{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "6b629695", "metadata": { "tags": [ "hide-input" ] }, "source": [ "# DAQA - Extended analysis\n", "\n", "\n", "\n", "This analysis will answer the following questions...\n", "\n", "**For the whole data set:**\n", "- what % of projects have addresses?\n", "- what % of projects have completion dates?\n", "- what % of projects have associated firms but no architects?\n", "- what % of projects have associated architects but no firms?\n", "- what % of firms have ‘operating years’ recorded\n", "\n", "**Number of projects:**\n", "- Before 1940\n", "- Between 1940-1980\n", "- Post 1980\n", "- Undated\n", "\n", "**For the 40-80 data set:**\n", "- what % of projects have addresses?\n", "- what % of projects have completion dates? \n", "- what % of projects have associated firms but no architects?\n", "- what % of projects have associated architects but no firms?\n", "- what % of firms have ‘operating years’ recorded\n", "\n", "**Analytic questions:**\n", "- Which architects were associated with Queensland governement projects i.e., Brisbane City Council, Department of Works, etc.?\n", "- Which architects have registrations recorded in DAQA between 1940-1980?\n", "- UQ vs (BCTC, QIT, QUT) vs the rest for whole data, 1940-1980, and also 1940 to present.\n", "- Repeat ‘how many completed related to a person’. 1940-1980 and the Pareto distribution\n", "- Number completed projects 1940-80\n", "- Number of works by year, most active vs rest 1940-1980\n", "- what % of the different typologies 1940-1980\n", "- Number of works by year by typology\n", "- what % of projects extant/demolished/modified 1940-1980\n", "- average and mean number of employers of the DAQA interviewed architects\n", "- names of Architects associated with the highest number of projects 1940-1980\n", "- names of firms associated with the highest number of projects 1940-1980\n", "- names of top 5 Architects associated with the highest number of each typology 1940-1980\n", "- names of top 5 firms associated with the highest number of projects 1940-1980\n", "- what % of architects who are women associated with projects 1940-1980\n", "- what % of architects who are women associated with projects after 1980\n", "- 5 Firms with the longest timespan with the same name.\n", "- 5 Firms with the longest timespan with successive names/known predecessor firms." ] }, { "cell_type": "code", "execution_count": 1, "id": "6f51a588", "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "import requests, gzip, io, os, json\n", "\n", "# for data mgmt\n", "import pandas as pd\n", "import numpy as np\n", "from collections import Counter\n", "from datetime import datetime\n", "import ast\n", "\n", "# for plotting\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import PercentFormatter\n", "import plotly.graph_objects as go\n", "import seaborn as sns\n", "from matplotlib.colors import to_rgba\n", "import plotly.express as px\n", "\n", "# for hypothesis testing\n", "from scipy.stats import chi2_contingency\n", "from scipy.stats import pareto\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "# provide folder_name which contains uncompressed data i.e., csv and jsonl files\n", "# only need to change this if you have already downloaded data\n", "# otherwise data will be fetched from google drive\n", "global folder_name\n", "folder_name = 'data/local'\n", "\n", "def fetch_small_data_from_github(fname):\n", " url = f\"https://raw.githubusercontent.com/acd-engine/jupyterbook/master/data/analysis/{fname}\"\n", " response = requests.get(url)\n", " rawdata = response.content.decode('utf-8')\n", " return pd.read_csv(io.StringIO(rawdata))\n", "\n", "def fetch_date_suffix():\n", " url = f\"https://raw.githubusercontent.com/acd-engine/jupyterbook/master/data/analysis/date_suffix\"\n", " response = requests.get(url)\n", " rawdata = response.content.decode('utf-8')\n", " try: return rawdata[:12]\n", " except: return None\n", "\n", "def check_if_csv_exists_in_folder(filename):\n", " try: return pd.read_csv(os.path.join(folder_name, filename), low_memory=False)\n", " except: return None\n", "\n", "def fetch_data(filetype='csv', acdedata='organization'):\n", " filename = f'acde_{acdedata}_{fetch_date_suffix()}.{filetype}'\n", "\n", " # first check if the data exists in current directory\n", " data_from_path = check_if_csv_exists_in_folder(filename)\n", " if data_from_path is not None: return data_from_path\n", "\n", " urls = fetch_small_data_from_github('acde_data_gdrive_urls.csv')\n", " sharelink = urls[urls.data == acdedata][filetype].values[0]\n", " url = f'https://drive.google.com/u/0/uc?id={sharelink}&export=download&confirm=yes'\n", "\n", " response = requests.get(url)\n", " decompressed_data = gzip.decompress(response.content)\n", " decompressed_buffer = io.StringIO(decompressed_data.decode('utf-8'))\n", "\n", " try:\n", " if filetype == 'csv': df = pd.read_csv(decompressed_buffer, low_memory=False)\n", " else: df = [json.loads(jl) for jl in pd.read_json(decompressed_buffer, lines=True, orient='records')[0]]\n", " return pd.DataFrame(df)\n", " except: return None \n", "\n", "def fetch_all_DAQA_data():\n", " daqa_data_dict = dict()\n", " for entity in ['event', 'organization', 'person', 'place', 'recognition', 'resource', 'work']:\n", " daqa_this_entity = fetch_data(acdedata=entity)\n", " daqa_data_dict[entity] = daqa_this_entity[daqa_this_entity.data_source.str.contains('DAQA')]\n", " return daqa_data_dict\n", "\n", "df_daqa_dict = fetch_all_DAQA_data() # 1 min if data is already downloaded\n", "daqa_work = df_daqa_dict['work']\n", "daqa_persons = df_daqa_dict['person']\n", "daqa_orgs = df_daqa_dict['organization']\n", "daqa_resources = df_daqa_dict['resource']" ] }, { "attachments": {}, "cell_type": "markdown", "id": "593399d1", "metadata": {}, "source": [ "## High-level summary of DAQA entities\n", "\n", "Before we jump into the analysis in response to the questions above, let's take a look at a high-level of each DAQA entity i.e., `person`, `organisation`, `work`, `resource`. \n", "\n", "Each entity has a `class` and we provide the count for each class for a given entity along with the total count of all classes. Next we output a count of all relationships between entities, and to this end, we generate detailled counts for all class-level relationships for each entity." ] }, { "cell_type": "code", "execution_count": 2, "id": "f9ad06cf", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of persons: 1103\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
Type
architect9120.827
non-architect1910.173
\n", "
" ], "text/plain": [ " Frequency Proportion\n", "Type \n", "architect 912 0.827\n", "non-architect 191 0.173" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number of organisations: 967\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
Type
\"firm\"9070.938
\"education\"390.040
\"organisation\"150.016
\"government\"60.006
\n", "
" ], "text/plain": [ " Frequency Proportion\n", "Type \n", "\"firm\" 907 0.938\n", "\"education\" 39 0.040\n", "\"organisation\" 15 0.016\n", "\"government\" 6 0.006" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number of works i.e, projects: 2203\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
Type
\"structure\"22031.0
\n", "
" ], "text/plain": [ " Frequency Proportion\n", "Type \n", "\"structure\" 2203 1.0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number of resources i.e., articles, interviews: 7696\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FrequencyProportion
Type
\"Photograph\"37840.492
\"LineDrawing\"11020.143
\"article\"7830.102
\"Image\"7420.096
\"Article\"6860.089
\"Audio\"1420.018
\"Transcript\"1280.017
\"Portrait\"1020.013
\"interview\"920.012
\"Youtube\"460.006
\"publication\"460.006
\"Video\"400.005
\"Spreadsheet\"30.000
\n", "
" ], "text/plain": [ " Frequency Proportion\n", "Type \n", "\"Photograph\" 3784 0.492\n", "\"LineDrawing\" 1102 0.143\n", "\"article\" 783 0.102\n", "\"Image\" 742 0.096\n", "\"Article\" 686 0.089\n", "\"Audio\" 142 0.018\n", "\"Transcript\" 128 0.017\n", "\"Portrait\" 102 0.013\n", "\"interview\" 92 0.012\n", "\"Youtube\" 46 0.006\n", "\"publication\" 46 0.006\n", "\"Video\" 40 0.005\n", "\"Spreadsheet\" 3 0.000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# architects\n", "print('Total number of persons:', daqa_persons.shape[0])\n", "architect_count = daqa_persons['longterm_roles'].value_counts().reset_index()\n", "architect_count['Proportion'] = round(architect_count['longterm_roles']/architect_count['longterm_roles'].sum(),3)\n", "architect_count['Type'] = np.where(architect_count['index'].str.contains('non-architect'), 'non-architect', 'architect')\n", "display(architect_count\\\n", " .groupby('Type')\\\n", " .sum()\\\n", " .reset_index()\\\n", " .rename(columns={'longterm_roles':'Frequency'})\\\n", " .set_index('Type')\n", " .sort_values('Frequency', ascending=False))\n", "\n", "# firms\n", "print('\\nTotal number of organisations:', daqa_orgs.shape[0])\n", "firm_count = daqa_orgs['_class_ori'].value_counts().reset_index()\n", "firm_count['Proportion'] = round(firm_count['_class_ori']/firm_count['_class_ori'].sum(),3)\n", "display(firm_count\\\n", " .groupby('index')\\\n", " .sum()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'Type', '_class_ori':'Frequency'})\\\n", " .set_index('Type')\n", " .sort_values('Frequency', ascending=False))\n", "\n", "# projects\n", "print('\\nTotal number of works i.e, projects:', daqa_work.shape[0])\n", "project_count = daqa_work['_class_ori'].value_counts().reset_index()\n", "project_count['Proportion'] = round(project_count['_class_ori']/project_count['_class_ori'].sum(),3)\n", "display(project_count\\\n", " .groupby('index')\\\n", " .sum()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'Type', '_class_ori':'Frequency'})\\\n", " .set_index('Type')\n", " .sort_values('Frequency', ascending=False))\n", "\n", "# articles\n", "print('\\nTotal number of resources i.e., articles, interviews:', daqa_resources.shape[0])\n", "article_count = daqa_resources['_class_ori'].value_counts().reset_index()\n", "article_count['Proportion'] = round(article_count['_class_ori']/article_count['_class_ori'].sum(),3)\n", "display(article_count\\\n", " .groupby('index')\\\n", " .sum()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'Type', '_class_ori':'Frequency'})\\\n", " .set_index('Type')\n", " .sort_values('Frequency', ascending=False))" ] }, { "cell_type": "code", "execution_count": 3, "id": "a05a93aa", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of relationships: 17451\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
relation_class
Work_RelatedResource48890.280
Person_RelatedWork23130.133
Person_RelatedPerson19520.112
Work_RelatedPlace18430.106
Person_RelatedOrganization15680.090
Organization_RelatedWork14840.085
Resource_RelatedResource9240.053
Person_RelatedResource6710.038
Resource_RelatedPerson6100.035
Organization_RelatedOrganization4200.024
Resource_RelatedOrganization1950.011
Work_RelatedOrganization1770.010
Resource_RelatedWork900.005
Organization_RelatedResource830.005
Resource_RelatedPlace770.004
Person_RelatedRecognition600.003
Resource_RelatedRecognition480.003
Work_RelatedPerson310.002
Organization_RelatedPerson70.000
Person_RelatedPlace70.000
Recognition_RelatedOrganization10.000
Work_RelatedRecognition10.000
\n", "
" ], "text/plain": [ " Frequency Proportion\n", "relation_class \n", "Work_RelatedResource 4889 0.280\n", "Person_RelatedWork 2313 0.133\n", "Person_RelatedPerson 1952 0.112\n", "Work_RelatedPlace 1843 0.106\n", "Person_RelatedOrganization 1568 0.090\n", "Organization_RelatedWork 1484 0.085\n", "Resource_RelatedResource 924 0.053\n", "Person_RelatedResource 671 0.038\n", "Resource_RelatedPerson 610 0.035\n", "Organization_RelatedOrganization 420 0.024\n", "Resource_RelatedOrganization 195 0.011\n", "Work_RelatedOrganization 177 0.010\n", "Resource_RelatedWork 90 0.005\n", "Organization_RelatedResource 83 0.005\n", "Resource_RelatedPlace 77 0.004\n", "Person_RelatedRecognition 60 0.003\n", "Resource_RelatedRecognition 48 0.003\n", "Work_RelatedPerson 31 0.002\n", "Organization_RelatedPerson 7 0.000\n", "Person_RelatedPlace 7 0.000\n", "Recognition_RelatedOrganization 1 0.000\n", "Work_RelatedRecognition 1 0.000" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number of unique predicates 33\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
predicate.term
HasMedia63000.361
WorkedOn35020.201
LocatedIn18430.106
Employment12890.074
Reference11880.068
RelatedTo6360.036
WorkedWith4020.023
TaughtBy3570.020
InfluencedBy2440.014
StudiedWith2430.014
PrecededBy2210.013
KnewSocially2030.012
succeededby1890.011
KnewProfessionally1320.008
StudiedAt1180.007
IsInvolvedIn970.006
PartnerOf830.005
DoneIn770.004
DesignedBy630.004
CollaboratedWith480.003
KnewOf420.002
TravelledTo280.002
Awarded220.001
ClientOf210.001
MentoredBy180.001
Founded160.001
Became150.001
WasInfluenceBy140.001
Attended120.001
TaughtAt90.001
MergedWith70.000
Read60.000
Authored60.000
\n", "
" ], "text/plain": [ " Frequency Proportion\n", "predicate.term \n", "HasMedia 6300 0.361\n", "WorkedOn 3502 0.201\n", "LocatedIn 1843 0.106\n", "Employment 1289 0.074\n", "Reference 1188 0.068\n", "RelatedTo 636 0.036\n", "WorkedWith 402 0.023\n", "TaughtBy 357 0.020\n", "InfluencedBy 244 0.014\n", "StudiedWith 243 0.014\n", "PrecededBy 221 0.013\n", "KnewSocially 203 0.012\n", "succeededby 189 0.011\n", "KnewProfessionally 132 0.008\n", "StudiedAt 118 0.007\n", "IsInvolvedIn 97 0.006\n", "PartnerOf 83 0.005\n", "DoneIn 77 0.004\n", "DesignedBy 63 0.004\n", "CollaboratedWith 48 0.003\n", "KnewOf 42 0.002\n", "TravelledTo 28 0.002\n", "Awarded 22 0.001\n", "ClientOf 21 0.001\n", "MentoredBy 18 0.001\n", "Founded 16 0.001\n", "Became 15 0.001\n", "WasInfluenceBy 14 0.001\n", "Attended 12 0.001\n", "TaughtAt 9 0.001\n", "MergedWith 7 0.000\n", "Read 6 0.000\n", "Authored 6 0.000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "relationship_cols = daqa_persons.iloc[:, 62:].columns\n", "relevant_datasets = df_daqa_dict.keys()\n", "\n", "relations = []\n", "for this_df in relevant_datasets:\n", " for idx,row in df_daqa_dict[this_df].iterrows():\n", " for col in relationship_cols:\n", " try: \n", " if isinstance(row[col], str): relations.append(pd.json_normalize(ast.literal_eval(row[col])))\n", " except: continue\n", "\n", "relations = pd.concat(relations)\n", "relations = relations.drop_duplicates()\n", "\n", "# replace person with specfic role\n", "# create dictionary of architects and their ids\n", "arch_nonarch_dict = daqa_persons[['ori_id','longterm_roles']]\n", "arch_nonarch_dict['_class_ori'] = np.where(arch_nonarch_dict['longterm_roles'].str.contains('non-architect'), 'non-architect', 'architect')\n", "arch_nonarch_dict = arch_nonarch_dict.drop('longterm_roles', axis=1).set_index('ori_id').to_dict()['_class_ori']\n", "\n", "relations['subject.ori_id'] = relations['subject.ori_id'].astype(str)\n", "relations['object.ori_id'] = relations['object.ori_id'].astype(str)\n", "\n", "relations['subject._class_ori'] = np.where(relations['subject._class'] == 'person', \n", " relations['subject.ori_id'].map(arch_nonarch_dict), \n", " relations['subject._class_ori'])\n", "relations['object._class_ori'] = np.where(relations['object._class'] == 'person', \n", " relations['object.ori_id'].map(arch_nonarch_dict), \n", " relations['object._class_ori'])\n", "\n", "# relations\n", "print('Total number of relationships:', relations.shape[0])\n", "display(relations.relation_class.value_counts()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'relation_class','relation_class':'Frequency'})\\\n", " .assign(Proportion = lambda x: round(x['Frequency']/x['Frequency'].sum(),3))\\\n", " .set_index('relation_class')\n", " .sort_values('Frequency', ascending=False))\n", "\n", "# predicate terms\n", "print('\\nTotal number of unique predicates', relations['predicate.term'].nunique())\n", "display(relations['predicate.term'].value_counts()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'predicate.term','predicate.term':'Frequency'})\\\n", " .assign(Proportion = lambda x: round(x['Frequency']/x['Frequency'].sum(),3))\\\n", " .set_index('predicate.term')\n", " .sort_values('Frequency', ascending=False))" ] }, { "cell_type": "code", "execution_count": 4, "id": "397012bb", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###################### PERSON RELATIONSHIPS ######################\n", "\n", "\n", "Total number of Person-Work relations: 2344\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectstructureWorkedOn2157
1architectstructureReference97
2structurearchitectDesignedBy30
3architectstructureInfluencedBy20
4architectstructureStudiedAt8
5architectstructureEmployment7
6architectstructureTravelledTo6
7architectstructureTaughtAt4
9architectstructureAttended3
8architectstructureRelatedTo3
10architectstructureKnewOf2
11non-architectstructureReference2
12architectstructureRead1
13architectstructureClientOf1
14architectstructureWorkedWith1
15non-architectstructureWorkedOn1
16structurearchitectClientOf1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect structure WorkedOn 2157\n", "1 architect structure Reference 97\n", "2 structure architect DesignedBy 30\n", "3 architect structure InfluencedBy 20\n", "4 architect structure StudiedAt 8\n", "5 architect structure Employment 7\n", "6 architect structure TravelledTo 6\n", "7 architect structure TaughtAt 4\n", "9 architect structure Attended 3\n", "8 architect structure RelatedTo 3\n", "10 architect structure KnewOf 2\n", "11 non-architect structure Reference 2\n", "12 architect structure Read 1\n", "13 architect structure ClientOf 1\n", "14 architect structure WorkedWith 1\n", "15 non-architect structure WorkedOn 1\n", "16 structure architect ClientOf 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Person relations: 1952\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectarchitectWorkedWith359
1architectarchitectTaughtBy323
2architectarchitectReference219
3architectarchitectStudiedWith210
4architectarchitectInfluencedBy192
5architectarchitectKnewSocially171
6architectarchitectEmployment97
7architectarchitectKnewProfessionally97
8architectarchitectKnewOf36
9architectnon-architectKnewProfessionally32
10architectnon-architectStudiedWith31
11architectnon-architectKnewSocially29
12architectnon-architectTaughtBy24
13architectnon-architectCollaboratedWith16
14architectarchitectMentoredBy16
15architectnon-architectWorkedWith15
17architectnon-architectReference14
16architectarchitectCollaboratedWith14
18architectarchitectWasInfluenceBy12
19non-architectarchitectClientOf10
20architectnon-architectInfluencedBy8
21architectarchitectPartnerOf8
22architectarchitectClientOf4
23non-architectnon-architectEmployment2
24non-architectnon-architectKnewProfessionally2
25architectnon-architectEmployment2
26architectnon-architectMentoredBy2
27architectarchitectRelatedTo2
28non-architectnon-architectKnewSocially2
29non-architectarchitectEmployment1
30non-architectarchitectReference1
31architectnon-architectKnewOf1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect architect WorkedWith 359\n", "1 architect architect TaughtBy 323\n", "2 architect architect Reference 219\n", "3 architect architect StudiedWith 210\n", "4 architect architect InfluencedBy 192\n", "5 architect architect KnewSocially 171\n", "6 architect architect Employment 97\n", "7 architect architect KnewProfessionally 97\n", "8 architect architect KnewOf 36\n", "9 architect non-architect KnewProfessionally 32\n", "10 architect non-architect StudiedWith 31\n", "11 architect non-architect KnewSocially 29\n", "12 architect non-architect TaughtBy 24\n", "13 architect non-architect CollaboratedWith 16\n", "14 architect architect MentoredBy 16\n", "15 architect non-architect WorkedWith 15\n", "17 architect non-architect Reference 14\n", "16 architect architect CollaboratedWith 14\n", "18 architect architect WasInfluenceBy 12\n", "19 non-architect architect ClientOf 10\n", "20 architect non-architect InfluencedBy 8\n", "21 architect architect PartnerOf 8\n", "22 architect architect ClientOf 4\n", "23 non-architect non-architect Employment 2\n", "24 non-architect non-architect KnewProfessionally 2\n", "25 architect non-architect Employment 2\n", "26 architect non-architect MentoredBy 2\n", "27 architect architect RelatedTo 2\n", "28 non-architect non-architect KnewSocially 2\n", "29 non-architect architect Employment 1\n", "30 non-architect architect Reference 1\n", "31 architect non-architect KnewOf 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Organization relations: 1575\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectfirmEmployment1137
1architecteducationStudiedAt108
2architectfirmPartnerOf75
3architectfirmReference55
4architecteducationEmployment35
5architectfirmInfluencedBy19
6architectfirmFounded15
7architectfirmWorkedWith14
8architectfirmTaughtBy10
9architectfirmCollaboratedWith9
10architecteducationReference9
11architectorganisationBecame8
15architecteducationTaughtAt5
17architecteducationInfluencedBy5
16architecteducationCollaboratedWith5
13architectgovernmentWorkedWith5
14architecteducationAttended5
12architectorganisationReference5
18architectorganisationRelatedTo4
19non-architectfirmEmployment4
20architectorganisationWorkedWith3
21architecteducationRead3
27architecteducationAuthored2
31architectfirmStudiedWith2
30non-architectfirmReference2
29architecteducationTravelledTo2
28architectfirmMergedWith2
24architecteducationWorkedWith2
26architectorganisationAttended2
23firmarchitectClientOf2
22architectorganisationEmployment2
25firmarchitectKnewOf2
40architecteducationClientOf1
47architecteducationWorkedOn1
46architectfirmKnewOf1
45architectfirmKnewProfessionally1
44architectfirmKnewSocially1
43architecteducationRelatedTo1
42architectfirmWasInfluenceBy1
41architectgovernmentEmployment1
38architecteducationAwarded1
39architectorganisationCollaboratedWith1
37architectfirmClientOf1
36firmarchitectBecame1
35non-architecteducationStudiedAt1
34non-architecteducationEmployment1
33firmarchitectCollaboratedWith1
32firmarchitectMergedWith1
48non-architectorganisationFounded1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect firm Employment 1137\n", "1 architect education StudiedAt 108\n", "2 architect firm PartnerOf 75\n", "3 architect firm Reference 55\n", "4 architect education Employment 35\n", "5 architect firm InfluencedBy 19\n", "6 architect firm Founded 15\n", "7 architect firm WorkedWith 14\n", "8 architect firm TaughtBy 10\n", "9 architect firm CollaboratedWith 9\n", "10 architect education Reference 9\n", "11 architect organisation Became 8\n", "15 architect education TaughtAt 5\n", "17 architect education InfluencedBy 5\n", "16 architect education CollaboratedWith 5\n", "13 architect government WorkedWith 5\n", "14 architect education Attended 5\n", "12 architect organisation Reference 5\n", "18 architect organisation RelatedTo 4\n", "19 non-architect firm Employment 4\n", "20 architect organisation WorkedWith 3\n", "21 architect education Read 3\n", "27 architect education Authored 2\n", "31 architect firm StudiedWith 2\n", "30 non-architect firm Reference 2\n", "29 architect education TravelledTo 2\n", "28 architect firm MergedWith 2\n", "24 architect education WorkedWith 2\n", "26 architect organisation Attended 2\n", "23 firm architect ClientOf 2\n", "22 architect organisation Employment 2\n", "25 firm architect KnewOf 2\n", "40 architect education ClientOf 1\n", "47 architect education WorkedOn 1\n", "46 architect firm KnewOf 1\n", "45 architect firm KnewProfessionally 1\n", "44 architect firm KnewSocially 1\n", "43 architect education RelatedTo 1\n", "42 architect firm WasInfluenceBy 1\n", "41 architect government Employment 1\n", "38 architect education Awarded 1\n", "39 architect organisation CollaboratedWith 1\n", "37 architect firm ClientOf 1\n", "36 firm architect Became 1\n", "35 non-architect education StudiedAt 1\n", "34 non-architect education Employment 1\n", "33 firm architect CollaboratedWith 1\n", "32 firm architect MergedWith 1\n", "48 non-architect organisation Founded 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Resource relations: 1281\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0interviewarchitectReference364
1architectPhotographHasMedia257
2architectinterviewIsInvolvedIn91
3interviewarchitectRelatedTo86
4non-architectinterviewRelatedTo86
5interviewnon-architectRelatedTo85
6architectinterviewRelatedTo82
7architectImageHasMedia80
8interviewnon-architectReference75
9architectPortraitHasMedia66
10non-architectinterviewIsInvolvedIn6
11architectLineDrawingHasMedia1
12architectpublicationReference1
13non-architectPhotographHasMedia1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 interview architect Reference 364\n", "1 architect Photograph HasMedia 257\n", "2 architect interview IsInvolvedIn 91\n", "3 interview architect RelatedTo 86\n", "4 non-architect interview RelatedTo 86\n", "5 interview non-architect RelatedTo 85\n", "6 architect interview RelatedTo 82\n", "7 architect Image HasMedia 80\n", "8 interview non-architect Reference 75\n", "9 architect Portrait HasMedia 66\n", "10 non-architect interview IsInvolvedIn 6\n", "11 architect LineDrawing HasMedia 1\n", "12 architect publication Reference 1\n", "13 non-architect Photograph HasMedia 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Recognition relations: 60\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectawardAwarded20
1architectawardTravelledTo15
2architectawardReference8
3architectawardBecame5
4architectawardAuthored3
5architectawardAttended2
6architectawardRead2
7architectawardRelatedTo1
8architectawardWasInfluenceBy1
9architectawardWorkedWith1
10non-architectawardAuthored1
11non-architectawardReference1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect award Awarded 20\n", "1 architect award TravelledTo 15\n", "2 architect award Reference 8\n", "3 architect award Became 5\n", "4 architect award Authored 3\n", "5 architect award Attended 2\n", "6 architect award Read 2\n", "7 architect award RelatedTo 1\n", "8 architect award WasInfluenceBy 1\n", "9 architect award WorkedWith 1\n", "10 non-architect award Authored 1\n", "11 non-architect award Reference 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Place relations: 7\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0architectplaceTravelledTo5
1architectplaceReference1
2architectplaceRelatedTo1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect place TravelledTo 5\n", "1 architect place Reference 1\n", "2 architect place RelatedTo 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "###################### WORK RELATIONSHIPS ######################\n", "\n", "\n", "Total number of Work-Resource relations: 4979\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0structurePhotographHasMedia3143
1structureLineDrawingHasMedia1088
2structureImageHasMedia651
3interviewstructureReference90
4structurePortraitHasMedia7
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 structure Photograph HasMedia 3143\n", "1 structure LineDrawing HasMedia 1088\n", "2 structure Image HasMedia 651\n", "3 interview structure Reference 90\n", "4 structure Portrait HasMedia 7" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Work-Place relations: 1843\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0structureplaceLocatedIn1843
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 structure place LocatedIn 1843" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Work-Recognition relations: 1\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0structureawardAwarded1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 structure award Awarded 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "###################### ORGANISATION RELATIONSHIPS ######################\n", "\n", "\n", "Total number of Organization-Work relations: 1661\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0firmstructureWorkedOn1343
1structurefirmRelatedTo144
2firmstructureRelatedTo141
3structurefirmDesignedBy33
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 firm structure WorkedOn 1343\n", "1 structure firm RelatedTo 144\n", "2 firm structure RelatedTo 141\n", "3 structure firm DesignedBy 33" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Organization-Organization relations: 420\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0firmfirmPrecededBy221
1firmfirmsucceededby189
2firmfirmMergedWith4
3firmfirmCollaboratedWith2
4firmfirmWorkedWith2
5firmfirmBecame1
6governmentfirmClientOf1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 firm firm PrecededBy 221\n", "1 firm firm succeededby 189\n", "2 firm firm MergedWith 4\n", "3 firm firm CollaboratedWith 2\n", "4 firm firm WorkedWith 2\n", "5 firm firm Became 1\n", "6 government firm ClientOf 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Recognition-Organization relations: 1\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0awardeducationStudiedAt1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 award education StudiedAt 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "###################### RESOURCE RELATIONSHIPS ######################\n", "\n", "\n", "Total number of Resource-Resource relations: 924\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0articleArticleHasMedia646
1interviewAudioHasMedia127
2interviewTranscriptHasMedia83
3interviewYoutubeHasMedia45
4interviewVideoHasMedia22
5interviewpublicationReference1
\n", "
" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 article Article HasMedia 646\n", "1 interview Audio HasMedia 127\n", "2 interview Transcript HasMedia 83\n", "3 interview Youtube HasMedia 45\n", "4 interview Video HasMedia 22\n", "5 interview publication Reference 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Resource-Organization relations: 278\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0intervieweducationReference121
1interviewfirmReference68
2firmPhotographHasMedia57
3firmImageHasMedia15
4firmPortraitHasMedia9
5intervieworganisationReference6
6firmLineDrawingHasMedia2
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 interview education Reference 121\n", "1 interview firm Reference 68\n", "2 firm Photograph HasMedia 57\n", "3 firm Image HasMedia 15\n", "4 firm Portrait HasMedia 9\n", "5 interview organisation Reference 6\n", "6 firm LineDrawing HasMedia 2" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Resource-Place relations: 77\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0interviewplaceDoneIn77
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 interview place DoneIn 77" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Resource-Recognition relations: 48\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0interviewawardReference48
\n", "
" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 interview award Reference 48" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "def fetch_relation_details(relation_types, relations=relations):\n", " this_relation = relations[relations['relation_class'].isin(relation_types)].fillna('-')\n", " print(f'Total number of {relation_types[0].replace(\"Related\",\"\").replace(\"_\",\"-\")} relations:', this_relation.shape[0])\n", " display(this_relation[['subject._class_ori','object._class_ori','predicate.term']]\\\n", " .value_counts()\\\n", " .reset_index()\\\n", " .rename(columns={0:'Frequency'})\\\n", " .sort_values('Frequency', ascending=False))\n", "\n", "print('###################### PERSON RELATIONSHIPS ######################')\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedWork','Work_RelatedPerson'])\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedPerson'])\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedOrganization','Organization_RelatedPerson'])\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedResource','Resource_RelatedPerson'])\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedRecognition'])\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedPlace'])\n", "print('\\n')\n", "\n", "print('###################### WORK RELATIONSHIPS ######################')\n", "print('\\n')\n", "fetch_relation_details(['Work_RelatedResource','Resource_RelatedWork'])\n", "print('\\n')\n", "fetch_relation_details(['Work_RelatedPlace'])\n", "print('\\n')\n", "fetch_relation_details(['Work_RelatedRecognition'])\n", "print('\\n')\n", "\n", "print('###################### ORGANISATION RELATIONSHIPS ######################')\n", "print('\\n')\n", "fetch_relation_details(['Organization_RelatedWork','Work_RelatedOrganization'])\n", "print('\\n')\n", "fetch_relation_details(['Organization_RelatedOrganization'])\n", "print('\\n')\n", "fetch_relation_details(['Recognition_RelatedOrganization'])\n", "\n", "print('###################### RESOURCE RELATIONSHIPS ######################')\n", "print('\\n')\n", "fetch_relation_details(['Resource_RelatedResource'])\n", "print('\\n')\n", "fetch_relation_details(['Resource_RelatedOrganization','Organization_RelatedResource'])\n", "print('\\n')\n", "fetch_relation_details(['Resource_RelatedPlace'])\n", "print('\\n')\n", "fetch_relation_details(['Resource_RelatedRecognition'])\n", "print('\\n')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "583a3711", "metadata": {}, "source": [ "## Projects & firms\n", "\n", "Below are some statistics about project characteristics and firms in the DAQA dataset. Proportions under the `PROJECTS` subheading are calculated as a percentage of the total number of projects in the dataset. Proportions under the `FIRMS` subheading are calculated as a percentage of the total number of firms in the dataset.\n", "\n", "It should be noted that all people related to projects are architects, we found no non-architects in the project records. Also all organisations related to projects are firms, we found no non-firm organisations in the project records.\n", "\n", "- We define a project with an address as one that has a populated `address` field\n", "- We define a project with a geocode date as one that has a populated `longitude` and `latitude` field\n", "- We define a project with a completion date as one that has a populated `completion year` field\n", "- We define a project with an associated firm as one that has a populated `related_organizations` field\n", "- We define a project with no associated firms as one that has a no populated `related organizations` field\n", "- We define a project with an associated architect as one that has a populated `related_people` field\n", "- We define a project with no associated architects as one that has no populated `related_people` field\n", "- We define a firm with operating years as an organisation that has a populated `operation` field" ] }, { "cell_type": "code", "execution_count": 5, "id": "f2454208", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###################### PROJECTS ######################\n", "\n", "Q: How many projects are recorded in DAQA?\n", "A: There are 2203 projects in DAQA.\n", "\n", "Q: what % of projects have addresses?\n", "A: 84.29% (1857) of DAQA projects have addresses.\n", "\n", "Q: what % of projects have geocodes (lat/long)?\n", "A: 65.68% (1447) of DAQA projects have geocodes.\n", "\n", "Q: what % of projects have completion dates?\n", "A: 60.37% (1330) of DAQA projects have completion dates.\n", "\n", "Q: how many projects have associated firms?\n", "A: 58.87% (1297) of DAQA projects have associated firms.\n", "\n", "Q: what % of projects have associated firms but no architects?\n", "A: 14.21% (313) of DAQA projects have associated firms but no architects.\n", "\n", "Q: how many projects have associated architects?\n", "A: 81.03% (1785) of DAQA projects have associated architects.\n", "\n", "Q: what % of projects have associated architects but no firms?\n", "A: 36.36% (801) of DAQA projects have associated architects but no firms.\n", "\n", "###################### FIRMS ######################\n", "\n", "Q: How many firms are recorded in DAQA?\n", "A: There are 907 firms in DAQA.\n", "\n", "Q: what % of firms have ‘operating years’ recorded (just start)?\n", "A: 79.93% (725) of DAQA firms have operating years recorded.\n", "\n", "Q: what % of firms have ‘operating years’ recorded (start and end)?\n", "A: 39.69% (360) of DAQA firms have operating years recorded.\n", "\n", "Q: What are the top five firms with the longest timespan with the same name? (must have operating start and end years)\n" ] }, { "data": { "text/html": [ "
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primary_namestart_yrend_yrdiff
21223\"Evans Deakin & Company (Engineers & Shipbuild...19101980.070.0
21594\"Bates Smart & McCutcheon\"19261995.069.0
21165\"Brown and Broad LTD\"19051967.062.0
20909\"Queensland Housing Commission\"19452004.059.0
21164\"George Brockwell Gill Architect and Agent\"18891943.054.0
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" ], "text/plain": [ " primary_name start_yr end_yr \\\n", "21223 \"Evans Deakin & Company (Engineers & Shipbuild... 1910 1980.0 \n", "21594 \"Bates Smart & McCutcheon\" 1926 1995.0 \n", "21165 \"Brown and Broad LTD\" 1905 1967.0 \n", "20909 \"Queensland Housing Commission\" 1945 2004.0 \n", "21164 \"George Brockwell Gill Architect and Agent\" 1889 1943.0 \n", "\n", " diff \n", "21223 70.0 \n", "21594 69.0 \n", "21165 62.0 \n", "20909 59.0 \n", "21164 54.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('###################### PROJECTS ######################')\n", "\n", "# load data\n", "daqa_work = df_daqa_dict['work']\n", "\n", "print('\\nQ: How many projects are recorded in DAQA?')\n", "count_projects = len(daqa_work)\n", "print(f'A: There are {count_projects} projects in DAQA.')\n", "\n", "# we define a project with an address as one that has a populated \"address\" field\n", "print('\\nQ: what % of projects have addresses?')\n", "count_projects_with_address = len(daqa_work[daqa_work.coverage_range.apply(lambda x: \"address\" in x)])\n", "prop_projects_with_address = round((count_projects_with_address / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_address}% ({count_projects_with_address}) of DAQA projects have addresses.')\n", "\n", "# we define a project with a geocode date as one that has a populated \"longitude\" field\n", "print('\\nQ: what % of projects have geocodes (lat/long)?')\n", "count_projects_with_geocodes = len(daqa_work[daqa_work.coverage_range.apply(lambda x: \"latitude\" in x)])\n", "prop_projects_with_geocodes = round((count_projects_with_geocodes / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_geocodes}% ({count_projects_with_geocodes}) of DAQA projects have geocodes.')\n", "\n", "# we define a project with a completion date as one that has a populated \"completion year\" field\n", "print('\\nQ: what % of projects have completion dates?')\n", "count_projects_with_completion_dates = len(daqa_work[daqa_work.coverage_range.apply(lambda x: \"date_end\" in x)])\n", "prop_projects_with_completion_dates = round((count_projects_with_completion_dates / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_completion_dates}% ({count_projects_with_completion_dates}) of DAQA projects have completion dates.')\n", "\n", "# we conduct a sanity check to see if related people in daqa_work are all architects, we find no non-architects\n", "# # load data\n", "# daqa_persons = df_daqa_dict['person']\n", "# non_architects = daqa_persons[daqa_persons['longterm_roles'].str.contains('non-architect')]['ori_id'].unique()\n", "# len(daqa_work[daqa_work.related_people.apply(lambda x: pd.json_normalize(eval(x))['subject.ori_id'].values[0] in non_architects if isinstance(x, str) else False)])\n", "\n", "# we conduct a sanity check to see if related organisations in daqa_work are all firms, we find no non-firms\n", "# count_related_organizations_firms = len(daqa_work[daqa_work.related_organizations.apply(lambda x: \"firm\" in x if isinstance(x, str) else False)])\n", "# count_related_organizations = len(daqa_work[daqa_work.related_organizations.notnull()])\n", "# count_related_organizations_firms == count_related_organizations\n", "\n", "# we define a project with an associated firm as one that has a populated \"related_organizations\" field\n", "print('\\nQ: how many projects have associated firms?')\n", "count_projects_with_firms = len(daqa_work[daqa_work.related_organizations.notnull()])\n", "prop_projects_with_firms = round((count_projects_with_firms / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_firms}% ({count_projects_with_firms}) of DAQA projects have associated firms.')\n", "\n", "# we define a project with no associated architects as one that has no populated \"related_people\" field\n", "print('\\nQ: what % of projects have associated firms but no architects?')\n", "count_projects_with_firms_no_architects = len(daqa_work[(daqa_work.related_organizations.notnull()) &\\\n", " (daqa_work.related_people.isnull())])\n", "prop_projects_with_firms_no_architects = round((count_projects_with_firms_no_architects / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_firms_no_architects}% ({count_projects_with_firms_no_architects}) of DAQA projects have associated firms but no architects.')\n", "\n", "# we define a project with an associated architect as one that has a populated \"related_people\" field\n", "print('\\nQ: how many projects have associated architects?')\n", "count_projects_with_architects = len(daqa_work[daqa_work.related_people.notnull()])\n", "prop_projects_with_architects = round((count_projects_with_architects / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_architects}% ({count_projects_with_architects}) of DAQA projects have associated architects.')\n", "\n", "# we define a project with an associated architects as one that has a populated \"related people\" field\n", "# and we define a project with no associated firms as one that has a no populated \"related organizations\" field\n", "print('\\nQ: what % of projects have associated architects but no firms?')\n", "count_projects_with_architects_no_firms = len(daqa_work[(daqa_work.related_organizations.isnull()) &\\\n", " (daqa_work.related_people.notnull())])\n", "prop_projects_with_architects_no_firms = round((count_projects_with_architects_no_firms / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_architects_no_firms}% ({count_projects_with_architects_no_firms}) of DAQA projects have associated architects but no firms.')\n", "\n", "print('\\n###################### FIRMS ######################')\n", "\n", "# load data\n", "daqa_orgs = df_daqa_dict['organization']\n", "daqa_firms = daqa_orgs[daqa_orgs['_class_ori'].str.contains('firm')].copy()\n", "\n", "print('\\nQ: How many firms are recorded in DAQA?')\n", "count_firms = len(daqa_firms)\n", "print(f'A: There are {count_firms} firms in DAQA.')\n", "\n", "# we define an operating firm as an organisation that has a populated \"operation\" field with a start date\n", "print('\\nQ: what % of firms have ‘operating years’ recorded (just start)?')\n", "count_firms_with_operating_start = len(daqa_firms[daqa_firms.operation.apply(lambda x: \"date_start\" in x if isinstance(x, str) else False)])\n", "prop_firms_with_operating_start = round((count_firms_with_operating_start / count_firms) * 100, 2)\n", "print(f'A: {prop_firms_with_operating_start}% ({count_firms_with_operating_start}) of DAQA firms have operating years recorded.')\n", "\n", "# we define an operating firm as an organisation that has a populated \"operation\" field with start and end dates\n", "print('\\nQ: what % of firms have ‘operating years’ recorded (start and end)?')\n", "count_firms_with_operating_years = len(daqa_firms[daqa_firms.operation.apply(lambda x: (\"date_start\" in x) & (\"date_end\" in x) if isinstance(x, str) else False)])\n", "prop_firms_with_operating_years = round((count_firms_with_operating_years / count_firms) * 100, 2)\n", "print(f'A: {prop_firms_with_operating_years}% ({count_firms_with_operating_years}) of DAQA firms have operating years recorded.')\n", "\n", "# top 5 firms - longest timespan \n", "daqa_firms_with_op_yrs = daqa_firms[daqa_firms.operation.apply(lambda x: \"date_start\" in x if isinstance(x, str) else False)]\n", "\n", "# extract the start and end years from the \"operation\" field\n", "start_dates = []; end_dates = []\n", "\n", "for index, row in daqa_firms_with_op_yrs.iterrows():\n", " start_dates.append(int(pd.json_normalize(ast.literal_eval(row['operation']))['date_start.year'].values[0]))\n", "\n", " try: end_dates.append(int(pd.json_normalize(ast.literal_eval(row['operation']))['date_end.year'].values[0]))\n", " except: end_dates.append(None)\n", "\n", "daqa_firms_with_op_yrs['start_yr'] = start_dates\n", "daqa_firms_with_op_yrs['end_yr'] = end_dates\n", "daqa_firms_with_op_yrs['diff'] = abs(daqa_firms_with_op_yrs['end_yr'] - daqa_firms_with_op_yrs['start_yr'])\n", "\n", "print('\\nQ: What are the top five firms with the longest timespan with the same name? (must have operating start and end years)')\n", "display(daqa_firms_with_op_yrs.sort_values(by='diff', ascending=False)\\\n", " .head(5)[['primary_name', 'start_yr', 'end_yr', 'diff']])" ] }, { "attachments": {}, "cell_type": "markdown", "id": "744a0675", "metadata": {}, "source": [ "### Top five firms with the longest timespan with successive names/known predecessor firms.\n", "\n", "We inspect the top five firms with the longest timespan with successive names/known predecessor firms. We use network graphs to visualise the predecessor/successor relationships between firms. The graphs are interactive, so you can click on the nodes to see the firm names and the years they were active." ] }, { "cell_type": "code", "execution_count": 6, "id": "60dffa7f", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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first_datelast_datenodesdiff
6118621967[4795, 3838, 3846]105
418931995[2310, 2446, 2447, 2449, 4628, 2456, 2338, 502...102
218641963[5033, 2387, 2356, 2457, 4890, 4891, 4892, 4286]99
1719262010[4576, 2497, 3868, 2440, 4459, 4460, 2445, 446...84
2819382002[2336, 4994, 2501, 4511, 2413, 2478, 4527, 363...64
\n", "
" ], "text/plain": [ " first_date last_date nodes \\\n", "61 1862 1967 [4795, 3838, 3846] \n", "4 1893 1995 [2310, 2446, 2447, 2449, 4628, 2456, 2338, 502... \n", "2 1864 1963 [5033, 2387, 2356, 2457, 4890, 4891, 4892, 4286] \n", "17 1926 2010 [4576, 2497, 3868, 2440, 4459, 4460, 2445, 446... \n", "28 1938 2002 [2336, 4994, 2501, 4511, 2413, 2478, 4527, 363... \n", "\n", " diff \n", "61 105 \n", "4 102 \n", "2 99 \n", "17 84 \n", "28 64 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "daqa_firms_with_related_orgs = daqa_firms[daqa_firms.related_organizations.notnull()]\n", "\n", "related_orgs_df = pd.DataFrame()\n", "\n", "for index, row in daqa_firms_with_related_orgs.iterrows():\n", " this_row_id = int(row['ori_id'])\n", " this_org_related = pd.json_normalize(ast.literal_eval(row['related_organizations']))\n", " related_orgs_df = related_orgs_df.append(this_org_related)\n", "\n", "related_orgs_df.drop_duplicates(inplace=True)\n", "related_orgs_df = related_orgs_df[related_orgs_df['predicate.term'].isin(['succeededby', 'PrecededBy','MergedWith'])]\n", "related_orgs_ls = related_orgs_df[['subject.ori_id','object.ori_id']].values.tolist()\n", "\n", "def get_connected_nodes(snapshot):\n", " connected_nodes = []\n", " for node_list in snapshot:\n", " connected_node_set = set(node_list)\n", " for connected_node in connected_nodes:\n", " if connected_node & connected_node_set:\n", " connected_node |= connected_node_set\n", " break\n", " else:\n", " connected_nodes.append(connected_node_set)\n", " \n", " connected_node_lists = [list(connected_node) for connected_node in connected_nodes]\n", " return connected_node_lists\n", "\n", "connected_node_lists = get_connected_nodes(related_orgs_ls)\n", "connected_node_lists = get_connected_nodes(connected_node_lists)\n", "connected_node_lists = get_connected_nodes(connected_node_lists)\n", "\n", "first_date = []\n", "last_date = []\n", "\n", "for nodes in connected_node_lists[0:]:\n", " this_nodes = daqa_firms_with_op_yrs[daqa_firms_with_op_yrs['ori_id'].astype(int).isin(nodes)]\n", " all_dates = this_nodes['start_yr'].to_list()\n", " all_dates.extend(this_nodes['start_yr'].to_list())\n", "\n", " if len(all_dates) > 0:\n", " first_date.append(min(all_dates))\n", " last_date.append(max(all_dates))\n", "\n", " else:\n", " first_date.append(None)\n", " last_date.append(None)\n", "\n", "\n", "# dates\n", "dts = pd.DataFrame([first_date, last_date]).T\n", "\n", "# add nodes as column\n", "dts.columns = ['first_date', 'last_date']\n", "dts['nodes'] = connected_node_lists\n", "dts['diff'] = dts['last_date'] - dts['first_date']\n", "\n", "display(dts.sort_values(by='diff', ascending=False).head(5))" ] }, { "attachments": {}, "cell_type": "markdown", "id": "bc7abf4c", "metadata": {}, "source": [ "
Firm 1 (105 years)" ] }, { "cell_type": "code", "execution_count": 7, "id": "4163c5d6", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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primary_nameori_idstart_yrend_yr
21757\"Backhouse & Taylor\"3838.018621863.0
21759\"Furnival & Taylor\"3846.01864NaN
21265\"T Taylor\"4795.01967NaN
\n", "
" ], "text/plain": [ " primary_name ori_id start_yr end_yr\n", "21757 \"Backhouse & Taylor\" 3838.0 1862 1863.0\n", "21759 \"Furnival & Taylor\" 3846.0 1864 NaN\n", "21265 \"T Taylor\" 4795.0 1967 NaN" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def output_graph(data, iteration = 0):\n", " import networkx as nx\n", " from pyvis import network as net\n", "\n", " g = net.Network(notebook=True, \n", " height='500px',\n", " width=\"100%\",\n", " cdn_resources = 'remote', \n", " select_menu=True)\n", " \n", " df = related_orgs_df[related_orgs_df['subject.ori_id']\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[iteration]['nodes'])][['subject.label','object.label','predicate.term']]\n", "\n", " dd = daqa_firms_with_op_yrs.copy()\n", " dd['primary_name'] = dd['primary_name'].apply(lambda x: ast.literal_eval(x))\n", "\n", " # Iterate over each row in the dataset\n", " for _, row in df.iterrows():\n", " subject = row['subject.label']\n", " predicate = row['predicate.term']\n", " obj = row['object.label']\n", " \n", " try:\n", " sub_start = dd[dd['primary_name'].str.contains(subject)]['start_yr'].values[0]\n", " sub_end = dd[dd['primary_name'].str.contains(subject)]['end_yr'].values[0]\n", " except:\n", " sub_start = None\n", " sub_end = None\n", "\n", " try:\n", " obj_start = dd[dd['primary_name'].str.contains(obj)]['start_yr'].values[0]\n", " obj_end = dd[dd['primary_name'].str.contains(obj)]['end_yr'].values[0]\n", " except:\n", " obj_start = None\n", " obj_end = None\n", "\n", " g.add_node(subject, subject, title=f\"{subject} \\nStart: {sub_start} \\nEnd: {sub_end}\", color='blue')\n", " g.add_node(obj, obj, title=f\"{obj} \\nStart: {obj_start} \\nEnd: {obj_end}\", color='blue')\n", "\n", " # Add edges to the graph based on the relationship type\n", " if predicate == 'succeededby':\n", " g.add_edge(subject, obj, color='red', title=predicate, arrows='to')\n", " elif predicate == 'PrecededBy':\n", " g.add_edge(obj, subject, color='red', title='succeededby', arrows='to')\n", " elif predicate == 'MergedWith':\n", " g.add_edge(subject, obj, color='green', title=predicate, width=3)\n", " g.add_edge(obj, subject, color='green', title=predicate, width=3)\n", "\n", " var_options = \"\"\"var_options = {\n", " \"nodes\": {\"font\": {\"size\": 12}},\n", " \"interaction\": {\"hover\": \"True\"}}\n", " \"\"\"\n", "\n", " g.set_options(var_options)\n", " return g\n", "\n", "display(daqa_firms_with_op_yrs[daqa_firms_with_op_yrs['ori_id'].astype(int)\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[0]['nodes'])][['primary_name','ori_id','start_yr','end_yr']]\\\n", " .sort_values(by='start_yr', ascending=True))\n", "\n", "g = output_graph(related_orgs_df[related_orgs_df['subject.ori_id']\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[0]['nodes'])][['subject.label','object.label','predicate.term']], 0)\n", "\n", "print('\\n')\n", "g.show(f\"networkgraph_0.html\")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "00b908f1", "metadata": {}, "source": [ "
Firm 2 (102 years)" ] }, { "cell_type": "code", "execution_count": 8, "id": "89cb460a", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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primary_nameori_idstart_yrend_yr
20931\"C.W. Chambers Architect and Consulting Engineer\"4415.018931910.0
20932\"McCredie Bros & Chambers\"4416.018991892.0
21556\"H.W. Atkinson & Chas. McLay\"2338.019071918.0
21715\"Chambers and Powell\"2499.019111920.0
21273\"Arnold Henry Conrad Architect\"4808.019171917.0
20911\"Atkinson and Conrad (1918-1927)\"4316.019181927.0
21673\"H.W. Atkinson & A.H. Conrad (1918-1927)\"2456.019181927.0
21531\"Chambers & Ford\"2310.019201935.0
20934\"Powell & Hutton Architects\"4419.019221924.0
21626\"Lange Powell Architect\"2408.019241927.0
21591\"Atkinson, Powell & Conrad (1927-1931)\"2373.019271931.0
21572\"H.W Atkinson & A.H Conrad (1931-1939)\"2354.019311939.0
20933\"Lange L Powell & Geo Rae Architects\"4417.019311933.0
21716\"Chambers & Hutton\"2500.019311940.0
21637\"Lange Powell\"2419.019331938.0
21691\"(Lange L.) Powell, Dods & Thorpe (PDT)\"2475.01938NaN
21664\"A.H Conrad & T.B.F Gargett\"2447.019391965.0
21612\"Ford, Hutton & Newell\"2394.019521958.0
21595\"Lund, Hutton & Newell\"2377.019591959.0
21704\"Lund, Hutton, Newell, Black & Paulsen\"2488.019601964.0
21296\"Ian Black and Company (Cairns)\"4852.01964NaN
21666\"Conrad Gargett & Partners (1965-1972)\"2449.019641966.0
21104\"Lund Hutton Newell Paulsen Pty Ltd\"4628.01965NaN
21663\"Conrad Gargett & Partners (1972-1995)\"2446.019721995.0
21418\"Conrad & Gargett\"5027.01995NaN
\n", "
" ], "text/plain": [ " primary_name ori_id start_yr \\\n", "20931 \"C.W. Chambers Architect and Consulting Engineer\" 4415.0 1893 \n", "20932 \"McCredie Bros & Chambers\" 4416.0 1899 \n", "21556 \"H.W. Atkinson & Chas. McLay\" 2338.0 1907 \n", "21715 \"Chambers and Powell\" 2499.0 1911 \n", "21273 \"Arnold Henry Conrad Architect\" 4808.0 1917 \n", "20911 \"Atkinson and Conrad (1918-1927)\" 4316.0 1918 \n", "21673 \"H.W. Atkinson & A.H. Conrad (1918-1927)\" 2456.0 1918 \n", "21531 \"Chambers & Ford\" 2310.0 1920 \n", "20934 \"Powell & Hutton Architects\" 4419.0 1922 \n", "21626 \"Lange Powell Architect\" 2408.0 1924 \n", "21591 \"Atkinson, Powell & Conrad (1927-1931)\" 2373.0 1927 \n", "21572 \"H.W Atkinson & A.H Conrad (1931-1939)\" 2354.0 1931 \n", "20933 \"Lange L Powell & Geo Rae Architects\" 4417.0 1931 \n", "21716 \"Chambers & Hutton\" 2500.0 1931 \n", "21637 \"Lange Powell\" 2419.0 1933 \n", "21691 \"(Lange L.) Powell, Dods & Thorpe (PDT)\" 2475.0 1938 \n", "21664 \"A.H Conrad & T.B.F Gargett\" 2447.0 1939 \n", "21612 \"Ford, Hutton & Newell\" 2394.0 1952 \n", "21595 \"Lund, Hutton & Newell\" 2377.0 1959 \n", "21704 \"Lund, Hutton, Newell, Black & Paulsen\" 2488.0 1960 \n", "21296 \"Ian Black and Company (Cairns)\" 4852.0 1964 \n", "21666 \"Conrad Gargett & Partners (1965-1972)\" 2449.0 1964 \n", "21104 \"Lund Hutton Newell Paulsen Pty Ltd\" 4628.0 1965 \n", "21663 \"Conrad Gargett & Partners (1972-1995)\" 2446.0 1972 \n", "21418 \"Conrad & Gargett\" 5027.0 1995 \n", "\n", " end_yr \n", "20931 1910.0 \n", "20932 1892.0 \n", "21556 1918.0 \n", "21715 1920.0 \n", "21273 1917.0 \n", "20911 1927.0 \n", "21673 1927.0 \n", "21531 1935.0 \n", "20934 1924.0 \n", "21626 1927.0 \n", "21591 1931.0 \n", "21572 1939.0 \n", "20933 1933.0 \n", "21716 1940.0 \n", "21637 1938.0 \n", "21691 NaN \n", "21664 1965.0 \n", "21612 1958.0 \n", "21595 1959.0 \n", "21704 1964.0 \n", "21296 NaN \n", "21666 1966.0 \n", "21104 NaN \n", "21663 1995.0 \n", "21418 NaN " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(daqa_firms_with_op_yrs[daqa_firms_with_op_yrs['ori_id'].astype(int)\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[1]['nodes'])][['primary_name','ori_id','start_yr','end_yr']]\\\n", " .sort_values(by='start_yr', ascending=True))\n", "\n", "g = output_graph(related_orgs_df[related_orgs_df['subject.ori_id']\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[1]['nodes'])][['subject.label','object.label','predicate.term']], 1)\n", "\n", "print('\\n')\n", "g.show(f\"networkgraph_1.html\")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "5467c7f8", "metadata": {}, "source": [ "
Firm 3 (99 years)" ] }, { "cell_type": "code", "execution_count": 9, "id": "6218d67b", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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primary_nameori_idstart_yrend_yr
21605\"John Hall & Son\"2387.018641896.0
21574\"Hall & Dods\"2356.018961916.0
21324\"HENNESSY AND HENNESSY AND F.R. HALL\"4890.01916NaN
21325\"F.R. HALL AND W. ALAN DEVEREUX\"4891.019231927.0
21326\"F R Hall Architect\"4892.019271930.0
20907\"F.R Hall and Cook\"4286.019301939.0
21674\"Harold M Cook & Walter J E Kerrison\"2457.019391962.0
21424\"Cook & Kerrison & Partners\"5033.01963NaN
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" ], "text/plain": [ " primary_name ori_id start_yr end_yr\n", "21605 \"John Hall & Son\" 2387.0 1864 1896.0\n", "21574 \"Hall & Dods\" 2356.0 1896 1916.0\n", "21324 \"HENNESSY AND HENNESSY AND F.R. HALL\" 4890.0 1916 NaN\n", "21325 \"F.R. HALL AND W. ALAN DEVEREUX\" 4891.0 1923 1927.0\n", "21326 \"F R Hall Architect\" 4892.0 1927 1930.0\n", "20907 \"F.R Hall and Cook\" 4286.0 1930 1939.0\n", "21674 \"Harold M Cook & Walter J E Kerrison\" 2457.0 1939 1962.0\n", "21424 \"Cook & Kerrison & Partners\" 5033.0 1963 NaN" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(daqa_firms_with_op_yrs[daqa_firms_with_op_yrs['ori_id'].astype(int)\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[2]['nodes'])][['primary_name','ori_id','start_yr','end_yr']]\\\n", " .sort_values(by='start_yr', ascending=True))\n", "\n", "g = output_graph(related_orgs_df[related_orgs_df['subject.ori_id']\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[2]['nodes'])][['subject.label','object.label','predicate.term']], 2)\n", "\n", "print('\\n')\n", "g.show(f\"networkgraph_2.html\")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "6040c7a1", "metadata": {}, "source": [ "
Firm 4 (84 years)" ] }, { "cell_type": "code", "execution_count": 10, "id": "7a04fdce", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
primary_nameori_idstart_yrend_yr
21731\"Arthur W. F. Bligh\"3574.01926NaN
21763\"Bligh & Jessup\"3870.019471952.0
20976\"Bligh Jessup & Partners\"4499.019531956.0
21662\"Bligh Jessup Bretnall & Partners\"2445.01957NaN
21052\"Colin W Jessup\"4576.019611975.0
20944\"Callaghan Robinson Architects\"4459.019721972.0
20945\"Noel Robinson Architects (& Partners)\"4460.019731977.0
20946\"Noel Robinson Built Environments\"4461.019771986.0
21713\"Bligh Jessup Robinson\"2497.019871960.0
20947\"Bligh Robinson\"4462.019891990.0
21658\"Noel Robinson Architects (2)\"2440.019901999.0
21614\"Bligh Voller Nield\"2396.019972009.0
20948\"Design Inc\"4463.020002010.0
21761\"BVN Architecture\"3868.02009NaN
20949\"NRACOLAB\"4464.020102020.0
\n", "
" ], "text/plain": [ " primary_name ori_id start_yr end_yr\n", "21731 \"Arthur W. F. Bligh\" 3574.0 1926 NaN\n", "21763 \"Bligh & Jessup\" 3870.0 1947 1952.0\n", "20976 \"Bligh Jessup & Partners\" 4499.0 1953 1956.0\n", "21662 \"Bligh Jessup Bretnall & Partners\" 2445.0 1957 NaN\n", "21052 \"Colin W Jessup\" 4576.0 1961 1975.0\n", "20944 \"Callaghan Robinson Architects\" 4459.0 1972 1972.0\n", "20945 \"Noel Robinson Architects (& Partners)\" 4460.0 1973 1977.0\n", "20946 \"Noel Robinson Built Environments\" 4461.0 1977 1986.0\n", "21713 \"Bligh Jessup Robinson\" 2497.0 1987 1960.0\n", "20947 \"Bligh Robinson\" 4462.0 1989 1990.0\n", "21658 \"Noel Robinson Architects (2)\" 2440.0 1990 1999.0\n", "21614 \"Bligh Voller Nield\" 2396.0 1997 2009.0\n", "20948 \"Design Inc\" 4463.0 2000 2010.0\n", "21761 \"BVN Architecture\" 3868.0 2009 NaN\n", "20949 \"NRACOLAB\" 4464.0 2010 2020.0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(daqa_firms_with_op_yrs[daqa_firms_with_op_yrs['ori_id'].astype(int)\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[3]['nodes'])][['primary_name','ori_id','start_yr','end_yr']]\\\n", " .sort_values(by='start_yr', ascending=True))\n", "\n", "g = output_graph(related_orgs_df[related_orgs_df['subject.ori_id']\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[3]['nodes'])][['subject.label','object.label','predicate.term']], 3)\n", "\n", "print('\\n')\n", "g.show(f\"networkgraph_3.html\")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "da4a6000", "metadata": {}, "source": [ "
Firm 5 (64 years)" ] }, { "cell_type": "code", "execution_count": 11, "id": "8950af01", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
primary_nameori_idstart_yrend_yr
21741\"Job & Collin\"3636.019381954.0
21694\"C W T Fulton in Assn with A H Job & J M Collin\"2478.019541955.0
21649\"Aubrey H. Job & R. P. Froud (Job & Froud)\"2431.019551976.0
21738\"C W T Fulton in Assn with J M Collin\"3633.019551960.0
20988\"JM Collin & CWT Fulton\"4511.019611966.0
21014\"J G Gilmour\"4537.019611966.0
21386\"G B Boys\"4994.019611966.0
21717\"J M Collin & C W T Fulton\"2501.019611966.0
21631\"Fulton Collin Boys Gilmour Trotter & Partners\"2413.019661981.0
21476\"C W T Fulton\"5085.01967NaN
21004\"RP Froud Architect\"4527.019741986.0
21739\"Fulton Gilmour Trotter & Moss\"3634.019811998.0
21740\"Fulton Trotter Moss\"3635.019982002.0
21554\"Fulton Trotter Architects\"2336.02002NaN
\n", "
" ], "text/plain": [ " primary_name ori_id start_yr \\\n", "21741 \"Job & Collin\" 3636.0 1938 \n", "21694 \"C W T Fulton in Assn with A H Job & J M Collin\" 2478.0 1954 \n", "21649 \"Aubrey H. Job & R. P. Froud (Job & Froud)\" 2431.0 1955 \n", "21738 \"C W T Fulton in Assn with J M Collin\" 3633.0 1955 \n", "20988 \"JM Collin & CWT Fulton\" 4511.0 1961 \n", "21014 \"J G Gilmour\" 4537.0 1961 \n", "21386 \"G B Boys\" 4994.0 1961 \n", "21717 \"J M Collin & C W T Fulton\" 2501.0 1961 \n", "21631 \"Fulton Collin Boys Gilmour Trotter & Partners\" 2413.0 1966 \n", "21476 \"C W T Fulton\" 5085.0 1967 \n", "21004 \"RP Froud Architect\" 4527.0 1974 \n", "21739 \"Fulton Gilmour Trotter & Moss\" 3634.0 1981 \n", "21740 \"Fulton Trotter Moss\" 3635.0 1998 \n", "21554 \"Fulton Trotter Architects\" 2336.0 2002 \n", "\n", " end_yr \n", "21741 1954.0 \n", "21694 1955.0 \n", "21649 1976.0 \n", "21738 1960.0 \n", "20988 1966.0 \n", "21014 1966.0 \n", "21386 1966.0 \n", "21717 1966.0 \n", "21631 1981.0 \n", "21476 NaN \n", "21004 1986.0 \n", "21739 1998.0 \n", "21740 2002.0 \n", "21554 NaN " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(daqa_firms_with_op_yrs[daqa_firms_with_op_yrs['ori_id'].astype(int)\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[4]['nodes'])][['primary_name','ori_id','start_yr','end_yr']]\\\n", " .sort_values(by='start_yr', ascending=True))\n", "\n", "g = output_graph(related_orgs_df[related_orgs_df['subject.ori_id']\\\n", " .isin(dts.sort_values(by='diff', ascending=False)\\\n", " .iloc[4]['nodes'])][['subject.label','object.label','predicate.term']], 4)\n", "\n", "print('\\n')\n", "g.show(f\"networkgraph_4.html\")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "13547dff", "metadata": {}, "source": [ "### Who worked on projects related to organisations such as the Queensland government, Brisbane City Council, and Department of Works?\n", "\n", "Below we list the architects who worked on government-related projects. We output three tables based on different search conditions." ] }, { "cell_type": "code", "execution_count": 12, "id": "c0ae5f85", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Person-organisation relationships for projects with type \"Government\"\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject.labelsubject._class_oripredicate.termobject.labelobject._class_ori
0Ruth WoodspersonWorkedWithBrisbane City Council Independent Design Advis...government
1Richard StringerpersonWorkedWithMitchell Librarygovernment
2Marion SullypersonEmploymentMitchell Librarygovernment
3Pamella MircovichpersonWorkedWithQueensland Government Architect’s Disciplinary...government
4Ruth WoodspersonWorkedWithQueensland Heritage Councilgovernment
5Ruth WoodspersonWorkedWithQueensland Urban Places Panelgovernment
\n", "
" ], "text/plain": [ " subject.label subject._class_ori predicate.term \\\n", "0 Ruth Woods person WorkedWith \n", "1 Richard Stringer person WorkedWith \n", "2 Marion Sully person Employment \n", "3 Pamella Mircovich person WorkedWith \n", "4 Ruth Woods person WorkedWith \n", "5 Ruth Woods person WorkedWith \n", "\n", " object.label object._class_ori \n", "0 Brisbane City Council Independent Design Advis... government \n", "1 Mitchell Library government \n", "2 Mitchell Library government \n", "3 Queensland Government Architect’s Disciplinary... government \n", "4 Queensland Heritage Council government \n", "5 Queensland Urban Places Panel government " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Person-organisation relationships for projects associated with organisations containing the term \"Brisbane City Council\" :\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject.labelsubject._class_oripredicate.termobject.labelobject._class_ori
0James BirrellpersonEmploymentBrisbane City Councilfirm
1Fedor MedekpersonEmploymentBrisbane City Councilfirm
2Louis Henry HaileypersonEmploymentBrisbane City Councilfirm
3John DaltonpersonEmploymentBrisbane City Councilfirm
4Ruth WoodspersonWorkedWithBrisbane City Councilfirm
5Robert RiddelpersonEmploymentBrisbane City Councilfirm
6Richard StringerpersonEmploymentBrisbane City Councilfirm
7Martin Louis ConradpersonEmploymentBrisbane City Councilfirm
8Peter RoypersonEmploymentBrisbane City Councilfirm
9Ruth WoodspersonReferenceBrisbane City Councilfirm
10Frank CostellopersonEmploymentBrisbane City Councilfirm
11John M. RailtonpersonEmploymentBrisbane City Councilfirm
12Arne FinkpersonEmploymentBrisbane City Councilfirm
13Ruth WoodspersonWorkedWithBrisbane City Council Independent Design Advis...government
14James BirrellpersonEmploymentBrisbane City Council, City Designfirm
15Robert FroudpersonEmploymentBrisbane City Council, City Designfirm
16Judy KraatzpersonEmploymentBrisbane City Council, City Designfirm
17Pamella MircovichpersonClientOfBrisbane City Council, City Designfirm
\n", "
" ], "text/plain": [ " subject.label subject._class_ori predicate.term \\\n", "0 James Birrell person Employment \n", "1 Fedor Medek person Employment \n", "2 Louis Henry Hailey person Employment \n", "3 John Dalton person Employment \n", "4 Ruth Woods person WorkedWith \n", "5 Robert Riddel person Employment \n", "6 Richard Stringer person Employment \n", "7 Martin Louis Conrad person Employment \n", "8 Peter Roy person Employment \n", "9 Ruth Woods person Reference \n", "10 Frank Costello person Employment \n", "11 John M. Railton person Employment \n", "12 Arne Fink person Employment \n", "13 Ruth Woods person WorkedWith \n", "14 James Birrell person Employment \n", "15 Robert Froud person Employment \n", "16 Judy Kraatz person Employment \n", "17 Pamella Mircovich person ClientOf \n", "\n", " object.label object._class_ori \n", "0 Brisbane City Council firm \n", "1 Brisbane City Council firm \n", "2 Brisbane City Council firm \n", "3 Brisbane City Council firm \n", "4 Brisbane City Council firm \n", "5 Brisbane City Council firm \n", "6 Brisbane City Council firm \n", "7 Brisbane City Council firm \n", "8 Brisbane City Council firm \n", "9 Brisbane City Council firm \n", "10 Brisbane City Council firm \n", "11 Brisbane City Council firm \n", "12 Brisbane City Council firm \n", "13 Brisbane City Council Independent Design Advis... government \n", "14 Brisbane City Council, City Design firm \n", "15 Brisbane City Council, City Design firm \n", "16 Brisbane City Council, City Design firm \n", "17 Brisbane City Council, City Design firm " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Person-organisation relationships for projects associated with organisations containing the term \"Department\" :\n" ] }, { "data": { "text/html": [ "
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subject.labelsubject._class_oripredicate.termobject.labelobject._class_ori
0Don(ald) C. RoderickpersonEmploymentCommonwealth Department of Worksfirm
1George HallandalepersonEmploymentCommonwealth Department of Worksfirm
2Harold DavispersonEmploymentCommonwealth Department of Worksfirm
3Reg PembertonpersonEmploymentCommonwealth Department of Worksfirm
4John DaltonpersonEmploymentCommonwealth Department of Worksfirm
5George Owen CowlishawpersonEmploymentCommonwealth Department of Worksfirm
6Roman PavylshynpersonEmploymentCommonwealth Department of Worksfirm
7Ian BlackpersonEmploymentCommonwealth Department of Worksfirm
8Margaret WardpersonEmploymentCommonwealth Department of Worksfirm
9Allan JagopersonEmploymentCommonwealth Department of Worksfirm
10Marion SullypersonReferenceCommonwealth Department of Worksfirm
11Balwant SainipersonEmploymentCommonwealth Department of Worksfirm
12Leonard (Lynn) J. GamblepersonEmploymentCommonwealth Department of Worksfirm
13Edwin OribinpersonEmploymentCommonwealth Department of Worksfirm
14Athol BretnallpersonEmploymentCommonwealth Department of Worksfirm
15Aubrey Clayton MuddpersonEmploymentCommonwealth Department of Worksfirm
16Marion SullypersonEmploymentCommonwealth Department of Worksfirm
17Harold AndersonpersonEmploymentCommonwealth Department of Worksfirm
18Donald WatsonpersonEmploymentCommonwealth Department of Worksfirm
19James BirrellpersonEmploymentCommonwealth Department of Worksfirm
20Harold PaulsenpersonEmploymentCommonwealth Department of Worksfirm
21Eric P. TrewernpersonEmploymentQueensland Department of Agriculturefirm
22Judy KraatzpersonEmploymentQueensland Government Department of Housingfirm
23Ted CroftspersonEmploymentQueensland Government Department of Housingfirm
24Margaret WardpersonEmploymentQueensland Government Department of Housingfirm
25Pamella MircovichpersonClientOfQueensland Government Department of Housingfirm
\n", "
" ], "text/plain": [ " subject.label subject._class_ori predicate.term \\\n", "0 Don(ald) C. Roderick person Employment \n", "1 George Hallandale person Employment \n", "2 Harold Davis person Employment \n", "3 Reg Pemberton person Employment \n", "4 John Dalton person Employment \n", "5 George Owen Cowlishaw person Employment \n", "6 Roman Pavylshyn person Employment \n", "7 Ian Black person Employment \n", "8 Margaret Ward person Employment \n", "9 Allan Jago person Employment \n", "10 Marion Sully person Reference \n", "11 Balwant Saini person Employment \n", "12 Leonard (Lynn) J. Gamble person Employment \n", "13 Edwin Oribin person Employment \n", "14 Athol Bretnall person Employment \n", "15 Aubrey Clayton Mudd person Employment \n", "16 Marion Sully person Employment \n", "17 Harold Anderson person Employment \n", "18 Donald Watson person Employment \n", "19 James Birrell person Employment \n", "20 Harold Paulsen person Employment \n", "21 Eric P. Trewern person Employment \n", "22 Judy Kraatz person Employment \n", "23 Ted Crofts person Employment \n", "24 Margaret Ward person Employment \n", "25 Pamella Mircovich person ClientOf \n", "\n", " object.label object._class_ori \n", "0 Commonwealth Department of Works firm \n", "1 Commonwealth Department of Works firm \n", "2 Commonwealth Department of Works firm \n", "3 Commonwealth Department of Works firm \n", "4 Commonwealth Department of Works firm \n", "5 Commonwealth Department of Works firm \n", "6 Commonwealth Department of Works firm \n", "7 Commonwealth Department of Works firm \n", "8 Commonwealth Department of Works firm \n", "9 Commonwealth Department of Works firm \n", "10 Commonwealth Department of Works firm \n", "11 Commonwealth Department of Works firm \n", "12 Commonwealth Department of Works firm \n", "13 Commonwealth Department of Works firm \n", "14 Commonwealth Department of Works firm \n", "15 Commonwealth Department of Works firm \n", "16 Commonwealth Department of Works firm \n", "17 Commonwealth Department of Works firm \n", "18 Commonwealth Department of Works firm \n", "19 Commonwealth Department of Works firm \n", "20 Commonwealth Department of Works firm \n", "21 Queensland Department of Agriculture firm \n", "22 Queensland Government Department of Housing firm \n", "23 Queensland Government Department of Housing firm \n", "24 Queensland Government Department of Housing firm \n", "25 Queensland Government Department of Housing firm " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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subject.labelsubject._class_oripredicate.termobject.labelobject._class_ori
0Arnold Henry ConradpersonEmploymentQueensland Government Department of Public Worksfirm
1Ursula KoroloffpersonEmploymentQueensland Government Department of Public Worksfirm
2Bruce PaulsenpersonEmploymentQueensland Government Department of Public Worksfirm
3Campbell R. ScottpersonEmploymentQueensland Government Department of Public Worksfirm
4Nellie McCrediepersonEmploymentQueensland Government Department of Public Worksfirm
5William HodgenpersonEmploymentQueensland Government Department of Public Worksfirm
6Cec FergopersonEmploymentQueensland Government Department of Public Worksfirm
7Colin S. TannettpersonEmploymentQueensland Government Department of Public Worksfirm
8Malcolm BunzlipersonEmploymentQueensland Government Department of Public Worksfirm
9Alf CarnellspersonEmploymentQueensland Government Department of Public Worksfirm
10Harold John HitchpersonEmploymentQueensland Government Department of Public Worksfirm
11Charles Ford WhitcombepersonEmploymentQueensland Government Department of Public Worksfirm
12Frank CostellopersonEmploymentQueensland Government Department of Public Worksfirm
13Susan SavagepersonWorkedWithQueensland Government Department of Public Worksfirm
14Fiona GardinerpersonEmploymentQueensland Government Department of Public Worksfirm
15Neville TwidalepersonEmploymentQueensland Government Department of Public Worksfirm
16Juanita PyepersonEmploymentQueensland Government Department of Public Worksfirm
17John DaltonpersonEmploymentQueensland Government Department of Public Worksfirm
18Jiri SvobodapersonEmploymentQueensland Government Department of Public Worksfirm
19Thomas Ramsay HallpersonEmploymentQueensland Government Department of Public Worksfirm
20Deborah CarlilepersonEmploymentQueensland Government Department of Public Worksfirm
21Peter PrystupapersonEmploymentQueensland Government Department of Public Worksfirm
22Eunice SlaughterpersonEmploymentQueensland Government Department of Public Worksfirm
23Ronald James CorbettpersonEmploymentQueensland Government Department of Public Worksfirm
24Alfred Barton BradypersonEmploymentQueensland Government Department of Public Worksfirm
25Ian T. SinnamonpersonEmploymentQueensland Government Department of Public Worksfirm
26Charles McLaypersonEmploymentQueensland Government Department of Public Worksfirm
27Paul MemmottpersonEmploymentQueensland Government Department of Public Worksfirm
28Frank CullenpersonEmploymentQueensland Government Department of Public Worksfirm
29Eric P. TrewernpersonEmploymentQueensland Government Department of Public Worksfirm
30Edwin T. CoddpersonEmploymentQueensland Government Department of Public Worksfirm
31John James ClarkpersonEmploymentQueensland Government Department of Public Worksfirm
32Roman PavylshynpersonEmploymentQueensland Government Department of Public Worksfirm
33Graham de GruchypersonEmploymentQueensland Government Department of Public Worksfirm
34David HunterpersonEmploymentQueensland Government Department of Public Worksfirm
35Charles TiffinpersonEmploymentQueensland Government Department of Public Worksfirm
36Henry AtkinsonpersonEmploymentQueensland Government Department of Public Worksfirm
37Alex Brown WilsonpersonEmploymentQueensland Government Department of Public Worksfirm
38Ury StukoffpersonEmploymentQueensland Government Department of Public Worksfirm
39Ronald James VollerpersonEmploymentQueensland Government Department of Public Worksfirm
40Garry MaypersonEmploymentQueensland Government Department of Public Worksfirm
41Ruth WoodspersonEmploymentQueensland Government Department of Public Worksfirm
42Margaret WestpersonEmploymentQueensland Government Department of Public Worksfirm
43Ralph TyrellpersonEmploymentQueensland Government Department of Public Worksfirm
44Ronald W. VollerpersonEmploymentQueensland Government Department of Public Worksfirm
45David MercerpersonEmploymentQueensland Government Department of Public Worksfirm
46Francis Drummond Greville StanleypersonEmploymentQueensland Government Department of Public Worksfirm
47Dorothy BrennanpersonEmploymentQueensland Government Department of Public Worksfirm
\n", "
" ], "text/plain": [ " subject.label subject._class_ori predicate.term \\\n", "0 Arnold Henry Conrad person Employment \n", "1 Ursula Koroloff person Employment \n", "2 Bruce Paulsen person Employment \n", "3 Campbell R. Scott person Employment \n", "4 Nellie McCredie person Employment \n", "5 William Hodgen person Employment \n", "6 Cec Fergo person Employment \n", "7 Colin S. Tannett person Employment \n", "8 Malcolm Bunzli person Employment \n", "9 Alf Carnells person Employment \n", "10 Harold John Hitch person Employment \n", "11 Charles Ford Whitcombe person Employment \n", "12 Frank Costello person Employment \n", "13 Susan Savage person WorkedWith \n", "14 Fiona Gardiner person Employment \n", "15 Neville Twidale person Employment \n", "16 Juanita Pye person Employment \n", "17 John Dalton person Employment \n", "18 Jiri Svoboda person Employment \n", "19 Thomas Ramsay Hall person Employment \n", "20 Deborah Carlile person Employment \n", "21 Peter Prystupa person Employment \n", "22 Eunice Slaughter person Employment \n", "23 Ronald James Corbett person Employment \n", "24 Alfred Barton Brady person Employment \n", "25 Ian T. Sinnamon person Employment \n", "26 Charles McLay person Employment \n", "27 Paul Memmott person Employment \n", "28 Frank Cullen person Employment \n", "29 Eric P. Trewern person Employment \n", "30 Edwin T. Codd person Employment \n", "31 John James Clark person Employment \n", "32 Roman Pavylshyn person Employment \n", "33 Graham de Gruchy person Employment \n", "34 David Hunter person Employment \n", "35 Charles Tiffin person Employment \n", "36 Henry Atkinson person Employment \n", "37 Alex Brown Wilson person Employment \n", "38 Ury Stukoff person Employment \n", "39 Ronald James Voller person Employment \n", "40 Garry May person Employment \n", "41 Ruth Woods person Employment \n", "42 Margaret West person Employment \n", "43 Ralph Tyrell person Employment \n", "44 Ronald W. Voller person Employment \n", "45 David Mercer person Employment \n", "46 Francis Drummond Greville Stanley person Employment \n", "47 Dorothy Brennan person Employment \n", "\n", " object.label object._class_ori \n", "0 Queensland Government Department of Public Works firm \n", "1 Queensland Government Department of Public Works firm \n", "2 Queensland Government Department of Public Works firm \n", "3 Queensland Government Department of Public Works firm \n", "4 Queensland Government Department of Public Works firm \n", "5 Queensland Government Department of Public Works firm \n", "6 Queensland Government Department of Public Works firm \n", "7 Queensland Government Department of Public Works firm \n", "8 Queensland Government Department of Public Works firm \n", "9 Queensland Government Department of Public Works firm \n", "10 Queensland Government Department of Public Works firm \n", "11 Queensland Government Department of Public Works firm \n", "12 Queensland Government Department of Public Works firm \n", "13 Queensland Government Department of Public Works firm \n", "14 Queensland Government Department of Public Works firm \n", "15 Queensland Government Department of Public Works firm \n", "16 Queensland Government Department of Public Works firm \n", "17 Queensland Government Department of Public Works firm \n", "18 Queensland Government Department of Public Works firm \n", "19 Queensland Government Department of Public Works firm \n", "20 Queensland Government Department of Public Works firm \n", "21 Queensland Government Department of Public Works firm \n", "22 Queensland Government Department of Public Works firm \n", "23 Queensland Government Department of Public Works firm \n", "24 Queensland Government Department of Public Works firm \n", "25 Queensland Government Department of Public Works firm \n", "26 Queensland Government Department of Public Works firm \n", "27 Queensland Government Department of Public Works firm \n", "28 Queensland Government Department of Public Works firm \n", "29 Queensland Government Department of Public Works firm \n", "30 Queensland Government Department of Public Works firm \n", "31 Queensland Government Department of Public Works firm \n", "32 Queensland Government Department of Public Works firm \n", "33 Queensland Government Department of Public Works firm \n", "34 Queensland Government Department of Public Works firm \n", "35 Queensland Government Department of Public Works firm \n", "36 Queensland Government Department of Public Works firm \n", "37 Queensland Government Department of Public Works firm \n", "38 Queensland Government Department of Public Works firm \n", "39 Queensland Government Department of Public Works firm \n", "40 Queensland Government Department of Public Works firm \n", "41 Queensland Government Department of Public Works firm \n", "42 Queensland Government Department of Public Works firm \n", "43 Queensland Government Department of Public Works firm \n", "44 Queensland Government Department of Public Works firm \n", "45 Queensland Government Department of Public Works firm \n", "46 Queensland Government Department of Public Works firm \n", "47 Queensland Government Department of Public Works firm " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filtered_cols = ['subject.label','subject._class_ori','predicate.term', 'object.label','object._class_ori']\n", "\n", "gov_projects = []\n", "\n", "for idx,row in daqa_orgs[daqa_orgs['_class_ori'].str.contains('government',na=False)].iterrows():\n", " try: gov_projects.append(pd.json_normalize(json.loads(row['related_people']))[filtered_cols])\n", " except: pass\n", "\n", "gov_projects = pd.concat(gov_projects)\n", "\n", "bcc_projects = []\n", "\n", "for idx,row in daqa_orgs[daqa_orgs['primary_name'].str.contains('Brisbane City',na=False)].iterrows():\n", " try: bcc_projects.append(pd.json_normalize(json.loads(row['related_people']))[filtered_cols])\n", " except: pass\n", "\n", "bcc_projects = pd.concat(bcc_projects)\n", "bcc_projects = bcc_projects[bcc_projects['object._class_ori'] != \"person\"]\n", "\n", "bcc_projects = pd.concat([bcc_projects, pd.DataFrame({'subject.label': \"Pamella Mircovich\",\n", " 'subject._class_ori': \"person\",\n", " 'predicate.term': \"ClientOf\",\n", " 'object.label': \"Brisbane City Council, City Design\",\n", " 'object._class_ori': \"firm\"}, index=[0])])\n", "\n", "dept_projects = []\n", "\n", "for idx,row in daqa_orgs[daqa_orgs['primary_name'].str.contains('Department',na=False)].iterrows():\n", " try: dept_projects.append(pd.json_normalize(json.loads(row['related_people']))[filtered_cols])\n", " except: pass\n", "\n", "dept_projects = pd.concat(dept_projects)\n", "dept_projects = dept_projects[dept_projects['object._class_ori'] != \"person\"]\n", "\n", "dept_projects = pd.concat([dept_projects, pd.DataFrame({'subject.label': \"Pamella Mircovich\",\n", " 'subject._class_ori': \"person\",\n", " 'predicate.term': \"ClientOf\",\n", " 'object.label': \"Queensland Government Department of Housing\",\n", " 'object._class_ori': \"firm\"}, index=[0])])\n", "\n", "print('Person-organisation relationships for projects with type \"Government\"')\n", "display(gov_projects.sort_values(by='object.label').drop_duplicates().reset_index(drop=True))\n", "\n", "print('\\n\\nPerson-organisation relationships for projects associated with organisations containing the term \"Brisbane City Council\" :')\n", "display(bcc_projects.sort_values(by='object.label').drop_duplicates().reset_index(drop=True))\n", "\n", "print('\\n\\nPerson-organisation relationships for projects associated with organisations containing the term \"Department\" :')\n", "display(dept_projects.sort_values(by='object.label').drop_duplicates().head(26).reset_index(drop=True))\n", "display(dept_projects.sort_values(by='object.label').drop_duplicates().tail(48).reset_index(drop=True))" ] }, { "attachments": {}, "cell_type": "markdown", "id": "fa92ee79", "metadata": {}, "source": [ "### Projects by completion date\n", "\n", "Below are some temporal statistics about completed projects in the DAQA dataset. We divide the data into four periods: before 1940, between 1940-1980, post 1980, and undated. Proportions are calculated as a percentage of the total number of completed projects in the dataset." ] }, { "cell_type": "code", "execution_count": 13, "id": "48cef0bd", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "There are 2203 projects in DAQA.\n", "There are 465 (21.11%) projects with completion dates before 1940.\n", "There are 608 (27.6%) projects with completion dates between 1940 and 1980.\n", "There are 257 (11.67%) projects with completion dates after 1980.\n", "There are 873 (39.63%) projects with no completion dates.\n", "\n", "\n" ] }, { "data": { "image/png": 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6enpQqVQaj5iYGDx+/LjAeQsSHx8PIUSB6w4gX5+srKw0nuvr6wNAoR/aeW28uN7ayk6dOgUfHx8AwJIlS/DPP/8gNDQUEyZMeOly8jx8+BDnz5/Pt51MTEwghJC2VdOmTbFt2zZkZWXhf//7H8qVKwcPDw+sX7++0Pbj4uKgVCphY2OjUa5QKGBvb1/o6/hf5H15Pf9lFBAQAAMDA7Rt2xYJCQlISEhAjRo14OzsjBUrViA7OxsAUL58eQAo9H1x69YtAND4EgsICAAAdO3aVWrfzMwMH3zwAbZs2YKEhIQi9b2g1/7FbaVtX4yLi3ulffR5Dx8+xI4dO/LtC9WqVQMAaV949OgRHBwcoKPzel8Nv/76K7777jts27YNzZs3h6WlJT788MN8lyy/ihffa0Du++3594CxsTFq1apVpEfeZ9LzhBAYOHAg1qxZgxUrVqBLly4F9uf27dto3rw5lEolDh06lO8zr6DPsNTUVGRkZGj97KxWrRrq1auHjz/+GHv37oWTkxO++uqrQrcL5VKWdgeoZOjq6qJly5bYs2cP7t27h3LlyhVaP++DIjo6Ol/dBw8ewNrautj7GBMTg7Jly0rPs7KyEBcXp/VDK4+1tTWsrKywd+9erdOf/4+tqCwsLKCjo4Po6Oh80x48eCAt97/KW6+YmJh8014s27BhA1QqFXbu3AkDAwOp/FXuzWFtbQ21Wl3gSMHz69SlSxd06dIF6enpOHHiBGbMmIFevXrB2dkZDRs2LHB9srKy8OjRI40gI4RATEyMNMJT3LZv3w4A0v2Drl27hmPHjgH4N6S8aN++fWjfvj18fHwwfvx4bNu2DW3bttVaN28b542EJCYmYsuWLQBQ4DqtW7cOQ4cOfWnfC3rtXV1dNcq0/bduZWX12vuotbU1atSogenTp2udnheEbGxscOzYMeTk5LxWkDEyMsLkyZMxefJkPHz4UBqV6dSpE65cufLK7RVVcHAwmjdvXqS6UVFRGvdkygswy5cvR0BAAPr06VPgvLdv34a3tzeEEAgKCtL6uVq9enVs2LABMTExGqH1woULAAAPD49C+6dUKlG7dm1s2rSpSOvzvuNIzDts3LhxEELgiy++QEZGRr7pmZmZ2LFjB4B/P7DXrFmjUSc0NBSXL19Gy5Yti71/a9eu1Xi+adMmZGVlFXpzu44dOyIuLg7Z2dmoU6dOvoe7u7tU98X/1gpiZGSE+vXr46+//tKon5OTgzVr1qBcuXKoVKnSq6/gC9zd3VGmTBmsX79e45DI7du3893cSqFQQKlUSocugNzRl9WrV+drt6D17NixIyIjI2FlZaV1W2m7uZ6+vj6aNWuGWbNmAQDOnj1b4Prk7RMv7jNbtmxBampqiewzBw4cwNKlS9GoUSN88MEHAP4dJVmyZAkCAwM1Hrt374ZKpZKCnJeXF9q0aYOAgAD8888/+do/duwYli1bhsaNG6NOnToAcgNKWloapk6dmq/9wMBAWFtbF/mQ0ov7fEhIiPTF+DItW7bEpUuXcObMGY3yVatWQaFQFPol3rFjR0RERKBixYpa94W8ENOuXTs8e/bspTdPLMp7y87ODv3798enn36Kq1ev4unTpy9dx9f1uoeT8j4fly9fjj/++AOfffZZgcu4c+cOvL29kZ2djcOHD8PJyUlrvS5dukChUGDlypUa5StWrIBarS4wPOd59uwZTpw4kS/YknYciXmHNWzYEIsWLcLQoUPh5eWFL7/8EtWqVUNmZibOnj2LxYsXw8PDA506dYK7uzsGDRqE+fPnQ0dHB+3atcOtW7fwww8/wNHREaNGjSr2/v31119QKpVo3bo1Ll68iB9++AE1a9ZE9+7dC5ynZ8+eWLt2Ldq3b4+vvvoK9erVg0qlwr179xAYGIguXbqga9euAP79j2jjxo2oUKECDAwMUL16da3tzpgxA61bt0bz5s0xZswY6OnpYeHChYiIiMD69esLPcZdVDo6Opg6dSoGDhyIrl274osvvkBCQgL8/PzyHWbo0KED5syZg169emHQoEGIi4vDTz/9JB22el5B6+nr64stW7agadOmGDVqFGrUqIGcnBzcuXMH+/fvx+jRo1G/fn1MnDgR9+7dQ8uWLVGuXDkkJCTgl19+gUqlQrNmzQpcn9atW6NNmzb47rvvkJSUhMaNG+P8+fOYNGkSPD090bdv39feVjk5OThx4gQAID09HXfu3MGePXuwadMmVKlSRfovNSsrC6tWrUKVKlUwcOBArW116tQJ27dvl0aMVq5ciZYtW8LHxwcjR46Uwtbhw4fxyy+/wN7eHhs3bpTmDwgIgIWFBcaMGaMxKpbnf//7H+bMmYNz586hZs2aha7X6dOnMXDgQHzyySe4e/cuJkyYgLJlyxZpFGfUqFFYtWoVOnTogClTpsDJyQm7du3CwoUL8eWXXxYatKdMmYIDBw6gUaNGGDlyJNzd3fHs2TPcunULu3fvxu+//45y5crh008/xfLlyzFkyBBcvXoVzZs3R05ODk6ePIkqVaqgZ8+eAHL3uaCgIOzYsQNlypSBiYkJ3N3dUb9+fXTs2BE1atSAhYUFLl++jNWrV6Nhw4YwNDQEkHu4zsXFBf369Su2O02bmJhIofNVjBw5EgEBAfj8889RvXp1aZ8DcoOap6cnACA2NhbNmzdHdHQ0AgICEBsbq3FeWrly5aRRmWrVqmHAgAGYNGkSdHV1UbduXezfvx+LFy/GtGnTNA4nNWrUCJ07d0aVKlVgZmaGW7duYdGiRYiMjMTWrVtfd3O8X0rjbGJ6s8LDw0W/fv1E+fLlhZ6ennQJ7MSJEzUudc27T0ylSpWESqUS1tbWok+fPgXeJ+ZFTk5OokOHDvnK8cKVHnlXdYSFhYlOnToJY2NjYWJiIj799NN8N5/Sdp+YzMxM8dNPP4maNWsKAwMDYWxsLCpXriwGDx4srl+/LtW7deuW8PHxESYmJq90nxgjIyOhVqtFgwYN8t2PIu8qkxev+Mm7OiIwMDDf+r9o6dKlws3NTejp6YlKlSqJZcuWab1PzLJly4S7u7vQ19cXFSpUEDNmzBABAQH5rrAoaD2FyL2s8/vvv5fuqWNmZiaqV68uRo0aJWJiYoQQuff1adeunShbtqzQ09MTtra2on379lpvBveitLQ08d133wknJyehUqlEmTJlxJdffqlxybAQr351Ep67mkStVovy5cuLTp06iWXLlon09HSp7rZt2wQAMW/evALby7uK6ueff9bYLtOnTxc1a9YUhoaG0rK6dOkinjx5ItU7d+6cACB8fX0LbP/KlSsCgBgxYkSBdZ6/T0zfvn2Fubm5dJny8/usEAW/v4TIvU9Mr169hJWVlVCpVMLd3V38+OOPWu8T4+fnp1H26NEjMXLkSOHi4iJUKpWwtLQUXl5eYsKECRr3DUpLSxMTJ06U9lErKyvRokULERISItUJDw8XjRs3lrZd3nt07Nixok6dOsLCwkLab0eNGiUeP34szXvhwgUBQIwdO7bA7fX8ejz/2ZHHycmpWG62p+0+S3mP599HL7v66cV78mRkZIhJkyZJn7mVKlXSuEIzz+jRo0XNmjWFmZmZUCqVwt7eXnTt2lW6Jw+9nEIILaf6E5UgPz8/TJ48GY8ePSqRc22IXlVSUhKaNWuGhw8f4ujRo6hYsWKxtr9ixQp89tlnCA0Nfa0Rg1eRmJgIc3NzzJ8/H8OHDy/RZb2OhQsX4ttvv0VkZCTs7OxKuzskczwnhojee6amptLlxS1btsTdu3dLu0uv5cSJE9IPPxZ0QnZpCwwMxMiRIxlgqFjwnBgiIuRe6nzz5s3S7sZ/0qtXL2RnZ+Pnn3/WuI/T2+TPP/8s7S7QO4SHk4iIiEiWXvlw0pEjR9CpUyc4ODhAoVDku2+FEAJ+fn5wcHCAWq2Gt7c3Ll68qFEnPT0dI0aMgLW1NYyMjNC5c2fcu3dPo058fDz69u0LMzMzmJmZoW/fvkW+oRQRERG9+145xKSmpqJmzZpYsGCB1umzZ8/GnDlzsGDBAoSGhsLe3h6tW7dGcnKyVMfX1xdbt27Fhg0bcOzYMaSkpKBjx47SXTWB3GHR8PBw7N27F3v37kV4ePh/umSTiIiI3i3/6XCSQqHA1q1b8eGHHwLIHYVxcHCAr68vvvvuOwC5oy52dnaYNWsWBg8ejMTERNjY2GD16tXo0aMHgNw7Tjo6OmL37t1o06YNLl++jKpVq+LEiROoX78+gNwT1ho2bIgrV65o3NCMiIiI3k/FemJvVFQUYmJipN98Af69A2hISAgGDx6MsLAwZGZmatRxcHCAh4cHQkJC0KZNGxw/fhxmZmZSgAGABg0awMzMDCEhIVpDTHp6OtLT06XnOTk5ePLkCaysrIrlRmVERERU8oQQSE5OLtLveBVriMn7XZAXL52zs7OTfrQtJiYGenp6sLCwyFcnb/6YmBjY2trma9/W1lbrb48AuXdcnTx58n9eByIiIip9d+/efenv/pXIJdYvjnwIIV46GvJiHW31C2tn3Lhx+Prrr6XniYmJKF++PO7evQtTU9NX6T4RERGVkqSkJDg6OhbpB32LNcTk/f5LTEyMxk/Gx8bGSqMz9vb2yMjIQHx8vMZoTGxsLBo1aiTVefjwYb72Hz16VOANkvT19bX+roypqSlDDBERkcwU5VSQYr1jr4uLC+zt7XHgwAGpLCMjA8HBwVJA8fLygkql0qgTHR2NiIgIqU7Dhg2RmJiIU6dOSXVOnjyJxMREqQ4RERG93155JCYlJQU3btyQnkdFRSE8PByWlpYoX748fH194e/vDzc3N7i5ucHf3x+Ghobo1asXAMDMzAwDBgzA6NGjYWVlBUtLS4wZMwbVq1dHq1atAABVqlRB27Zt8cUXX+CPP/4AAAwaNAgdO3bklUlEREQE4DVCzOnTp9G8eXPped55KHk/q/7tt98iLS0NQ4cORXx8POrXr4/9+/drHNuaO3culEolunfvjrS0NLRs2RIrVqyArq6uVGft2rUYOXKkdBVT586dC7w3DREREb1/3tmfHUhKSoKZmRkSExN5TgwREZFMvMr3N3/FmoiIiGSJIYaIiIhkiSGGiIiIZIkhhoiIiGSJIYaIiIhkiSGGiIiIZIkhhoiIiGSJIYaIiIhkiSGGiIiIZIkhhoiIiGSJIYaIiIhkiSGGiIiIZIkhhoiIiGSJIYaIiIhkiSGGiIiIZIkhhoiIiGSJIYaIiIhkiSGGiIiIZIkhhoiIiGSJIYaIiIhkiSGGiIiIZIkhhoiIqJicPXsWH374IRwcHGBoaIjKlStjypQpePr0qVRHoVAU+KhcubJULzU1FT179oS7uztMTExgZGSEatWqYdq0aUhNTS2N1XvrKEu7A0RERO+CS5cuoVGjRnB3d8e8efNgbW2NI0eOYMqUKQgLC8Pff/8NADh+/Hi+eU+ePAlfX1907dpVKsvMzIQQAl9//TVcXFygo6MjtRcUFISDBw++sXV7WzHEEBERFYN169bh2bNn2LJlCypWrAgAaNGiBaKjo7F48WLEx8fDwsICDRo0yDfvH3/8AYVCgQEDBkhl5ubm2Lhxo0a9Vq1aIT09HbNnz8bNmzdRoUKFkl2ptxwPJxERERUDlUoFADAzM9MoNzc3h46ODvT09LTOl5ycjD///BPNmjWDq6vrS5djY2MDAFAqOQ7BEENERFQM+vXrB3Nzc3z55Ze4efMmkpOTsXPnTvzxxx8YNmwYjIyMtM63YcMGpKamYuDAgVqnCyGQlZWFpKQk7N27Fz///DM+/fRTlC9fviRXRxYY44iIiIqBs7Mzjh8/jq5du0qHkwBg5MiRmDdvXoHzBQQEwNzcHB999JHW6Rs3bsSnn34qPf/ss8+wePHiYuu3nDHEEBERFYNbt26hU6dOsLOzw+bNm2FjY4OTJ09i2rRpSElJQUBAQL55Ll68iJMnT2LYsGEwMDDQ2m6bNm0QGhqK5ORkHD9+HLNmzUJcXBy2bt0KHZ33+4AKQwwREVExGDt2LJKSkhAeHi4dOmratCmsra3x+eef43//+x+aNWumMU9esCnoUBIAWFhYoE6dOgCA5s2bo2LFiujZsyf+/vtvjauZ3kfvd4QjIiIqJuHh4ahatWq+c1/q1q0LAIiIiNAoz8jIwOrVq+Hl5YVatWoVeTn16tUDAFy7du2/dfgdwBBDRERUDBwcHHDx4kWkpKRolOfdF6ZcuXIa5du3b8fjx481LqsuisDAQAAo0pVM7zqFEEKUdidKQlJSEszMzJCYmAhTU9PS7g4REb3jtm/fjg8//BD169fHqFGjYG1tjRMnTmDGjBkoX748zp49q3GZdbt27RAcHIzo6Oh8l2UDufeOOXr0KHx8fODo6IjU1FQcPXoU8+fPh6enJ4KDg9/Jy6xf5fubIYaIiKiYBAYGYubMmTh//jwSExPh6OiITp06Ydy4cbCyspLq3b17F87OzujTpw9Wrlypta2QkBDMmDEDZ86cwePHj6FUKuHm5oaPP/4YX3/9NQwNDd/Uar1RDDFgiCEiIpKjV/n+5jkxREREJEsMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkS+/eXXKIiOi94jx2V2l34b11a2aHUl0+R2KIiIhIlhhiiIiISJYYYoiIiEiWGGKIiIhIlhhiiIiISJYYYoiIiEiWGGKIiIhIlhhiiIiISJYYYoiIiEiWGGKIiIhIlhhiiIiISJYYYoiIiEiWGGKIiIhIlhhiiIiISJYYYoiIiEiWlKXdASIiordd1TKmGNPGHe72JrAy0sOzzGzcfJSKVcdvY1v4fY26/Rs5o28DJ5SzVCPxaSb2X3qI2fuuICktq0jLUqt0McS7IjrVKIOyFmo8Tc/GlZhkjPvrPG7FPQUAlDEzgF/naqhibwprEz1k5QjcffIUG0PvYu3JO8jOEVJ79VwsMalTVThZGeHig0RM2BqBG7EpGsuc0qUa3O1M0GPxif+4pd4shhgiIqKXMFUrEZ2Qhu3hD/Aw6RnUerr4sJYD5vWshXKWaiw4fAMAMKFDFXze2AVLjtzEsRuP4WZnjFGtK6FGOTN0WxiCrOfChTaGerpY/0UD2JkaYFFwJK5EJ8HEQAkvJwuo9XQ16qU8y8L8w9dxPyENero68K5siyldPFDVwRRjt1zI7beBEn/08cKeiBjM2H0F/Rs7448+Xmg9Nxh5XfF0NEf3Oo7o8OvRktl4JYghhoiI6CVO3HyCEzefaJQdvhILR0tDfFqvPBYcvgE7U3181sgZq47fwsy9VwAAx248RlxKBn791BMfe5XDhtC7hS5njI87XG2N0faXI7j7JE0qP3g5VqNe5KNUjP7znEZZ0LVHsDbSw0e1y2HitovIyM5B7fIW0FPqYOLfEcjKEbj2MBmnJrSCi7URIh+lQqmjgH+36vg9OBKRj1L/yyYqFTwnhoiI6DU9Sc1AdnbukIZneQsodXUQdPWRRp1Dlx8CANp52BfaloFKBz3qOmL3hWiNAPMq4lIzkCMEskVun/RVOsjIypFGgJ5mZOeWK3NHdQY1rQA9XR0sDIx8reWVNo7EEBERFZFCAegoFDBTq9C+ehk0rWSDSX9fBADo6eaOC2Rk5WjMk5UjkJMjULmMaaFtVy9rBiN9JaIep2Lahx7oVNMBapUursQkYe6B6wi8Gqt1Pl0dBYz0ddHUzQYfe5XD0qNR0jkx5+8lwkhfiT71y2P7uQf40rsinqRmIPJRCspbGmJ4C1f0Xx6KjOwcrW2/7RhiiIiIimhaFw/0buAEAEjPysbk7Rex7tQdAMD12GQAgJezBY7fjJPmqe1kAR0dBcwNVYW2bW9qAAAY4l0RV2OS8fWmcOTkAF80dUFAvzrov/wUjlx/rDHPl80q4rt2lQEAOTkCvwXdwM/7r0nToxOfYcqOi/ihU1VM61odSWmZ+HpTONKzcjC9qwe2n3uAU1Gah8nkhCGGiIioiH4LvIENoXdhZayHVlXsMLmLB9R6Siw5ehOXo5Nx8mYcBjWtgJuPUnDs+mO42ppg+oceyMrOgSj8nF4oFAoAQGZWDvovO4XU/z/0c/zmYwSNaY4RLd3yhZjNYfdw7MZjmBuq0KiiFQY1rQATAxX8tl+U6qw5eQdbztyHg7kB7j5JQ0Z2Drp6lkVle1MMW3cGZmoVJneuhiZu1khJz8LiIzex9uSd4t1wJaTYz4nJysrC999/DxcXF6jValSoUAFTpkxBTs6/Q1VCCPj5+cHBwQFqtRre3t64ePGiRjvp6ekYMWIErK2tYWRkhM6dO+PevXvF3V0iIqIie5D4DBfuJyLo6iN8vy0C60/dwbdt3WFppAcAGLr2DMJuxWNhby+c92uD9YPqY9/FGFyKTkJM4rNC2054mgEAOHMnXgowAPAsMwcno+Lg4WCWb55HKem4cD8RR68/xqy9VzH3wDX0b+SMag6ah67SMrMR+SgVGdk5MDdUYUKHKpi68xKS0rIwqVNVmBuq4P1jEEasP4vx7augYQWr/7qp3ohiDzGzZs3C77//jgULFuDy5cuYPXs2fvzxR8yfP1+qM3v2bMyZMwcLFixAaGgo7O3t0bp1ayQnJ0t1fH19sXXrVmzYsAHHjh1DSkoKOnbsiOzsbG2LJSIieuPO3U2ASlcH5S0NAeSeWPvZilB4TT2AtvOOoM7Ug5hz4BpcrI1w6lbhh20uxyQXOE0BIOdlQzkAzt1NBAC4WBsVWOf7DlUQcT8R2889AAB4u9ti9YnbSE7Pwvl7uYHI293mpct6GxT74aTjx4+jS5cu6NChAwDA2dkZ69evx+nTpwHkjsLMmzcPEyZMQLdu3QAAK1euhJ2dHdatW4fBgwcjMTERAQEBWL16NVq1agUAWLNmDRwdHXHw4EG0adOmuLtNRET0yhpUtEJ2jsCdJ081yuNSMxCXmjuy0r+RMwz1lFgZcqvQth4lpyPsdjy8nCxhrK9ESnruzfEMVDqoX8EKZ+8kFKk/AHArTvvl0g0rWKF99TLwmXtEKlMAMFRp3oMm79DW267YQ8wHH3yA33//HdeuXUOlSpVw7tw5HDt2DPPmzQMAREVFISYmBj4+PtI8+vr6aNasGUJCQjB48GCEhYUhMzNTo46DgwM8PDwQEhKiNcSkp6cjPT1dep6UlFTcq0ZERO8p/67VkZKehXN3E/A4JR0WRnroUL0MOtV0wO/BkXjy/4GlZ11HAMDtJ09haqCCt7sNetRxxI/7ruLiA83vpRvT2+Fk1BP0XnpSKpu+6zLWD6qPVZ/Xw+/BkRAABjZxgYWhHuYcuCrVG9XKDdbG+jgZ9QQPk57BVK1Cs0o26FnXETvPP0DE/fzfgXq6Opje1QPzDl7Hvfh/L+E+cv0RRrZ0Q3J6FlysjdDY1Rp/HJHHJdfFHmK+++47JCYmonLlytDV1UV2djamT5+OTz/9FAAQExMDALCzs9OYz87ODrdv35bq6OnpwcLCIl+dvPlfNGPGDEyePLm4V4eIiAhn78Tj4zrl8FHtsjBVq/A0IxuXo5PguyFc42cHFArg88YuKGuhRo4ALj5IxOA1YThw6WG+NpW6OtDV0RzxOHMnHr2XnMSYNu6Y17PW/y87AT0Xn8CZ50Zizt9PRP9GzvCpZgdzQz2kZ+XgxsNkTN11GWtO3Na6DsNbuOJZZjYCjkVplE/ecQlTu3jg156eSE7PwrRdl/DPjTitbbxtij3EbNy4EWvWrMG6detQrVo1hIeHw9fXFw4ODujXr59U78WhKiHES4evCqszbtw4fP3119LzpKQkODo6/oc1ISIiyvVn2D38Gfbyi0vWn7qL9acKvytvHuexu7SWn74dj54v+Q2jQ5djceiy9vvGFGTOgWuYc+BavvInqRkYtu7MK7X1tij2EPPNN99g7Nix6NmzJwCgevXquH37NmbMmIF+/frB3j73joUxMTEoU6aMNF9sbKw0OmNvb4+MjAzEx8drjMbExsaiUaNGWperr68PfX394l4dIiIieksV+9VJT58+hY6OZrO6urrSJdYuLi6wt7fHgQMHpOkZGRkIDg6WAoqXlxdUKpVGnejoaERERBQYYoiIiOj9UuwjMZ06dcL06dNRvnx5VKtWDWfPnsWcOXPw+eefA8g9jOTr6wt/f3+4ubnBzc0N/v7+MDQ0RK9evQAAZmZmGDBgAEaPHg0rKytYWlpizJgxqF69unS1EhEREb3fij3EzJ8/Hz/88AOGDh2K2NhYODg4YPDgwZg4caJU59tvv0VaWhqGDh2K+Ph41K9fH/v374eJiYlUZ+7cuVAqlejevTvS0tLQsmVLrFixArq6utoWS0RERO8ZhRBFuHuODCUlJcHMzAyJiYkwNS38R7eIiEi+CjpBlkrerZkdir3NV/n+LvZzYoiIiIjeBIYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhiit9ixY8fQvn17WFhYQK1Ww83NDVOnTtWoc+bMGbRq1QrGxsYwNzdHt27dcPPmzXxtxcTEYPjw4ahQoQLUajWcnJwwYMAA3Llz502tDhFRsWKIIXpLrVu3Ds2aNYOZmRlWrVqF3bt347vvvoMQQqpz5coVeHt7IyMjA5s2bcKyZctw7do1NGnSBI8ePZLqpaeno2nTpti4cSPGjBmDPXv2YPz48di1axcaNWqE5OTk0lhFIqL/RFnaHSCi/O7fv49BgwZh8ODBWLhwoVTevHlzjXoTJ06Evr4+du7cCVNTUwCAl5cX3Nzc8NNPP2HWrFkAgKNHj+L69etYunQpBgwYAADw9vaGqakpevXqhYMHD6Jr165vaO2IiIoHR2KI3kJLly5FamoqvvvuuwLrZGVlYefOnfjoo4+kAAMATk5OaN68ObZu3SqVqVQqAICZmZlGG+bm5gAAAwODYuw9EdGbwRBD9BY6cuQILC0tceXKFdSqVQtKpRK2trYYMmQIkpKSAACRkZFIS0tDjRo18s1fo0YN3LhxA8+ePQMANG7cGF5eXvDz80NoaChSUlJw5swZjB8/HrVr10arVq3e6PoRERUHhhiit9D9+/fx9OlTfPLJJ+jRowcOHjyIb775BqtWrUL79u0hhEBcXBwAwNLSMt/8lpaWEEIgPj4eAKBUKhEYGIgKFSqgXr16MDExgZeXF8zNzXHgwAFppIaISE54TgzRWygnJwfPnj3DpEmTMHbsWAC557Do6enB19cXhw4dgqGhIQBAoVAU2E7etMzMTPTo0QMRERFYsmQJ3N3dERUVhWnTpqF169Y4fPhwvkNNRERvO47EEL2FrKysAABt2rTRKG/Xrh2A3Muq8+rkjcg878mTJ1AoFNI5LwEBAdizZw/++usvDBw4EE2aNMH//vc/7N27F2fOnMG8efNKbmWIiEoIQwzRW0jbeS4ApMurdXR0ULFiRajValy4cCFfvQsXLsDV1VU6YTc8PBy6urqoXbu2Rr0KFSrAysoKERERxbwGREQljyGG6C300UcfAQD27NmjUb57924AQIMGDaBUKtGpUyf89ddfGvd5uXPnDgIDA9GtWzepzMHBAdnZ2QgNDdVo79q1a4iLi0O5cuVKalWIiEqMQjx/56x3SFJSEszMzJCYmKhx+SmRXHTu3Bn79+/H999/jwYNGuD06dOYPHkyWrVqhR07dgDIvdld3bp1Ubt2bYwdOxbPnj3DxIkT8eTJE4SHh8PGxgYAcPfuXdSoUQNGRkb4/vvv4e7ujps3b8Lf3x8PHz5EWFgY3N3dS3N1iV6b89hdpd2F99atmR2Kvc1X+f7mSAzRW2rjxo3w9fXF4sWL0a5dOyxatAijRo3C5s2bpTqVK1dGUFAQVCoVPv74Y/Tv3x+urq44cuSIFGAAwNHREaGhoWjRogVmzZqFdu3aYcqUKahbty5OnTrFAENEssSRGCIikjWOxJQejsQQERERvQaGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpKlEgkx9+/fR58+fWBlZQVDQ0PUqlULYWFh0nQhBPz8/ODg4AC1Wg1vb29cvHhRo4309HSMGDEC1tbWMDIyQufOnXHv3r2S6C4RERHJULH/AGR8fDwaN26M5s2bY8+ePbC1tUVkZKT0Gy4AMHv2bMyZMwcrVqxApUqVpB+hu3r1KkxMTAAAvr6+2LFjBzZs2AArKyuMHj0aHTt2RFhYGHR1dYu720QAeKlmaSqJSzWJ6N1W7CFm1qxZcHR0xPLly6UyZ2dn6W8hBObNm4cJEyZIt0VfuXIl7OzssG7dOgwePBiJiYkICAjA6tWr0apVKwDAmjVr4OjoiIMHD+b7UTwiIiJ6/xT74aTt27ejTp06+OSTT2BrawtPT08sWbJEmh4VFYWYmBj4+PhIZfr6+mjWrBlCQkIAAGFhYcjMzNSo4+DgAA8PD6nOi9LT05GUlKTxICIiondXsYeYmzdvYtGiRXBzc8O+ffswZMgQjBw5EqtWrQIAxMTEAADs7Ow05rOzs5OmxcTEQE9PDxYWFgXWedGMGTNgZmYmPRwdHYt71YiIiOgtUuwhJicnB7Vr14a/vz88PT0xePBgfPHFF1i0aJFGPYVCofFcCJGv7EWF1Rk3bhwSExOlx927d//bihAREdFbrdhDTJkyZVC1alWNsipVquDOnTsAAHt7ewDIN6ISGxsrjc7Y29sjIyMD8fHxBdZ5kb6+PkxNTTUeRERE9O4q9hDTuHFjXL16VaPs2rVrcHJyAgC4uLjA3t4eBw4ckKZnZGQgODgYjRo1AgB4eXlBpVJp1ImOjkZERIRUh4iIiN5vxX510qhRo9CoUSP4+/uje/fuOHXqFBYvXozFixcDyD2M5OvrC39/f7i5ucHNzQ3+/v4wNDREr169AABmZmYYMGAARo8eDSsrK1haWmLMmDGoXr26dLUSERERvd+KPcTUrVsXW7duxbhx4zBlyhS4uLhg3rx56N27t1Tn22+/RVpaGoYOHYr4+HjUr18f+/fvl+4RAwBz586FUqlE9+7dkZaWhpYtW2LFihW8RwwREREBABRCCFHanSgJSUlJMDMzQ2JiIs+PoSLjze5KD292R6+L79vSUxLv21f5/uZvJxEREZEsMcQQERGRLDHEEBERkSwxxBAREZEsMcQQERGRLDHEEBERkSwV+31iiKhwDStaoatnWXiVt0AZcwMkpWXhwv0E/HLoOiLu//vr64VduhgZm4KWc4Jfuiy1ShdDvCuiU40yKGuhxtP0bFyJSca4v87jVtxTAICpWolpXTzQzN0WiU8zsSj4Btaf0vztsVqO5tgwqAE6/HoMkY9SXnPNiYiKF0MM0RvWp74TLIxUWP5PFK7HpsDSSA9fNKmArUMb43/LTuF4ZBwAoOtv/+Sbt1Z5c0zqVA37Lmr/NffnGerpYv0XDWBnaoBFwZG4Ep0EEwMlvJwsoNb796aR33eoimoOZvDdEI4KNkaY9mF13IhNQeit3N8u09VRYEa36vgjOJIBhojeKgwxRG/YxL8jEJeaoVEWfO0Rgr/xxjBvVynEnL2bkG/eXvXLIydHYOPpl/9K+xgfd7jaGqPtL0dw90maVH7wcqxGvebutpi68xICr8Yi8CrQrJINWlS2lULMoCYVoKerg98CI191VYmIShRDDNEb9mKAAYCnGdm4HpuCMuYGBc5npKeL9tXL4GTUE9z+/0NBBTFQ6aBHXUfsvhCtEWC00Vfp4GlGlkZf9JW5IzWOlmqMaOmKz5aHIiM7p9B2iIjeNIYYoreAib4SHg5mCPn/URhtOtV0gJG+EhtC77y0veplzWCkr0TU41RM+9ADnWo6QK3SxZWYJMw9cB2BV/8djTlzOx7/a+iMs3cS4GxthKaVrPHNn+cBANM/rI4d56JxMurJf19JIqJixquTiN4CUz70gFpPFwsCrxdYp0ddRySmZWJvxMvPh7E3zR3RGeJdEe72Jvh6UzgGrw5DSnoWAvrVQVM363+XveMSylmoEfZDa2z5shF2nIvGrgvR+LBWWVQpYwr/3Zf/+woSEZUAjsQQlbKvW1dCV8+ymPh3hMbVSc9zszWGZ3kLrAy5hfSslx/WUSgUAIDMrBz0X3YKqRnZAIDjNx8jaExzjGjphiPXHwMAbj5ORcs5wShvaYiktEzEP82EmVqF7ztWwZQdl5CYlok+DZzwRRMXmBiocOTaI0zcHoGktKwCl09E9CZwJIaoFH3V0g0jW7ph9t4rWHX8doH1etR1BABsDH35Cb0AkPA097ybM3fipQADAM8yc3AyKg4eDmYa9YUAbsc9RfzTTADAhA5VcOlBErafe4BGFa0wtl1lDF93Ft4/BsLSSA+TOlZ7pfUkIioJDDFEpeSrlm4Y1boS5h64hoVBBV/5o9JVoKtnWZy/l4BL0dpHal50OSa5wGkKADlCFDi9QQVLdKxRBhO2XQAAeLvb4uj1R7hwPxFJz7Kw6vgteLvbFKkfREQliSGGqBSMaOGKUa0r4ddD1/HLoYLPgwGAVlXsYGWsj01FHIUBgEfJ6Qi7HQ8vJ0sY6/971NhApYP6Faxw9k6C1vn0dHUwvWt1/HLwunRVk0IBGKr+va+MoZ5SOlxFRFSaGGKI3rCBTVww2scdQVdjEXglFp6O5hqPF/Wo64i0jGz8Hf6gwDZvTG+HtQPra5RN33UZRvq6WPV5PfhUtUPrqnZY+Xk9WBjqYc6Bq1rbGdbCFRlZOVh6LEoqO3LtERq7WqN/I2d4u9vgq5ZuCL726PVWnoioGPHEXqI3rFUVOwC5h2m83W3zTXceu0v6u4yZAZq42WDb2ftITi/4RFqlrg50dTRHR87ciUfvJScxpo075vWsBQA4eycBPRefwBktIzEVbYwxuGkF9Fx8Atk5/x5uOnr9MWbsuYKBTVxgqlbh6LXHmLLj4qusMhFRiVAIUcjBcRlLSkqCmZkZEhMTYWpqWtrdIZl4PkDQm1XYb0URFYbv29JTEu/bV/n+5uEkIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhph3zNKlS6FQKGBsbJxvWmZmJubMmYPq1atDrVbD3NwcjRo1QkhIiEY9hUKh9TFz5sw3tRpEREQvxfvEvEPu37+PMWPGwMHBAYmJiRrTsrOz0bVrVxw7dgzffvstGjVqhNTUVISFhSE1NTVfWx9//DFGjx6tUVa+fPkS7T8REdGrYIh5hwwZMgRNmzaFpaUlNm/erDFt/vz52LNnD/755x80aNBAKu/QQfs1/nZ2dhr1iIiI3jY8nPSOWLNmDYKDg7Fw4UKt03/55Rc0bdqUwYSIiN4ZDDHvgNjYWPj6+mLmzJkoV65cvul3797FrVu3UL16dYwfPx52dnZQKpWoVq0aVq5cqbXNdevWQa1WQ19fH15eXli+fHlJrwYREdEr4eGkd8DQoUPh7u6OL7/8Uuv0+/fvAwBWrlyJcuXKYcGCBTAzM8OSJUvQv39/ZGRk4IsvvpDq9+rVCx06dICjoyNiY2MREBCAzz//HDdv3sTUqVPfyDoRERG9DEOMzG3ZsgU7duzA2bNnoVAotNbJyckBADx79gy7d++Gk5MTAKB169aoU6cOpkyZohFi1q5dqzH/Rx99hE6dOmHmzJkYOXIkbGxsSmhtiIiIio6Hk2QsJSUFw4YNw4gRI+Dg4ICEhAQkJCQgIyMDAJCQkIDU1FRYWVkBACpXriwFGCD3Uuo2bdrg3r17iI2NLXRZffr0QVZWFk6fPl1yK0RERPQKGGJk7PHjx3j48CF+/vlnWFhYSI/169cjNTUVFhYW6N27NypWrAhDQ0OtbeT9iLmOTuG7QlHrERERvSk8nCRj9vb2CAwMzFc+c+ZMBAcHY8+ePbC2toZSqUSXLl2wefNm3Lp1C87OzgByg8nevXtRsWJFWFtbF7qs1atXQ6VSwcvLqyRWhYiI6JUxxMiYgYEBvL2985WvWLECurq6GtOmTp2KPXv2oG3btvDz84OpqSmWLl2Kc+fOYdOmTVK9H3/8EZcuXULLli1Rrlw56cTe/fv3w8/P76Vhh4iI6E1hiHlPVKxYEUePHsXYsWMxaNAgZGZmolatWti+fTs6duwo1atcuTK2b9+OXbt2IT4+Hmq1GrVq1cL69evRs2fPUlwDIiIiTQqRd7LDOyYpKQlmZmZITEyEqalpaXeHZMJ57K7S7sJ769ZM7XePJnoZvm9LT0m8b1/l+5tnaRIREZEsMcQQERGRLDHEEBERkSwxxBAREZEsMcQQERGRLPES69fEs+FLD69iISIigCMxREREJFMMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEvK0u4AFaxhRSt09SwLr/IWKGNugKS0LFy4n4BfDl1HxP0kqV4dJwt8XKccqpUxQyV7Y+grdfHBrMO4F59WpOW0qGyLDjXKoJqDKSraGEOlqwPnsbu01h3tUwlVy5iimoMZ7M0MsDnsLsb8eT5fvXoulpjUqSqcrIxw8UEiJmyNwI3YFI06U7pUg7udCXosPvEKW4WIiCgXR2LeYn3qO6GchRrL/4nCZ8tDMXnHRVgZ6WPr0MZoWNFKqtfY1RofuFrjQWIaztxOeOXltKlmD09Hc9x4mILL0UmF1h3wgQssDPVw8PJDpGdla61jaqDEH328cO5uIoasDkPysyz80ccLOop/63g6mqN7HUeM33rhlftLREQEcCTmrTbx7wjEpWZolAVfe4Tgb7wxzNsVxyPjAAC/Hr6OXw5dBwB80aSCRsApirF/nYcQuX9P7lwNNcqZF1i32qR9Ut2unmW11qld3gJ6Sh1M/DsCWTkC1x4m49SEVnCxNkLko1QodRTw71YdvwdHIvJR6iv1lYiIKA9HYt5iLwYYAHiakY3rsSkoY24gleWFitf1KvMXpa6+SgcZWTnIysmt/DQjd8RGX6kLABjUtAL0dHWwMDDylftKRESUp8RDzIwZM6BQKODr6yuVCSHg5+cHBwcHqNVqeHt74+LFixrzpaenY8SIEbC2toaRkRE6d+6Me/fulXR333om+kp4OJjh+sOUl1cuJefvJcJIX4k+9cvD1ECJYc0r4klqBiIfpaC8pSGGt3DFuK0XkJGdU9pdJSIiGSvREBMaGorFixejRo0aGuWzZ8/GnDlzsGDBAoSGhsLe3h6tW7dGcnKyVMfX1xdbt27Fhg0bcOzYMaSkpKBjx47IztZ+Hsb7YsqHHlDr6WJB4PXS7kqBohOfYcqOi/ihU1Wc92uD3vWd8O3mc0jPysH0rh7Yfu4BTkU9Ke1uEhGRzJXYOTEpKSno3bs3lixZgmnTpknlQgjMmzcPEyZMQLdu3QAAK1euhJ2dHdatW4fBgwcjMTERAQEBWL16NVq1agUAWLNmDRwdHXHw4EG0adMm3/LS09ORnp4uPU9KKvwEVTn6unUldPUsi4l/R2hcnfQ2WnPyDracuQ8HcwPcfZKGjOwcdPUsi8r2phi27gzM1CpM7lwNTdyskZKehcVHbmLtyTul3W0iIpKREhuJGTZsGDp06CCFkDxRUVGIiYmBj4+PVKavr49mzZohJCQEABAWFobMzEyNOg4ODvDw8JDqvGjGjBkwMzOTHo6OjiWwVqXnq5ZuGNnSDbP3XsGq47dLuztFkpaZjchHqcjIzoG5oQoTOlTB1J2XkJSWhUmdqsLcUAXvH4MwYv1ZjG9fBQ0rvNoJyURE9H4rkRCzYcMGnDlzBjNmzMg3LSYmBgBgZ2enUW5nZydNi4mJgZ6eHiwsLAqs86Jx48YhMTFRety9e7c4VuWt8FVLN4xqXQlzD1zDwiB5ngz7fYcqiLifiO3nHgAAvN1tsfrEbSSnZ+H8vUQcvf4Y3u42pdxLIiKSk2I/nHT37l189dVX2L9/PwwMDAqsp1AoNJ4LIfKVvaiwOvr6+tDX13/1Dr/lRrRwxajWlfDroX8vo5abhhWs0L56GfjMPSKVKQAYqnSl54Z6ui99/YmIiJ5X7CEmLCwMsbGx8PLyksqys7Nx5MgRLFiwAFevXgWQO9pSpkwZqU5sbKw0OmNvb4+MjAzEx8drjMbExsaiUaNGxd3lt9bAJi4Y7eOOoKuxCLwSC09Hc43pZ+8mAAAsjfRQ38USAFDZ3gQA4F3JBnGpGXiSmoGTz51Ee2N6O5yMeoLeS09KZWXN1ahRzgwA4GRlCABo52EPALgXn4YL9xOluvVdLGFppAcA0NVRoKy5oVT3ZNQTPHnhsnA9XR1M7+qBeQeva9xB+Mj1RxjZ0g3J6VlwsTZCY1dr/HFEnqNMRERUOoo9xLRs2RIXLmjehfWzzz5D5cqV8d1336FChQqwt7fHgQMH4OnpCQDIyMhAcHAwZs2aBQDw8vKCSqXCgQMH0L17dwBAdHQ0IiIiMHv27OLu8lurVZXcUOftbgtvd9t80/N+GqCSnTEW9fHSmData3UAwImbcej53G39lbo60NXRHPFoWNEKP31SU6Msr70Xf1ZgVOtKaPDcuSsNK1pJN9frufg4TtzUvOpoeAtXPMvMRsCxKI3yyTsuYWoXD/za0xPJ6VmYtusS/rkRV9CmICIiyqfYQ4yJiQk8PDw0yoyMjGBlZSWV+/r6wt/fH25ubnBzc4O/vz8MDQ3Rq1cvAICZmRkGDBiA0aNHw8rKCpaWlhgzZgyqV6+e70Thd1nPIv6m0ImbTwr8raMXaau3OeweNocV7R48Re1TnjkHrmHOgWv5yp+kZmDYujOv1BYREdHzSuVnB7799lukpaVh6NChiI+PR/369bF//36YmJhIdebOnQulUonu3bsjLS0NLVu2xIoVK6Crq1tIy0RERPS+eCMhJigoSOO5QqGAn58f/Pz8CpzHwMAA8+fPx/z580u2c0RERCRL/O0kIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIqI36PDhw/j8889RuXJlGBkZoWzZsujSpQvCwsI06vXv3x8KhSLfo3Llyhr1VqxYobVe3mPmzJlvcvWI3ihlaXeAiOh9smjRIsTFxeGrr75C1apV8ejRI/z8889o0KAB9u3bhxYtWkh11Wo1Dh8+rDG/Wq3WeN6hQwccP34833ImTpyIAwcOoGvXriWzIkRvAYYYIqI36LfffoOtra1GWdu2beHq6gp/f3+NEKOjo4MGDRoU2p6NjQ1sbGw0ylJTU3H8+HF88MEHcHd3L77OE71leDiJiOgNejHAAICxsTGqVq2Ku3fvFssyNm7ciJSUFAwcOLBY2iN6WzHEEBGVssTERJw5cwbVqlXTKE9LS4O9vT10dXVRrlw5DB8+HE+ePHlpewEBATA1NcUnn3xSUl0meivwcBIRUSkbNmwYUlNTMWHCBKmsZs2aqFmzJjw8PAAAwcHBmDt3Lg4dOoTQ0FAYGxtrbevKlSsICQnB4MGDYWho+Eb6T1RaGGKIiErRDz/8gLVr12L+/Pnw8vKSykeNGqVRr3Xr1vD09MTHH3+MJUuW5JueJyAgAAB4KIneCzycRERUSiZPnoxp06Zh+vTpGD58+Evrd+3aFUZGRjhx4oTW6ZmZmVi1ahVq1qyJOnXqFHd3id46DDFERKVg8uTJ8PPzg5+fH8aPH1/k+YQQ0NHR/tG9c+dOxMbGchSG3hsMMUREb9jUqVPh5+eH77//HpMmTSryfJs3b8bTp08LvOw6ICAABgYG6N27d3F1leitxnNiiIjeoJ9//hkTJ05E27Zt0aFDh3yHhho0aIDbt2+jV69e6NmzJ1xdXaFQKBAcHIx58+ahWrVqWkdaHjx4gL1796JHjx6wsLB4U6tDVKoYYoiI3qAdO3YAAPbu3Yu9e/fmmy6EgKmpKezs7DBnzhw8fPgQ2dnZcHJywsiRIzF+/HgYGRnlm2/FihXIzs7moSR6rzDEEBG9QUFBQS+tY2Fhgb/++uuV2h0/fvwrnVtD9C7gOTFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEu8OomI3gvOY3eVdhfeW7dmdijtLtA7iiMxREREJEsMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEsMMURERCRLDDFEREQkSwwxREREJEvK0u4AEdG7xEhPFyNauqFqGVNUczCFlbE+5h28hnkHr2vUq+NkgY/rlEO1MmaoZG8MfaUuPph1GPfi04q8LLVKF0O8K6JTjTIoa6HG0/RsXIlJxri/zuNW3FMAgG8rN/i2qlRgGyPWncGO89EAgHoulpjUqSqcrIxw8UEiJmyNwI3YFI36U7pUg7udCXosPlHkfhKVlGIfiZkxYwbq1q0LExMT2Nra4sMPP8TVq1c16ggh4OfnBwcHB6jVanh7e+PixYsaddLT0zFixAhYW1vDyMgInTt3xr1794q7u0RExcrCSA+96pWHvlIH+y89LLBeY1drfOBqjQeJaThzO+GVl2Oop4sNgxqgRx1HrDx+G/8LOIVvNp9D+N14qPV0pXobTt1F19/+yfe4EpOEtIxsBF97BAAwNVDijz5eOHc3EUNWhyH5WRb+6OMFHcW/y/R0NEf3Oo4Yv/XCK/eXqCQU+0hMcHAwhg0bhrp16yIrKwsTJkyAj48PLl26BCMjIwDA7NmzMWfOHKxYsQKVKlXCtGnT0Lp1a1y9ehUmJiYAAF9fX+zYsQMbNmyAlZUVRo8ejY4dOyIsLAy6urqFdYGIqNTci09Djcn7AQAWhip8Wq+81nq/Hr6OXw7ljs580aQCGla0eqXljPFxh6utMdr+cgR3n/w7enPwcqxGvZikZ4hJeqZRVs5CjUq2JtgWfh9Jz7IAALXLW0BPqYOJf0cgK0fg2sNknJrQCi7WRoh8lAqljgL+3arj9+BIRD5KfaW+EpWUYg8xe/fu1Xi+fPly2NraIiwsDE2bNoUQAvPmzcOECRPQrVs3AMDKlSthZ2eHdevWYfDgwUhMTERAQABWr16NVq1aAQDWrFkDR0dHHDx4EG3atMm33PT0dKSnp0vPk5KSinvViIiKjRCvP6+BSgc96jpi94VojQBTVJ/UcYSOjgIbQ+9KZfoqHWRk5SArJ7djTzOyc8uVuf80DmpaAXq6OlgYGPn6HScqZiV+Ym9iYiIAwNLSEgAQFRWFmJgY+Pj4SHX09fXRrFkzhISEAADCwsKQmZmpUcfBwQEeHh5SnRfNmDEDZmZm0sPR0bGkVomIqFRVL2sGI30loh6nYtqHHjg3yQfXprXD9uGN0dzdttB5FQrgY69yiHqcipNRT6Ty8/cSYaSvRJ/65WFqoMSw5hXxJDUDkY9SUN7SEMNbuGLc1gvIyM4p6dUjKrISDTFCCHz99df44IMP4OHhAQCIiYkBANjZ2WnUtbOzk6bFxMRAT08PFhYWBdZ50bhx45CYmCg97t69q7UeEZHc2ZsaAACGeFeEu70Jvt4UjsGrw5CSnoWAfnXQ1M26wHmbutmgrLkam0I1PyOjE59hyo6L+KFTVZz3a4Pe9Z3w7eZzSM/KwfSuHth+7gFOPRd6iN4GJXp10vDhw3H+/HkcO3Ys3zSFQqHxXAiRr+xFhdXR19eHvr7+63eWiEgm8j4HM7Ny0H/ZKaT+/6Gf4zcfI2hMc4xo6YYj1x9rnbd7HUdkZudgc1j+CyXWnLyDLWfuw8HcAHefpCEjOwddPcuisr0phq07AzO1CpM7V0MTN2ukpGdh8ZGbWHvyTsmtKNFLlNhIzIgRI7B9+3YEBgaiXLlyUrm9vT0A5BtRiY2NlUZn7O3tkZGRgfj4+ALrEBG9rxKeZgAAztyJlwIMADzLzMHJqDh4OJhpnc/CUIVWVW0ReCUWj1LStdZJy8xG5KNUZGTnwNxQhQkdqmDqzktISsvCpE5VYW6ogvePQRix/izGt6+ChhVe7YRkouJU7CFGCIHhw4fjr7/+wuHDh+Hi4qIx3cXFBfb29jhw4IBUlpGRgeDgYDRq1AgA4OXlBZVKpVEnOjoaERERUh0iovfV5ZjkAqcpAOQUcNZwt9rloK/UxYbQoh1u/75DFUTcT8T2cw8AAN7utlh94jaS07Nw/l4ijl5/DG93m1fuP1FxKfbDScOGDcO6devw999/w8TERBpxMTMzg1qthkKhgK+vL/z9/eHm5gY3Nzf4+/vD0NAQvXr1kuoOGDAAo0ePhpWVFSwtLTFmzBhUr15dulqJiOh99Sg5HWG34+HlZAljfSVS0nMvkzZQ6aB+BSucvZOgdb7udRwRk/gMQVdjtU5/XsMKVmhfvQx85h6RyhQADFX/3uLCUE/3pacBEJWkYg8xixYtAgB4e3trlC9fvhz9+/cHAHz77bdIS0vD0KFDER8fj/r162P//v3SPWIAYO7cuVAqlejevTvS0tLQsmVLrFixgveIIaK3nnclG6j1dGGsn/sR62prjHYeuYfSA6/G4llmDiyN9FDfJfeqzcr2JtJ8cakZeJKaoXHl0I3p7XAy6gl6Lz0plU3fdRnrB9XHqs/r4ffgSAgAA5u4wMJQD3MOaN5gFABqOZrD3d4ECw7fQM5LLu/W09XB9K4emHfwusYdhI9cf4SRLd2QnJ4FF2sjNHa1xh9HeMk1lZ5iDzGiCDc/UCgU8PPzg5+fX4F1DAwMMH/+fMyfP78Ye0dEVPKmdfVAOQtD6XnHGg7oWMMBAKSfFqhkZ4xFfbxemK86AODEzTj0fO62/kpdHejqaI54nLkTj95LTmJMG3fM61kLAHD2TgJ6Lj6BM1pGYrrXcUROjsDG0y8/EXd4C1c8y8xGwLEojfLJOy5hahcP/NrTE8npWZi26xL+uRH30vaISopCFCV1yFBSUhLMzMyQmJgIU1PTYm/feeyuYm+TiubWzA4l1jZf19JTkq8rwNe2NPG1fXeVxGv7Kt/f/BVrIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikiWGGCIiIpIlhhgiIiKSJYYYIiIikqW3PsQsXLgQLi4uMDAwgJeXF44ePVraXSIiIqK3wFsdYjZu3AhfX19MmDABZ8+eRZMmTdCuXTvcuXOntLtGREREpeytDjFz5szBgAEDMHDgQFSpUgXz5s2Do6MjFi1aVNpdIyIiolKmLO0OFCQjIwNhYWEYO3asRrmPjw9CQkLy1U9PT0d6err0PDExEQCQlJRUIv3LSX9aIu3Sy5XUawrwdS1NJfm6AnxtSxNf23dXSby2eW0KIV5a960NMY8fP0Z2djbs7Ow0yu3s7BATE5Ov/owZMzB58uR85Y6OjiXWRyodZvNKuwdUEvi6vrv42r67SvK1TU5OhpmZWaF13toQk0ehUGg8F0LkKwOAcePG4euvv5ae5+Tk4MmTJ7CystJa/32VlJQER0dH3L17F6ampqXdHSpGfG3fXXxt3018XbUTQiA5ORkODg4vrfvWhhhra2vo6urmG3WJjY3NNzoDAPr6+tDX19coMzc3L8kuypqpqSnfNO8ovrbvLr627ya+rvm9bAQmz1t7Yq+enh68vLxw4MABjfIDBw6gUaNGpdQrIiIielu8tSMxAPD111+jb9++qFOnDho2bIjFixfjzp07GDJkSGl3jYiIiErZWx1ievTogbi4OEyZMgXR0dHw8PDA7t274eTkVNpdky19fX1MmjQp36E3kj++tu8uvrbvJr6u/51CFOUaJiIiIqK3zFt7TgwRERFRYRhiiIiISJYYYoiIiEiWGGKIiIhIlhhiiIiI3gBvb2/4+vqWyrKDgoKgUCiQkJBQKssvKQwxpah///5QKBRQKBRQqVSoUKECxowZg9TU1GJdzsWLF/HRRx/B2dkZCoUC8+bNy1cnOTkZvr6+cHJyglqtRqNGjRAaGlpgm4MHD9baVnp6OkaMGAFra2sYGRmhc+fOuHfvXrGuj5wcOXIEnTp1goODAxQKBbZt26Yx/eHDh+jfvz8cHBxgaGiItm3b4vr161rbEkKgXbt2WtuJj49H3759YWZmBjMzM/Tt27dIH1b79u1DgwYNYGJiAhsbG3z00UeIiorSqBMcHAwvLy8YGBigQoUK+P33319lE8heSb1Pb926BYVCgfDw8JfWfdl+BBRtX4qJiUHfvn1hb28PIyMj1K5dG5s3b9ao87r70ruioKCxbdu2N/4TNu9q8ChODDGlrG3btoiOjsbNmzcxbdo0LFy4EGPGjMlXLzMz87WX8fTpU1SoUAEzZ86Evb291joDBw7EgQMHsHr1aly4cAE+Pj5o1aoV7t+/n6/utm3bcPLkSa2/a+Hr64utW7diw4YNOHbsGFJSUtCxY0dkZ2e/dv/lLDU1FTVr1sSCBQvyTRNC4MMPP8TNmzfx999/4+zZs3ByckKrVq20fkHOmzevwA/RXr16ITw8HHv37sXevXsRHh6Ovn37Ftq3mzdvokuXLmjRogXCw8Oxb98+PH78GN26dZPqREVFoX379mjSpAnOnj2L8ePHY+TIkdiyZcsrbgl5K+r7tKQUth8BRd+X+vbti6tXr2L79u24cOECunXrhh49euDs2bNSndfZl4hKjaBS069fP9GlSxeNsoEDBwp7e3sxadIkUbNmTREQECBcXFyEQqEQOTk5IiEhQXzxxRfCxsZGmJiYiObNm4vw8PAiL9PJyUnMnTtXo+zp06dCV1dX7Ny5U6O8Zs2aYsKECRpl9+7dE2XLlhURERH52kpISBAqlUps2LBBKrt//77Q0dERe/fuLXIf31UAxNatW6XnV69eFQBERESEVJaVlSUsLS3FkiVLNOYNDw8X5cqVE9HR0fnauXTpkgAgTpw4IZUdP35cABBXrlwpsD9//vmnUCqVIjs7Wyrbvn27UCgUIiMjQwghxLfffisqV66sMd/gwYNFgwYNXmnd5ayw9+mzZ8/EiBEjhI2NjdDX1xeNGzcWp06dkuo9efJE9OrVS1hbWwsDAwPh6uoqli1bJoTI3R+efzRr1qxI/Xnx9Rei6PuSkZGRWLVqlca8lpaWYunSpUKI19+X3iXNmjUTX331Vb7yrVu3iryvzLzP51WrVgknJydhamoqevToIZKSkqT6KSkpom/fvsLIyEjY29uLn376KV/bq1evFl5eXsLY2FjY2dmJTz/9VDx8+FAIIURUVFS+faRfv35CCCFycnLErFmzhIuLizAwMBA1atQQf/75p0Z/d+3aJdzc3ISBgYHw9vYWy5cvFwBEfHx8sW6v0saRmLeMWq2WRl1u3LiBTZs2YcuWLdKQc4cOHRATE4Pdu3cjLCwMtWvXRsuWLfHkyZPXXmZWVhays7NhYGCQry/Hjh2Tnufk5KBv37745ptvUK1atXzthIWFITMzEz4+PlKZg4MDPDw8EBIS8tr9e1elp6cDgMZ219XVhZ6ensZ2f/r0KT799FMsWLBA60ja8ePHYWZmhvr160tlDRo0gJmZWaHbvU6dOtDV1cXy5cuRnZ2NxMRErF69Gj4+PlCpVFLbz7+eANCmTRucPn36P40Oyl3e+/Tbb7/Fli1bsHLlSpw5cwaurq5o06aN9H784YcfcOnSJezZsweXL1/GokWLYG1tDQA4deoUAODgwYOIjo7GX3/99dr9Keq+9MEHH2Djxo148uQJcnJysGHDBqSnp8Pb2xvA6+9L76PIyEhs27YNO3fuxM6dOxEcHIyZM2dK07/55hsEBgZi69at2L9/P4KCghAWFqbRRkZGBqZOnYpz585h27ZtiIqKQv/+/QEAjo6O0ojn1atXER0djV9++QUA8P3332P58uVYtGgRLl68iFGjRqFPnz4IDg4GANy9exfdunVD+/btER4ejoEDB2Ls2LFvYKuUgtJOUe+zF//DO3nypLCyshLdu3cXkyZNEiqVSsTGxkrTDx06JExNTcWzZ8802qlYsaL4448/irRMbSMxQgjRsGFD0axZM3H//n2RlZUlVq9eLRQKhahUqZJUx9/fX7Ru3Vrk5ORobWvt2rVCT08vX9utW7cWgwYNKlL/3mV44T/ojIwM4eTkJD755BPx5MkTkZ6eLmbMmCEACB8fH6neoEGDxIABAwpsZ/r06cLNzS3f8tzc3IS/v3+hfQoODha2trZCV1dXABANGzbU+E/Nzc1NTJ8+XWOef/75RwAQDx48KOKay1tB79OPP/5YqFQqsXbtWmlaRkaGcHBwELNnzxZCCNGpUyfx2WefaW037z/ts2fPvlJ/Xnz985ZblH0pISFBtGnTRgAQSqVSmJqaiv3790vT/8u+9K4o6kiMoaGhxsjLN998I+rXry+EECI5OVno6elpjErHxcUJtVqtte08p06dEgBEcnKyEEKIwMDAfKMnKSkpwsDAQISEhGjMO2DAAPHpp58KIYQYN26cqFKlivRZLYQQ3333HUdiqPjt3LkTxsbGMDAwQMOGDdG0aVPMnz8fAODk5AQbGxupblhYGFJSUmBlZQVjY2PpERUVhcjISNy5c0ej3N/fv8j9WL16NYQQKFu2LPT19fHrr7+iV69e0NXVlZb9yy+/YMWKFa98cpsQ4o2fECcHKpUKW7ZswbVr12BpaQlDQ0MEBQWhXbt20nbfvn07Dh8+rPVk7Odp277Pb/dq1apJ+0W7du0A5J7kOXDgQPTr1w+hoaEIDg6Gnp4ePv74Y4jnfo3kxbbzpr1Pr6m29+mIESOQmZmJxo0bS/VUKhXq1auHy5cvAwC+/PJLbNiwAbVq1cK333770tGMo0eParyH165dW6T+FWVfAnL/g4+Pj8fBgwdx+vRpfP311/jkk09w4cIFqc7L9iXK5ezsDBMTE+l5mTJlEBsbCyB3lCYjIwMNGzaUpltaWsLd3V2jjbNnz6JLly5wcnKCiYmJNCJ2586dApd76dIlPHv2DK1bt9bYV1atWoXIyEgAwOXLl9GgQQON1+z5vrxL3uofgHwfNG/eHIsWLYJKpYKDg4M0jA8ARkZGGnVzcnJQpkwZBAUF5WvH3Nwc5ubmGlc6WFpaFrkfFStWRHBwMFJTU5GUlIQyZcqgR48ecHFxAZD74RobG4vy5ctL82RnZ2P06NGYN28ebt26BXt7e2RkZCA+Ph4WFhZSvdjYWDRq1KjIfXmfeHl5ITw8HImJicjIyICNjQ3q16+POnXqAAAOHz6MyMhImJuba8z30UcfoUmTJggKCoK9vT0ePnyYr+1Hjx7Bzs4OALB7927p8I9arQYA/PbbbzA1NcXs2bOledasWQNHR0ecPHkSDRo0gL29PWJiYjTajY2NhVKphJWVVbFth7edtvfpuXPnAGgPeXll7dq1w+3bt7Fr1y4cPHgQLVu2xLBhw/DTTz9pXU6dOnU03sN5r19RvGxfioyMxIIFCxARESEdDq5ZsyaOHj2K3377Db///nuR9qV3nampKRITE/OVJyQkwNTUVHr+/Gc1kLsf5OTkAIDGPwEFSU1NhY+PD3x8fLBmzRrY2Njgzp07aNOmDTIyMgqcL28Zu3btQtmyZTWm5f2QZFGW/65giCllRkZGcHV1LVLd2rVrIyYmBkqlEs7OzlrrFLWtwvpjZGSE+Ph47Nu3T/qC69u3L1q1aqVRt02bNujbty8+++wzALkfoiqVCgcOHED37t0BANHR0YiIiND4oqT8zMzMAADXr1/H6dOnMXXqVADA2LFjMXDgQI261atXx9y5c9GpUycAuf9hJSYm4tSpU6hXrx4A4OTJk0hMTJTCo7Zffn/69KnGf+kApOd5H5QNGzbEjh07NOrs378fderUyfch/i7T9j51dXWVzjnp1asXgNyrCE+fPq1xia6NjQ369++P/v37o0mTJvjmm2/w008/QU9PDwA0rtxTq9X/+T1c0L709OlTAICOjuYAvK6ursbr/bJ96V1XuXJl7NmzJ195aGhovpGUgri6ukKlUuHEiRPSP37x8fG4du0amjVrBgC4cuUKHj9+jJkzZ8LR0REAcPr0aY12tO0jVatWhb6+Pu7cuSO19aKqVavmuwz/xIkTReq77JTagSzSetVDnryz35+Xk5MjPvjgA1GzZk2xd+9eERUVJf755x8xYcIEERoaWuBy0tPTxdmzZ8XZs2dFmTJlxJgxY8TZs2fF9evXpTp79+4Ve/bsETdv3hT79+8XNWvWFPXq1ZOuUtFG2/k1Q4YMEeXKlRMHDx4UZ86cES1atBA1a9YUWVlZL90e76Lk5GRp2wMQc+bMEWfPnhW3b98WQgixadMmERgYKCIjI8W2bduEk5OT6NatW6FtQss5EW3bthU1atQQx48fF8ePHxfVq1cXHTt2LLSdQ4cOCYVCISZPniyuXbsmwsLCRJs2bYSTk5N4+vSpEEKImzdvCkNDQzFq1Chx6dIlERAQIFQqldi8efPrbxSZKex9+tVXXwkHBwexZ88ecfHiRdGvXz9hYWEhnjx5IoQQ4ocffhDbtm0T169fFxEREaJjx46iXr16QgghMjMzhVqtFtOmTRMxMTEiISGhwD68bD8S4uX7UkZGhnB1dRVNmjQRJ0+eFDdu3BA//fSTUCgUYteuXVK919mX3iVRUVFCrVaLoUOHivDwcHH16lWxYMECoa+vLzZt2iSE0P75PHfuXOHk5CQ9HzJkiChfvrw4ePCguHDhgujcubMwNjaWzomJjY0Venp64ptvvhGRkZHi77//FpUqVdI4T+revXtCoVCIFStWiNjYWOlcmQkTJggrKyuxYsUKcePGDXHmzBmxYMECsWLFCiGEELdv3xZ6enpi1KhR4sqVK2Lt2rXC3t7+nTwnhiGmFL1qiBFCiKSkJDFixAjh4OAgVCqVcHR0FL179xZ37twpcDnaLtXDC5d0bty4UVSoUEHo6ekJe3t7MWzYsEI/VIXQHmLS0tLE8OH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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# store all rows with date_start in coverage_range\n", "completion_dates_dict = {'Pre-1940': 0, '1940-80': 0, 'Post-1980': 0, 'Undated': 0}\n", "projects_1940_80 = []\n", "projects_1940_80_firms_dict = dict()\n", "firms_with_projects_1940_80 = []\n", "person_with_projects_1940_80 = []\n", "resource_with_projects_1940_80 = []\n", "\n", "for idx,row in daqa_work.iterrows():\n", " if isinstance(row['coverage_range'], str): \n", " if \"date_end\" in row['coverage_range']:\n", " comp_yr = pd.json_normalize(ast.literal_eval(row['coverage_range'])['date_range'])['date_end.year'].values[0]\n", " # add counter for comp_yr to dict\n", " if int(comp_yr) < 1940: completion_dates_dict['Pre-1940'] += 1\n", " elif int(comp_yr) >= 1940 and int(comp_yr) <= 1980: \n", " completion_dates_dict['1940-80'] += 1\n", " projects_1940_80.append(row['_id'])\n", "\n", " # add related firm to list\n", " if isinstance(row['related_organizations'], str):\n", " related_firm = pd.json_normalize(ast.literal_eval(row['related_organizations']))['subject.ori_id'].values[0]\n", " firms_with_projects_1940_80.append(related_firm)\n", " projects_1940_80_firms_dict[row['_id']] = related_firm\n", "\n", " # add related person to list\n", " if isinstance(row['related_people'], str):\n", " related_person = pd.json_normalize(ast.literal_eval(row['related_people']))['subject.ori_id'].values[0]\n", " person_with_projects_1940_80.append(related_person)\n", "\n", " # add related firm to list\n", " if isinstance(row['related_resources'], str):\n", " related_resource = pd.json_normalize(ast.literal_eval(row['related_resources']))['object.ori_id'].values[0]\n", " resource_with_projects_1940_80.append(related_resource)\n", "\n", " elif int(comp_yr) > 1980: completion_dates_dict['Post-1980'] += 1\n", " else:\n", " completion_dates_dict['Undated'] += 1\n", "\n", "# number of projects in DAQA\n", "print('\\nThere are {} projects in DAQA.'.format(count_projects))\n", "\n", "# number of pre-1940 projects\n", "print('There are {} ({}%) projects with completion dates before 1940.'.\\\n", " format(completion_dates_dict['Pre-1940'], round((completion_dates_dict['Pre-1940']/count_projects)*100,2)))\n", "\n", "# number of 1940-1980 projects\n", "print('There are {} ({}%) projects with completion dates between 1940 and 1980.'.\\\n", " format(completion_dates_dict['1940-80'], round((completion_dates_dict['1940-80']/count_projects)*100,2)))\n", "\n", "# number of post-1980 projects\n", "print('There are {} ({}%) projects with completion dates after 1980.'.\\\n", " format(completion_dates_dict['Post-1980'], round((completion_dates_dict['Post-1980']/count_projects)*100,2)))\n", "\n", "# number of undated projects\n", "print('There are {} ({}%) projects with no completion dates.'\\\n", " .format(completion_dates_dict['Undated'], round((completion_dates_dict['Undated']/count_projects)*100,2)))\n", "\n", "print('\\n')\n", "\n", "# plot completion_dates_dict as a bar chart\n", "plt.bar(range(len(completion_dates_dict)), list(completion_dates_dict.values()), align='center')\n", "plt.xticks(range(len(completion_dates_dict)), list(completion_dates_dict.keys()))\n", "plt.title('Completion dates of DAQA projects, n=2203')\n", "\n", "# add labels to bars with propotion in white inside top of bar\n", "for i, v in enumerate(completion_dates_dict.values()):\n", " plt.text(i - 0.125, v + 20, str(v), size=12)\n", " plt.text(i - 0.2, v - 75, str(round((v/count_projects)*100,2)) + '%', color='white', size=12)\n", "\n", "# make y axis start at 0\n", "plt.ylim(0, 1000)\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "7545e63b", "metadata": {}, "source": [ "### High-level summary of DAQA entities between 1940-1980\n", "\n", "We are particularly interested in data between 1940-1980. Similar to what we generated for the whole dataset, we output high-level statistics for data related to projects in this 1940-1980 period." ] }, { "cell_type": "code", "execution_count": 14, "id": "ac1b14f8", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of persons with a related project between 1940-1980: 113\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
Type
architect1120.991
non-architect10.009
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" ], "text/plain": [ " Frequency Proportion\n", "Type \n", "architect 112 0.991\n", "non-architect 1 0.009" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number of organisations with a related project between 1940-1980: 101\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
Type
\"firm\"1011.0
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" ], "text/plain": [ " Frequency Proportion\n", "Type \n", "\"firm\" 101 1.0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number of works between 1940-1980 i.e, projects: 608\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
Type
\"structure\"6081.0
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" ], "text/plain": [ " Frequency Proportion\n", "Type \n", "\"structure\" 608 1.0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number of resources with a related project between 1940-1980 i.e., articles, interviews: 471\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
Type
\"Photograph\"2880.611
\"LineDrawing\"670.142
\"Image\"620.132
\"article\"470.100
\"interview\"30.006
\"Portrait\"20.004
\"publication\"20.004
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" ], "text/plain": [ " Frequency Proportion\n", "Type \n", "\"Photograph\" 288 0.611\n", "\"LineDrawing\" 67 0.142\n", "\"Image\" 62 0.132\n", "\"article\" 47 0.100\n", "\"interview\" 3 0.006\n", "\"Portrait\" 2 0.004\n", "\"publication\" 2 0.004" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# architects\n", "# filter data accordingly\n", "daqapersons_1940_80 = daqa_persons[daqa_persons['ori_id'].astype(int).isin(person_with_projects_1940_80)].copy()\n", "print('Total number of persons with a related project between 1940-1980:', daqapersons_1940_80.shape[0])\n", "architect_count = daqapersons_1940_80['longterm_roles'].value_counts().reset_index()\n", "architect_count['Proportion'] = round(architect_count['longterm_roles']/architect_count['longterm_roles'].sum(),3)\n", "architect_count['Type'] = np.where(architect_count['index'].str.contains('non-architect'), 'non-architect', 'architect')\n", "display(architect_count\\\n", " .groupby('Type')\\\n", " .sum()\\\n", " .reset_index()\\\n", " .rename(columns={'longterm_roles':'Frequency'})\\\n", " .set_index('Type')\n", " .sort_values('Frequency', ascending=False))\n", "\n", "# firms\n", "# filter data accordingly\n", "daqaorgs_1940_80 = daqa_orgs[daqa_orgs['ori_id'].astype(int).isin(firms_with_projects_1940_80)].copy()\n", "print('\\nTotal number of organisations with a related project between 1940-1980:', daqaorgs_1940_80.shape[0])\n", "firm_count = daqaorgs_1940_80['_class_ori'].value_counts().reset_index()\n", "firm_count['Proportion'] = round(firm_count['_class_ori']/firm_count['_class_ori'].sum(),3)\n", "display(firm_count\\\n", " .groupby('index')\\\n", " .sum()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'Type', '_class_ori':'Frequency'})\\\n", " .set_index('Type')\n", " .sort_values('Frequency', ascending=False))\n", "\n", "# projects\n", "# filter data accordingly\n", "daqawork_1940_80 = daqa_work[daqa_work['_id'].isin(projects_1940_80)].copy()\n", "print('\\nTotal number of works between 1940-1980 i.e, projects:', daqawork_1940_80.shape[0])\n", "project_count = daqawork_1940_80['_class_ori'].value_counts().reset_index()\n", "project_count['Proportion'] = round(project_count['_class_ori']/project_count['_class_ori'].sum(),3)\n", "display(project_count\\\n", " .groupby('index')\\\n", " .sum()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'Type', '_class_ori':'Frequency'})\\\n", " .set_index('Type')\n", " .sort_values('Frequency', ascending=False))\n", "\n", "# articles\n", "# filter data accordingly\n", "daqaresources_1940_80 = daqa_resources[daqa_resources['ori_id'].astype(int).isin(resource_with_projects_1940_80)].copy()\n", "print('\\nTotal number of resources with a related project between 1940-1980 i.e., articles, interviews:', daqaresources_1940_80.shape[0])\n", "article_count = daqaresources_1940_80['_class_ori'].value_counts().reset_index()\n", "article_count['Proportion'] = round(article_count['_class_ori']/article_count['_class_ori'].sum(),3)\n", "display(article_count\\\n", " .groupby('index')\\\n", " .sum()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'Type', '_class_ori':'Frequency'})\\\n", " .set_index('Type')\n", " .sort_values('Frequency', ascending=False))" ] }, { "cell_type": "code", "execution_count": 15, "id": "5561fda0", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of relationships for entities related to 1940-1980 works: 8399\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
relation_class
Work_RelatedResource20170.240
Person_RelatedWork14720.175
Person_RelatedPerson12180.145
Organization_RelatedWork10910.130
Person_RelatedOrganization9030.108
Work_RelatedPlace5800.069
Person_RelatedResource2930.035
Resource_RelatedPerson2530.030
Organization_RelatedOrganization1830.022
Work_RelatedOrganization1680.020
Resource_RelatedResource490.006
Organization_RelatedResource430.005
Resource_RelatedOrganization430.005
Resource_RelatedWork380.005
Person_RelatedRecognition220.003
Work_RelatedPerson150.002
Organization_RelatedPerson50.001
Resource_RelatedRecognition30.000
Resource_RelatedPlace20.000
Person_RelatedPlace10.000
