Project_Proposal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "161ed1d5-ce56-49ed-abb5-8ef1732b969e",
   "metadata": {},
   "source": [
    "# DID THEFT INCREASE IN KITSILANO DURING THE PANDEMIC?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa755fb6-4569-42ce-8e5b-7d8c12b182c9",
   "metadata": {},
   "source": [
    "## INTRODUCTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b37918-44e0-44e0-9971-9737c1cbd150",
   "metadata": {},
   "source": [
    "A study co-authored by Simon Fraser University researchers found that theft-related crimes increased in wealthier neighbourhoods, such as Kitsilano, during the pandemic (Andresen & Hodgkinson, 2022). They suspect this is related to the decrease in theft around downtown and surrounding neighbourhoods, as businesses closed down and employees started working from home. We will explore whether the proportion of theft crimes in Kitsilano increased during the pandemic. Our response variables would be the proportion of theft crimes in Kitsilano in 2018 and 2020. The location parameter would be the difference in proportions between the two years, while the scale parameter would be the standard deviation of that difference. We hypothesize an increase in theft crimes from 2018 to 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48cacfbd-f051-464b-8e36-b32c28c186dd",
   "metadata": {},
   "source": [
    "We will use crime data from the Vancouver Police Department: https://geodash.vpd.ca/opendata/. Within the website, we're able to select a specific year and neighbourhood(s) to view data from. We selected crime data from all neighbourhoods during 2018 and 2020. Each tuple lists the type of crime, the date (across several columns), and the location it occurred. Theft-related crimes are classified as either \"Theft of Vehicle\", \"Theft of Bicycle\", \"Theft from Vehicle\", or \"Other Theft\" under the TYPE column. To find the proportions of theft crime in Kitsilano for a specific year, we'll first filter for all theft-related crimes in our dataset. Then, we find the number of theft crimes in Kitsilano by filtering NEIGHBOURHOOD==\"Kitsilano\", and divide that by the total number of theft crimes across all neighbourhoods that year. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92aa3028-b2f9-4d95-996d-e6c8e902969f",
   "metadata": {},
   "source": [
    "## PRELIMINARY RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2b65527-d845-451e-9106-fac6b426774a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.2 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.6      \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.8      \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.10\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.1      \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.1 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.2      \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.2 \n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n"
     ]
    }
   ],
   "source": [
    "#Importing the libraries\n",
    "library(tidyverse)\n",
    "library(infer)\n",
    "library(repr)\n",
    "options(repr.plot.width=5, repr.plot.height=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14daccfd-47a7-464d-ba7e-7a4a6379e218",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m44280\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m10\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (3): TYPE, HUNDRED_BLOCK, NEIGHBOURHOOD\n",
      "\u001b[32mdbl\u001b[39m (7): YEAR, MONTH, DAY, HOUR, MINUTE, X, Y\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m37516\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m10\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (3): TYPE, HUNDRED_BLOCK, NEIGHBOURHOOD\n",
      "\u001b[32mdbl\u001b[39m (7): YEAR, MONTH, DAY, HOUR, MINUTE, X, Y\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>MONTH</th><th scope=col>DAY</th><th scope=col>HOUR</th><th scope=col>MINUTE</th><th scope=col>HUNDRED_BLOCK</th><th scope=col>NEIGHBOURHOOD</th><th scope=col>X</th><th scope=col>Y</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td> 6</td><td>16</td><td>18</td><td> 0</td><td>10XX ALBERNI ST </td><td>West End                 </td><td>491102.2</td><td>5459092</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td>12</td><td>12</td><td> 0</td><td> 0</td><td>10XX BEACH AVE  </td><td>West End                 </td><td>490228.8</td><td>5458208</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td> 4</td><td> 9</td><td> 6</td><td> 0</td><td>10XX BEACH AVE  </td><td>Central Business District</td><td>490249.2</td><td>5458167</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td>10</td><td> 2</td><td>18</td><td>31</td><td>10XX BEACH AVE  </td><td>Central Business District</td><td>490258.4</td><td>5458155</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td> 2</td><td>17</td><td>15</td><td> 0</td><td>10XX BEACH AVE  </td><td>Central Business District</td><td>490269.9</td><td>5458141</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td> 5</td><td>16</td><td>17</td><td> 0</td><td>10XX BOUNDARY RD</td><td>Hastings-Sunrise         </td><td>498275.6</td><td>5458125</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " TYPE & YEAR & MONTH & DAY & HOUR & MINUTE & HUNDRED\\_BLOCK & NEIGHBOURHOOD & X & Y\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Break and Enter Commercial & 2018 &  6 & 16 & 18 &  0 & 10XX ALBERNI ST  & West End                  & 491102.2 & 5459092\\\\\n",
