From 800b38efb50d7789b1a05d902535df7750b78092 Mon Sep 17 00:00:00 2001
From: Keshav Prasath <kprasath@student.unimelb.edu.au>
Date: Thu, 25 May 2023 11:22:33 +0000
Subject: [PATCH] Upload New File
---
Analysis/CCC_data_wrangling.ipynb | 675 ++++++++++++++++++++++++++++++
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "144ae6e4",
+ "metadata": {},
+ "source": [
+ "## Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b755e6df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import json\n",
+ "import geopandas as gpd\n",
+ "\n",
+ "#twitter-data-small.json"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30a0611e",
+ "metadata": {},
+ "source": [
+ "# Different Data's"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14a18af1",
+ "metadata": {},
+ "source": [
+ "## 1) Data from Sudo (Crime)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "021d9519",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>a40_abduction_and_related_offences</th>\n",
+ " <th>b30_burglary_break_and_enter</th>\n",
+ " <th>a10_homicide_and_related_offences</th>\n",
+ " <th>f30_other_government_regulatory_offences</th>\n",
+ " <th>lga_code11</th>\n",
+ " <th>total_division_c_offences</th>\n",
+ " <th>a30_sexual_offences</th>\n",
+ " <th>a70_stalking_harassment_and_threatening_behaviour</th>\n",
+ " <th>e10_justice_procedures</th>\n",
+ " <th>total_division_f_offences</th>\n",
+ " <th>...</th>\n",
+ " <th>total_division_e_offences</th>\n",
+ " <th>e20_breaches_of_orders</th>\n",
+ " <th>f10_regulatory_driving_offences</th>\n",
+ " <th>a60_blackmail_and_extortion</th>\n",
+ " <th>b40_theft</th>\n",
+ " <th>c90_other_drug_offences</th>\n",
+ " <th>f20_transport_regulation_offences</th>\n",
+ " <th>a20_assault_and_related_offences</th>\n",
+ " <th>b60_bribery</th>\n",
+ " <th>c10_drug_dealing_and_trafficking</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1.0</td>\n",
+ " <td>27</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20110</td>\n",
+ " <td>53</td>\n",
+ " <td>95</td>\n",
+ " <td>11</td>\n",
+ " <td>11</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>68</td>\n",
+ " <td>57</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>90</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>81</td>\n",
+ " <td>0.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.0</td>\n",
+ " <td>69</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20260</td>\n",
+ " <td>137</td>\n",
+ " <td>58</td>\n",
+ " <td>38</td>\n",
+ " <td>84</td>\n",
+ " <td>2</td>\n",
+ " <td>...</td>\n",
+ " <td>411</td>\n",
+ " <td>327</td>\n",
+ " <td>0.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>222</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>176</td>\n",
+ " <td>0.0</td>\n",
+ " <td>24.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>21.0</td>\n",
+ " <td>983</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20570</td>\n",
+ " <td>414</td>\n",
+ " <td>345</td>\n",
+ " <td>236</td>\n",
+ " <td>376</td>\n",
+ " <td>23</td>\n",
+ " <td>...</td>\n",
+ " <td>1884</td>\n",
+ " <td>1508</td>\n",
+ " <td>0.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>3501</td>\n",
+ " <td>5.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1007</td>\n",
+ " <td>0.0</td>\n",
+ " <td>68.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>9.0</td>\n",
+ " <td>832</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>20660</td>\n",
+ " <td>513</td>\n",
+ " <td>185</td>\n",
+ " <td>266</td>\n",
+ " <td>192</td>\n",
+ " <td>13</td>\n",