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" ], "text/plain": [ " Frequency Proportion\n", "relation_class \n", "Work_RelatedResource 2017 0.240\n", "Person_RelatedWork 1472 0.175\n", "Person_RelatedPerson 1218 0.145\n", "Organization_RelatedWork 1091 0.130\n", "Person_RelatedOrganization 903 0.108\n", "Work_RelatedPlace 580 0.069\n", "Person_RelatedResource 293 0.035\n", "Resource_RelatedPerson 253 0.030\n", "Organization_RelatedOrganization 183 0.022\n", "Work_RelatedOrganization 168 0.020\n", "Resource_RelatedResource 49 0.006\n", "Organization_RelatedResource 43 0.005\n", "Resource_RelatedOrganization 43 0.005\n", "Resource_RelatedWork 38 0.005\n", "Person_RelatedRecognition 22 0.003\n", "Work_RelatedPerson 15 0.002\n", "Organization_RelatedPerson 5 0.001\n", "Resource_RelatedRecognition 3 0.000\n", "Resource_RelatedPlace 2 0.000\n", "Person_RelatedPlace 1 0.000" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number of unique predicates for entities related to 1940-1980 works: 32\n" ] }, { "data": { "text/html": [ "
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FrequencyProportion
predicate.term
WorkedOn23030.274
HasMedia22880.272
Employment7600.090
Reference6020.072
LocatedIn5800.069
RelatedTo4360.052
TaughtBy2450.029
WorkedWith2300.027
InfluencedBy1600.019
StudiedWith1100.013
KnewSocially1040.012
PrecededBy930.011
succeededby860.010
KnewProfessionally850.010
StudiedAt570.007
PartnerOf540.006
IsInvolvedIn400.005
DesignedBy380.005
KnewOf300.004
CollaboratedWith190.002
TravelledTo150.002
ClientOf110.001
WasInfluenceBy110.001
MentoredBy100.001
Founded70.001
Became60.001
Awarded50.001
Read40.000
Attended30.000
TaughtAt30.000
MergedWith20.000
DoneIn20.000
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" ], "text/plain": [ " Frequency Proportion\n", "predicate.term \n", "WorkedOn 2303 0.274\n", "HasMedia 2288 0.272\n", "Employment 760 0.090\n", "Reference 602 0.072\n", "LocatedIn 580 0.069\n", "RelatedTo 436 0.052\n", "TaughtBy 245 0.029\n", "WorkedWith 230 0.027\n", "InfluencedBy 160 0.019\n", "StudiedWith 110 0.013\n", "KnewSocially 104 0.012\n", "PrecededBy 93 0.011\n", "succeededby 86 0.010\n", "KnewProfessionally 85 0.010\n", "StudiedAt 57 0.007\n", "PartnerOf 54 0.006\n", "IsInvolvedIn 40 0.005\n", "DesignedBy 38 0.005\n", "KnewOf 30 0.004\n", "CollaboratedWith 19 0.002\n", "TravelledTo 15 0.002\n", "ClientOf 11 0.001\n", "WasInfluenceBy 11 0.001\n", "MentoredBy 10 0.001\n", "Founded 7 0.001\n", "Became 6 0.001\n", "Awarded 5 0.001\n", "Read 4 0.000\n", "Attended 3 0.000\n", "TaughtAt 3 0.000\n", "MergedWith 2 0.000\n", "DoneIn 2 0.000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "relations_1940_1980 = []\n", "for this_df in [daqapersons_1940_80,daqaorgs_1940_80,daqawork_1940_80,daqaresources_1940_80]:\n", " for idx,row in this_df.iterrows():\n", " for col in relationship_cols:\n", " try: \n", " if isinstance(row[col], str): relations_1940_1980.append(pd.json_normalize(ast.literal_eval(row[col])))\n", " except: continue\n", "\n", "relations_1940_1980 = pd.concat(relations_1940_1980)\n", "relations_1940_1980 = relations_1940_1980.drop_duplicates()\n", "\n", "relations_1940_1980['subject.ori_id'] = relations_1940_1980['subject.ori_id'].astype(str)\n", "relations_1940_1980['object.ori_id'] = relations_1940_1980['object.ori_id'].astype(str)\n", "\n", "relations_1940_1980['subject._class_ori'] = np.where(relations_1940_1980['subject._class'] == 'person', \n", " relations_1940_1980['subject.ori_id'].map(arch_nonarch_dict), \n", " relations_1940_1980['subject._class_ori'])\n", "relations_1940_1980['object._class_ori'] = np.where(relations_1940_1980['object._class'] == 'person', \n", " relations_1940_1980['object.ori_id'].map(arch_nonarch_dict), \n", " relations_1940_1980['object._class_ori'])\n", "\n", "# relations\n", "print('Total number of relationships for entities related to 1940-1980 works:', relations_1940_1980.shape[0])\n", "display(relations_1940_1980.relation_class.value_counts()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'relation_class','relation_class':'Frequency'})\\\n", " .assign(Proportion = lambda x: round(x['Frequency']/x['Frequency'].sum(),3))\\\n", " .set_index('relation_class')\n", " .sort_values('Frequency', ascending=False))\n", "\n", "# predicate terms\n", "print('\\nTotal number of unique predicates for entities related to 1940-1980 works:', relations_1940_1980['predicate.term'].nunique())\n", "display(relations_1940_1980['predicate.term'].value_counts()\\\n", " .reset_index()\\\n", " .rename(columns={'index':'predicate.term','predicate.term':'Frequency'})\\\n", " .assign(Proportion = lambda x: round(x['Frequency']/x['Frequency'].sum(),3))\\\n", " .set_index('predicate.term')\n", " .sort_values('Frequency', ascending=False))" ] }, { "cell_type": "code", "execution_count": 16, "id": "6ee2375d", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###################### PERSON RELATIONSHIPS ######################\n", "\n", "\n", "Total number of Person-Work relations: 1487\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectstructureWorkedOn1352
1architectstructureReference86
2architectstructureInfluencedBy16
3structurearchitectDesignedBy14
4architectstructureStudiedAt4
5architectstructureEmployment3
6architectstructureTravelledTo3
7architectstructureAttended2
8architectstructureTaughtAt2
9non-architectstructureReference2
10architectstructureClientOf1
11architectstructureKnewOf1
12structurearchitectClientOf1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect structure WorkedOn 1352\n", "1 architect structure Reference 86\n", "2 architect structure InfluencedBy 16\n", "3 structure architect DesignedBy 14\n", "4 architect structure StudiedAt 4\n", "5 architect structure Employment 3\n", "6 architect structure TravelledTo 3\n", "7 architect structure Attended 2\n", "8 architect structure TaughtAt 2\n", "9 non-architect structure Reference 2\n", "10 architect structure ClientOf 1\n", "11 architect structure KnewOf 1\n", "12 structure architect ClientOf 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Person relations: 1218\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectarchitectTaughtBy232
1architectarchitectWorkedWith209
2architectarchitectReference180
3architectarchitectInfluencedBy127
4architectarchitectStudiedWith100
5architectarchitectKnewSocially99
6architectarchitectEmployment84
7architectarchitectKnewProfessionally71
8architectarchitectKnewOf26
9architectnon-architectKnewProfessionally13
10architectarchitectMentoredBy10
11architectarchitectWasInfluenceBy9
13architectnon-architectStudiedWith8
12architectnon-architectReference8
14architectarchitectCollaboratedWith5
15architectarchitectPartnerOf5
16architectnon-architectKnewSocially5
17architectnon-architectCollaboratedWith4
18architectnon-architectWorkedWith4
19non-architectarchitectClientOf4
20architectnon-architectTaughtBy3
21architectarchitectClientOf3
22architectnon-architectInfluencedBy3
23architectnon-architectEmployment2
24architectarchitectRelatedTo2
25non-architectarchitectEmployment1
26non-architectarchitectReference1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect architect TaughtBy 232\n", "1 architect architect WorkedWith 209\n", "2 architect architect Reference 180\n", "3 architect architect InfluencedBy 127\n", "4 architect architect StudiedWith 100\n", "5 architect architect KnewSocially 99\n", "6 architect architect Employment 84\n", "7 architect architect KnewProfessionally 71\n", "8 architect architect KnewOf 26\n", "9 architect non-architect KnewProfessionally 13\n", "10 architect architect MentoredBy 10\n", "11 architect architect WasInfluenceBy 9\n", "13 architect non-architect StudiedWith 8\n", "12 architect non-architect Reference 8\n", "14 architect architect CollaboratedWith 5\n", "15 architect architect PartnerOf 5\n", "16 architect non-architect KnewSocially 5\n", "17 architect non-architect CollaboratedWith 4\n", "18 architect non-architect WorkedWith 4\n", "19 non-architect architect ClientOf 4\n", "20 architect non-architect TaughtBy 3\n", "21 architect architect ClientOf 3\n", "22 architect non-architect InfluencedBy 3\n", "23 architect non-architect Employment 2\n", "24 architect architect RelatedTo 2\n", "25 non-architect architect Employment 1\n", "26 non-architect architect Reference 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Organization relations: 908\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectfirmEmployment652
1architecteducationStudiedAt53
2architectfirmReference49
3architectfirmPartnerOf49
4architecteducationEmployment15
5architectfirmInfluencedBy11
6architectfirmWorkedWith11
7architectfirmTaughtBy10
8architectfirmFounded7
9architectfirmCollaboratedWith6
10architecteducationReference5
11architecteducationRead3
12architecteducationInfluencedBy3
13architectorganisationBecame3
14architectgovernmentWorkedWith3
20architectfirmStudiedWith2
19non-architectfirmReference2
18architectorganisationReference2
17firmarchitectKnewOf2
15architectorganisationWorkedWith2
16architectorganisationEmployment2
29architectfirmWasInfluenceBy1
35architecteducationClientOf1
34architectfirmKnewProfessionally1
33architectorganisationAttended1
32architectfirmKnewOf1
31architectorganisationCollaboratedWith1
30architecteducationWorkedOn1
27architecteducationTaughtAt1
28architecteducationTravelledTo1
26firmarchitectBecame1
25firmarchitectClientOf1
24architecteducationCollaboratedWith1
23firmarchitectMergedWith1
22non-architectfirmEmployment1
21architectorganisationRelatedTo1
36architecteducationAwarded1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect firm Employment 652\n", "1 architect education StudiedAt 53\n", "2 architect firm Reference 49\n", "3 architect firm PartnerOf 49\n", "4 architect education Employment 15\n", "5 architect firm InfluencedBy 11\n", "6 architect firm WorkedWith 11\n", "7 architect firm TaughtBy 10\n", "8 architect firm Founded 7\n", "9 architect firm CollaboratedWith 6\n", "10 architect education Reference 5\n", "11 architect education Read 3\n", "12 architect education InfluencedBy 3\n", "13 architect organisation Became 3\n", "14 architect government WorkedWith 3\n", "20 architect firm StudiedWith 2\n", "19 non-architect firm Reference 2\n", "18 architect organisation Reference 2\n", "17 firm architect KnewOf 2\n", "15 architect organisation WorkedWith 2\n", "16 architect organisation Employment 2\n", "29 architect firm WasInfluenceBy 1\n", "35 architect education ClientOf 1\n", "34 architect firm KnewProfessionally 1\n", "33 architect organisation Attended 1\n", "32 architect firm KnewOf 1\n", "31 architect organisation CollaboratedWith 1\n", "30 architect education WorkedOn 1\n", "27 architect education TaughtAt 1\n", "28 architect education TravelledTo 1\n", "26 firm architect Became 1\n", "25 firm architect ClientOf 1\n", "24 architect education CollaboratedWith 1\n", "23 firm architect MergedWith 1\n", "22 non-architect firm Employment 1\n", "21 architect organisation RelatedTo 1\n", "36 architect education Awarded 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Resource relations: 546\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0interviewarchitectReference176
1architectPhotographHasMedia107
2interviewarchitectRelatedTo57
3architectinterviewRelatedTo55
4architectImageHasMedia49
5architectinterviewIsInvolvedIn39
6architectPortraitHasMedia23
7interviewnon-architectRelatedTo18
8non-architectinterviewRelatedTo18
9interviewnon-architectReference2
10architectpublicationReference1
11non-architectinterviewIsInvolvedIn1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 interview architect Reference 176\n", "1 architect Photograph HasMedia 107\n", "2 interview architect RelatedTo 57\n", "3 architect interview RelatedTo 55\n", "4 architect Image HasMedia 49\n", "5 architect interview IsInvolvedIn 39\n", "6 architect Portrait HasMedia 23\n", "7 interview non-architect RelatedTo 18\n", "8 non-architect interview RelatedTo 18\n", "9 interview non-architect Reference 2\n", "10 architect publication Reference 1\n", "11 non-architect interview IsInvolvedIn 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Recognition relations: 22\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectawardTravelledTo10
1architectawardAwarded4
2architectawardReference4
3architectawardBecame2
4architectawardRead1
5architectawardWasInfluenceBy1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect award TravelledTo 10\n", "1 architect award Awarded 4\n", "2 architect award Reference 4\n", "3 architect award Became 2\n", "4 architect award Read 1\n", "5 architect award WasInfluenceBy 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Person-Place relations: 1\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0architectplaceTravelledTo1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 architect place TravelledTo 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "###################### WORK RELATIONSHIPS ######################\n", "\n", "\n", "Total number of Work-Resource relations: 2055\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0structurePhotographHasMedia1477
1structureLineDrawingHasMedia397
2structureImageHasMedia141
3interviewstructureReference38
4structurePortraitHasMedia2
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subject._class_oriobject._class_oripredicate.termFrequency
0structureplaceLocatedIn580
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subject._class_oriobject._class_oripredicate.termFrequency
0firmstructureWorkedOn950
1structurefirmRelatedTo144
2firmstructureRelatedTo141
3structurefirmDesignedBy24
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subject._class_oriobject._class_oripredicate.termFrequency
0firmfirmPrecededBy93
1firmfirmsucceededby86
2firmfirmCollaboratedWith2
3firmfirmMergedWith1
4firmfirmWorkedWith1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 firm firm PrecededBy 93\n", "1 firm firm succeededby 86\n", "2 firm firm CollaboratedWith 2\n", "3 firm firm MergedWith 1\n", "4 firm firm WorkedWith 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Recognition-Organization relations: 1\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0awardeducationStudiedAt1
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 award education StudiedAt 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "###################### RESOURCE RELATIONSHIPS ######################\n", "\n", "\n", "Total number of Resource-Resource relations: 49\n" ] }, { "data": { "text/html": [ "
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subject._class_oriobject._class_oripredicate.termFrequency
0articleArticleHasMedia36
1interviewAudioHasMedia6
2interviewTranscriptHasMedia5
3interviewYoutubeHasMedia2
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subject._class_oriobject._class_oripredicate.termFrequency
0interviewfirmReference41
1firmPhotographHasMedia31
2firmPortraitHasMedia9
3firmLineDrawingHasMedia2
4firmImageHasMedia1
5intervieweducationReference1
6intervieworganisationReference1
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subject._class_oriobject._class_oripredicate.termFrequency
0interviewplaceDoneIn2
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 interview place DoneIn 2" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Total number of Resource-Recognition relations: 3\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
subject._class_oriobject._class_oripredicate.termFrequency
0interviewawardReference3
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" ], "text/plain": [ " subject._class_ori object._class_ori predicate.term Frequency\n", "0 interview award Reference 3" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "print('###################### PERSON RELATIONSHIPS ######################')\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedWork','Work_RelatedPerson'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedPerson'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedOrganization','Organization_RelatedPerson'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedResource','Resource_RelatedPerson'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedRecognition'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Person_RelatedPlace'], relations=relations_1940_1980)\n", "print('\\n')\n", "\n", "print('###################### WORK RELATIONSHIPS ######################')\n", "print('\\n')\n", "fetch_relation_details(['Work_RelatedResource','Resource_RelatedWork'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Work_RelatedPlace'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Work_RelatedRecognition'], relations=relations_1940_1980)\n", "print('\\n')\n", "\n", "print('###################### ORGANISATION RELATIONSHIPS ######################')\n", "print('\\n')\n", "fetch_relation_details(['Organization_RelatedWork','Work_RelatedOrganization'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Organization_RelatedOrganization'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Recognition_RelatedOrganization'])\n", "\n", "print('###################### RESOURCE RELATIONSHIPS ######################')\n", "print('\\n')\n", "fetch_relation_details(['Resource_RelatedResource'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Resource_RelatedOrganization','Organization_RelatedResource'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Resource_RelatedPlace'], relations=relations_1940_1980)\n", "print('\\n')\n", "fetch_relation_details(['Resource_RelatedRecognition'], relations=relations_1940_1980)\n", "print('\\n')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "dfd61f64", "metadata": {}, "source": [ "### 1940-1980 projects and firms\n", "\n", "Below are some statistics about project characteristics and firms in the DAQA dataset of only projected completed between 1940 and 1980. Proportions under the `1940-1980 PROJECTS` subheading are calculated as a percentage of the total number of projects completed between 1940 and 1980 in the dataset. Proportions under the `1940-1980 FIRMS` subheading are calculated as a percentage of the total number of firms related to projects completed between 1940 and 1980 in the dataset.\n", "\n", "- We define an extant project as one that has a populated `is_demolished` field and the value is `False`\n", "- We define a demolished project as one that has a populated `is_demolished` field and the value is `True`" ] }, { "cell_type": "code", "execution_count": 17, "id": "aed47b64", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###################### 1940-1980 PROJECTS ######################\n", "\n", "Q: How many projects are recorded in DAQA between 1940-1980?\n", "A: There are 608 projects between 1940-1980.\n", "\n", "Q: what % of projects completed in 1940-1980 are extant?\n", "A: 86.84% (528) of DAQA projects between 1940-1980 are extant.\n", "\n", "Q: what % of projects completed in 1940-1980 are demolished?\n", "A: 12.34% (75) of DAQA projects between 1940-1980 are demolished.\n", "\n", "*It should be noted that there are 5 DAQA projects between 1940-1980 with missing demolished information.\n", "\n", "Q: what % of projects between 1940-1980 have addresses?\n", "A: 96.05% (584) of DAQA projects between 1940-1980 have addresses.\n", "\n", "Q: what % of projects between 1940-1980 have geocodes (lat/long)?\n", "A: 85.86% (522) of DAQA projects between 1940-1980 have geocodes.\n", "\n", "Q: how many projects between 1940-1980 have associated firms?\n", "A: 78.29% (476) of DAQA projects between 1940-1980 have associated firms.\n", "\n", "Q: what % of projects between 1940-1980 have associated firms but no architects?\n", "A: 16.94% (103) of DAQA projects between 1940-1980 have associated firms but no architects.\n", "\n", "Q: how many projects between 1940-1980 have associated architects?\n", "A: 82.07% (499) of DAQA projects between 1940-1980 have associated architects.\n", "\n", "Q: what % of projects between 1940-1980 have associated architects but no firms?\n", "A: 20.72% (126) of DAQA projects between 1940-1980 have associated architects but no firms.\n", "\n", "###################### 1940-1980 FIRMS ######################\n", "\n", "Q: How many firms are recorded in DAQA between 1940-1980?\n", "A: There are 101 firms between 1940-1980.\n", "\n", "Q: what % of firms between 1940-1980 have ‘operating years’ recorded (just start)?\n", "A: 81.19% (82) of DAQA firms between 1940-1980 have operating years recorded (just start).\n", "\n", "Q: what % of firms between 1940-1980 have ‘operating years’ recorded (start and end)?\n", "A: 67.33% (68) of DAQA firms between 1940-1980 have operating years recorded (start and end).\n" ] } ], "source": [ "print('###################### 1940-1980 PROJECTS ######################')\n", "\n", "# filter data accordingly\n", "daqawork_1940_80 = daqa_work[daqa_work['_id'].isin(projects_1940_80)].copy()\n", "\n", "print('\\nQ: How many projects are recorded in DAQA between 1940-1980?')\n", "count_projects = len(daqawork_1940_80)\n", "print(f'A: There are {count_projects} projects between 1940-1980.')\n", "\n", "# we define a extant buiulding where \"is_demolished\" field is False\n", "print('\\nQ: what % of projects completed in 1940-1980 are extant?')\n", "count_projects_extant = len(daqawork_1940_80[daqawork_1940_80['is_demolished'] == False])\n", "prop_projects_extant = round((count_projects_extant / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_extant}% ({count_projects_extant}) of DAQA projects between 1940-1980 are extant.')\n", "\n", "# we define a extant buiulding where \"is_demolished\" field is True\n", "print('\\nQ: what % of projects completed in 1940-1980 are demolished?')\n", "count_projects_demolished = len(daqawork_1940_80[daqawork_1940_80['is_demolished'] == True])\n", "prop_projects_demolished = round((count_projects_demolished / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_demolished}% ({count_projects_demolished}) of DAQA projects between 1940-1980 are demolished.')\n", "\n", "print('\\n*It should be noted that there are 5 DAQA projects between 1940-1980 with missing demolished information.')\n", "\n", "# we define a project with an address as one that has a populated \"address\" field\n", "print('\\nQ: what % of projects between 1940-1980 have addresses?')\n", "count_projects_with_address = len(daqawork_1940_80[daqawork_1940_80.coverage_range.apply(lambda x: \"address\" in x)])\n", "prop_projects_with_address = round((count_projects_with_address / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_address}% ({count_projects_with_address}) of DAQA projects between 1940-1980 have addresses.')\n", "\n", "# we define a project with a geocode date as one that has a populated \"longitude\" field\n", "print('\\nQ: what % of projects between 1940-1980 have geocodes (lat/long)?')\n", "count_projects_with_geocodes = len(daqawork_1940_80[daqawork_1940_80.coverage_range.apply(lambda x: \"latitude\" in x)])\n", "prop_projects_with_geocodes = round((count_projects_with_geocodes / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_geocodes}% ({count_projects_with_geocodes}) of DAQA projects between 1940-1980 have geocodes.')\n", "\n", "# we define a project with an associated firm as one that has a populated \"related_organizations\" field\n", "print('\\nQ: how many projects between 1940-1980 have associated firms?')\n", "daqawork_1940_80_firms = daqawork_1940_80[daqawork_1940_80.related_organizations.notnull()]\n", "count_projects_with_firms = len(daqawork_1940_80_firms)\n", "prop_projects_with_firms = round((count_projects_with_firms / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_firms}% ({count_projects_with_firms}) of DAQA projects between 1940-1980 have associated firms.')\n", "\n", "# we define a project with no associated architects as one that has no populated \"related_people\" field\n", "print('\\nQ: what % of projects between 1940-1980 have associated firms but no architects?')\n", "count_projects_with_firms_no_architects = len(daqawork_1940_80[(daqawork_1940_80.related_organizations.notnull()) &\\\n", " (daqawork_1940_80.related_people.isnull())])\n", "prop_projects_with_firms_no_architects = round((count_projects_with_firms_no_architects / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_firms_no_architects}% ({count_projects_with_firms_no_architects}) of DAQA projects between 1940-1980 have associated firms but no architects.')\n", "\n", "# we define a project with an associated architect as one that has a populated \"related_people\" field\n", "print('\\nQ: how many projects between 1940-1980 have associated architects?')\n", "count_projects_with_architects = len(daqawork_1940_80[daqawork_1940_80.related_people.notnull()])\n", "prop_projects_with_architects = round((count_projects_with_architects / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_architects}% ({count_projects_with_architects}) of DAQA projects between 1940-1980 have associated architects.')\n", "\n", "# we define a project with an associated architects as one that has a populated \"related people\" field\n", "# and we define a project with no associated firms as one that has a no populated \"related organizations\" field\n", "print('\\nQ: what % of projects between 1940-1980 have associated architects but no firms?')\n", "count_projects_with_architects_no_firms = len(daqawork_1940_80[(daqawork_1940_80.related_organizations.isnull()) &\\\n", " (daqawork_1940_80.related_people.notnull())])\n", "prop_projects_with_architects_no_firms = round((count_projects_with_architects_no_firms / count_projects) * 100, 2)\n", "print(f'A: {prop_projects_with_architects_no_firms}% ({count_projects_with_architects_no_firms}) of DAQA projects between 1940-1980 have associated architects but no firms.')\n", "\n", "\n", "print('\\n###################### 1940-1980 FIRMS ######################')\n", "\n", "# filter data accordingly\n", "daqafirms_1940_80 = daqa_firms[daqa_firms['ori_id'].astype(int).isin(firms_with_projects_1940_80)].copy()\n", "\n", "print('\\nQ: How many firms are recorded in DAQA between 1940-1980?')\n", "count_firms = len(daqafirms_1940_80)\n", "print(f'A: There are {count_firms} firms between 1940-1980.')\n", "\n", "# we define an operating firm as an organisation that has a populated \"operation\" field with a start date\n", "print('\\nQ: what % of firms between 1940-1980 have ‘operating years’ recorded (just start)?')\n", "count_firms_with_operating_start = len(daqafirms_1940_80[daqafirms_1940_80.operation.apply(lambda x: \"date_start\" in x if isinstance(x, str) else False)])\n", "prop_firms_with_operating_start = round((count_firms_with_operating_start / count_firms) * 100, 2)\n", "print(f'A: {prop_firms_with_operating_start}% ({count_firms_with_operating_start}) of DAQA firms between 1940-1980 have operating years recorded (just start).')\n", "\n", "# we define an operating firm as an organisation that has a populated \"operation\" field with start and end dates\n", "print('\\nQ: what % of firms between 1940-1980 have ‘operating years’ recorded (start and end)?')\n", "count_firms_with_operating_years = len(daqafirms_1940_80[daqafirms_1940_80.operation.apply(lambda x: (\"date_start\" in x) & (\"date_end\" in x) if isinstance(x, str) else False)])\n", "prop_firms_with_operating_years = round((count_firms_with_operating_years / count_firms) * 100, 2)\n", "print(f'A: {prop_firms_with_operating_years}% ({count_firms_with_operating_years}) of DAQA firms between 1940-1980 have operating years recorded (start and end).')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "4980df40", "metadata": {}, "source": [ "## Education\n", "\n", "Below are some education qualification characteristics of people records in the DAQA dataset. Proportions are calculated as a percentage of the total number of people in the dataset. \n", "\n", "We focus on the data of four universities: `UQ`, `BCTC`, `QIT`, and `QUT`. The latter three also grouped together, and represented as `BCTC/QIT/QUT`. We compute statistics for each university separately and then visualise the data for `UQ` and `BCTC/QIT/QUT`.\n", "\n", "- We define a person with a education data as one that has a populated `education_trainings` field\n", "- We define a person with a education year data as one that has a populated `date_end` field in the `education_trainings` field" ] }, { "cell_type": "code", "execution_count": 18, "id": "f49718fa", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Q: How many persons are recorded in DAQA?\n", "A: There are 1103 people in DAQA.\n", "\n", "Q: what % of people have education data?\n", "A: 20.58% (227) of people with education data.\n", "\n", "Q: what % of people have education data (with years)?\n", "A: 17.86% (197) of people with education year data.\n" ] } ], "source": [ "# load data\n", "daqa_persons = df_daqa_dict['person']\n", "daqa_persons_with_education_data = daqa_persons[daqa_persons['education_trainings'].notnull()].copy()\n", "\n", "print('\\nQ: How many persons are recorded in DAQA?')\n", "count_persons = len(daqa_persons)\n", "print(f'A: There are {count_persons} people in DAQA.')\n", "\n", "# we define a person with a education data as one that has a populated \"education_trainings\" field\n", "print('\\nQ: what % of people have education data?')\n", "count_persons_with_education_data = len(daqa_persons_with_education_data)\n", "prop_persons_with_education_data = round((count_persons_with_education_data / count_persons) * 100, 2)\n", "print(f'A: {prop_persons_with_education_data}% ({count_persons_with_education_data}) of people with education data.')\n", "\n", "# we define a person with a education year data as one that has a populated \"date_end\" field in the \"education_trainings\" field\n", "print('\\nQ: what % of people have education data (with years)?')\n", "daqa_persons_with_education_year_data = daqa_persons_with_education_data[daqa_persons_with_education_data\\\n", " .education_trainings.apply(lambda x: \"date_end\" in x)]\n", "count_persons_with_education_year_data = len(daqa_persons_with_education_year_data)\n", "prop_persons_with_education_year_data = round((count_persons_with_education_year_data / count_persons) * 100, 2)\n", "print(f'A: {prop_persons_with_education_year_data}% ({count_persons_with_education_year_data}) of people with education year data.')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "ef87d833", "metadata": {}, "source": [ "### Queensland universities" ] }, { "attachments": {}, "cell_type": "markdown", "id": "dc977345", "metadata": {}, "source": [ "#### UQ" ] }, { "cell_type": "code", "execution_count": 19, "id": "b399785b", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Q: How many education records from UQ exist in DAQA?\n" ] }, { "data": { "text/html": [ "
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organizationcount
0UQ99
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2QUT, UQ1
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" ], "text/plain": [ " organization count\n", "0 UQ 99\n", "1 BCTC/UQ 11\n", "2 QUT, UQ 1\n", "3 BCTC?UQ 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "A: There are 112 education records with from UQ.\n" ] } ], "source": [ "education_training_df = pd.DataFrame()\n", "\n", "for idx, row in daqa_persons_with_education_data.iterrows():\n", " person_id = row['ori_id']\n", " education_training_data = pd.json_normalize(ast.literal_eval(row['education_trainings']))\n", " education_training_data['ori_id'] = person_id\n", " education_training_df = education_training_df.append(education_training_data)\n", "\n", "# remove erroneous row\n", "education_training_df = education_training_df[~education_training_df['organization.name'].str.contains('note: interview UQ',na=False)]\n", "\n", "def print_filtered_data(df, filter, display_output=True):\n", " filtered_df = df[df['organization.name'].str.contains('|'.join(filter),na=False)]\n", " if display_output:\n", " display(filtered_df['organization.name']\\\n", " .value_counts()\\\n", " .reset_index()\\\n", " .rename(columns={'index': 'organization',\n", " 'organization.name': 'count'}))\n", " return filtered_df.shape[0]\n", "\n", "print('\\nQ: How many education records from UQ exist in DAQA?')\n", "filterd_count_uq = print_filtered_data(education_training_df, ['UQ'])\n", "print('A: There are {} education records with from UQ.'.format(filterd_count_uq))" ] }, { "attachments": {}, "cell_type": "markdown", "id": "8cb19126", "metadata": {}, "source": [ "#### BCTC" ] }, { "cell_type": "code", "execution_count": 20, "id": "de8f24ff", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: How many people have education data from BCTC?\n" ] }, { "data": { "text/html": [ "
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organizationcount
0BCTC42
1BCTC/UQ11
2BRISBANE CENTRAL TECHNICAL COLLEGE3
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" ], "text/plain": [ " organization count\n", "0 BCTC 42\n", "1 BCTC/UQ 11\n", "2 BRISBANE CENTRAL TECHNICAL COLLEGE 3\n", "3 BCTC?UQ 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "A: There are 57 people with education data from BCTC.\n" ] } ], "source": [ "print('Q: How many people have education data from BCTC?')\n", "filterd_count_bctc = print_filtered_data(education_training_df, ['BCTC','BRISBANE'])\n", "print('A: There are {} people with education data from BCTC.'.format(filterd_count_bctc))" ] }, { "attachments": {}, "cell_type": "markdown", "id": "b93021db", "metadata": {}, "source": [ "#### QIT" ] }, { "cell_type": "code", "execution_count": 21, "id": "0142f1d9", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: How many people have education data from QIT?\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
organizationcount
0QIT21
\n", "
" ], "text/plain": [ " organization count\n", "0 QIT 21" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "A: There are 21 people with education data from QIT.\n" ] } ], "source": [ "print('Q: How many people have education data from QIT?')\n", "filterd_count_qit = print_filtered_data(education_training_df, ['QIT'])\n", "print('A: There are {} people with education data from QIT.'.format(filterd_count_qit))" ] }, { "attachments": {}, "cell_type": "markdown", "id": "fb8ff59b", "metadata": {}, "source": [ "#### QUT" ] }, { "cell_type": "code", "execution_count": 22, "id": "b22681a8", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: How many people have education data from QUT?\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
organizationcount
0QUT10
1OHIO_STATE_UNIandQUT1
2QUT, UQ1
\n", "
" ], "text/plain": [ " organization count\n", "0 QUT 10\n", "1 OHIO_STATE_UNIandQUT 1\n", "2 QUT, UQ 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "A: There are 12 people with education data from QUT.\n" ] } ], "source": [ "print('Q: How many people have education data from QUT?')\n", "filterd_count_qut = print_filtered_data(education_training_df, ['QUT'])\n", "print('A: There are {} people with education data from QUT.'.format(filterd_count_qut))" ] }, { "attachments": {}, "cell_type": "markdown", "id": "59175230", "metadata": {}, "source": [ "#### Visual comparison" ] }, { "cell_type": "code", "execution_count": 23, "id": "9dc49cd5", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a bar chart with all the filterd_count values\n", "fig, ax = plt.subplots(figsize=(10, 5))\n", "ax.bar(['UQ', 'BCTC', 'QIT', 'QUT'], [filterd_count_uq, filterd_count_bctc, filterd_count_qit, filterd_count_qut])\n", "ax.set_ylabel('Count')\n", "ax.set_title('Count of education records from each institution')\n", "ax.text(0.5, -0.125, 'Note: there are some non-disjoint cases in the data.', size=12, ha=\"center\", transform=ax.transAxes)\n", "for p in ax.patches: ax.annotate(str(p.get_height()), (p.get_x()+.32, p.get_height() + 3), size=15)\n", "ax.set_ylim([0, 130])\n", "plt.show()\n", "\n", "# create a bar chart with all the filterd_count values\n", "fig, ax = plt.subplots(figsize=(10, 5))\n", "ax.bar(['UQ', 'BCTC/QIT/QUT'], [filterd_count_uq, filterd_count_bctc + filterd_count_qit + filterd_count_qut])\n", "ax.set_ylabel('Count')\n", "ax.set_title('Count of education records from each institution (combined)')\n", "ax.text(0.5, -0.125, 'Note: there are some non-disjoint cases in the data.', size=12, ha=\"center\", transform=ax.transAxes)\n", "for p in ax.patches: ax.annotate(str(p.get_height()), (p.get_x()+.32, p.get_height() + 3), size=15)\n", "ax.set_ylim([0, 130])\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "ade7fbda", "metadata": {}, "source": [ "### Education by period\n", "\n", "Below are some temporal statistics of education qualification data of people records in the DAQA dataset - but before computing summary statistics the data requires some cleaning. Under the heading `DATA QUALITY OF EDUCATION RECORDS`, we walk through the applied cleaning steps.\n", "\n", "Summary statistics are presented for two periods: before 1940 and between 1940-1980. These outputs can be found under their respective subheadings. Proportions under the `1940-1980 EDUCATION` subheading are calculated as a percentage of the total number of people with education records in the dataset during this period. Proportions under the `1940-Present EDUCATION` subheading are calculated as a percentage of the total number of people with education records in the dataset during this period. " ] }, { "cell_type": "code", "execution_count": 24, "id": "ce15273e", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "###################### DATA QUALITY OF EDUCATION RECORDS ######################\n", "\n", "For some records, the data is messy in terms of the end year for a corresponding education record.\n", "\n", "Below we provide all the eductional records with unconventional values in the end year field\n" ] }, { "data": { "text/html": [ "
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nametypequalplaceend_yearori_id
0UQUNINaNQLDincomplete254
0UQUNIBArchQldincomplete267
1UQUNIDipArchQLDincomplete384
0UQUNIDipArchQLD1945?376
0UQUNINaNQLDincomplete736
1UQUNIBArchQLD1969?117
3UNSWUNIArchivesNSWincomplete384
0STCNSWNaNColleincom538
2QUT, UQUNIPhD (hon)QLD2003, 2008174
0QITINSTITUTECERTQLD1968?13
0QITINSTITUTECERTQLD1968?14
0QITINSTITUTECERTQLD1968?10
0QITCOLLEGEDipArchQLD1979?380
0NANGLE IoTDISTANCE EDUCATIONDipArchSYDNEY1960s355
0MelbourneUNIBArchVIC1939?349
1DurhanUNILandscapeUK1965?713
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" ], "text/plain": [ " name type qual place \\\n", "0 UQ UNI DipArch QLD \n", "1 UQ UNI BArch QLD \n", "2 QUT, UQ UNI PhD (hon) QLD \n", "0 QIT INSTITUTE CERT QLD \n", "0 QIT INSTITUTE CERT QLD \n", "0 QIT INSTITUTE CERT QLD \n", "0 QIT COLLEGE DipArch QLD \n", "0 NANGLE IoT DISTANCE EDUCATION DipArch SYDNEY \n", "0 Melbourne UNI BArch VIC \n", "1 Durhan UNI Landscape UK \n", "0 BCTC COLLEGE CertArch QLD \n", "0 AA INDEPENDENT DipArch UK \n", "0 NaN NaN NaN Vienna \n", "1 NaN UNI BACHELOR OF ARCHITECTURE NaN \n", "\n", " end_year ori_id \n", "0 1945? 376 \n", "1 1969? 117 \n", "2 2003, 2008 174 \n", "0 1968? 13 \n", "0 1968? 14 \n", "0 1968? 10 \n", "0 1979? 380 \n", "0 1960s 355 \n", "0 1939? 349 \n", "1 1965? 713 \n", "0 1961? 627 \n", "0 1890? 636 \n", "0 1940s 166 \n", "1 1968? 4 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "2. Remove non-numeric suffixal characters such as \"?\" and \"s\". As we are interested in a certain period, granularity is not important.\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " name type qual place \\\n", "0 UQ UNI DipArch QLD \n", "1 UQ UNI BArch QLD \n", "2 QUT, UQ UNI PhD (hon) QLD \n", "0 QIT INSTITUTE CERT QLD \n", "0 QIT INSTITUTE CERT QLD \n", "0 QIT INSTITUTE CERT QLD \n", "0 QIT COLLEGE DipArch QLD \n", "0 NANGLE IoT DISTANCE EDUCATION DipArch SYDNEY \n", "0 Melbourne UNI BArch VIC \n", "1 Durhan UNI Landscape UK \n", "0 BCTC COLLEGE CertArch QLD \n", "0 AA INDEPENDENT DipArch UK \n", "0 NaN NaN NaN Vienna \n", "1 NaN UNI BACHELOR OF ARCHITECTURE NaN \n", "\n", " end_year ori_id \n", "0 1945 376 \n", "1 1969 117 \n", "2 2003, 2008 174 \n", "0 1968 13 \n", "0 1968 14 \n", "0 1968 10 \n", "0 1979 380 \n", "0 1960 355 \n", "0 1939 349 \n", "1 1965 713 \n", "0 1961 627 \n", "0 1890 636 \n", "0 1940 166 \n", "1 1968 4 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "3. Fix the row with two years separated by a comma. Again as we are interested in a certain period, we can temporarily replace this with the most recent year.\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " name type qual place end_year \\\n", "0 UQ UNI DipArch QLD 1945 \n", "1 UQ UNI BArch QLD 1969 \n", "2 QUT, UQ UNI PhD (hon) QLD 2008 \n", "0 QIT INSTITUTE CERT QLD 1968 \n", "0 QIT INSTITUTE CERT QLD 1968 \n", "0 QIT INSTITUTE CERT QLD 1968 \n", "0 QIT COLLEGE DipArch QLD 1979 \n", "0 NANGLE IoT DISTANCE EDUCATION DipArch SYDNEY 1960 \n", "0 Melbourne UNI BArch VIC 1939 \n", "1 Durhan UNI Landscape UK 1965 \n", "0 BCTC COLLEGE CertArch QLD 1961 \n", "0 AA INDEPENDENT DipArch UK 1890 \n", "0 NaN NaN NaN Vienna 1940 \n", "1 NaN UNI BACHELOR OF ARCHITECTURE NaN 1968 \n", "\n", " ori_id \n", "0 376 \n", "1 117 \n", "2 174 \n", "0 13 \n", "0 14 \n", "0 10 \n", "0 380 \n", "0 355 \n", "0 349 \n", "1 713 \n", "0 627 \n", "0 636 \n", "0 166 \n", "1 4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('\\n###################### DATA QUALITY OF EDUCATION RECORDS ######################')\n", "\n", "education_training_df_with_dates = education_training_df[education_training_df['coverage_range.date_range.date_end.year'].notnull()].copy()\n", "\n", "clean_columns_dict = {'organization.name': 'name',\n", " 'organization.type': 'type',\n", " 'organization.qualification': 'qual',\n", " 'coverage_range.place': 'place',\n", " 'coverage_range.date_range.date_end.year': 'end_year',\n", " 'ori_id': 'ori_id'}\n", "\n", "print('\\nFor some records, the data is messy in terms of the end year for a corresponding education record.')\n", "print('\\nBelow we provide all the eductional records with unconventional values in the end year field')\n", "\n", "check_string_len_cond = (education_training_df_with_dates['coverage_range.date_range.date_end.year'].str.len() > 4)\n", "display(education_training_df_with_dates[check_string_len_cond]\\\n", " .sort_values(by=['organization.name'], ascending=False)\\\n", " .rename(columns=clean_columns_dict))\n", "\n", "print('\\n1. Remove incomplete records. These records do not hold any temporal information.')\n", "disregard_incomplete_records = (education_training_df_with_dates['coverage_range.date_range.date_end.year'].str.contains('incom', na=False))\n", "\n", "display(education_training_df_with_dates[check_string_len_cond & ~disregard_incomplete_records]\\\n", " .sort_values(by=['organization.name'], ascending=False)\\\n", " .rename(columns=clean_columns_dict))\n", "\n", "print('\\n2. Remove non-numeric suffixal characters such as \"?\" and \"s\". As we are interested in a certain period, granularity is not important.')\n", "\n", "# remove non-numeric characters such as \"?\" and \"s\" for the end year field\n", "education_training_df_with_dates['coverage_range.date_range.date_end.year'] = education_training_df_with_dates['coverage_range.date_range.date_end.year']\\\n", " .str.replace('?', '', regex=False).str.replace('s', '', regex=False)\n", "\n", "display(education_training_df_with_dates[check_string_len_cond & ~disregard_incomplete_records]\\\n", " .sort_values(by=['organization.name'], ascending=False)\\\n", " .rename(columns=clean_columns_dict))\n", "\n", "print('\\n3. Fix the row with two years separated by a comma. Again as we are interested in a certain period, we can temporarily replace this with the most recent year.')\n", "\n", "# remove non-numeric characters such as \"?\" and \"s\" for the end year field\n", "education_training_df_with_dates['coverage_range.date_range.date_end.year'] = education_training_df_with_dates['coverage_range.date_range.date_end.year']\\\n", " .str.replace('2003, 2008', '2008', regex=False)\n", "\n", "display(education_training_df_with_dates[check_string_len_cond & ~disregard_incomplete_records]\\\n", " .sort_values(by=['organization.name'], ascending=False)\\\n", " .rename(columns=clean_columns_dict))" ] }, { "cell_type": "code", "execution_count": 25, "id": "eb32b17b", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "###################### 1940-1980 EDUCATION ######################\n", "\n", "Q: How many education records exist in DAQA between 1940-1980?\n", "A: There are 158 education records between 1940-1980.\n", "\n", "Q: How many education records exist from UQ between 1940-1980?\n", "A: There are 83 (52.53%) education records from UQ between 1940-1980.\n", "\n", "Q: How many education records exist from BCTC/QIT/QIT between 1940-1980?\n", "A: There are 42 (26.58%) education records from BCTC/QIT/QUT between 1940-1980.\n", "\n", "###################### 1940-Present EDUCATION ######################\n", "\n", "Q: How many education records exist in DAQA between 1940-Present?\n", "A: There are 193 education records between 1940-Present.\n", "\n", "Q: How many education records exist from UQ between 1940-Present?\n", "A: There are 98 (50.78%) education records from UQ between 1940-Present.\n", "\n", "Q: How many education records exist from BCTC/QIT/QIT between 1940-Present?\n", "A: There are 57 (29.53%) education records from BCTC/QIT/QUT between 1940-Present.\n" ] } ], "source": [ "print('\\n###################### 1940-1980 EDUCATION ######################')\n", "\n", "# clean year data for temporal analysis\n", "education_training_df_with_clean_dates = education_training_df_with_dates[~check_string_len_cond].copy()\n", "education_training_df_with_clean_dates = education_training_df_with_clean_dates\\\n", " .append(education_training_df_with_dates[check_string_len_cond & ~disregard_incomplete_records])\n", "\n", "education_training_df_with_clean_dates['coverage_range.date_range.date_end.year'] = education_training_df_with_clean_dates['coverage_range.date_range.date_end.year'].astype(int)\n", "\n", "education_training_df_1940_1980 = education_training_df_with_clean_dates[(education_training_df_with_clean_dates['coverage_range.date_range.date_end.year'] >= 1940) & \\\n", " (education_training_df_with_clean_dates['coverage_range.date_range.date_end.year'] <= 1980)].copy()\n", "\n", "print('\\nQ: How many education records exist in DAQA between 1940-1980?')\n", "count_ed_records_1940_1980 = len(education_training_df_1940_1980)\n", "print(f'A: There are {count_ed_records_1940_1980} education records between 1940-1980.')\n", "\n", "print('\\nQ: How many education records exist from UQ between 1940-1980?')\n", "count_ed_records_1940_1980_uq = print_filtered_data(education_training_df_1940_1980, ['UQ'], display_output=False)\n", "prop_ed_records_1940_1980_uq = count_ed_records_1940_1980_uq / count_ed_records_1940_1980\n", "print(f'A: There are {count_ed_records_1940_1980_uq} ({prop_ed_records_1940_1980_uq:.2%}) education records from UQ between 1940-1980.')\n", "\n", "print('\\nQ: How many education records exist from BCTC/QIT/QIT between 1940-1980?')\n", "count_ed_records_1940_1980_rest = print_filtered_data(education_training_df_1940_1980, ['BCTC', 'QIT', 'QUT','BRISBANE'], display_output=False)\n", "prop_ed_records_1940_1980_rest = count_ed_records_1940_1980_rest / count_ed_records_1940_1980\n", "print(f'A: There are {count_ed_records_1940_1980_rest} ({prop_ed_records_1940_1980_rest:.2%}) education records from BCTC/QIT/QUT between 1940-1980.')\n", "\n", "\n", "print('\\n###################### 1940-Present EDUCATION ######################')\n", "\n", "# remove rows with more than 4 characters in string\n", "education_training_df_1940_present = education_training_df_with_clean_dates[(education_training_df_with_clean_dates['coverage_range.date_range.date_end.year'] >= 1940)].copy()\n", "\n", "print('\\nQ: How many education records exist in DAQA between 1940-Present?')\n", "count_ed_records_1940_present = len(education_training_df_1940_present)\n", "print(f'A: There are {count_ed_records_1940_present} education records between 1940-Present.')\n", "\n", "print('\\nQ: How many education records exist from UQ between 1940-Present?')\n", "count_ed_records_1940_present_uq = print_filtered_data(education_training_df_1940_present, ['UQ'], display_output=False)\n", "prop_ed_records_1940_present_uq = count_ed_records_1940_present_uq / count_ed_records_1940_present\n", "print(f'A: There are {count_ed_records_1940_present_uq} ({prop_ed_records_1940_present_uq:.2%}) education records from UQ between 1940-Present.')\n", "\n", "print('\\nQ: How many education records exist from BCTC/QIT/QIT between 1940-Present?')\n", "count_ed_records_1940_present_rest = print_filtered_data(education_training_df_1940_present, ['BCTC', 'QIT', 'QUT','BRISBANE'], display_output=False)\n", "prop_ed_records_1940_present_rest = count_ed_records_1940_present_rest / count_ed_records_1940_present\n", "print(f'A: There are {count_ed_records_1940_present_rest} ({prop_ed_records_1940_present_rest:.2%}) education records from BCTC/QIT/QUT between 1940-Present.')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "9edf11c2", "metadata": {}, "source": [ "## Persons with related works\n", "\n", "In this section, we explore project activity of people in DAQA. We start by first categorising people into five groups: People with one related work, 2-3 works, 4-10 works, 11-50 works, and +50 works. We then visualise the distribution of these categories. We are particulatly interested in architects with more than 50 related works. \n", "\n", "Given the distribution, we also explore the presence of the Pareto principle - in other words, are a majority of completed projects related to only a small number of architects? We plot the cumulative distribution of architects and their related works." ] }, { "cell_type": "code", "execution_count": 26, "id": "4ded8199", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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3\"Lange Powell\"145
5\"Karl Langer\"82
4\"Graham W. Bligh\"78
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1\"Robin Gibson\"70
2\"John Dalton\"67
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" ], "text/plain": [ " display_name count\n", "3 \"Lange Powell\" 145\n", "5 \"Karl Langer\" 82\n", "4 \"Graham W. Bligh\" 78\n", "0 \"Rex Addison\" 75\n", "1 \"Robin Gibson\" 70\n", "2 \"John Dalton\" 67" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "###################### PARETO PRINCIPLE ######################\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# load data\n", "daqa_persons_with_related_works = daqa_persons[daqa_persons['related_works'].notnull()].copy()\n", "\n", "related_works_raw = pd.DataFrame()\n", "for index, row in daqa_persons_with_related_works.iterrows():\n", " related_works_raw = related_works_raw.append(pd.json_normalize(ast.literal_eval(row['related_works'])))\n", "\n", "# get all person ids with related works\n", "related_works_raw = related_works_raw[['relation_class','subject.ori_id','object.ori_id']].drop_duplicates()\n", "\n", "Person_RelatedWork_cond = (related_works_raw['relation_class']=='Person_RelatedWork')\n", "Work_RelatedPerson_cond = (related_works_raw['relation_class']=='Work_RelatedPerson')\n", "\n", "person_ids_related_works = related_works_raw[Person_RelatedWork_cond]['subject.ori_id']\\\n", " .value_counts().reset_index().rename(columns={'index': 'id', 'subject.ori_id': 'count'})\\\n", " .append(related_works_raw[Work_RelatedPerson_cond]['object.ori_id']\\\n", " .value_counts().reset_index().rename(columns={'index': 'id', 'object.ori_id': 'count'}))\n", "\n", "person_ids_related_works = person_ids_related_works.groupby('id').sum().reset_index()\n", "\n", "person_ids_related_works['count_cat'] = np.where((person_ids_related_works['count'] < 2), person_ids_related_works['count'],\n", " np.where((person_ids_related_works['count'] <= 3),'2-3',\n", " np.where((person_ids_related_works['count'] <= 10),'4-10',\n", " np.where((person_ids_related_works['count'] <= 50),'11-50','+50'))))\n", "\n", "\n", "# plot horizontal bar chart\n", "ax = person_ids_related_works['count_cat'].value_counts().reindex(['1','2-3','4-10','11-50','+50']).sort_values()\\\n", " .plot(kind='barh', figsize=(10,5), title='Number of (unique) Related Works per Person')\n", "\n", "# add bar labels with proportions\n", "for p in ax.patches:\n", " ax.annotate(f'{p.get_width()} ({p.get_width()/len(person_ids_related_works):.2%})', (p.get_width()+1.05, p.get_y()+0.15), size=12)\n", "\n", "# increase x-axis\n", "ax.set_xlim(0, 110)\n", "\n", "plt.show()\n", "\n", "print('\\nQ: Who has 50 or more related works?')\n", "person_ids_with_over50works = person_ids_related_works[person_ids_related_works['count_cat']=='+50']['id'].values.tolist()\n", "daqa_persons['ori_id'] = daqa_persons['ori_id'].astype(int)\n", "\n", "top_six = pd.merge(daqa_persons[daqa_persons['ori_id'].astype(int).isin(person_ids_with_over50works)][['ori_id','display_name']].rename(columns={'ori_id': 'id'}),\n", " person_ids_related_works[person_ids_related_works['id'].isin(person_ids_with_over50works)], on='id')\n", "\n", "# get person records with 50 or more related works\n", "display(top_six[['display_name','count']].sort_values(by='count', ascending=False))\n", "\n", "print('\\n###################### PARETO PRINCIPLE ######################')\n", "\n", "#check for pareto principle\n", "samples = pd.DataFrame({\"Number of projects\": person_ids_related_works['count'].sort_values(ascending=False).values})\n", "\n", "# Add cumulative percentage column\n", "samples[\"cum_percentage\"] = round(samples[\"Number of projects\"].cumsum()/samples[\"Number of projects\"].sum()*100,2)\n", "\n", "# Set figure and axis\n", "fig, ax = plt.subplots(figsize=(10,5))\n", "\n", "# Plot bars (i.e. frequencies)\n", "ax.bar(samples.index, samples[\"Number of projects\"])\n", "ax.set_title('Pareto-like distribution')\n", "ax.set_xlabel(\"Architects (ordered by related work frequency)\")\n", "ax.set_ylabel(\"Frequency\")\n", "\n", "# Second y axis (i.e. cumulative percentage)\n", "ax2 = ax.twinx()\n", "ax2.plot(samples.index, samples[\"cum_percentage\"], color=\"red\")\n", "ax2.axhline(77, color=\"orange\", linestyle=\"dashed\", alpha=1)\n", "ax2.yaxis.set_major_formatter(PercentFormatter())\n", "\n", "# add more ticks to y axis\n", "ax2.set_yticks([0,25,50,75,100])\n", "ax2.set_ylabel(\"Cumulative Percentage\")\n", "\n", "# color regions on the x-axis\n", "ax.axvspan(0, 70, alpha=0.05, color='orange')\n", "\n", "# add an annotation for the 80% line\n", "ax2.annotate('25% of architects (70)', xy=(0, 100), xytext=(5, 100))\n", "ax2.annotate('77% of completed projects (1628)', xy=(0, 100), xytext=(195, 79))\n", "\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "e2fb1eed", "metadata": {}, "source": [ "```{epigraph}\n", "25% of architects are related to 77% of the completed projects.\n", "```" ] }, { "attachments": {}, "cell_type": "markdown", "id": "87ab7828", "metadata": {}, "source": [ "### Persons with related works (1940-1980)\n", "\n", "We now explore the frequency of completed projects over time specifically between 1940 and 1980. We also visualise the frequency of completed projects by architects with more than 50 related works with the rest of the architects.\n", "\n", "Out of the top six architects, we can see that `Lange Powell` and `Rex Addison` are not very active during this period with 0 and 20 projects respectively. We provide a breakdown for these two architects to understand which period they were most active in." ] }, { "cell_type": "code", "execution_count": 27, "id": "029dcfcf", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Completion Date=%{x}
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"white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Total number of (completed) works by year, most active architects vs. rest, 1940-1980\n\n" }, "width": 900, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Completion Date" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "range": [ 0, 40 ], "title": {} } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "###################### PERSONS WITH RELATED WORK (1940-1980) ######################\n", "all_completion_dates = []\n", "all_completion_with_name = pd.DataFrame()\n", "\n", "for idx,row in daqawork_1940_80.iterrows():\n", " comp_date = int(pd.json_normalize(ast.literal_eval(row['coverage_range']))['date_range.date_end.year'].values[0])\n", " all_completion_dates.append(comp_date)\n", "\n", " try: this_work = pd.json_normalize(ast.literal_eval(row['related_people']))[['subject.label','object.ori_id']].drop_duplicates()\n", " except: this_work = pd.DataFrame({'subject.label': None, 'object.ori_id': row['ori_id']}, index=[0])\n", "\n", " this_work['comp_date'] = comp_date\n", " all_completion_with_name = all_completion_with_name.append(this_work)\n", "\n", "\n", "total_completions = pd.DataFrame(all_completion_dates)[0]\\\n", ".value_counts()\\\n", ".reset_index()\\\n", ".rename({'index':'Completion Date',0:'Frequency'}, axis=1)\\\n", ".sort_values('Completion Date')\n", "\n", "# make interactive line plot, set figure size to 1000x500\n", "fig = px.line(total_completions, x='Completion Date', y='Frequency', title='Total number of completed works by year, 1940-1980', \n", "width=900, height=500, color_discrete_sequence=['steelblue'])\n", "\n", "fig.update_yaxes(range=[0, 55])\n", "fig.update_yaxes(title=None)\n", "fig.show()\n", "\n", "print('\\n###################### 1940-1980 - TOP 6 vs REST ######################')\n", "top_six_ls = top_six['display_name'].apply(lambda x: ast.literal_eval(x)).to_list()\n", "\n", "rest_completions = all_completion_with_name[~all_completion_with_name['subject.label'].isin(top_six_ls)]['comp_date']\\\n", ".value_counts()\\\n", ".reset_index()\\\n", ".rename({'index':'Completion Date','comp_date':'Frequency'}, axis=1)\\\n", ".sort_values('Completion Date')\n", "rest_completions[''] = 'Rest'\n", "\n", "lp_completions = all_completion_with_name[all_completion_with_name['subject.label']=='Lange Powell']['comp_date']\\\n", ".value_counts()\\\n", ".reset_index()\\\n", ".rename({'index':'Completion Date','comp_date':'Frequency'}, axis=1)\\\n", ".sort_values('Completion Date')\n", "lp_completions[''] = 'Lange Powell'\n", "\n", "rg_completions = all_completion_with_name[all_completion_with_name['subject.label']=='Robin Gibson']['comp_date']\\\n", ".value_counts()\\\n", ".reset_index()\\\n", ".rename({'index':'Completion Date','comp_date':'Frequency'}, axis=1)\\\n", ".sort_values('Completion Date')\n", "rg_completions[''] = 'Robin Gibson'\n", "\n", "jd_completions = all_completion_with_name[all_completion_with_name['subject.label']=='John Dalton']['comp_date']\\\n", ".value_counts()\\\n", ".reset_index()\\\n", ".rename({'index':'Completion Date','comp_date':'Frequency'}, axis=1)\\\n", ".sort_values('Completion Date')\n", "jd_completions[''] = 'John Dalton'\n", "\n", "gb_completions = all_completion_with_name[all_completion_with_name['subject.label']=='Graham W. Bligh']['comp_date']\\\n", ".value_counts()\\\n", ".reset_index()\\\n", ".rename({'index':'Completion Date','comp_date':'Frequency'}, axis=1)\\\n", ".sort_values('Completion Date')\n", "gb_completions[''] = 'Graham Bligh'\n", "\n", "kl_completions = all_completion_with_name[all_completion_with_name['subject.label']=='Karl Langer']['comp_date']\\\n", ".value_counts()\\\n", ".reset_index()\\\n", ".rename({'index':'Completion Date','comp_date':'Frequency'}, axis=1)\\\n", ".sort_values('Completion Date')\n", "kl_completions[''] = 'Karl Langer'\n", "\n", "ra_completions = all_completion_with_name[all_completion_with_name['subject.label']=='Rex Addison']['comp_date']\\\n", ".value_counts()\\\n", ".reset_index()\\\n", ".rename({'index':'Completion Date','comp_date':'Frequency'}, axis=1)\\\n", ".sort_values('Completion Date')\n", "ra_completions[''] = 'Rex Addison'\n", "\n", "allcompletions = rest_completions.append(lp_completions)\n", "allcompletions = allcompletions.append(rg_completions)\n", "allcompletions = allcompletions.append(jd_completions)\n", "allcompletions = allcompletions.append(gb_completions)\n", "allcompletions = allcompletions.append(kl_completions)\n", "allcompletions = allcompletions.append(ra_completions)\n", "\n", "# get a table of the sum of the frequency column for each architect\n", "allcompletions.rename({'':'Architect'}, axis=1, inplace=True)\n", "display(allcompletions\\\n", " .groupby('Architect')['Frequency']\\\n", " .sum()\\\n", " .sort_values(ascending=False)\\\n", " .reset_index()\\\n", " .append({'Architect':'Lange Powell', 'Frequency':0}, ignore_index=True)\\\n", " .set_index('Architect')\\\n", " .T)\n", "\n", "# plot interactive time series for each architect\n", "fig = px.line(allcompletions, x='Completion Date', y='Frequency', \n", "title='Total number of (completed) works by year, most active architects vs. rest, 1940-1980\\n\\n',\n", "width=900, height=500, color='Architect')\n", "\n", "# move legend to top\n", "fig.update_layout(legend=dict(\n", " orientation=\"h\",\n", " yanchor=\"bottom\",\n", " y=1.02,\n", " xanchor=\"right\",\n", " x=0.9,\n", " title=None\n", "))\n", "\n", "fig.update_yaxes(range=[0, 40])\n", "fig.update_yaxes(title=None)\n", "fig.show()\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "6c82328f", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "###################### WHERE IS LANGE POWELL? ######################\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
decade182019101920193020102020
count129763211
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" ], "text/plain": [ "decade 1820 1910 1920 1930 2010 2020\n", "count 1 29 76 32 1 1" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('\\n###################### WHERE IS LANGE POWELL? ######################')\n", "\n", "langepowell_relatedworks = pd.json_normalize(eval(daqa_persons.loc[daqa_persons['display_name']\\\n", " .str.contains('Powell', na=False)]['related_works'].values[0]))['object.ori_id'].values.tolist()\n", "\n", "langepowell_relatedworks_years = []\n", "\n", "for i,x in daqa_work[daqa_work['ori_id'].isin(langepowell_relatedworks)].iterrows():\n", " try: \n", " this_yr = pd.json_normalize(ast.literal_eval(x['coverage_range']))['date_range.date_end.year'].values[0]\n", " langepowell_relatedworks_years.append(int(str(this_yr)[:3]+'0'))\n", " except: pass\n", "\n", "pd.DataFrame(langepowell_relatedworks_years)[0]\\\n", " .value_counts()\\\n", " .reset_index()\\\n", " .sort_values('index')\\\n", " .rename({'index':'decade',0:'count'}, axis=1)\\\n", " .set_index('decade')\\\n", " .T" ] }, { "cell_type": "code", "execution_count": 29, "id": "89d6642a", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "###################### WHERE IS REX ADDISON? ######################\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
decade19601970198019902000
count4142542
\n", "
" ], "text/plain": [ "decade 1960 1970 1980 1990 2000\n", "count 4 14 25 4 2" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('\\n###################### WHERE IS REX ADDISON? ######################')\n", "\n", "rex_addison_relatedworks = pd.json_normalize(eval(daqa_persons.loc[daqa_persons['display_name']\\\n", " .str.contains('Rex Addison', na=False)]['related_works'].values[0]))['object.ori_id'].values.tolist()\n", "\n", "rex_addison_relatedworks_years = []\n", "\n", "for i,x in daqa_work[daqa_work['ori_id'].isin(rex_addison_relatedworks)].iterrows():\n", " try: \n", " this_yr = pd.json_normalize(ast.literal_eval(x['coverage_range']))['date_range.date_end.year'].values[0]\n", " rex_addison_relatedworks_years.append(int(str(this_yr)[:3]+'0'))\n", " except: pass\n", "\n", "pd.DataFrame(rex_addison_relatedworks_years)[0]\\\n", " .value_counts()\\\n", " .reset_index()\\\n", " .sort_values('index')\\\n", " .rename({'index':'decade',0:'count'}, axis=1)\\\n", " .set_index('decade')\\\n", " .T" ] }, { "attachments": {}, "cell_type": "markdown", "id": "45821361", "metadata": {}, "source": [ "
\n", "\n", "We continue our analysis during this period by identiyfing the most active architects and most active firms. We list below the top 20 for each respective entity." ] }, { "cell_type": "code", "execution_count": 30, "id": "a94b17e4", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###################### ARCHITECTS WITH THE HIGHEST NUMBER OF ASSOCIATED PROJECTS (1940-1980) ######################\n", "\n", "There are 148 unique architects with associated projects between 1940-1980.\n", "Below is a list of the top 20 architects with the highest number of associated projects within this period.\n" ] }, { "data": { "text/html": [ "
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ArchitectCount
0John Dalton63
1Robin Gibson44
2Graham W. Bligh40
3Karl Langer39
4Rex Addison20
5Jon Voller19
6James Birrell17
7James Gibson14
8Stephen Trotter14
9Vitaly Gzell13
10Edwin Oribin12
11Donald Spencer10
12Peter Heathwood10
13Sidney Barnes9
14Don Winsen9
15Martin Louis Conrad8
16Christina Metcalfe8
17John M. Railton7
18Robert Froud6
19Gabriel Poole6
20Alexander Ian Ferrier6
21Geoffrey Pie6
22Lindy Wissler6
\n", "
" ], "text/plain": [ " Architect Count\n", "0 John Dalton 63\n", "1 Robin Gibson 44\n", "2 Graham W. Bligh 40\n", "3 Karl Langer 39\n", "4 Rex Addison 20\n", "5 Jon Voller 19\n", "6 James Birrell 17\n", "7 James Gibson 14\n", "8 Stephen Trotter 14\n", "9 Vitaly Gzell 13\n", "10 Edwin Oribin 12\n", "11 Donald Spencer 10\n", "12 Peter Heathwood 10\n", "13 Sidney Barnes 9\n", "14 Don Winsen 9\n", "15 Martin Louis Conrad 8\n", "16 Christina Metcalfe 8\n", "17 John M. Railton 7\n", "18 Robert Froud 6\n", "19 Gabriel Poole 6\n", "20 Alexander Ian Ferrier 6\n", "21 Geoffrey Pie 6\n", "22 Lindy Wissler 6" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "###################### FIRMS WITH THE HIGHEST NUMBER OF ASSOCIATED PROJECTS (1940-1980) ######################\n", "\n", "There 113 unique firms with associated projects between 1940-1980.\n", "Below is a list of the top 20 firms with the highest number of associated projects within this period.\n" ] }, { "data": { "text/html": [ "
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FirmCount
59\"John Dalton Architect & Associates\"55
36\"Karl Langer Architect\"37
62\"Bligh Jessup Bretnall & Partners\"29
31\"Robin Gibson & Partners\"21
56\"Aubrey H. Job & R. P. Froud (Job & Froud)\"19
86\"R F Gibson Architect\"18
67\"Hayes & Scott\"17
28\"Fulton Trotter Architects\"16
48\"THA Cross & D Bain\"14
64\"A.H Conrad & T.B.F Gargett\"11
82\"S.G. Barnes & Oribin\"11
25\"W L Douglas & B Barnes\"10
75\"Theo Thynne & Associates\"9
81\"Lund, Hutton, Newell, Black & Paulsen\"8
38\"Cullen Hargraves Mooney\"8
83\"John Railton Architect\"7
89\"Brisbane City Council, City Design\"7
68\"James Birrell & Partners\"6
61\"Prangley & Crofts\"6
66\"Conrad Gargett & Partners (1965-1972)\"6
\n", "
" ], "text/plain": [ " Firm Count\n", "59 \"John Dalton Architect & Associates\" 55\n", "36 \"Karl Langer Architect\" 37\n", "62 \"Bligh Jessup Bretnall & Partners\" 29\n", "31 \"Robin Gibson & Partners\" 21\n", "56 \"Aubrey H. Job & R. P. Froud (Job & Froud)\" 19\n", "86 \"R F Gibson Architect\" 18\n", "67 \"Hayes & Scott\" 17\n", "28 \"Fulton Trotter Architects\" 16\n", "48 \"THA Cross & D Bain\" 14\n", "64 \"A.H Conrad & T.B.F Gargett\" 11\n", "82 \"S.G. Barnes & Oribin\" 11\n", "25 \"W L Douglas & B Barnes\" 10\n", "75 \"Theo Thynne & Associates\" 9\n", "81 \"Lund, Hutton, Newell, Black & Paulsen\" 8\n", "38 \"Cullen Hargraves Mooney\" 8\n", "83 \"John Railton Architect\" 7\n", "89 \"Brisbane City Council, City Design\" 7\n", "68 \"James Birrell & Partners\" 6\n", "61 \"Prangley & Crofts\" 6\n", "66 \"Conrad Gargett & Partners (1965-1972)\" 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('###################### ARCHITECTS WITH THE HIGHEST NUMBER OF ASSOCIATED PROJECTS (1940-1980) ######################')\n", "\n", "# names of Architects associated with the highest number of projects 1940-1980\n", "print(f'\\nThere are {all_completion_with_name[\"subject.label\"].nunique()} unique architects with associated projects between 1940-1980.')\n", "print('Below is a list of the top 20 architects with the highest number of associated projects within this period.')\n", "display(all_completion_with_name['subject.label'].value_counts().head(23).reset_index().rename({'index':'Architect','subject.label':'Count'}, axis=1))\n", "\n", "print('\\n###################### FIRMS WITH THE HIGHEST NUMBER OF ASSOCIATED PROJECTS (1940-1980) ######################')\n", "# dataframe of value counts of items in firms_with_projects_1940_80 list\n", "firms_with_projects_1940_80_count = pd.DataFrame(firms_with_projects_1940_80, columns=['firm'])['firm']\\\n", " .value_counts()\\\n", " .reset_index()\\\n", " .rename({'index':'ori_id','firm':'count'}, axis=1)\n", "\n", "# names of firms associated with the highest number of projects 1940-1980\n", "print(f'\\nThere {firms_with_projects_1940_80_count[\"ori_id\"].nunique()} unique firms with associated projects between 1940-1980.')\n", "print('Below is a list of the top 20 firms with the highest number of associated projects within this period.')\n", "\n", "display(pd.merge(daqa_orgs[['ori_id', 'primary_name']], firms_with_projects_1940_80_count, on='ori_id')\\\n", " .sort_values('count', ascending=False)\\\n", " .rename({'primary_name':'Firm', 'count':'Count'}, axis=1)\\\n", " .head(20)[['Firm','Count']])" ] }, { "attachments": {}, "cell_type": "markdown", "id": "1f72055c", "metadata": {}, "source": [ "### Registered architects\n", "\n", "Below we identify all registered architects in the DAQA dataset. This information is embedded within the `career` field of each person record. We visualise a comparison of registration year and degree completion year for each architect. Note that not all architects have corresponding education data." ] }, { "cell_type": "code", "execution_count": 31, "id": "dacb8ffb", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "There are 86 architects (active in 1940-1980) with registration details in their biographical summary.\n", "- 77 of these are male architects.\n", "- 9 of these are female architects.\n" ] } ], "source": [ "registered_architects = []\n", "registered_architects_years = []\n", "for idx,row in daqapersons_1940_80.iterrows():\n", " try:\n", " reg_df = pd.json_normalize(ast.literal_eval(row['career'])['registrations'])\n", " reg_df = reg_df[reg_df['coverage.date.year'].notnull()].sort_values('coverage.date.year')\n", " if reg_df.shape[0] > 0:\n", " closest_year = reg_df['coverage.date.year'].values[0]\n", " registered_architects.append(row[['display_name','ori_id', 'gender']])\n", " registered_architects_years.append(closest_year)\n", " except: pass\n", "\n", "registered_architects = pd.DataFrame(registered_architects)\n", "registered_architects['reg_year'] = registered_architects_years\n", "registered_architects['gender'] = registered_architects.gender.fillna('Missing')\n", "\n", "# fetch education details for registered architects\n", "education_registered = education_training_df_with_dates[education_training_df_with_dates.ori_id.isin(registered_architects.ori_id)]\n", "education_registered = education_registered[education_registered['coverage_range.date_range.date_end.year'] != 'incomplete']\n", "education_registered = education_registered.sort_values('coverage_range.date_range.date_end.year')\\\n", " .drop_duplicates(subset=['ori_id'], keep='first')\n", "registered_architects = pd.merge(registered_architects, education_registered[['ori_id','coverage_range.date_range.date_end.year']], on='ori_id', how='left')\n", "\n", "# change column name\n", "registered_architects = registered_architects.rename({'coverage_range.date_range.date_end.year':'completion_year_first_qualification'}, axis=1)\n", "\n", "print(f'\\nThere are {registered_architects.shape[0]} architects (active in 1940-1980) with registration details in their biographical summary.')\n", "print('-',registered_architects[registered_architects.gender == '\"male\"'].shape[0], 'of these are male architects.')\n", "print('-',registered_architects[registered_architects.gender == '\"female\"'].shape[0], 'of these are female architects.')\n", "# print('-',registered_architects[registered_architects.gender == 'Missing'].shape[0], 'of these does not have a recorded gender.\\n')\n", "# display(registered_architects.sort_values('reg_year', ascending=True).head())" ] }, { "cell_type": "code", "execution_count": 32, "id": "34ab13eb", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot a lollipop chart of reg_year and completion_year_first_qualification\n", "registered_architects_with_qual = registered_architects[registered_architects['completion_year_first_qualification'].notnull()]\n", "registered_architects_with_qual['reg_year'] = registered_architects_with_qual['reg_year'].astype(int)\n", "registered_architects_with_qual['completion_year_first_qualification'] = registered_architects_with_qual['completion_year_first_qualification'].astype(int)\n", "registered_architects_with_qual.sort_values('completion_year_first_qualification', ascending=False, inplace=True)\n", "registered_architects_with_qual.reset_index(drop=True, inplace=True)\n", "registered_architects_with_qual['display_name'] = registered_architects_with_qual['display_name'].apply(lambda x: x.replace('\"',''))\n", "registered_architects_with_qual['diff'] = registered_architects_with_qual['reg_year'] - registered_architects_with_qual['completion_year_first_qualification']\n", "\n", "# The horizontal plot is made using the hline function\n", "# add grid lines for y-axis\n", "plt.grid(axis='y', linestyle='--', alpha=0.4)\n", "\n", "plt.scatter(registered_architects_with_qual['completion_year_first_qualification'], \n", " registered_architects_with_qual.index, color='tab:orange', alpha=0.7, label='value1')\n", "plt.scatter(registered_architects_with_qual['reg_year'], \n", " registered_architects_with_qual.index, color='tab:green', alpha=0.7 , label='value2')\n", " \n", "# add legend under title and make horizontal\n", "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.03), ncol=2, frameon=False,\n", " labels=['Completion year of first qualification', 'Registration year'])\n", "\n", "# if architect is female, make line red\n", "plt.hlines(y=registered_architects_with_qual.index, \n", " xmin=registered_architects_with_qual['completion_year_first_qualification'], \n", " xmax=registered_architects_with_qual['reg_year'], color='grey',\n", " alpha=0.4, label=False)\n", "\n", "# Add title and axis names\n", "plt.yticks(registered_architects_with_qual.index, registered_architects_with_qual.display_name)\n", "\n", "# change colour of yticks based on condition\n", "for idx,row in registered_architects_with_qual.iterrows():\n", " if \"fe\" in row['gender']: plt.gca().get_yticklabels()[idx].set_color('tab:red')\n", "\n", "# add annoation in bottom left\n", "plt.annotate('*Female architects highlighted in red', (0,0), (15,25), \n", " xycoords='axes fraction', textcoords='offset points', va='top', color='tab:red')\n", "\n", "plt.title(\"Year comparison of graduation (of first degree) and registration\\n\\n\", loc='left')\n", "plt.xlabel('')\n", "plt.ylabel('')\n", "\n", "# make plot longer \n", "plt.gcf().set_size_inches(7, 16)\n", "\n", "# Show the graph\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a1e536c8", "metadata": {}, "source": [ "```{epigraph}\n", "Based on the 78 registered architects with corresponding education data, we find that architects tend to become registered **one** year after completing their first education qualification.\n", "```" ] }, { "cell_type": "code", "execution_count": 33, "id": "ca5f802e", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "# import re\n", "\n", "# def find_word_with_regis(text):\n", "# # Split the text into individual words\n", "# words = re.findall(r'\\b\\w+\\b', text)\n", "\n", "# # Search for a word that contains \"regis\" or \"Regis\"\n", "# for word in words:\n", "# if re.search(r'regis|Regis', word):\n", "# return word\n", "\n", "# return None # No matching word found\n", "\n", "# def find_closest_4_digit_number(string, target_word=\"registering\"):\n", "# numbers = re.findall(r'\\d{4}', string) # Find all 4-digit numbers in the string\n", " \n", "# closest_number = None\n", "# min_distance = float('inf')\n", " \n", "# for number in numbers:\n", "# try:\n", "# distance = abs(string.index(target_word) - string.index(number))\n", "# if distance < min_distance:\n", "# closest_number = number\n", "# min_distance = distance\n", "# except: pass\n", " \n", "# return closest_number\n", "\n", "# registered_architects = []\n", "# registered_architects_years = []\n", "# for idx,row in daqapersons_1940_80.iterrows():\n", "# try:\n", "# if (\"registering\" in row['summary']) | (\"registered\" in row['summary']) | \\\n", "# (\"Registered\" in row['summary']) | (\"architectural registration\" in row['summary']) | (\"Registration\" in row['summary']):\n", "# registered_architects.append(row[['display_name','ori_id', 'gender']])\n", "# this_summary = pd.Series(row['summary'].split('. '))\n", "# register_pos = this_summary.apply(lambda x: x.strip()).str.contains('regis|Regis').values.tolist().index(True)\n", "# result = find_word_with_regis(this_summary[register_pos])\n", "# closest_year = find_closest_4_digit_number(this_summary[register_pos], target_word=result)\n", "# registered_architects_years.append(closest_year)\n", "# except: pass\n", "\n", "# registered_architects = pd.DataFrame(registered_architects)\n", "# registered_architects['reg_year'] = registered_architects_years\n", "# registered_architects['gender'] = registered_architects.gender.fillna('Missing')\n", "\n", "# # fetch education details for registered architects\n", "# education_registered = education_training_df_with_dates[education_training_df_with_dates.ori_id.isin(registered_architects.ori_id)]\n", "# education_registered = education_registered[education_registered['coverage_range.date_range.date_end.year'] != 'incomplete']\n", "# education_registered = education_registered.sort_values('coverage_range.date_range.date_end.year')\\\n", "# .drop_duplicates(subset=['ori_id'], keep='first')\n", "# registered_architects = pd.merge(registered_architects, education_registered[['ori_id','coverage_range.date_range.date_end.year']], on='ori_id', how='left')\n", "\n", "# # change column name\n", "# registered_architects = registered_architects.rename({'coverage_range.date_range.date_end.year':'completion_year_first_qualification'}, axis=1)\n", "\n", "# print(f'\\nThere are {registered_architects.shape[0]} architects (active in 1940-1980) with registration details in their biographical summary.')\n", "# print('-',registered_architects[registered_architects.gender == '\"male\"'].shape[0], 'of these are male architects.')\n", "# print('-',registered_architects[registered_architects.gender == '\"female\"'].shape[0], 'of these are female architects.')\n", "# print('-',registered_architects[registered_architects.gender == 'Missing'].shape[0], 'of these does not have a recorded gender.\\n')\n", "# display(registered_architects.sort_values('reg_year', ascending=True))" ] }, { "cell_type": "code", "execution_count": 34, "id": "ef83fb5b", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "# # plot a lollipop chart of reg_year and completion_year_first_qualification\n", "# registered_architects_with_qual = registered_architects[registered_architects['completion_year_first_qualification'].notnull()]\n", "# registered_architects_with_qual['reg_year'] = registered_architects_with_qual['reg_year'].astype(int)\n", "# registered_architects_with_qual['completion_year_first_qualification'] = registered_architects_with_qual['completion_year_first_qualification'].astype(int)\n", "# registered_architects_with_qual.sort_values('completion_year_first_qualification', ascending=True, inplace=True)\n", "# registered_architects_with_qual.reset_index(drop=True, inplace=True)\n", "# registered_architects_with_qual['display_name'] = registered_architects_with_qual['display_name'].apply(lambda x: x.replace('\"',''))\n", "# registered_architects_with_qual['diff'] = registered_architects_with_qual['reg_year'] - registered_architects_with_qual['completion_year_first_qualification']\n", "\n", "# # The horizontal plot is made using the hline function\n", "# # add grid lines for y-axis\n", "# plt.grid(axis='y', linestyle='--', alpha=0.4)\n", "\n", "# plt.scatter(registered_architects_with_qual['completion_year_first_qualification'], \n", "# registered_architects_with_qual.index, color='tab:orange', alpha=0.7, label='value1')\n", "# plt.scatter(registered_architects_with_qual['reg_year'], \n", "# registered_architects_with_qual.index, color='tab:green', alpha=0.7 , label='value2')\n", " \n", "# # add legend under title and make horizontal\n", "# plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.065), ncol=3, frameon=False,\n", "# labels=['Completion year of first qualification', 'Registration year'])\n", "\n", "# plt.hlines(y=registered_architects_with_qual.index, \n", "# xmin=registered_architects_with_qual['completion_year_first_qualification'], \n", "# xmax=registered_architects_with_qual['reg_year'], color='grey', alpha=0.4, label=False)\n", "\n", "# # Add title and axis names\n", "# plt.yticks(registered_architects_with_qual.index, registered_architects_with_qual.display_name)\n", "# plt.title(\"Year comparison of graduation (of first degree) and registration\\n\\n\", loc='left')\n", "# plt.xlabel('')\n", "# plt.ylabel('')\n", "\n", "# # make plot longer \n", "# plt.gcf().set_size_inches(7, 8)\n", "\n", "# # Show the graph\n", "# plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "1189b74b", "metadata": {}, "source": [ "### Women with related works\n", "\n", "We compare the frequency of completed projects by males and females between two periods 1940-1980, and 1980-Present." ] }, { "cell_type": "code", "execution_count": 35, "id": "1cf27265", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "###################### FEMAlE ARCHITECTS (1940-1980) ######################\n", "\n", "Q: what % of projects completed in 1940-1980 were associated with a male architect?\n", "A: 92.54% (571) of architects associated with projects completed in 1940-1980 were male.\n", "\n", "Q: what % of projects completed in 1940-1980 were associated with a female architect?\n", "A: 6.48% (40) of architects associated with projects completed in 1940-1980 were female.\n", "\n", "*It should be noted that there are 6 DAQA projects between 1940-1980 with missing gender information.\n", "\n", "###################### FEMAlE ARCHITECTS (1980-Present) ######################\n", "\n", "Q: what % of projects completed after 1980 were associated with a male architect?\n", "A: 64.34% (184) of architects associated with projects completed affter 1980 were male.\n", "\n", "Q: what % of projects completed after 1980 were associated with a female architect?\n", "A: 32.87% (94) of architects associated with projects completed after 1980 were female.\n", "\n", "*It should be noted that there are 8 DAQA projects after 1980 with missing gender information.\n" ] } ], "source": [ "print('\\n###################### FEMAlE ARCHITECTS (1940-1980) ######################')\n", "projects_1940_80 = []\n", "projects_1980_present = []\n", "\n", "for idx,row in daqa_work.iterrows():\n", " if isinstance(row['coverage_range'], str): \n", " if \"date_end\" in row['coverage_range']:\n", " comp_yr = pd.json_normalize(ast.literal_eval(row['coverage_range'])['date_range'])['date_end.year'].values[0]\n", " # add counter for comp_yr to dict\n", " if int(comp_yr) >= 1940 and int(comp_yr) <= 1980: \n", " projects_1940_80.append(row['_id']) \n", "\n", " if int(comp_yr) > 1980: \n", " projects_1980_present.append(row['_id'])\n", "\n", "daqawork_1940_80 = daqa_work[daqa_work['_id'].isin(projects_1940_80)]\n", "daqawork_1980_present = daqa_work[daqa_work['_id'].isin(projects_1980_present)]\n", "\n", "# store all rows with date_start in coverage_range\n", "all_completion_with_name_1940_80 = pd.DataFrame()\n", "\n", "for idx,row in daqawork_1940_80.iterrows():\n", " comp_date = int(pd.json_normalize(ast.literal_eval(row['coverage_range']))['date_range.date_end.year'].values[0])\n", "\n", " try: this_work = pd.json_normalize(ast.literal_eval(row['related_people']))[['subject.label','object.ori_id']].drop_duplicates()\n", " except: this_work = pd.DataFrame({'subject.label': None, 'object.ori_id': row['ori_id']}, index=[0])\n", "\n", " this_work['comp_date'] = comp_date\n", " all_completion_with_name_1940_80 = all_completion_with_name_1940_80.append(this_work)\n", "\n", "# store all rows with date_start in coverage_range\n", "all_completion_with_name_1980_present = pd.DataFrame()\n", "\n", "for idx,row in daqawork_1980_present.iterrows():\n", " comp_date = int(pd.json_normalize(ast.literal_eval(row['coverage_range']))['date_range.date_end.year'].values[0])\n", "\n", " try: this_work = pd.json_normalize(ast.literal_eval(row ['related_people']))[['subject.label','object.ori_id']].drop_duplicates()\n", " except: this_work = pd.DataFrame({'subject.label': None, 'object.ori_id': row['ori_id']}, index=[0])\n", "\n", " this_work['comp_date'] = comp_date\n", " all_completion_with_name_1980_present = all_completion_with_name_1980_present.append(this_work)\n", "\n", "persons_gender = daqa_persons[['display_name','gender']].copy()\n", "persons_gender['display_name'] = persons_gender['display_name'].apply(lambda x: ast.literal_eval(x))\n", "\n", "persons_gender_1940_1980 = pd.merge(all_completion_with_name_1940_80, persons_gender, right_on='display_name', left_on='subject.label')\n", "persons_gender_1940_1980['gender'].fillna('Missing', inplace=True)\n", "persons_gender_1940_1980 = persons_gender_1940_1980['gender'].value_counts().reset_index().rename({'index':'Gender', 'gender':'Count'},axis=1)\n", "\n", "print('\\nQ: what % of projects completed in 1940-1980 were associated with a male architect?')\n", "count_projects_all = persons_gender_1940_1980['Count'].sum()\n", "male_count = persons_gender_1940_1980[persons_gender_1940_1980['Gender'].str.contains('''\"male\"''')]['Count'].values[0]\n", "prop_projects_male = round((male_count / count_projects_all) * 100, 2)\n", "print(f'A: {prop_projects_male}% ({male_count}) of architects associated with projects completed in 1940-1980 were male.')\n", "\n", "print('\\nQ: what % of projects completed in 1940-1980 were associated with a female architect?')\n", "female_count = persons_gender_1940_1980[persons_gender_1940_1980['Gender'].str.contains('''\"female\"''')]['Count'].values[0]\n", "prop_projects_female = round((female_count / count_projects_all) * 100, 2)\n", "print(f'A: {prop_projects_female}% ({female_count}) of architects associated with projects completed in 1940-1980 were female.')\n", "\n", "print('\\n*It should be noted that there are 6 DAQA projects between 1940-1980 with missing gender information.')\n", "\n", "print('\\n###################### FEMAlE ARCHITECTS (1980-Present) ######################')\n", "\n", "persons_gender_1980_present = pd.merge(all_completion_with_name_1980_present, persons_gender, right_on='display_name', left_on='subject.label')\n", "persons_gender_1980_present['gender'].fillna('Missing', inplace=True)\n", "persons_gender_1980_present = persons_gender_1980_present['gender'].value_counts().reset_index().rename({'index':'Gender', 'gender':'Count'},axis=1)\n", "\n", "print('\\nQ: what % of projects completed after 1980 were associated with a male architect?')\n", "count_projects_all = persons_gender_1980_present['Count'].sum()\n", "male_count = persons_gender_1980_present[persons_gender_1980_present['Gender'].str.contains('''\"male\"''')]['Count'].values[0]\n", "prop_projects_male = round((male_count / count_projects_all) * 100, 2)\n", "print(f'A: {prop_projects_male}% ({male_count}) of architects associated with projects completed affter 1980 were male.')\n", "\n", "print('\\nQ: what % of projects completed after 1980 were associated with a female architect?')\n", "female_count = persons_gender_1980_present[persons_gender_1980_present['Gender'].str.contains('''\"female\"''')]['Count'].values[0]\n", "prop_projects_female = round((female_count / count_projects_all) * 100, 2)\n", "print(f'A: {prop_projects_female}% ({female_count}) of architects associated with projects completed after 1980 were female.')\n", "\n", "print('\\n*It should be noted that there are 8 DAQA projects after 1980 with missing gender information.')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "a77305f4", "metadata": {}, "source": [ "## Typologies\n", "\n", "Below we visualise the frequency of typologies in the DAQA dataset. We continue our analysis by focusing on the period of 1940 to 1980, as well as idenyifing the arcihtects and firms with the most related projects for each typology within this period." ] }, { "cell_type": "code", "execution_count": 36, "id": "c801830e", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "variable=Commercial buildings
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daqa_work_years.append(comp_yr)\n", " daqa_work_ori_ids.append(row['ori_id'])\n", "\n", "# create a dummy variable for each typology\n", "typology_dummies_daqa_work = pd.get_dummies(daqa_work['typologies'].\\\n", " fillna('Missing')\\\n", " .str.replace('[^a-zA-Z, ]', '', regex=True)\\\n", " .str.split(', ')\\\n", " .apply(pd.Series).stack())\\\n", " .sum(level=0)\n", "\n", "typology_dummies_daqa_work['extracted_compyear'] = daqa_work_years\n", "\n", "# create a dataframe with value counts for each typology and year\n", "typology_dummies_counts = typology_dummies_daqa_work.groupby('extracted_compyear')\\\n", " .sum()\\\n", " .reset_index()\n", "\n", "typology_dummies_counts['extracted_compyear'] = typology_dummies_counts['extracted_compyear'].astype(int)\n", "\n", "# remove erronoous year 1220 and 2029\n", "typology_dummies_counts = typology_dummies_counts.iloc[1:,].head(-1)\n", "\n", "# add row for missing years between 1820 and 2022\n", "typology_dummies_counts = typology_dummies_counts\\\n", " .append(pd.DataFrame({'extracted_compyear':list(range(1820,2022))}), ignore_index=True)\\\n", " .fillna(0)\\\n", " .drop_duplicates('extracted_compyear',keep='first')\\\n", " .sort_values('extracted_compyear')\n", "\n", "# plot interactve time series for each typology\n", "fig = px.line(typology_dummies_counts, x='extracted_compyear', y=typology_dummies_counts.columns[1:],\n", "title='Number of works by typology and year, 1820-2021',\n", "width=1000, height=500, color_discrete_sequence=px.colors.qualitative.Pastel)\n", "\n", "fig.update_yaxes(range=[0, 30])\n", "fig.update_yaxes(title=None)\n", "fig.update_xaxes(title='Year of completion')\n", "\n", "# add subtitle\n", "fig.add_annotation(x=0.5, y=1.07, xref='paper', yref='paper',\n", "text='Note: there are some non-disjoint cases in the data.', showarrow=False, font=dict(size=12))\n", "\n", "# change legend title from \"variable\" to \"typology\"\n", "fig.update_layout(legend_title_text='Typology')\n", "\n", "fig.show()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "40f3f572", "metadata": {}, "source": [ "### Typologies (1940-1980)" ] }, { "cell_type": "code", "execution_count": 37, "id": "45f0f2f4", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a dummy variable for each typology\n", "typology_dummies = pd.get_dummies(daqawork_1940_80['typologies'].\\\n", " fillna('Missing')\\\n", " .str.replace('[^a-zA-Z, ]', '', regex=True)\\\n", " .str.split(', ')\\\n", " .apply(pd.Series).stack())\\\n", " .sum(level=0)\n", "\n", "# create a dataframe with value counts for each typology and their proportion of the total\n", "typology_dummies_counts = typology_dummies.sum()\\\n", " .reset_index()\\\n", " .rename({'index':'typology',0:'count'}, axis=1)\\\n", " .sort_values('count', ascending=False)\\\n", " .reset_index(drop=True)\n", "\n", "# matplotlib plot the freuqencies of typologies with bar labels and switch order of y axis\n", "fig, ax = plt.subplots(figsize=(10,5))\n", "ax.barh(typology_dummies_counts['typology'], typology_dummies_counts['count'], color='steelblue')\n", "ax.set_yticklabels(typology_dummies_counts['typology'], fontsize=12)\n", "ax.set_xlim(0, 300)\n", "ax.set_xlabel('Number of works', fontsize=12)\n", "ax.set_ylabel('')\n", "ax.set_title('Typologies of works, 1940-1980\\n\\n', fontsize=14)\n", "ax.invert_yaxis()\n", "\n", "# add bar labels with proportions in brackets\n", "for p in ax.patches:\n", " ax.annotate(f'{p.get_width()}', (p.get_width()+3, p.get_y()+0.55), size=12)\n", "\n", "ax.text(0.5, 1.05, 'Note: there are some non-disjoint cases in the data.', size=12, ha=\"center\", transform=ax.transAxes)\n", "\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "d0a05075", "metadata": {}, "source": [ "#### Top 5 architects" ] }, { "cell_type": "code", "execution_count": 38, "id": "000bb5c9", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Commercial buildings\n" ] }, { "data": { "text/html": [ "
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ArchitectCommercial buildings count
60Graham W. Bligh22
125Robin Gibson16
92Karl Langer9
54Geoffrey Pie4
132Stephen Trotter4
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" ], "text/plain": [ " Architect Commercial buildings count\n", "60 Graham W. Bligh 22\n", "125 Robin Gibson 16\n", "92 Karl Langer 9\n", "54 Geoffrey Pie 4\n", "132 Stephen Trotter 4" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dwellings\n" ] }, { "data": { "text/html": [ "
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ArchitectDwellings count
82John Dalton62
92Karl Langer15
125Robin Gibson10
34Donald Spencer9
137Vitaly Gzell9
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" ], "text/plain": [ " Architect Dwellings count\n", "82 John Dalton 62\n", "92 Karl Langer 15\n", "125 Robin Gibson 10\n", "34 Donald Spencer 9\n", "137 Vitaly Gzell 9" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Educational facilities\n" ] }, { "data": { "text/html": [ "
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ArchitectEducational facilities count
125Robin Gibson6
60Graham W. Bligh6
118Rex Addison6
132Stephen Trotter5
90Jon Voller5
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" ], "text/plain": [ " Architect Educational facilities count\n", "125 Robin Gibson 6\n", "60 Graham W. Bligh 6\n", "118 Rex Addison 6\n", "132 Stephen Trotter 5\n", "90 Jon Voller 5" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Government buildings\n" ] }, { "data": { "text/html": [ "
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ArchitectGovernment buildings count
92Karl Langer5
90Jon Voller3
125Robin Gibson3
60Graham W. Bligh3
127Roman Pavylshyn3
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" ], "text/plain": [ " Architect Government buildings count\n", "92 Karl Langer 5\n", "90 Jon Voller 3\n", "125 Robin Gibson 3\n", "60 Graham W. Bligh 3\n", "127 Roman Pavylshyn 3" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Health care facilities\n" ] }, { "data": { "text/html": [ "
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ArchitectHealth care facilities count
131Sidney Barnes5
22Charles W. T. Fulton2
17Bruce Paulsen1
46Elmars A. Kraams1
101Margaret Ward1
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" ], "text/plain": [ " Architect Health care facilities count\n", "131 Sidney Barnes 5\n", "22 Charles W. T. Fulton 2\n", "17 Bruce Paulsen 1\n", "46 Elmars A. Kraams 1\n", "101 Margaret Ward 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "HighRise\n" ] }, { "data": { "text/html": [ "
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ArchitectHighRise count
68Ian D. Charlton1
125Robin Gibson1
134Ted Crofts1
77Jan van den Broek1
\n", "
" ], "text/plain": [ " Architect HighRise count\n", "68 Ian D. Charlton 1\n", "125 Robin Gibson 1\n", "134 Ted Crofts 1\n", "77 Jan van den Broek 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Industrial buildings\n" ] }, { "data": { "text/html": [ "
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ArchitectIndustrial buildings count
125Robin Gibson6
131Sidney Barnes1
137Vitaly Gzell1
92Karl Langer1
48Frank Costello1
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" ], "text/plain": [ " Architect Industrial buildings count\n", "125 Robin Gibson 6\n", "131 Sidney Barnes 1\n", "137 Vitaly Gzell 1\n", "92 Karl Langer 1\n", "48 Frank Costello 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Missing\n" ] }, { "data": { "text/html": [ "
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ArchitectMissing count
104Martin Louis Conrad1
115Peter Newell1
108Neville H. Lund1
\n", "
" ], "text/plain": [ " Architect Missing count\n", "104 Martin Louis Conrad 1\n", "115 Peter Newell 1\n", "108 Neville H. Lund 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Recreation and sports facilities\n" ] }, { "data": { "text/html": [ "
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ArchitectRecreation and sports facilities count
63Guy Crick4
75James Birrell3
42Edwin Oribin3
118Rex Addison3
74Jack McElroy3
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" ], "text/plain": [ " Architect Recreation and sports facilities count\n", "63 Guy Crick 4\n", "75 James Birrell 3\n", "42 Edwin Oribin 3\n", "118 Rex Addison 3\n", "74 Jack McElroy 3" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Religious buildings\n" ] }, { "data": { "text/html": [ "
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ArchitectReligious buildings count
76James Gibson14
92Karl Langer5
42Edwin Oribin4
1Alexander Ian Ferrier3
81John Buckeridge3
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" ], "text/plain": [ " Architect Religious buildings count\n", "76 James Gibson 14\n", "92 Karl Langer 5\n", "42 Edwin Oribin 4\n", "1 Alexander Ian Ferrier 3\n", "81 John Buckeridge 3" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Transport infrastructure\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ArchitectTransport infrastructure count
10Balwant Saini1
92Karl Langer1
60Graham W. Bligh1
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" ], "text/plain": [ " Architect Transport infrastructure count\n", "10 Balwant Saini 1\n", "92 Karl Langer 1\n", "60 Graham W. Bligh 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "typology_dummies_daqa_work['ori_id'] = daqa_work_ori_ids\n", "typology_architects = pd.merge(typology_dummies_daqa_work, \n", " all_completion_with_name[['subject.label','object.ori_id']], \n", " left_on='ori_id', right_on='object.ori_id').drop(['extracted_compyear','ori_id','object.ori_id'], axis=1)\n", "\n", "# get the sum of each typology for each firm\n", "typology_architects = typology_architects.groupby('subject.label').sum().reset_index()\n", "\n", "for typology in typology_architects.columns[1:]:\n", " print(f'\\n{typology}')\n", " \n", " typology_architects_top5 = typology_architects[['subject.label', typology]]\\\n", " .sort_values(typology, ascending=False)\\\n", " .rename({typology:f'{typology} count'}, axis=1)\n", " \n", " typology_architects_top5 = typology_architects_top5[typology_architects_top5[f'{typology} count'] > 0].head(5)\n", "\n", " display(typology_architects_top5\\\n", " .sort_values(f'{typology} count', ascending=False)\\\n", " .rename({'subject.label':'Architect'}, axis=1)[['Architect', f'{typology} count']])" ] }, { "attachments": {}, "cell_type": "markdown", "id": "dfe3f83b", "metadata": {}, "source": [ "#### Top 5 firms" ] }, { "cell_type": "code", "execution_count": 39, "id": "07f77f6d", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dwellings\n" ] }, { "data": { "text/html": [ "
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FirmDwellings count
2\"John Dalton Architect & Associates\"54
1\"Aubrey H. Job & R. P. Froud (Job & Froud)\"18
3\"Hayes & Scott\"16
0\"Karl Langer Architect\"13
4\"John Railton Architect\"7
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" ], "text/plain": [ " Firm Dwellings count\n", "2 \"John Dalton Architect & Associates\" 54\n", "1 \"Aubrey H. Job & R. P. Froud (Job & Froud)\" 18\n", "3 \"Hayes & Scott\" 16\n", "0 \"Karl Langer Architect\" 13\n", "4 \"John Railton Architect\" 7" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Commercial buildings\n" ] }, { "data": { "text/html": [ "
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FirmCommercial buildings count
2\"Bligh Jessup Bretnall & Partners\"18
1\"Karl Langer Architect\"8
4\"R F Gibson Architect\"8
0\"Robin Gibson & Partners\"5
3\"Theo Thynne & Associates\"4
\n", "
" ], "text/plain": [ " Firm Commercial buildings count\n", "2 \"Bligh Jessup Bretnall & Partners\" 18\n", "1 \"Karl Langer Architect\" 8\n", "4 \"R F Gibson Architect\" 8\n", "0 \"Robin Gibson & Partners\" 5\n", "3 \"Theo Thynne & Associates\" 4" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Educational facilities\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FirmEducational facilities count
2\"Cullen Hargraves Mooney\"6
0\"Fulton Trotter Architects\"5
1\"Robin Gibson & Partners\"5
4\"Bligh Jessup Bretnall & Partners\"4
3\"Goodsir Baker Wilde\"3
\n", "
" ], "text/plain": [ " Firm Educational facilities count\n", "2 \"Cullen Hargraves Mooney\" 6\n", "0 \"Fulton Trotter Architects\" 5\n", "1 \"Robin Gibson & Partners\" 5\n", "4 \"Bligh Jessup Bretnall & Partners\" 4\n", "3 \"Goodsir Baker Wilde\" 3" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Religious buildings\n" ] }, { "data": { "text/html": [ "
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FirmReligious buildings count
3\"THA Cross & D Bain\"14
2\"Karl Langer Architect\"5
4\"S.G. Barnes & Oribin\"5
0\"Ian A Ferrier\"3
1\"W L Douglas & B Barnes\"3
\n", "
" ], "text/plain": [ " Firm Religious buildings count\n", "3 \"THA Cross & D Bain\" 14\n", "2 \"Karl Langer Architect\" 5\n", "4 \"S.G. Barnes & Oribin\" 5\n", "0 \"Ian A Ferrier\" 3\n", "1 \"W L Douglas & B Barnes\" 3" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Government buildings\n" ] }, { "data": { "text/html": [ "
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FirmGovernment buildings count
1\"Karl Langer Architect\"5
2\"Bligh Jessup Bretnall & Partners\"3
3\"Conrad Gargett & Partners (1965-1972)\"3
4\"Commonwealth Department of Works\"3
0\"Robin Gibson & Partners\"2
\n", "
" ], "text/plain": [ " Firm Government buildings count\n", "1 \"Karl Langer Architect\" 5\n", "2 \"Bligh Jessup Bretnall & Partners\" 3\n", "3 \"Conrad Gargett & Partners (1965-1972)\" 3\n", "4 \"Commonwealth Department of Works\" 3\n", "0 \"Robin Gibson & Partners\" 2" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Recreation and sports facilities\n" ] }, { "data": { "text/html": [ "
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FirmRecreation and sports facilities count
1\"A.H Conrad & T.B.F Gargett\"3
4\"Brisbane City Council, City Design\"3
0\"Karl Langer Architect\"2
3\"S.G. Barnes & Oribin\"2
2\"James Birrell & Partners\"1
\n", "
" ], "text/plain": [ " Firm \\\n", "1 \"A.H Conrad & T.B.F Gargett\" \n", "4 \"Brisbane City Council, City Design\" \n", "0 \"Karl Langer Architect\" \n", "3 \"S.G. Barnes & Oribin\" \n", "2 \"James Birrell & Partners\" \n", "\n", " Recreation and sports facilities count \n", "1 3 \n", "4 3 \n", "0 2 \n", "3 2 \n", "2 1 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Health care facilities\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FirmHealth care facilities count
2\"S.G. Barnes\"4
4\"J M Collin & C W T Fulton\"4
1\"A.H Conrad & T.B.F Gargett\"2
0\"Donald W Spencer\"1
3\"Theo Thynne & Associates\"1
\n", "
" ], "text/plain": [ " Firm Health care facilities count\n", "2 \"S.G. Barnes\" 4\n", "4 \"J M Collin & C W T Fulton\" 4\n", "1 \"A.H Conrad & T.B.F Gargett\" 2\n", "0 \"Donald W Spencer\" 1\n", "3 \"Theo Thynne & Associates\" 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Industrial buildings\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FirmIndustrial buildings count
0\"Robin Gibson & Partners\"3
4\"R F Gibson Architect\"3
1\"Karl Langer Architect\"1
2\"S.G. Barnes\"1
3\"Theo Thynne & Associates\"1
\n", "
" ], "text/plain": [ " Firm Industrial buildings count\n", "0 \"Robin Gibson & Partners\" 3\n", "4 \"R F Gibson Architect\" 3\n", "1 \"Karl Langer Architect\" 1\n", "2 \"S.G. Barnes\" 1\n", "3 \"Theo Thynne & Associates\" 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "HighRise\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FirmHighRise count
3\"Prangley & Crofts\"4
4\"Theo Thynne, Denham & Associates\"2
0\"Curro Nutter & Charlton\"1
1\"Robin Gibson & Partners\"1
2\"Peddle Thorp & Walker\"1
\n", "
" ], "text/plain": [ " Firm HighRise count\n", "3 \"Prangley & Crofts\" 4\n", "4 \"Theo Thynne, Denham & Associates\" 2\n", "0 \"Curro Nutter & Charlton\" 1\n", "1 \"Robin Gibson & Partners\" 1\n", "2 \"Peddle Thorp & Walker\" 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Missing\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FirmMissing count
0\"Martin L Conrad Architects\"1
1\"Hennessy, Hennessy & Co\"1
2\"Lund, Hutton & Newell\"1
3\"Ford, Hutton & Newell\"1
\n", "
" ], "text/plain": [ " Firm Missing count\n", "0 \"Martin L Conrad Architects\" 1\n", "1 \"Hennessy, Hennessy & Co\" 1\n", "2 \"Lund, Hutton & Newell\" 1\n", "3 \"Ford, Hutton & Newell\" 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Transport infrastructure\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FirmTransport infrastructure count
0\"Karl Langer Architect\"1
1\"Bligh Jessup Bretnall & Partners\"1
\n", "
" ], "text/plain": [ " Firm Transport infrastructure count\n", "0 \"Karl Langer Architect\" 1\n", "1 \"Bligh Jessup Bretnall & Partners\" 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# convert projects_1940_80_firms_dict to dataframe\n", "typology_dummies['_id'] = daqawork_1940_80[['_id']]\n", "typology_firms = pd.merge(typology_dummies,\n", " pd.DataFrame.from_dict(projects_1940_80_firms_dict, orient='index')\\\n", " .reset_index()\\\n", " .rename({'index':'_id', 0:'ori_id'}, axis=1))\n", "\n", "# get the sum of each typology for each firm\n", "typology_firms = typology_firms.groupby('ori_id').sum().reset_index()\n", "\n", "for typology in typology_dummies_counts['typology'].values:\n", " print(f'\\n{typology}')\n", " \n", " typology_firms_top5 = typology_firms[['ori_id', typology]]\\\n", " .sort_values(typology, ascending=False)\\\n", " .rename({typology:f'{typology} count'}, axis=1)\n", " \n", " typology_firms_top5 = typology_firms_top5[typology_firms_top5[f'{typology} count'] > 0].head(5)\n", " \n", " display(pd.merge(daqa_orgs[['ori_id', 'primary_name']], typology_firms_top5, on='ori_id')\\\n", " .sort_values(f'{typology} count', ascending=False)\\\n", " .rename({'primary_name':'Firm'}, axis=1)[['Firm', f'{typology} count']])" ] }, { "attachments": {}, "cell_type": "markdown", "id": "bc927e38", "metadata": {}, "source": [ "## Interviews\n", "\n", "Below we explore the frequency of interviews by interviewers. We focus only on intereviews with architect interviewees." ] }, { "cell_type": "code", "execution_count": 40, "id": "68b18bad", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# load data\n", "daqa_resources = df_daqa_dict['resource']\n", "daqa_interviews = daqa_resources[daqa_resources['_class_ori'].str.contains('interview', na=False)]\n", "\n", "interviewers = []\n", "interview_data = []\n", "\n", "for idx,row in daqa_interviews.iterrows():\n", " try: \n", " interviewees_isarchitect = pd.json_normalize(json.loads(row['interviewee']))['architect'].values.tolist()\n", " if True in interviewees_isarchitect:\n", " interview_data.append(pd.json_normalize(json.loads(row['interviewee'])))\n", " interviewers.extend(pd.json_normalize(json.loads(row['interviewer']))['label'].values.tolist())\n", " except: pass\n", "\n", "\n", "# plot bar plot of interviewers\n", "interviewers = pd.Series(interviewers)\n", "interviewers.value_counts().plot(kind='barh', figsize=(10,5), title='Number of interviews by interviewers (only architect interviewees)')\n", "\n", "# inverse y-axis\n", "plt.gca().invert_yaxis()\n", "\n", "# add bar labels and propotions\n", "for i, v in enumerate(interviewers.value_counts()):\n", " plt.text(v + .4, i + .15, str(v), color='black')\n", "\n", "plt.xlim(0, 56.5)\n", "plt.text(28, 13.25, 'Note: there are some non-disjoint cases in the data.', size=12, ha=\"center\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "id": "e2aca1e9", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Count of interviewed architects by organisation, Top 8:\n" ] }, { "data": { "text/plain": [ "Conrad Gargett & Partners (1965-1972) 19\n", "Queensland Government Department of Public Works 12\n", "Bligh Jessup Bretnall & Partners 11\n", "The University of Queensland 8\n", "Commonwealth Department of Works 7\n", "A.H Conrad & T.B.F Gargett 7\n", "Conrad Gargett & Partners (1972-1995) 6\n", "Brisbane City Council 5\n", "Name: object.label, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "daqa_persons_interviewed = daqa_persons[daqa_persons[\"display_name\"].apply(lambda x: x.replace('\"','')).isin(pd.concat(interview_data)['label'].unique())]\n", "\n", "interviewed_orgs = []\n", "for idx,row in daqa_persons_interviewed.iterrows():\n", " try: interviewed_orgs.append(pd.json_normalize(json.loads(row['related_organizations'])))\n", " except: pass\n", "\n", "interviewed_orgs = pd.concat(interviewed_orgs)\n", "print('Count of interviewed architects by organisation, Top 8:')\n", "interviewed_orgs = interviewed_orgs[interviewed_orgs[\"predicate.term\"] == 'Employment'][[\"subject.label\", \"object.label\"]].drop_duplicates()\n", "display(interviewed_orgs['object.label'].value_counts().head(8))\n", "\n", "avg_no_emnployers = interviewed_orgs.groupby('subject.label').count().reset_index().rename({'object.label':'count'}, axis=1)['count'].median()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "de274624", "metadata": {}, "source": [ "```{epigraph}\n", "On average, interviewed architects have worked for **5 employers**.\n", "```" ] }, { "attachments": {}, "cell_type": "markdown", "id": "e6d78830", "metadata": {}, "source": [ "## Socioeconomic factors \n", "Below we overlay the DAQA dataset (completed projects) with socioeconomic data from the Australian Bureau of Statistics (ABS), Queensland Government Statistician's Office (QGSO) and Department of Foreign Affairs and Trade (DFAT). Factors include GDP, overseas migration, interstate migration, employment and buildings approved. Most measures are specific to Queensland.\n", "\n", "As data is recorded in two forms, by calendar year and financial year, we have developed two applications to explore the data. Applications can be accessed using the following links:\n", "\n", "**By calendar year**: [https://daqa-socioecon-cy-dashapp.onrender.com](https://daqa-socioecon-cy-dashapp.onrender.com)\n", "\n", "**By financial year**: [https://daqa-socioeconomic-fy.onrender.com/](https://daqa-socioeconomic-fy.onrender.com)\n", "
\n", "\n", "The applications consist of the following demographic variables:" ] }, { "attachments": {}, "cell_type": "markdown", "id": "6f0b3775", "metadata": {}, "source": [ "### ABS data\n", "\n", "Dataset: 3105.0.65.001 Australian Historical Population Statistics, Migration, 2014\n", "\n", "Source: [https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3105.0.65.0012014](https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3105.0.65.0012014)\n", "\n", "Sheet name: Table 7.2\n", "\n", "- **QLD_Net overseas migration**\n", "
\n", "\n", " Net overseas migration (a)(b)(c), Queensland, 1972-2011, FY\n", "
\n", "\n", " (a) From September quarter 1971 to June quarter 2006 inclusive, net overseas migration (NOM) was the difference between permanent and long-term arrivals and permanent and long-term departures. For September quarter 2006 onwards estimates for NOM are the difference between the number of incoming travellers who stay in Australia for 12 months or more and are added to the population (NOM arrivals) and the number of outgoing travellers who leave Australia for 12 months or more and are subtracted from the population (NOM departures). See paragraphs 51 to 54 of the Explanatory Notes.\t\t\t\t\t\t\t\t\t\t\t\t\t\n", " \n", " (b) Estimates for net overseas migration (NOM) contain a break in time series. Estimates for September quarter 2006 onwards use an improved methodology based on the 12/16 rule and are not comparable with NOM estimates prior to this based on the 12/12 rule. See paragraph 51 of the Explanatory Notes.\t\t\t\t\t\t\t\t\t\t\t\t\t\n", " \n", " (c) An adjustment for category jumping (later referred to as migration adjustments) was included for estimates for September quarter 1976 to June quarter 2006, except for September quarter 1997 to June quarter 2001 when it was set to zero. See paragraphs 55 to 57 of the Explanatory Notes.\t\t\t\t\t\t\t\t\t\t\t\t\t\n", "
\n", " \n", " Sources: ABS data available on request, Population Estimates (for years 1972 - 1981); Australian Demographic Statistics (cat. no. 3101.0) (for years 1982 - 2011)\n", "\n", "
\n", "\n", "Sheet name: Table 7.3\n", "\n", "- **QLD_InterstateMigration_Arrivals_To**\n", "
\n", "\n", " Interstate migration, arrivals, Queensland, 1972-2010, CY\n", "
\n", "\n", " Sources: ABS data available on request, Population Estimates (for years 1972 - 1981); Australian Demographic Statistics (cat. no. 3101.0) (1982 - 2010)\n", "\n", "- **QLD_InterstateMigration_Departures_from**\n", "
\n", "\n", " Interstate migration, departures, Queensland, 1972-2010, CY\n", "
\n", "\n", " Sources: ABS data available on request, Population Estimates (for years 1972 - 1981); Australian Demographic Statistics (cat. no. 3101.0) (1982 - 2010)\n", "\n", "- **QLD_Net interstate migration**\n", "
\n", "\n", " Net interstate migration, arrivals, Queensland, 1972-2010, CY\n", "
\n", "\n", " Sources: ABS data available on request, Population Estimates (for years 1972 - 1981); Australian Demographic Statistics (cat. no. 3101.0) (1982 - 2010)\n", "\n", "
\n", "\n", "Sheet name: Table 7.5\n", "\n", "- **QLD_Net interstate and overseas migration**\n", "
\n", "\n", " Net interstate and overseas migration(a)(b)(c), Queensland, 1860-2010, CY\n", "
\n", "\n", " (a) Until June quarter 1971, net overseas migration (NOM) was the difference between total arrivals and total departures, including short-term movements. From September quarter 1971 to June quarter 2006 inclusive, NOM was the difference between permanent and long-term arrivals and permanent and long-term departures. For September quarter 2006 onwards estimates for NOM are the difference between the number of incoming travellers who stay in Australia for 12 months or more and are added to the population (NOM arrivals) and the number of outgoing travellers who leave Australia for 12 months or more and are subtracted from the population (NOM departures). See paragraphs 51 to 54 of the Explanatory Notes.\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", " \n", " (b) Estimates for net overseas migration (NOM) contain a break in time series. Estimates for September quarter 2006 onwards use an improved methodology based on the 12/16 rule and are not comparable with NOM estimates prior to this based on the 12/12 rule. See paragraph 51 of the Explanatory Notes.\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", " \n", " (c) An adjustment for category jumping (later referred to as migration adjustments) was included for estimates for September quarter 1976 to June quarter 2006, except for September quarter 1997 to June quarter 2001 when it was set to zero. See paragraphs 55 to 57 of the Explanatory Notes.\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", "
\n", "\n", " Sources: Demography (1910) (for years 1860 - 1901); Australian Demographic Trends, 1997 (cat. no. 3102.0) (for years 1902 - 1980); Australian Demographic Statistics (cat. no. 3101.0) (for years 1981 - 2010)\n", "\n", "
\n", "\n", "Sheet name: Table 7.6\n", "\n", "- **QLD_Net interstate and overseas migration rate**\n", "
\n", "\n", " Net interstate and overseas migration rate(a)(b)(c)(d), Queensland, 1860-2010, CY\n", "
\n", "\n", " (a) Net movement per 1,000 population. The net interstate and overseas migration rate is the number of net movements in a year per 1,000 mid-year (30 June) estimated resident population. For years prior to 1981, the net interstate and overseas migration rate was based on the mean estimated resident population for the calendar year.\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", " \n", " (b) Until June quarter 1971, net overseas migration (NOM) was the difference between total arrivals and total departures, including short-term movements. From September quarter 1971 to June quarter 2006 inclusive, NOM was the difference between permanent and long-term arrivals and permanent and long-term departures. For September quarter 2006 onwards estimates for NOM are the difference between the number of incoming travellers who stay in Australia for 12 months or more and are added to the population (NOM arrivals) and the number of outgoing travellers who leave Australia for 12 months or more and are subtracted from the population (NOM departures). See paragraphs 51 to 54 of the Explanatory Notes.\t\n", "\n", " (c) Estimates for net overseas migration (NOM) contain a break in time series. Estimates for September quarter 2006 onwards use an improved methodology based on the 12/16 rule and are not comparable with NOM estimates prior to this based on the 12/12 rule. See paragraph 51 of the Explanatory Notes.\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", " (d) An adjustment for category jumping (later referred to as migration adjustments) was included for estimates for September quarter 1976 to June quarter 2006, except for September quarter 1997 to June quarter 2001 when it was set to zero. See paragraphs 55 to 57 of the Explanatory Notes.\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", "
\n", "\n", " Sources: Demography (1910) (for years 1860 - 1901); Australian Demographic Trends, 1997 (cat. no. 3102.0) (for years 1902 - 1980); Australian Demographic Statistics (cat. no. 3101.0) (for years 1981 - 2010)\n", "\n", "
\n", "\n", "Dataset: 6204055001TS0004 Labour Force Historical Timeseries, Australia - Labour Force Status by State, Queensland\n", "\n", "Source: https://www.abs.gov.au/statistics/labour/employment-and-unemployment/labour-force-australia-detailed/latest-release\n", "\n", "- **QLD_Males_Employed_000**\n", "
\n", "\n", " Labour Force Status, Employed, Males, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Males_Looking for full-time work_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Looking for full-time work, Males, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Males_Looking for part-time work_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Looking for part-time work, Males, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Males_Total_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Total, Males, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Males_Labour force_000**\n", "
\n", "\n", " Labour Force Status, Labour force, Males, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Males_Not in labour force_000**\n", "
\n", "\n", " Labour Force Status, Not in labour force, Males, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Males_Civilian pop'n aged 15 years and over_000**\n", "
\n", "\n", " Labour Force Status, Civilian population aged 15 years and over, Males, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Males_Unemployment rate_%**\n", "
\n", "\n", " Labour Force Status, Unemployment Rate, Males, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Males_Partic-ipation rate_%**\n", "
\n", "\n", " Labour Force Status, Participation Rate, Males, Queensland - 1966 – 1977, CY\n", "\n", "
\n", "\n", "- **QLD_Females_Employed_000**\n", "
\n", "\n", " Labour Force Status, Employed, Females, Queensland - 1966 – 1977, CY\n", "
\n", " \n", "- **QLD_Females_Looking for full-time work_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Looking for full-time work, Females, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Females_Looking for part-time work_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Looking for part-time work, Females, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Females_Total_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Total, Females, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Females_Labour force_000**\n", "
\n", "\n", " Labour Force Status, Labour force, Females, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Females_Not in labour force_000**\n", "
\n", "\n", " Labour Force Status, Not in labour force, Females, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Females_Civilian pop'n aged 15 years and over_000**\n", "
\n", "\n", " Labour Force Status, Civilian population aged 15 years and over, Females, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Females_Unemployment rate_%**\n", "
\n", "\n", " Labour Force Status, Unemployment Rate, Females, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_Females_Partic-ipation rate_%**\n", "
\n", "\n", " Labour Force Status, Participation Rate, Females, Queensland - 1966 – 1977, CY\n", "\n", "
\n", "\n", "- **QLD_TotalPersons_Employed_000**\n", "
\n", "\n", " Labour Force Status, Employed, Persons, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_TotalPersons_Looking for full-time work_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Looking for full-time work, Persons, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_TotalPersons_Looking for part-time work_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Looking for part-time work, Persons, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_TotalPersons_Total_000**\n", "
\n", "\n", " Labour Force Status, Unemployed - Total, Persons, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_TotalPersons_Labour force_000**\n", "
\n", "\n", " Labour Force Status, Labour force, Persons, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_TotalPersons_Not in labour force_000**\n", "
\n", "\n", " Labour Force Status, Not in labour force, Persons, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_TotalPersons_Civilian pop'n aged 15 years and over_000**\n", "
\n", "\n", " Labour Force Status, Civilian population aged 15 years and over, Persons, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_TotalPersons_Unemployment rate_%**\n", "
\n", "\n", " Labour Force Status, Unemployment Rate, Persons, Queensland - 1966 – 1977, CY\n", "
\n", "\n", "- **QLD_TotalPersons_Partic-ipation rate_%**\n", "
\n", "\n", " Labour Force Status, Participation Rate, Persons, Queensland - 1966 – 1977, CY\n", "\n", "\n", "
\n", "\n", "Dataset: 3105.0.65.001 Australian Historical Population Statistics, Overseas Arrivals and Departures, 2014\n", "\n", "Source: [https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3105.0.65.0012014](https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3105.0.65.0012014)\n", "\n", "Sheet name: Table 9.4\n", "\n", "- **QLD_Overseas_Arrivals_Males_Total_movement**\n", "
\n", "\n", " Overseas arrivals (b)(c)(d), total movement, Males, Queensland (a), 1901-2010 (h), CY\n", "
\n", "\n", " (a) Prior to 1974, data relates to state/territory of disembarkation (for arrivals) or embarkation (for departures). For 1974 and 1975, data relates to state/territory of clearance. From 1976 onwards data relates to state/territory of usual residence.\t\n", "\n", " (b) Includes arrivals of troops for the years 1915 to 1920 and departures of troops for the years 1914 to 1919.\t\n", " \n", " (c) Excludes troop movements for the period September 1939 to June 1947.\n", "\n", " (d) Includes U.S. troops visiting Australia on rest and recreation leave during the years 1970 to 1971.\n", "\n", " (h) Overseas arrivals and departures data are rounded to whole numbers. As a result, sums of the componenets may not add exactly to totals. \n", "
\n", "\n", " Sources: Demography (1911, 1922, 1933, 1947, 1949, 1960, 1966) (for years 1901 - 1964); Overseas Arrivals and Departures, Australia (1972) (CBCS ref. no. 4.23) (for years 1965 - 1972); Overseas Arrivals and Departures, Australia (1977) (cat. no. 3404.0) (for years 1973 - 1975); ABS data available on request, Overseas Arrivals and Departures collection (for years 1976 - ); Overseas Arrivals and Departures, Australia (cat. no. 3401.0) (for years 1976 - )\n", "