       "\t Break and Enter Commercial & 2018 & 12 & 12 &  0 &  0 & 10XX BEACH AVE   & West End                  & 490228.8 & 5458208\\\\\n",
       "\t Break and Enter Commercial & 2018 &  4 &  9 &  6 &  0 & 10XX BEACH AVE   & Central Business District & 490249.2 & 5458167\\\\\n",
       "\t Break and Enter Commercial & 2018 & 10 &  2 & 18 & 31 & 10XX BEACH AVE   & Central Business District & 490258.4 & 5458155\\\\\n",
       "\t Break and Enter Commercial & 2018 &  2 & 17 & 15 &  0 & 10XX BEACH AVE   & Central Business District & 490269.9 & 5458141\\\\\n",
       "\t Break and Enter Commercial & 2018 &  5 & 16 & 17 &  0 & 10XX BOUNDARY RD & Hastings-Sunrise          & 498275.6 & 5458125\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 10\n",
       "\n",
       "| TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | MONTH &lt;dbl&gt; | DAY &lt;dbl&gt; | HOUR &lt;dbl&gt; | MINUTE &lt;dbl&gt; | HUNDRED_BLOCK &lt;chr&gt; | NEIGHBOURHOOD &lt;chr&gt; | X &lt;dbl&gt; | Y &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| Break and Enter Commercial | 2018 |  6 | 16 | 18 |  0 | 10XX ALBERNI ST  | West End                  | 491102.2 | 5459092 |\n",
       "| Break and Enter Commercial | 2018 | 12 | 12 |  0 |  0 | 10XX BEACH AVE   | West End                  | 490228.8 | 5458208 |\n",
       "| Break and Enter Commercial | 2018 |  4 |  9 |  6 |  0 | 10XX BEACH AVE   | Central Business District | 490249.2 | 5458167 |\n",
       "| Break and Enter Commercial | 2018 | 10 |  2 | 18 | 31 | 10XX BEACH AVE   | Central Business District | 490258.4 | 5458155 |\n",
       "| Break and Enter Commercial | 2018 |  2 | 17 | 15 |  0 | 10XX BEACH AVE   | Central Business District | 490269.9 | 5458141 |\n",
       "| Break and Enter Commercial | 2018 |  5 | 16 | 17 |  0 | 10XX BOUNDARY RD | Hastings-Sunrise          | 498275.6 | 5458125 |\n",
       "\n"
      ],
      "text/plain": [
       "  TYPE                       YEAR MONTH DAY HOUR MINUTE HUNDRED_BLOCK   \n",
       "1 Break and Enter Commercial 2018  6    16  18    0     10XX ALBERNI ST \n",
       "2 Break and Enter Commercial 2018 12    12   0    0     10XX BEACH AVE  \n",
       "3 Break and Enter Commercial 2018  4     9   6    0     10XX BEACH AVE  \n",
       "4 Break and Enter Commercial 2018 10     2  18   31     10XX BEACH AVE  \n",
       "5 Break and Enter Commercial 2018  2    17  15    0     10XX BEACH AVE  \n",
       "6 Break and Enter Commercial 2018  5    16  17    0     10XX BOUNDARY RD\n",
       "  NEIGHBOURHOOD             X        Y      \n",
       "1 West End                  491102.2 5459092\n",
       "2 West End                  490228.8 5458208\n",
       "3 Central Business District 490249.2 5458167\n",
       "4 Central Business District 490258.4 5458155\n",
       "5 Central Business District 490269.9 5458141\n",
       "6 Hastings-Sunrise          498275.6 5458125"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>MONTH</th><th scope=col>DAY</th><th scope=col>HOUR</th><th scope=col>MINUTE</th><th scope=col>HUNDRED_BLOCK</th><th scope=col>NEIGHBOURHOOD</th><th scope=col>X</th><th scope=col>Y</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>6</td><td>19</td><td> 3</td><td>40</td><td>10XX ALBERNI ST</td><td>West End</td><td>491059.5</td><td>5459122</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>1</td><td> 3</td><td> 6</td><td>43</td><td>10XX ALBERNI ST</td><td>West End</td><td>491068.7</td><td>5459126</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>9</td><td>27</td><td>20</td><td> 0</td><td>10XX ALBERNI ST</td><td>West End</td><td>491073.1</td><td>5459109</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>6</td><td>28</td><td> 6</td><td>50</td><td>10XX ALBERNI ST</td><td>West End</td><td>491102.2</td><td>5459092</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>2</td><td> 5</td><td> 0</td><td> 0</td><td>10XX BEACH AVE </td><td>West End</td><td>490227.2</td><td>5458210</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>2</td><td>11</td><td>13</td><td>35</td><td>10XX BEACH AVE </td><td>West End</td><td>490227.2</td><td>5458210</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " TYPE & YEAR & MONTH & DAY & HOUR & MINUTE & HUNDRED\\_BLOCK & NEIGHBOURHOOD & X & Y\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Break and Enter Commercial & 2020 & 6 & 19 &  3 & 40 & 10XX ALBERNI ST & West End & 491059.5 & 5459122\\\\\n",