+ " <td>...</td>\n",
+ " <td>2038</td>\n",
+ " <td>1846</td>\n",
+ " <td>0.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>2931</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>611</td>\n",
+ " <td>0.0</td>\n",
+ " <td>47.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.0</td>\n",
+ " <td>205</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>20740</td>\n",
+ " <td>154</td>\n",
+ " <td>74</td>\n",
+ " <td>119</td>\n",
+ " <td>48</td>\n",
+ " <td>5</td>\n",
+ " <td>...</td>\n",
+ " <td>378</td>\n",
+ " <td>330</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>640</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>277</td>\n",
+ " <td>0.0</td>\n",
+ " <td>29.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 37 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " a40_abduction_and_related_offences b30_burglary_break_and_enter \\\n",
+ "0 1.0 27 \n",
+ "1 0.0 69 \n",
+ "2 21.0 983 \n",
+ "3 9.0 832 \n",
+ "4 0.0 205 \n",
+ "\n",
+ " a10_homicide_and_related_offences \\\n",
+ "0 0.0 \n",
+ "1 0.0 \n",
+ "2 4.0 \n",
+ "3 4.0 \n",
+ "4 2.0 \n",
+ "\n",
+ " f30_other_government_regulatory_offences lga_code11 \\\n",
+ "0 1.0 20110 \n",
+ "1 1.0 20260 \n",
+ "2 1.0 20570 \n",
+ "3 1.0 20660 \n",
+ "4 2.0 20740 \n",
+ "\n",
+ " total_division_c_offences a30_sexual_offences \\\n",
+ "0 53 95 \n",
+ "1 137 58 \n",
+ "2 414 345 \n",
+ "3 513 185 \n",
+ "4 154 74 \n",
+ "\n",
+ " a70_stalking_harassment_and_threatening_behaviour \\\n",
+ "0 11 \n",
+ "1 38 \n",
+ "2 236 \n",
+ "3 266 \n",
+ "4 119 \n",
+ "\n",
+ " e10_justice_procedures total_division_f_offences ... \\\n",
+ "0 11 1 ... \n",
+ "1 84 2 ... \n",
+ "2 376 23 ... \n",
+ "3 192 13 ... \n",
+ "4 48 5 ... \n",
+ "\n",
+ " total_division_e_offences e20_breaches_of_orders \\\n",
+ "0 68 57 \n",
+ "1 411 327 \n",
+ "2 1884 1508 \n",
+ "3 2038 1846 \n",
+ "4 378 330 \n",
+ "\n",
+ " f10_regulatory_driving_offences a60_blackmail_and_extortion b40_theft \\\n",
+ "0 0.0 0.0 90 \n",
+ "1 0.0 5.0 222 \n",
+ "2 0.0 7.0 3501 \n",
+ "3 0.0 4.0 2931 \n",
+ "4 0.0 2.0 640 \n",
+ "\n",
+ " c90_other_drug_offences f20_transport_regulation_offences \\\n",
+ "0 0.0 0.0 \n",
+ "1 0.0 0.0 \n",
+ "2 5.0 4.0 \n",
+ "3 1.0 3.0 \n",
+ "4 1.0 2.0 \n",
+ "\n",
+ " a20_assault_and_related_offences b60_bribery \\\n",
+ "0 81 0.0 \n",
+ "1 176 0.0 \n",
+ "2 1007 0.0 \n",
+ "3 611 0.0 \n",
+ "4 277 0.0 \n",
+ "\n",
+ " c10_drug_dealing_and_trafficking \n",
+ "0 9.0 \n",
+ "1 24.0 \n",
+ "2 68.0 \n",
+ "3 47.0 \n",
+ "4 29.0 \n",
+ "\n",
+ "[5 rows x 37 columns]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "crime_data = pd.read_csv('csa_crime_stats_offences_recorded_offence_type_lga_2010_2019-8538075499928749341.csv')\n",
+ "crime_data.fillna(0, inplace=True)\n",
+ "\n",
+ "crime_data.head()\n",
+ "\n",
+ "# column (lga_code11) will be used for matching. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "ff495427",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#crime_data.to_csv('crime_data.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c3080fe1",
+ "metadata": {},
+ "source": [
+ "## 2) Data regarding Suburbs and Geocoordinates (for matching) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "0b1894c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>geometry</th>\n",
+ " <th>lon</th>\n",
+ " <th>lat</th>\n",
+ " <th>lga_code</th>\n",
+ " <th>lga_name</th>\n",
+ " <th>scc_code</th>\n",