\n", "\n", "- **QLD_Overseas_Arrivals_Females_Total_movement**\n", "
\n", "\n", " Overseas arrivals (b)(c)(d), total movement, Females, Queensland (a), 1901-2010 (h), CY\n", "
\n", "\n", " (a) Prior to 1974, data relates to state/territory of disembarkation (for arrivals) or embarkation (for departures). For 1974 and 1975, data relates to state/territory of clearance. From 1976 onwards data relates to state/territory of usual residence.\t\n", "\n", " (b) Includes arrivals of troops for the years 1915 to 1920 and departures of troops for the years 1914 to 1919.\t\n", " \n", " (c) Excludes troop movements for the period September 1939 to June 1947.\n", "\n", " (d) Includes U.S. troops visiting Australia on rest and recreation leave during the years 1970 to 1971.\n", " \n", " (h) Overseas arrivals and departures data are rounded to whole numbers. As a result, sums of the componenets may not add exactly to totals. \n", "
\n", "\n", " Sources: Demography (1911, 1922, 1933, 1947, 1949, 1960, 1966) (for years 1901 - 1964); Overseas Arrivals and Departures, Australia (1972) (CBCS ref. no. 4.23) (for years 1965 - 1972); Overseas Arrivals and Departures, Australia (1977) (cat. no. 3404.0) (for years 1973 - 1975); ABS data available on request, Overseas Arrivals and Departures collection (for years 1976 - ); Overseas Arrivals and Departures, Australia (cat. no. 3401.0) (for years 1976 - )\n", "