       "\t Break and Enter Commercial & 2020 & 1 &  3 &  6 & 43 & 10XX ALBERNI ST & West End & 491068.7 & 5459126\\\\\n",
       "\t Break and Enter Commercial & 2020 & 9 & 27 & 20 &  0 & 10XX ALBERNI ST & West End & 491073.1 & 5459109\\\\\n",
       "\t Break and Enter Commercial & 2020 & 6 & 28 &  6 & 50 & 10XX ALBERNI ST & West End & 491102.2 & 5459092\\\\\n",
       "\t Break and Enter Commercial & 2020 & 2 &  5 &  0 &  0 & 10XX BEACH AVE  & West End & 490227.2 & 5458210\\\\\n",
       "\t Break and Enter Commercial & 2020 & 2 & 11 & 13 & 35 & 10XX BEACH AVE  & West End & 490227.2 & 5458210\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 10\n",
       "\n",
       "| TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | MONTH &lt;dbl&gt; | DAY &lt;dbl&gt; | HOUR &lt;dbl&gt; | MINUTE &lt;dbl&gt; | HUNDRED_BLOCK &lt;chr&gt; | NEIGHBOURHOOD &lt;chr&gt; | X &lt;dbl&gt; | Y &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| Break and Enter Commercial | 2020 | 6 | 19 |  3 | 40 | 10XX ALBERNI ST | West End | 491059.5 | 5459122 |\n",
       "| Break and Enter Commercial | 2020 | 1 |  3 |  6 | 43 | 10XX ALBERNI ST | West End | 491068.7 | 5459126 |\n",
       "| Break and Enter Commercial | 2020 | 9 | 27 | 20 |  0 | 10XX ALBERNI ST | West End | 491073.1 | 5459109 |\n",
       "| Break and Enter Commercial | 2020 | 6 | 28 |  6 | 50 | 10XX ALBERNI ST | West End | 491102.2 | 5459092 |\n",
       "| Break and Enter Commercial | 2020 | 2 |  5 |  0 |  0 | 10XX BEACH AVE  | West End | 490227.2 | 5458210 |\n",
       "| Break and Enter Commercial | 2020 | 2 | 11 | 13 | 35 | 10XX BEACH AVE  | West End | 490227.2 | 5458210 |\n",
       "\n"
      ],
      "text/plain": [
       "  TYPE                       YEAR MONTH DAY HOUR MINUTE HUNDRED_BLOCK  \n",
       "1 Break and Enter Commercial 2020 6     19   3   40     10XX ALBERNI ST\n",
       "2 Break and Enter Commercial 2020 1      3   6   43     10XX ALBERNI ST\n",
       "3 Break and Enter Commercial 2020 9     27  20    0     10XX ALBERNI ST\n",
       "4 Break and Enter Commercial 2020 6     28   6   50     10XX ALBERNI ST\n",
       "5 Break and Enter Commercial 2020 2      5   0    0     10XX BEACH AVE \n",
       "6 Break and Enter Commercial 2020 2     11  13   35     10XX BEACH AVE \n",
       "  NEIGHBOURHOOD X        Y      \n",
       "1 West End      491059.5 5459122\n",
       "2 West End      491068.7 5459126\n",
       "3 West End      491073.1 5459109\n",
       "4 West End      491102.2 5459092\n",
       "5 West End      490227.2 5458210\n",
       "6 West End      490227.2 5458210"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Reading the data from the web\n",
    "#Data is uploaded on github\n",
    "crimedata_all_2018 <- read_csv(url(\"https://raw.githubusercontent.com/Krithik1/STAT_201_PROJECT/main/data/crimedata_csv_AllNeighbourhoods_2018.csv\"))\n",
    "crimedata_all_2020 <- read_csv(url(\"https://raw.githubusercontent.com/Krithik1/STAT_201_PROJECT/main/data/crimedata_csv_AllNeighbourhoods_2020.csv\"))\n",
    "\n",
    "\n",
    "head(crimedata_all_2018)\n",
    "head(crimedata_all_2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10080ed-40f4-4fe0-b700-e887a9ab586a",
   "metadata": {},
   "source": [
    "### Cleaning the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "899c40da-62e2-45aa-93ad-213204cf6625",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>NEIGHBOURHOOD</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td>West End                 </td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td>West End                 </td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2018</td><td>Hastings-Sunrise         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " TYPE & YEAR & NEIGHBOURHOOD\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Break and Enter Commercial & 2018 & West End                 \\\\\n",
       "\t Break and Enter Commercial & 2018 & West End                 \\\\\n",
       "\t Break and Enter Commercial & 2018 & Central Business District\\\\\n",
       "\t Break and Enter Commercial & 2018 & Central Business District\\\\\n",
       "\t Break and Enter Commercial & 2018 & Central Business District\\\\\n",
       "\t Break and Enter Commercial & 2018 & Hastings-Sunrise         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | NEIGHBOURHOOD &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Break and Enter Commercial | 2018 | West End                  |\n",
       "| Break and Enter Commercial | 2018 | West End                  |\n",
       "| Break and Enter Commercial | 2018 | Central Business District |\n",
       "| Break and Enter Commercial | 2018 | Central Business District |\n",
       "| Break and Enter Commercial | 2018 | Central Business District |\n",
       "| Break and Enter Commercial | 2018 | Hastings-Sunrise          |\n",
       "\n"
      ],
      "text/plain": [