+ " <th>scc_name</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>POLYGON ((146.00107 -38.32214, 146.00770 -38.3...</td>\n",
+ " <td>146.021697</td>\n",
+ " <td>-38.292841</td>\n",
+ " <td>20830</td>\n",
+ " <td>Baw Baw</td>\n",
+ " <td>20024</td>\n",
+ " <td>Allambee</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>POLYGON ((145.27748 -36.41085, 145.29542 -36.4...</td>\n",
+ " <td>145.301089</td>\n",
+ " <td>-36.380130</td>\n",
+ " <td>22830</td>\n",
+ " <td>Greater Shepparton</td>\n",
+ " <td>20063</td>\n",
+ " <td>Ardmona</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>POLYGON ((143.86374 -36.70341, 143.86334 -36.7...</td>\n",
+ " <td>143.859519</td>\n",
+ " <td>-36.675198</td>\n",
+ " <td>23940</td>\n",
+ " <td>Loddon</td>\n",
+ " <td>20069</td>\n",
+ " <td>Arnold (Vic.)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>POLYGON ((143.18182 -36.89766, 143.17798 -36.9...</td>\n",
+ " <td>143.217873</td>\n",
+ " <td>-36.944447</td>\n",
+ " <td>25990</td>\n",
+ " <td>Pyrenees</td>\n",
+ " <td>20142</td>\n",
+ " <td>Barkly (Vic.)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>POLYGON ((145.15929 -38.18173, 145.15756 -38.1...</td>\n",
+ " <td>145.156779</td>\n",
+ " <td>-38.200888</td>\n",
+ " <td>25340</td>\n",
+ " <td>Mornington Peninsula</td>\n",
+ " <td>20169</td>\n",
+ " <td>Baxter</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " geometry lon lat \\\n",
+ "0 POLYGON ((146.00107 -38.32214, 146.00770 -38.3... 146.021697 -38.292841 \n",
+ "1 POLYGON ((145.27748 -36.41085, 145.29542 -36.4... 145.301089 -36.380130 \n",
+ "2 POLYGON ((143.86374 -36.70341, 143.86334 -36.7... 143.859519 -36.675198 \n",
+ "3 POLYGON ((143.18182 -36.89766, 143.17798 -36.9... 143.217873 -36.944447 \n",
+ "4 POLYGON ((145.15929 -38.18173, 145.15756 -38.1... 145.156779 -38.200888 \n",
+ "\n",
+ " lga_code lga_name scc_code scc_name \n",
+ "0 20830 Baw Baw 20024 Allambee \n",
+ "1 22830 Greater Shepparton 20063 Ardmona \n",
+ "2 23940 Loddon 20069 Arnold (Vic.) \n",
+ "3 25990 Pyrenees 20142 Barkly (Vic.) \n",
+ "4 25340 Mornington Peninsula 20169 Baxter "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load the GeoJSON file\n",
+ "gdf = gpd.read_file('georef-australia-state-suburb.geojson')\n",
+ "\n",
+ "# Convert the nested columns into separate columns\n",
+ "gdf['lon'] = gdf['geo_point_2d'].apply(lambda x: x.get('lon'))\n",
+ "gdf['lat'] = gdf['geo_point_2d'].apply(lambda x: x.get('lat'))\n",
+ "gdf['lga_code'] = gdf['lga_code'].apply(lambda x: x[0] if x else None)\n",
+ "gdf['lga_name'] = gdf['lga_name'].apply(lambda x: x[0] if x else None)\n",
+ "gdf['scc_code'] = gdf['scc_code'].apply(lambda x: x[0] if x else None)\n",
+ "gdf['scc_name'] = gdf['scc_name'].apply(lambda x: x[0] if x else None)\n",
+ "\n",
+ "# Keep only the required columns\n",
+ "gdf = gdf[['geometry', 'lon', 'lat', 'lga_code', 'lga_name', 'scc_code', 'scc_name']]\n",
+ "state_Data = pd.DataFrame(gdf)\n",
+ "state_Data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "7c6b9531",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#state_Data.to_csv('state_data.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb7ba3f1",
+ "metadata": {},
+ "source": [
+ "## 3) Main data from Twitter file.json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8c433b58",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 4.07 s, sys: 999 ms, total: 5.07 s\n",
+ "Wall time: 5.07 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "\n",
+ "#using a bigger data (produces 37k rows of data roughly)\n",