\n", "\n", "- **QLD_Overseas_Arrivals_Persons_Total_movement**\n", "
\n", "\n", " Overseas arrivals (b)(c)(d), total movement, Persons, Queensland (a), 1901-2010 (h), CY\n", "
\n", "\n", " (a) Prior to 1974, data relates to state/territory of disembarkation (for arrivals) or embarkation (for departures). For 1974 and 1975, data relates to state/territory of clearance. From 1976 onwards data relates to state/territory of usual residence.\t\n", "\n", " (b) Includes arrivals of troops for the years 1915 to 1920 and departures of troops for the years 1914 to 1919.\t\n", " \n", " (c) Excludes troop movements for the period September 1939 to June 1947.\n", "\n", " (d) Includes U.S. troops visiting Australia on rest and recreation leave during the years 1970 to 1971.\n", " \n", " (h) Overseas arrivals and departures data are rounded to whole numbers. As a result, sums of the componenets may not add exactly to totals. \n", "
\n", "\n", " Sources: Demography (1911, 1922, 1933, 1947, 1949, 1960, 1966) (for years 1901 - 1964); Overseas Arrivals and Departures, Australia (1972) (CBCS ref. no. 4.23) (for years 1965 - 1972); Overseas Arrivals and Departures, Australia (1977) (cat. no. 3404.0) (for years 1973 - 1975); ABS data available on request, Overseas Arrivals and Departures collection (for years 1976 - ); Overseas Arrivals and Departures, Australia (cat. no. 3401.0) (for years 1976 - )\n", "