       "  TYPE                       YEAR NEIGHBOURHOOD            \n",
       "1 Break and Enter Commercial 2018 West End                 \n",
       "2 Break and Enter Commercial 2018 West End                 \n",
       "3 Break and Enter Commercial 2018 Central Business District\n",
       "4 Break and Enter Commercial 2018 Central Business District\n",
       "5 Break and Enter Commercial 2018 Central Business District\n",
       "6 Break and Enter Commercial 2018 Hastings-Sunrise         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>NEIGHBOURHOOD</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Break and Enter Commercial</td><td>2020</td><td>West End</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " TYPE & YEAR & NEIGHBOURHOOD\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Break and Enter Commercial & 2020 & West End\\\\\n",
       "\t Break and Enter Commercial & 2020 & West End\\\\\n",
       "\t Break and Enter Commercial & 2020 & West End\\\\\n",
       "\t Break and Enter Commercial & 2020 & West End\\\\\n",
       "\t Break and Enter Commercial & 2020 & West End\\\\\n",
       "\t Break and Enter Commercial & 2020 & West End\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | NEIGHBOURHOOD &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Break and Enter Commercial | 2020 | West End |\n",
       "| Break and Enter Commercial | 2020 | West End |\n",
       "| Break and Enter Commercial | 2020 | West End |\n",
       "| Break and Enter Commercial | 2020 | West End |\n",
       "| Break and Enter Commercial | 2020 | West End |\n",
       "| Break and Enter Commercial | 2020 | West End |\n",
       "\n"
      ],
      "text/plain": [
       "  TYPE                       YEAR NEIGHBOURHOOD\n",
       "1 Break and Enter Commercial 2020 West End     \n",
       "2 Break and Enter Commercial 2020 West End     \n",
       "3 Break and Enter Commercial 2020 West End     \n",
       "4 Break and Enter Commercial 2020 West End     \n",
       "5 Break and Enter Commercial 2020 West End     \n",
       "6 Break and Enter Commercial 2020 West End     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Removing the columns MONTH, DAY, HOUR, MINUTE, HUNDRED_BLOCK, X and Y\n",
    "\n",
    "crimedata_all_2018 <- select(crimedata_all_2018, c(1,2,8))\n",
    "crimedata_all_2020 <- select(crimedata_all_2020, c(1,2,8))\n",
    "\n",
    "head(crimedata_all_2018)\n",
    "head(crimedata_all_2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd98e34-7f8b-448a-be77-36a569c11d99",
   "metadata": {},
   "source": [
    "### Filtering the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "952e3180-f3dc-47df-bf0e-03d2c2f5bbf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>NEIGHBOURHOOD</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>Mount Pleasant           </td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>West End                 </td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>West End                 </td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>West End                 </td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>West End                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " TYPE & YEAR & NEIGHBOURHOOD\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Other Theft & 2018 & Central Business District\\\\\n",
       "\t Other Theft & 2018 & Mount Pleasant           \\\\\n",
       "\t Other Theft & 2018 & West End                 \\\\\n",
       "\t Other Theft & 2018 & West End                 \\\\\n",
       "\t Other Theft & 2018 & West End                 \\\\\n",
       "\t Other Theft & 2018 & West End                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | NEIGHBOURHOOD &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Other Theft | 2018 | Central Business District |\n",
       "| Other Theft | 2018 | Mount Pleasant            |\n",
       "| Other Theft | 2018 | West End                  |\n",
       "| Other Theft | 2018 | West End                  |\n",
       "| Other Theft | 2018 | West End                  |\n",
       "| Other Theft | 2018 | West End                  |\n",
       "\n"
      ],
      "text/plain": [
       "  TYPE        YEAR NEIGHBOURHOOD            \n",
       "1 Other Theft 2018 Central Business District\n",
       "2 Other Theft 2018 Mount Pleasant           \n",
       "3 Other Theft 2018 West End                 \n",
       "4 Other Theft 2018 West End                 \n",
       "5 Other Theft 2018 West End                 \n",
       "6 Other Theft 2018 West End                 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "29561"
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       "29561"
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       "29561"
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      "text/plain": [