+ "with open('smallTwitter.json', 'r') as f:\n",
+ " data = json.load(f)\n",
+ "\n",
+ "filtered_data = []\n",
+ "\n",
+ "for tweet in data:\n",
+ " if 'places' in tweet['includes']:\n",
+ " for place in tweet['includes']['places']:\n",
+ " if place['full_name'].endswith(', Victoria'):\n",
+ " # Extract first part of the name for area_name and full_name\n",
+ " area_name, _, _ = place['full_name'].partition(', ')\n",
+ " # If there's a \"-\", take the part before it for the area_name\n",
+ " if '-' in area_name:\n",
+ " area_name = area_name.split('-')[0].strip()\n",
+ "\n",
+ " filtered_data.append({\n",
+ " '_id': tweet['_id'],\n",
+ " #'author_id': tweet['data']['author_id'],\n",
+ " 'text': tweet['data']['text'],\n",
+ " 'sentiment': tweet['data'].get('sentiment', None), # returns None if 'sentiment' key doesn't exist\n",
+ " 'language': tweet['data']['lang'],\n",
+ " 'area_name': area_name\n",
+ " })\n",
+ "\n",
+ "main_data = pd.DataFrame(filtered_data)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "bc8d2580",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>_id</th>\n",
+ " <th>text</th>\n",
+ " <th>sentiment</th>\n",
+ " <th>language</th>\n",
+ " <th>area_name</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1414031972041588744</td>\n",
+ " <td>Stella is spending the day with her cousin Mon...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>en</td>\n",
+ " <td>Ocean Grove</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1414032158197354497</td>\n",
+ " <td>@eleaud Oh no! I hope they don’t go up any fur...</td>\n",
+ " <td>0.090909</td>\n",
+ " <td>en</td>\n",
+ " <td>Ocean Grove</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1414036352308445186</td>\n",
+ " <td>Sunday @afl R17: #aflgiantssuns #afldogsswans ...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>en</td>\n",
+ " <td>Drysdale</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1414044890040733699</td>\n",
+ " <td>@BlancheVerlie Sorry to watch the declining si...</td>\n",
+ " <td>0.058824</td>\n",
+ " <td>en</td>\n",
+ " <td>Ocean Grove</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1414057294950649861</td>\n",
+ " <td>@VicGovAu One lady had over 40 saved photos of...</td>\n",
+ " <td>0.090909</td>\n",
+ " <td>en</td>\n",
+ " <td>Drysdale</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " _id text \\\n",
+ "0 1414031972041588744 Stella is spending the day with her cousin Mon... \n",
+ "1 1414032158197354497 @eleaud Oh no! I hope they don’t go up any fur... \n",
+ "2 1414036352308445186 Sunday @afl R17: #aflgiantssuns #afldogsswans ... \n",
+ "3 1414044890040733699 @BlancheVerlie Sorry to watch the declining si... \n",
+ "4 1414057294950649861 @VicGovAu One lady had over 40 saved photos of... \n",
+ "\n",
+ " sentiment language area_name \n",
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+ "1 0.090909 en Ocean Grove \n",
+ "2 0.000000 en Drysdale \n",
+ "3 0.058824 en Ocean Grove \n",
+ "4 0.090909 en Drysdale "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "main_data.head()"
+ ]
+ },
+ {
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+ "execution_count": null,
+ "id": "0dce35eb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
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+ "id": "adc72463",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
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+ "language": "python",
+ "name": "python3"
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--
GitLab