\n", "\n", "- **QLD_Overseas_Departures_Males_Total_movement**\n", "
\n", "\n", " Overseas departures (b)(c)(d), total movement, Males, Queensland (a), 1901-2010 (h), CY\n", "
\n", "\n", " (a) Prior to 1974, data relates to state/territory of disembarkation (for arrivals) or embarkation (for departures). For 1974 and 1975, data relates to state/territory of clearance. From 1976 onwards data relates to state/territory of usual residence.\t\n", "\n", " (b) Includes arrivals of troops for the years 1915 to 1920 and departures of troops for the years 1914 to 1919.\t\n", " \n", " (c) Excludes troop movements for the period September 1939 to June 1947.\n", "\n", " (d) Includes U.S. troops visiting Australia on rest and recreation leave during the years 1970 to 1971.\n", " \n", " (h) Overseas arrivals and departures data are rounded to whole numbers. As a result, sums of the componenets may not add exactly to totals. \n", "
\n", "\n", " Sources: Demography (1911, 1922, 1933, 1947, 1949, 1960, 1966) (for years 1901 - 1964); Overseas Arrivals and Departures, Australia (1972) (CBCS ref. no. 4.23) (for years 1965 - 1972); Overseas Arrivals and Departures, Australia (1977) (cat. no. 3404.0) (for years 1973 - 1975); ABS data available on request, Overseas Arrivals and Departures collection (for years 1976 - ); Overseas Arrivals and Departures, Australia (cat. no. 3401.0) (for years 1976 - )\n", "