       "[1] 29561"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>NEIGHBOURHOOD</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>West End</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>West End</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " TYPE & YEAR & NEIGHBOURHOOD\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Other Theft & 2020 & West End\\\\\n",
       "\t Other Theft & 2020 & West End\\\\\n",
       "\t Other Theft & 2020 & West End\\\\\n",
       "\t Other Theft & 2020 & West End\\\\\n",
       "\t Other Theft & 2020 & West End\\\\\n",
       "\t Other Theft & 2020 & West End\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | NEIGHBOURHOOD &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Other Theft | 2020 | West End |\n",
       "| Other Theft | 2020 | West End |\n",
       "| Other Theft | 2020 | West End |\n",
       "| Other Theft | 2020 | West End |\n",
       "| Other Theft | 2020 | West End |\n",
       "| Other Theft | 2020 | West End |\n",
       "\n"
      ],
      "text/plain": [
       "  TYPE        YEAR NEIGHBOURHOOD\n",
       "1 Other Theft 2020 West End     \n",
       "2 Other Theft 2020 West End     \n",
       "3 Other Theft 2020 West End     \n",
       "4 Other Theft 2020 West End     \n",
       "5 Other Theft 2020 West End     \n",
       "6 Other Theft 2020 West End     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "21915"
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       "21915"
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       "21915"
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      "text/plain": [
       "[1] 21915"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting all theft-related crimes rows in the data set, which are Theft of Vehicle, Theft of Bicycle, Theft from Vehicle and Other Theft.\n",
    "\n",
    "all_theft_2018 <- crimedata_all_2018 %>% filter(TYPE %in% c(\"Theft of Vehicle\", \"Theft of Bicycle\", \"Theft from Vehicle\", \"Other Theft\"))\n",
    "all_theft_2020 <- crimedata_all_2020 %>% filter(TYPE %in% c(\"Theft of Vehicle\", \"Theft of Bicycle\", \"Theft from Vehicle\", \"Other Theft\"))\n",
    "head(all_theft_2018)\n",
    "nrow(all_theft_2018)\n",
    "head(all_theft_2020)\n",
    "nrow(all_theft_2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88194c01-677c-4049-a973-41e12904c762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>NEIGHBOURHOOD</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2018</td><td>Kitsilano</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " TYPE & YEAR & NEIGHBOURHOOD\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Other Theft & 2018 & Kitsilano\\\\\n",
       "\t Other Theft & 2018 & Kitsilano\\\\\n",
       "\t Other Theft & 2018 & Kitsilano\\\\\n",
       "\t Other Theft & 2018 & Kitsilano\\\\\n",
       "\t Other Theft & 2018 & Kitsilano\\\\\n",
       "\t Other Theft & 2018 & Kitsilano\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | NEIGHBOURHOOD &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Other Theft | 2018 | Kitsilano |\n",
       "| Other Theft | 2018 | Kitsilano |\n",
       "| Other Theft | 2018 | Kitsilano |\n",
       "| Other Theft | 2018 | Kitsilano |\n",
       "| Other Theft | 2018 | Kitsilano |\n",
       "| Other Theft | 2018 | Kitsilano |\n",
       "\n"
      ],
      "text/plain": [
       "  TYPE        YEAR NEIGHBOURHOOD\n",
       "1 Other Theft 2018 Kitsilano    \n",
       "2 Other Theft 2018 Kitsilano    \n",
       "3 Other Theft 2018 Kitsilano    \n",
       "4 Other Theft 2018 Kitsilano    \n",
       "5 Other Theft 2018 Kitsilano    \n",
       "6 Other Theft 2018 Kitsilano    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "1393"
      ],
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       "1393"
      ],
      "text/markdown": [
       "1393"
      ],
      "text/plain": [
       "[1] 1393"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>NEIGHBOURHOOD</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>Kitsilano</td></tr>\n",
       "\t<tr><td>Other Theft</td><td>2020</td><td>Kitsilano</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " TYPE & YEAR & NEIGHBOURHOOD\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Other Theft & 2020 & Kitsilano\\\\\n",
       "\t Other Theft & 2020 & Kitsilano\\\\\n",
       "\t Other Theft & 2020 & Kitsilano\\\\\n",
       "\t Other Theft & 2020 & Kitsilano\\\\\n",
       "\t Other Theft & 2020 & Kitsilano\\\\\n",
       "\t Other Theft & 2020 & Kitsilano\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | NEIGHBOURHOOD &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Other Theft | 2020 | Kitsilano |\n",
       "| Other Theft | 2020 | Kitsilano |\n",
       "| Other Theft | 2020 | Kitsilano |\n",
       "| Other Theft | 2020 | Kitsilano |\n",
       "| Other Theft | 2020 | Kitsilano |\n",
       "| Other Theft | 2020 | Kitsilano |\n",
       "\n"
      ],
      "text/plain": [
       "  TYPE        YEAR NEIGHBOURHOOD\n",