\n", "\n", "- **QLD_Overseas_Departures_Females_Total_movement**\n", "
\n", " \n", " Overseas departures (b)(c)(d), total movement, Females, Queensland (a), 1901-2010 (h), CY\n", "
\n", "\n", " (a) Prior to 1974, data relates to state/territory of disembarkation (for arrivals) or embarkation (for departures). For 1974 and 1975, data relates to state/territory of clearance. From 1976 onwards data relates to state/territory of usual residence.\t\n", "\n", " (b) Includes arrivals of troops for the years 1915 to 1920 and departures of troops for the years 1914 to 1919.\t\n", " \n", " (c) Excludes troop movements for the period September 1939 to June 1947.\n", "\n", " (d) Includes U.S. troops visiting Australia on rest and recreation leave during the years 1970 to 1971.\n", " \n", " (h) Overseas arrivals and departures data are rounded to whole numbers. As a result, sums of the componenets may not add exactly to totals. \n", "
\n", "\n", " Sources: Demography (1911, 1922, 1933, 1947, 1949, 1960, 1966) (for years 1901 - 1964); Overseas Arrivals and Departures, Australia (1972) (CBCS ref. no. 4.23) (for years 1965 - 1972); Overseas Arrivals and Departures, Australia (1977) (cat. no. 3404.0) (for years 1973 - 1975); ABS data available on request, Overseas Arrivals and Departures collection (for years 1976 - ); Overseas Arrivals and Departures, Australia (cat. no. 3401.0) (for years 1976 - )\n", "

\n", "\n", "- **QLD_Overseas_Departures_Persons_Total_movement**\n", "
\n", "\n", " Overseas departures (b)(c)(d), total movement, Persons, Queensland (a), 1901-2010 (h), CY\n", "
\n", "\n", " (a) Prior to 1974, data relates to state/territory of disembarkation (for arrivals) or embarkation (for departures). For 1974 and 1975, data relates to state/territory of clearance. From 1976 onwards data relates to state/territory of usual residence.\t\n", "\n", " (b) Includes arrivals of troops for the years 1915 to 1920 and departures of troops for the years 1914 to 1919.\t\n", " \n", " (c) Excludes troop movements for the period September 1939 to June 1947.\n", "\n", " (d) Includes U.S. troops visiting Australia on rest and recreation leave during the years 1970 to 1971.\n", " \n", " (h) Overseas arrivals and departures data are rounded to whole numbers. As a result, sums of the componenets may not add exactly to totals. \n", "
\n", " \n", " Sources: Demography (1911, 1922, 1933, 1947, 1949, 1960, 1966) (for years 1901 - 1964); Overseas Arrivals and Departures, Australia (1972) (CBCS ref. no. 4.23) (for years 1965 - 1972); Overseas Arrivals and Departures, Australia (1977) (cat. no. 3404.0) (for years 1973 - 1975); ABS data available on request, Overseas Arrivals and Departures collection (for years 1976 - ); Overseas Arrivals and Departures, Australia (cat. no. 3401.0) (for years 1976 - )" ] }, { "attachments": {}, "cell_type": "markdown", "id": "ce0882d3", "metadata": {}, "source": [ "### QGSO data\n", "\n", "Dataset: cpi-all-groups-brisbane-weighted-average-eight-capital-cities-financial-year-1948-49-2021-22.csv\n", "\n", "Source: [https://www.qgso.qld.gov.au/statistics/theme/economy/prices-indexes/consumer-price-index-state](https://www.qgso.qld.gov.au/statistics/theme/economy/prices-indexes/consumer-price-index-state)\n", "
\n", "\n", "- **Brisbane_Index**\n", "
\n", "\n", " Consumer Price Index (a)(b): Brisbane, 1948-49 to 2021-22, FY\n", "
\n", "\n", " (a) 2011-12 = 100\n", "\n", " (b) Average of four quarters.\n", "
\n", "\n", " Source: ABS 6401.0, Consumer Price Index, Australia.\n", "