       "1 Other Theft 2020 Kitsilano    \n",
       "2 Other Theft 2020 Kitsilano    \n",
       "3 Other Theft 2020 Kitsilano    \n",
       "4 Other Theft 2020 Kitsilano    \n",
       "5 Other Theft 2020 Kitsilano    \n",
       "6 Other Theft 2020 Kitsilano    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "1135"
      ],
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       "1135"
      ],
      "text/markdown": [
       "1135"
      ],
      "text/plain": [
       "[1] 1135"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting rows which are in \"Kitsilano\" neighborhood\n",
    "\n",
    "kitsilano_all_theft_2018 <- all_theft_2018 %>% filter(NEIGHBOURHOOD == \"Kitsilano\")\n",
    "head(kitsilano_all_theft_2018)\n",
    "nrow(kitsilano_all_theft_2018)\n",
    "kitsilano_all_theft_2020 <- all_theft_2020 %>% filter(NEIGHBOURHOOD == \"Kitsilano\")\n",
    "head(kitsilano_all_theft_2020)\n",
    "nrow(kitsilano_all_theft_2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f3fcfe6-d114-43b3-8890-4a6d6b50dadb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0471228984134502"
      ],
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       "0.0471228984134502"
      ],
      "text/markdown": [
       "0.0471228984134502"
      ],
      "text/plain": [
       "[1] 0.0471229"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.0517910107232489"
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       "0.0517910107232489"
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       "0.0517910107232489"
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      "text/plain": [
       "[1] 0.05179101"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.00466811230979877"
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       "0.00466811230979877"
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       "0.00466811230979877"
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      "text/plain": [
       "[1] 0.004668112"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CALCULATING POPULATION PARAMETER\n",
    "# p1: proportion of theft crimes in kitsilano in 2018\n",
    "# p2: proportion of theft crimes in kitsilano in 2020\n",
    "# p: difference in the proportions of theft crimes in Kitsilano in 2018 and 2020 (population parameter)\n",
    "\n",
    "p1 <- nrow(kitsilano_all_theft_2018)/nrow(all_theft_2018)\n",
    "p2 <- nrow(kitsilano_all_theft_2020)/nrow(all_theft_2020)\n",
    "p <- p2-p1\n",
    "p1\n",
    "p2\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "60a64a30-138e-4586-b5db-3a8e33ca97ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>p1</th><th scope=col>p2</th><th scope=col>p</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.0471229</td><td>0.05179101</td><td>0.004668112</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 3\n",
       "\\begin{tabular}{lll}\n",
       " p1 & p2 & p\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.0471229 & 0.05179101 & 0.004668112\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 3\n",
       "\n",
       "| p1 &lt;dbl&gt; | p2 &lt;dbl&gt; | p &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 0.0471229 | 0.05179101 | 0.004668112 |\n",
       "\n"
      ],
      "text/plain": [
       "  p1        p2         p          \n",
       "1 0.0471229 0.05179101 0.004668112"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating table containing the proportions in both 2018 and 2020 and their difference (population)\n",
    "\n",
    "pop_proportion_table <- tibble(p1 = p1, p2 = p2, p = p)\n",
    "\n",
    "pop_proportion_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58094e26-4960-47a6-b699-890823a6ddf1",
   "metadata": {},
   "outputs": [
    {
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Xmypn5B4YKy0eTxPeSWl2Oxl2feaGvRa5+1WJBqi/tAyMfZ11Ubb5MqMCuT41oc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\nIDAgUgo+PgpzdGFydHhyZWYKMTM3NDUKJSVFT0YK",
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      ],
      "text/plain": [
       "Plot with title “Proportion of theft crimes in Kitsilano in 2018\n",
       "and 2020”"
      ]
     },
     "metadata": {
      "application/pdf": {
       "height": 300,
       "width": 300
      },
      "image/jpeg": {
       "height": 300,
       "width": 300
      },
      "image/png": {
       "height": 300,
       "width": 300
      },
      "image/svg+xml": {
       "height": 300,
       "isolated": true,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the proportion of theft-related crimes in Kitsilano in both 2018 and 2020 using barplot (population)\n",
    "\n",
    "barplot(c(p1, p2), main = \"Proportion of theft crimes in Kitsilano in 2018\\nand 2020\", names.arg = c(\"p1\", \"p2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43a2e4a4-0d81-411c-99be-fcec21aa79c9",
   "metadata": {},
   "outputs": [
    {
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ajiTAdQvWfOYskEfQEquY4dARLTBe5cafd3Olc9tmWR5Vji8EBJWyCnYGlfYN5A\nh/remy+8l/ZtsbCyvmB7r3vRHfPcTfU1cxRwL7yr1u1QOxc6V9SacuYVziif5kwwT6tat+jg\nkfv2W6f5XMbbaqb7chymmGSXZ34DuZ6/70FbKCLzyL+rRdv8+/x0W+y+WEpzxbigJKQI1J2E\n26CQZkujWVnpAbVA7JrTP2doDjenYsC6wKalIG1pQZutfIGTA66wYrSCHq+ACs1e0l1BpTbX\n7qsVxRRvixU81iErtVqNoRRTgTdE7Np3C+14yg7L7OSkifG6FIuKmBhRiu5mN4pQyMhm2e1y\nuPlTrt0WTQ7btM/fNAc3jBwUsB/08ZzNaodvfedE7a5nVvxnWsmK0pl1Zdm6V6Q5HUc2vfWz\naaXG9PiMimxvdUESp3NU3d7nWr6zftr/mH9XU3GL9blDG/ctTqd8acWqklRjToXXrG5c7H55\nZKIgVMtz3QZD6uzaWTPrSuUHy1f3FjfyYC5qqm5oZTqLGypv1L4Dr1JlmeUQRNGwiQ7zwHtg\nCCgAr+PYZ16qpwI7A6SY7MFKoU5oFziB50xY4zmdzgCRL91F5V6v2+Nt1nLTiSgvdpbXEube\nYsUsFGex/f0odEz8ABY9BSsO83P/8Owfv0xiH6U50ohrnIJn3TziJ7Xkorq7L3dPLu1T9ii0\nz7HHQfvS9qTRvqQ9SXR74mAi3Z4wmEC3x8J2w6CBbtcP6ul2bpCjfXQPpfVVa6tofc3aGto0\nb8M8OnOTbdompzHTucQGNqfNKZFNqrHMWUadZZ6yY2XccBmULRsIbrokgZRprOjz+RZ5+lL0\ni/qi/8mgpdlw3/jaC5iZu9qMFmW6Yroy9Un7+v4BN1gRU4TZxcx8It9+tAOjLZ3jbjlMJ9ya\n307x9Z27554X7/J5lqydVdpcrvi6n7qz52TXLKW82e9fv3D6b1PL2qsXrC5Ps5esC9V3zDa7\n0ip7li/pqpLlwJbapd2VDtjX9NimsrI7H2tcdPeKYpGPL1uxofS2XWt8JWt231ayYYU/lpeK\nV9xNFxY3lLtc5Q3F0xqCBQXBhvHveFuq8/Nva5s5b+OiadMW3UkiOxzHciAklvB0Mb7T0cdz\nGMn0k0lYBm1wN9wHj9DX6W/kbLlQLpGfUzImJ9n/tZDjsBRaEX9vFJ+AeN91/D+/APv4DXqE\no/At/Dse/Xsd/34CP9H+j+dfX+y7GNV0C9VcG/HfXwnaM/EGSEr0bUG7j1wC3vFayXZL6zTC\nIh1C9MSATx0GDuwykziSjHJiVwxJJSIaR+Sy/jcj/v/yEt4S3iL34i5kI9u0500XRnpWchch\nk+x/qm54Tqz4fzsKQ+R1lrxKTpPjN6H2kvuI9j9fN1wXyQ/JKa10hDz0L9i+RJ6Nlg6Sw+TB\nf0q3gexCPifI2RtgrQjdRr6JPV8gT6M6Z4AXe90Yxf6avPGPWcHv4A3yCMZYG/F5Hp9H0IXu\noJ+SR+hSson+ittJ7if7cI7HoJMMIX0rOQErySqERq5VZC3puoXpIBkmT5LtZOBrkLBz8j9J\n3FdP48j3IZ9DpJNsvqHFM/AFe3FOHPt3yYsabOcUUh/kNtBzlI4/ipVvkA682+ADHOdD3DxS\nKZjhJCFqVWNDfd2ypbWhJYsXLay5rTq4IFBVWTF/nlpe5p9bWuKbM3tW8YxCT0H+9Nyc7KxM\nV4biTLKaTcb4uBhJNOh16KEokOlVrkCrHM5uDfPZrmAwn9VdbQhouwHQGpYRFLiZJiy3amTy\nzZQqUq67hVKNUKrXKcEkzyVz86fLVS45/HalS74ATbUNWH6o0tUoh69o5UVamc/WKnFYURRs\nIVclra+Uw9AqV4UDW9cPVrVWIr+RGKnCVbFWyp9ORqQYLMZgKZzr6h6B3DLQCjS3qmSEEkMc\n6zbMZVW1tYdDtQ1VlamK0pg/vToc76rUUKRCYxnWVYT1Gku5kw2d7JdHpo8OHrhgIqtb3bHt\nrva22xvCXBu2HeSqBgcfDJvd4TxXZThv+4dJOPO14emuyqqwm3GtWXq9n5qvu4SwkGVyyYN/\nJTgd15U/3wxpi0J0Waa/ElYMoHgHBwMuOTDYOth2YXJgtUs2uQZHYmMHu6tQwiTUgK0uTH5v\nf2o4cKAxbGpdDyXRyQaW1oQTalc2hGlWQF7fhhD8lbuUOamKuXGKJvTP0AQFgeJAmSoKm/j+\nCypZjZXwQG1DpC6T1alniOpxN4ZpK8OMTmFs9QwzMIW53rzVhatZs6xhMMxnVbe7qlDG+9vC\nA6tRnzawpXCZwvGfpyquQYtZ9nkaNVoZR1Xd3imHhWwUC7a6sQFqCmsyaNIq8Z9HXldSsYNs\ns0X2uZAN41PlqmqN/rauT0IGcv70cNAdWfq6hrBaiQW1LbpGVSOFHmzR1opL1FmpLV/Y4+oO\nW13zr68nG1ZV57IGrUm0WdhaESata6Ktwp6qStazXDXYWhkZAuPlqm14iXgnx0ZmyqkveMlM\n0ljJiO0VqFfZVYMN7evCztbUdrS0dXJDqhJWG3GBG10NaxuZoqGE8sawO0XrMUwr6hpqlrlq\napsa5kQHEkEwdnxW1S1sXA2pETaocmFDlkFuoKlcIxKaECAHsOCaPxefYX2WAW8TClyDMlWd\nP1dugFQyRY3DCOfJVWsro3SsfhNTgalTRXCKm45VkU9FMFVpVCJX/nSKaDnaMbYwMKEGp1Bc\nFu4ECKPIRgMxWSYxnZcbXGtdja71clgNNbC5MfFoUo4KQ5N5dK3qbqrdICwUE1EQPVVhwgwH\n3Kk3Cje8QKtfrwZvQVdPoeVBg6tm2SBj7ooyJDjy6jBhKqzOMadq1s/s2RVoQyNGi9bseXBE\nVZktr2dmO+iqbh90LWuYq1HjDnJv6nbWl4XUQE3d/PzpuJnNH3HB3toRFfYua2p4CWMteW9d\nwxkKtKJ1fuNIJuIaXpLRV2hQyqAMyCoyqzBOS7Fi0OhTX1IJGdCwvAbQ6msuANFghikYkDUX\naARmmoJRhPERmKrB2IWrlLQeZYz7d5Xcztbnnsb1g62NTMeJHSWCPwiDqwyl4yobAaqLDUuu\ntfPDMa75DF7O4OURuI7B9agZYIf86dsHTVWuvyblaw6d3eZPvlrq/HmLce5fiTMSq1xMmdS8\n8TuPLPrdROv4o4YOfZCwQIZOBQLoZ8smFpMKw+hE68Rnhg6N042Xk/6ZVPI9JIR3B94evNfz\nfyAF+H4Y7xXURxx4O/Em9FmyF2HLdfgWlhMRfkz2YVi9At+DOh9pQ9gzWBfwnc/KWr2H7MF7\nbxTnYzzxPop3I/Y/nXwbpuOpYC5Xwp3grfzD/DX+mlAmfFffqv8vQ8DwikjErVJszFOx/xG3\nMG4wbiL+jvhfGnebCk1bzQFtNk5YSurIAYzRKUbiHtKEEce3hFGM+emIqL4GevafYtrzGPDq\nwzA6DqfHgYyDtORLkL+Ev4ZynZ8Gcp3/OzDNeS3gdrZc7b9KjVeXXG25OnT19FUh5o8fpjv/\n8PuA0/h7UH8fsDt/NxZwvjN2eezqGKeOeWcFxgJJzv/lv1z/Wz9Xfxm4+t9wk07jL5y/oNpD\nfTMpNfDOD+DV0bnO74eyna+8luucfAlCF7ovDFzgtI9lFyxFAef58vNLzned7z9/7Pzp8/ru\nM8fPhM9wxjMw/CKEXwTji2AwvlD+wtUXuIHwcJiGw6PhS2HOc7r8ND3+fPh5Ovr8peep57ny\n5+ixUzD67KVn6ZKTQyep52TXyYsnJ0/yR49kOkNHoOsQXDwEhwIO578dTHT2Hxw6OHmQK/yG\n+g068A3oHhoYosNDMDp0aYguOdByoOsA90Bg0nlsD+zeNcPZ21Pu7MEZdG2a69wUKHamQFJ9\nsjepXu/l6nU451bEteB9e2CGc2VT0NmE74QiS72AMuGLuPouDoxcOUev1k7WUrW2eE5Arc3K\nDbyj1oWgOiA7g8hzAd6nA3A5cDVABwJgL7LVm8FYbyoy1mMgVw8EnE5jubHF2G/kjUaPcYmx\nyzhkvGycNOrLEXbVyHURGLCDABdgeKRumdtdc0E/iYGBPrQyDHvDWcvYU61tCuv2hkl908qG\nEYCHG/c89BCZ76gJFy1rCLc6GmvC7VhQWWEACybHiJ3Mb+zt6e1zswsiBdLrdvf0sBKwmjuC\n00rg7kE0kvX09mClt4/0uHt6oaenl/T0IrwHVmG5p4eBewBb4N3jjrBHDsh4FTLAR2+EdU8P\n0vdg+56kVajy/xeQU1ANCmVuZHN0cmVhbQplbmRvYmoKOCAwIG9iagogICA5MDc4CmVuZG9i\nago5IDAgb2JqCjw8IC9MZW5ndGggMTAgMCBSCiAgIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+\nCnN0cmVhbQp4nF2STW+EIBCG7/wKjtvDxi+QbmJMmu3FQz9S2x+gMGxNKhJ0D/77Msxmm/Sg\nPA7vvDMyZOfuuXPTxrP3sOgeNm4nZwKsyzVo4CNcJseKkptJb7ev9Nbz4FkWk/t93WDunF1Y\n0/DsI26uW9j54cksIzwwznn2FgyEyV344evcU6i/ev8DM7iN56xtuQEb7V4G/zrMwLOUfOxM\n3J+2/RjT/hSfuwdepu+CWtKLgdUPGsLgLsCaPG95Y23LwJl/e1VOKaPV30NgTYXSPI9L5JK4\nRBbEArkmriNLikuM14+J4xJZEktk0teoV6RXqC+pVom1FNVSWKu2pLfIhthgLfKR6FOfKH5C\nrogrZE2sIwvyEegjyF+gv6JchbmSWCamfiT2I6h/gf0rYpX+BcgfUDOSZkQN9aBSDwVpCjwr\n4ioxnU9ccBC3E8eR4N25z1pfQ4hjThcszRcnOzm430G/eMxKzy/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      ],