\n", "\n", "\n", "- **Brisbane_YoY_change**\n", "
\n", "\n", " Consumer Price Index (a)(b): Brisbane, Annual (%) change, 1949-50 to 2021-22, FY\n", "
\n", "\n", " (a) 2011-12 = 100\n", "\n", " (b) Average of four quarters.\n", "
\n", "\n", " Source: ABS 6401.0, Consumer Price Index, Australia.\n", "

\n", "\n", "\n", "- **Weighted average of eight capital cities_Index**\n", "
\n", "\n", " Consumer Price Index (a)(b): Weighted average of eight capitals, 1948-49 to 2021-22, FY\n", "
\n", "\n", " (a) 2011-12 = 100\n", "\n", " (b) Average of four quarters.\n", "
\n", "\n", " Source: ABS 6401.0, Consumer Price Index, Australia.\n", "

\n", "\n", "\n", "- **Weighted average of eight capital cities_YoY_change**\n", "
\n", "\n", " Consumer Price Index (a)(b): Weighted average of eight capitals, Annual (%) change, 1949-50 to 2021-22, FY\n", "
\n", "\n", " (a) 2011-12 = 100\n", "\n", " (b) Average of four quarters.\n", "
\n", "\n", " Source: ABS 6401.0, Consumer Price Index, Australia.\n", "\n", "

\n", "\n", "Dataset: building-approvals-value-building-approved-type-qld-1970-71-2021-22.csv\n", "\n", "Source: [https://www.qgso.qld.gov.au/statistics/theme/industry-development/housing-construction/building-approvals](https://www.qgso.qld.gov.au/statistics/theme/industry-development/housing-construction/building-approvals)\n", "\n", "
\n", "\n", "- **QLD_BuildingsApproved_Residential houses_Value**\n", "
\n", "\n", " Building approvals: value of building approved (a), Residential houses, Queensland, 1970-71 to 2021-22, FY\n", "
\n", "\n", " (a) Based on original series as at June 2022.\n", "
\n", "\n", " Source: ABS, Building Approvals, Australia.\n", "

\n", "\n", "- **QLD_BuildingsApproved_Other residential_Value**\n", "
\n", "\n", " Building approvals: value of building approved (a), Other residential, Queensland, 1970-71 to 2021-22, FY\n", "
\n", "\n", " (a) Based on original series as at June 2022.\n", "
\n", "\n", " Source: ABS, Building Approvals, Australia.\n", "

\n", "\n", "- **QLD_BuildingsApproved_Alterations, additions and conversions_Value**\n", "
\n", "\n", " Building approvals: value of building approved (a), Alterations, additions and conversions, Queensland, 1973-74 to 2021-22, FY\n", "
\n", "\n", " (a) Based on original series as at June 2022.\n", "
\n", "\n", " Source: ABS, Building Approvals, Australia.\n", "

\n", "\n", "- **QLD_BuildingsApproved_Total residential_Value**\n", "
\n", "\n", " Building approvals: value of building approved (a), Total Residential, Queensland, 1973-74 to 2021-22, FY\n", "
\n", "\n", " (a) Based on original series as at June 2022.\n", "
\n", "\n", " Source: ABS, Building Approvals, Australia.\n", "

\n", "\n", "- **QLD_BuildingsApproved_Non-residential_Value**\n", "
\n", "\n", " Building approvals: value of building approved (a), Non-residential, Queensland, 1970-71 to 2021-22, FY\n", "
\n", "\n", " (a) Based on original series as at June 2022.\n", "
\n", "\n", " Source: ABS, Building Approvals, Australia.\n", "

\n", "\n", "- **QLD_BuildingsApproved_Total_Value**\n", "
\n", "\n", " Building approvals: value of building approved (a), Total, Queensland, 1973-74 to 2021-22, FY\n", "
\n", "\n", " (a) Based on original series as at June 2022.\n", "
\n", " \n", " Source: ABS, Building Approvals, Australia." ] }, { "attachments": {}, "cell_type": "markdown", "id": "90fdf9be", "metadata": {}, "source": [ "### DFAT data \n", "Dataset: australias-trade-and-economic-indicators-historical.xlsx\n", "\n", "Source: [https://www.dfat.gov.au/trade/trade-and-investment-data-information-and-publications/trade-statistics/trade-time-series-data](https://www.dfat.gov.au/trade/trade-and-investment-data-information-and-publications/trade-statistics/trade-time-series-data)\n", "\n", "
\n", "\n", "Sheet name: Population\n", "- **Aus_Population**\n", "
\n", "\n", " Population (1788-2017), Australia, CY (a)(b)\n", "
\n", "\n", " (a) Includes estimates of the Indigenous population from 1961 onwards.\n", " \n", " (b) For 2017 only, end September 2017\n", "

\n", "\n", "- **Aus_Short term overseas visitor arrivals**\n", "
\n", "\n", " Short-term overseas visitor arrivals (1925-2017), Australia, CY\n", "
\n", "\n", "- **Aus_Short term Australian resident departures**\n", "
\n", "\n", " Short-term Australian resident departures (1925-2017), Australia, CY\n", "\n", "
\n", "\n", "Sheet name: Economic\n", "\n", "- **AUS_GDP_Current**\n", "
\n", "\n", " Gross Domestic Product, Australia - current price, 1900-01 - 2016-17, FY (a)(b)\n", "
\n", "\n", " (a) Please note there is a time series break in the current price GDP series between 1958-59 and 1959-60.\n", " \n", " (b) Excludes livestock accumulation up to 1948-49.\n", "

\n", "\n", "- **AUS_GDP_Real_2015-16**\n", "
\n", "\n", " Real Gross Domestic Product, Australia - 2015-16 prices, 1900-01 - 2016-17, FY\n", "

\n", "\n", "- **AUS_GDP_Real_2015-16_Percentage**\n", "
\n", "\n", " Real Gross Domestic Product Percentage change (%), 1901-02 - 2016-17, Australia, FY\n", "

\n", "\n", "- **AUS_Terms of trade Index set to 2015-16**\n", "
\n", "\n", " Terms of trade Index (2015-16=100), 1900-01 - 2016-17, Australia, FY\n", "

\n", "\n", "- **AUS_Terms of trade percentage set to 2015-16**\n", "
\n", "\n", " Terms of trade precentage change (%), 1901-02 - 2016-17, Australia, FY\n", "

\n", "\n", "- **AUS_Unemployment rate**\n", "
\n", " \n", " Unemployment rate (%), 1900-01 - 2016-17, Australia, FY" ] }, { "cell_type": "markdown", "id": "c8f03aab", "metadata": {}, "source": [ "## Comparing DAQA and DAAO\n", "\n", "We visualise the number of active males and females over time. We capture this activity by filtering on people that have a career start date and end date. The first plot below shows a count of the cumulative career activity for both DAQA and DAAO. The second visualisation displays the proportion of active males and females over time. The visualisations suggest that the gender disparity is not as pronounced in DAAO as it is in DAQA." ] }, { "cell_type": "code", "execution_count": 44, "id": "327e9163", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# persons = fetch_data(acdedata='person')\n", "# daao_daqa = persons[persons.data_source.str.contains('DAQA|DAAO')][['gender', 'data_source', 'birth','death','career']]\n", "# daao_daqa = daao_daqa[daao_daqa['career'].notnull()]\n", "\n", "# selected_idx = []\n", "# selected_birth_year = [] \n", "# selected_start_year = []\n", "# selected_end_year = []\n", "# selected_death_year = []\n", "\n", "# for idx,row in daao_daqa.iterrows():\n", "# try: selected_birth_year.append(int(pd.json_normalize(json.loads(row['birth']))['coverage.date.year'].values[0]))\n", "# except: selected_birth_year.append(np.nan)\n", "\n", "# try:\n", "# career_df = pd.json_normalize(pd.json_normalize(json.loads(row['career']))['career_periods'].values[0])\n", "# selected_start_year.append(career_df['coverage_range.date_range.date_start.year'].min())\n", "# except: selected_start_year.append(np.nan)\n", "\n", "# try:\n", "# career_df = pd.json_normalize(pd.json_normalize(json.loads(row['career']))['career_periods'].values[0])\n", "# selected_end_year.append(career_df['coverage_range.date_range.date_end.year'].max())\n", "# except: selected_end_year.append(np.nan)\n", "\n", "# try: selected_death_year.append(int(pd.json_normalize(json.loads(row['death']))['coverage.date.year'].values[0]))\n", "# except: selected_death_year.append(np.nan)\n", "\n", "# # daao_daqa = daao_daqa[daao_daqa.index.isin(selected_idx)].copy()\n", "# daao_daqa['birth_year'] = selected_birth_year\n", "# daao_daqa['career_start'] = selected_start_year\n", "# # return max year of selected_end_year and death_year, there may be nan values\n", "# daao_daqa['career_end'] = selected_end_year\n", "# daao_daqa['death_year'] = selected_start_year\n", "\n", "# # manipulate career_start so that each value contains the first 4 digits\n", "# daao_daqa['career_start'] = daao_daqa['career_start'].apply(lambda x: np.nan if isinstance(x,float) else str(x)[:4])\n", "# daao_daqa['career_end'] = daao_daqa['career_end'].apply(lambda x: np.nan if isinstance(x,float) else str(x)[:4])\n", "# daao_daqa['death_year'] = daao_daqa['death_year'].apply(lambda x: np.nan if isinstance(x,float) else str(x)[:4])\n", "\n", "# # change all strings that are not numbers to nan\n", "# daao_daqa['career_start'] = daao_daqa['career_start'].apply(lambda x: int(x) if str(x).isdigit() else np.nan)\n", "# daao_daqa['career_end'] = daao_daqa['career_end'].apply(lambda x: int(x) if str(x).isdigit() else np.nan)\n", "# daao_daqa['death_year'] = daao_daqa['death_year'].apply(lambda x: int(x) if str(x).isdigit() else np.nan)\n", "\n", "# daao_daqa['gender'] = daao_daqa['gender'].apply(lambda x: str(x.replace('\"', '')))\n", "# daao_daqa['data_source'] = daao_daqa['data_source'].apply(lambda x: str(x.replace('\"', '')))\n", "# daao_daqa = daao_daqa[daao_daqa['gender']\\\n", "# .str.contains('male|female')][['gender','data_source','birth_year','death_year','career_start','career_end']]\n", "\n", "# daao_daqa = daao_daqa[(daao_daqa.death_year.notnull()) | (daao_daqa.career_end.notnull())].copy()\n", "# daao_daqa['career_end'] = daao_daqa['career_end'].fillna(-1)\n", "# daao_daqa['death_year'] = daao_daqa['death_year'].fillna(-1)\n", "# daao_daqa['career_end'] = np.where(daao_daqa['career_end'] > daao_daqa['death_year'], daao_daqa['career_end'], daao_daqa['death_year'])\n", "\n", "# daao_daqa = daao_daqa[daao_daqa['career_start'].notnull()][['gender','data_source','career_start','career_end']]\n", "# daao_daqa = daao_daqa[daao_daqa['career_start'] != daao_daqa['career_end']]\n", "# daao_daqa.reset_index(inplace=True, drop=True)\n", "\n", "# daao_daqa_wide = pd.DataFrame()\n", "\n", "# for i,row in daao_daqa.iterrows():\n", "# for year in range(int(row['career_start']), int(row['career_end'])+1): \n", "# daao_daqa_wide.loc[i, year] = 1\n", "\n", "# daao_daqa_wide = daao_daqa_wide.fillna(0)\n", "# daao_daqa_wide = pd.merge(daao_daqa, daao_daqa_wide, left_index=True, right_index=True)\n", "\n", "# daao_females_wide = daao_daqa_wide[(daao_daqa_wide.gender == 'female') & (daao_daqa_wide.data_source == 'DAAO')].copy()\n", "# daao_males_wide = daao_daqa_wide[(daao_daqa_wide.gender == 'male') & (daao_daqa_wide.data_source == 'DAAO')].copy()\n", "# daqa_females_wide = daao_daqa_wide[(daao_daqa_wide.gender == 'female') & (daao_daqa_wide.data_source == 'DAQA')].copy()\n", "# daqa_males_wide = daao_daqa_wide[(daao_daqa_wide.gender == 'male') & (daao_daqa_wide.data_source == 'DAQA')].copy()\n", "\n", "# daao_females_wide = daao_females_wide.drop(['gender','data_source','career_start','career_end'], axis=1)\n", "# daao_males_wide = daao_males_wide.drop(['gender','data_source','career_start','career_end'], axis=1)\n", "\n", "# daao_gender_count = pd.merge(daao_males_wide.sum().reset_index().sort_values('index', ascending=True),\n", "# daao_females_wide.sum().reset_index().sort_values('index', ascending=True), \n", "# on='index', how='outer')\n", "\n", "# daao_gender_count.columns = ['year','male','female']\n", "# daao_gender_count['data_source'] = 'DAAO'\n", "\n", "# daqa_females_wide = daqa_females_wide.drop(['gender','data_source','career_start','career_end'], axis=1)\n", "# daqa_males_wide = daqa_males_wide.drop(['gender','data_source','career_start','career_end'], axis=1)\n", "\n", "# daqa_gender_count = pd.merge(daqa_males_wide.sum().reset_index().sort_values('index', ascending=True),\n", "# daqa_females_wide.sum().reset_index().sort_values('index', ascending=True), \n", "# on='index', how='outer')\n", "\n", "# daqa_gender_count.columns = ['year','male','female']\n", "# daqa_gender_count['data_source'] = 'DAQA'\n", "\n", "# daqa_daao_gender_count = pd.concat([daao_gender_count, daqa_gender_count])\n", "# daqa_daao_gender_count.to_csv('data/local/DAQA_gender_comparison.csv', index=False)\n", "\n", "daqa_daao_gender_count = fetch_small_data_from_github('DAQA_gender_comparison.csv')\n", "\n", "# plot\n", "plt.plot(daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAAO']['year'], \n", "daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAAO']['male'], label='DAAO Male', color='tab:blue')\n", "\n", "plt.plot(daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAAO']['year'], \n", "daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAAO']['female'], \n", "label='DAAO Female', color='tab:orange')\n", "\n", "plt.plot(daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAQA']['year'], \n", "daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAQA']['male'], \n", "label='DAQA Male', color='tab:blue', alpha=0.75, linestyle='dashed')\n", "\n", "plt.plot(daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAQA']['year'], \n", "daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAQA']['female'], \n", "label='DAQA Female', color='tab:orange', alpha=0.75, linestyle='dashed')\n", "\n", "plt.title('Number of active males and females for DAAO and DAQA')\n", "plt.xlabel('Year of career activity')\n", "\n", "plt.legend()\n", "\n", "# increase the size of the plot\n", "fig = plt.gcf()\n", "fig.set_size_inches(12, 4)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 45, "id": "e9297af6", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "daqa_daao_gender_count['male_to_female'] = (daqa_daao_gender_count.male/(daqa_daao_gender_count.male + daqa_daao_gender_count.female))\n", "\n", "# plot\n", "plt.plot(daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAAO']['year'], \n", "daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAAO']['male_to_female'], label='DAAO Male-Female Ratio')\n", "\n", "plt.plot(daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAQA']['year'], \n", "daqa_daao_gender_count[daqa_daao_gender_count.data_source == 'DAQA']['male_to_female'], label='DAQA Male-Female Ratio')\n", "\n", "plt.title('Proportion of active males and females for DAAO and DAQA (represented as a ratio), 1800-2000')\n", "plt.ylabel('Male-to-female ratio')\n", "plt.xlabel('Year of career activity')\n", "\n", "plt.legend()\n", "\n", "# change x-axis limit\n", "plt.xlim(1800, 2025)\n", "\n", "# change x-axis tick frequency\n", "plt.xticks(np.arange(1800, 2025, 20))\n", "\n", "# add line at 50% mark\n", "plt.axhline(y=0.5, color='grey', linestyle='--', alpha=0.3)\n", "\n", "# increase the size of the plot\n", "fig = plt.gcf()\n", "fig.set_size_inches(12, 4)\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.7 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" }, "vscode": { "interpreter": { "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" } } }, "nbformat": 4, "nbformat_minor": 5 }