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "application/pdf": {
       "height": 300,
       "width": 300
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   ],
   "source": [
    "# Plotting the total number of crimes for each type of theft-related crimes in 2018\n",
    "\n",
    "kitsilano_theft_plot_type_2018 <- kitsilano_all_theft_2018 %>%\n",
    "        group_by(TYPE) %>%\n",
    "        summarise(count = n()) %>%\n",
    "        ggplot() +\n",
    "        geom_bar(aes(x = reorder(TYPE, -count), y = count), stat = \"identity\") +\n",
    "        xlab(\"TYPE\") +\n",
    "        ggtitle(\"Kitsilano Theft TYPE Plot 2018\")\n",
    "\n",
    "kitsilano_theft_plot_type_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "914581c4-7f7c-4c4d-b517-62ddb9c67a19",
   "metadata": {},
   "outputs": [
    {
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/jRsdrtTy3+zynFi0um1ZVlan4izVx96I43fj21xJAal1aR6a3OT+Q09qqlG12L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\nNzcgNTU2IDAgMCAwIDUwMCBdCiAgICAvVG9Vbmljb2RlIDkgMCB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      ],
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "application/pdf": {
       "height": 300,
       "width": 300
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       "height": 300,
       "width": 300
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   ],
   "source": [
    "# Plotting the total number of crimes for each type of theft-related crimes in 2020\n",
    "\n",
    "kitsilano_theft_plot_type_2020 <- kitsilano_all_theft_2020 %>%\n",
    "        group_by(TYPE) %>%\n",
    "        summarise(count = n()) %>%\n",
    "        ggplot() +\n",
    "        geom_bar(aes(x = reorder(TYPE, -count), y = count), stat = \"identity\") +\n",
    "        xlab(\"TYPE\") +\n",
    "        ggtitle(\"Kitsilano Theft TYPE Plot 2020\")\n",
    "\n",
    "kitsilano_theft_plot_type_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4de2e659-daaf-469d-8867-26e836e8b071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>replicate</th><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>NEIGHBOURHOOD</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>Theft from Vehicle</td><td>2018</td><td>Strathcona               </td></tr>\n",
       "\t<tr><td>1</td><td>Theft from Vehicle</td><td>2018</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>1</td><td>Other Theft       </td><td>2018</td><td>Renfrew-Collingwood      </td></tr>\n",
       "\t<tr><td>1</td><td>Other Theft       </td><td>2018</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>1</td><td>Theft of Vehicle  </td><td>2018</td><td>Riley Park               </td></tr>\n",
       "\t<tr><td>1</td><td>Theft of Vehicle  </td><td>2018</td><td>Renfrew-Collingwood      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " replicate & TYPE & YEAR & NEIGHBOURHOOD\\\\\n",
       " <int> & <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & Theft from Vehicle & 2018 & Strathcona               \\\\\n",
       "\t 1 & Theft from Vehicle & 2018 & Central Business District\\\\\n",
       "\t 1 & Other Theft        & 2018 & Renfrew-Collingwood      \\\\\n",
       "\t 1 & Other Theft        & 2018 & Central Business District\\\\\n",
       "\t 1 & Theft of Vehicle   & 2018 & Riley Park               \\\\\n",
       "\t 1 & Theft of Vehicle   & 2018 & Renfrew-Collingwood      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 6 × 4\n",
       "\n",
       "| replicate &lt;int&gt; | TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | NEIGHBOURHOOD &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Theft from Vehicle | 2018 | Strathcona                |\n",
       "| 1 | Theft from Vehicle | 2018 | Central Business District |\n",
       "| 1 | Other Theft        | 2018 | Renfrew-Collingwood       |\n",
       "| 1 | Other Theft        | 2018 | Central Business District |\n",
       "| 1 | Theft of Vehicle   | 2018 | Riley Park                |\n",
       "| 1 | Theft of Vehicle   | 2018 | Renfrew-Collingwood       |\n",
       "\n"
      ],
      "text/plain": [
       "  replicate TYPE               YEAR NEIGHBOURHOOD            \n",
       "1 1         Theft from Vehicle 2018 Strathcona               \n",
       "2 1         Theft from Vehicle 2018 Central Business District\n",
       "3 1         Other Theft        2018 Renfrew-Collingwood      \n",
       "4 1         Other Theft        2018 Central Business District\n",
       "5 1         Theft of Vehicle   2018 Riley Park               \n",
       "6 1         Theft of Vehicle   2018 Renfrew-Collingwood      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>replicate</th><th scope=col>TYPE</th><th scope=col>YEAR</th><th scope=col>NEIGHBOURHOOD</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>Other Theft       </td><td>2020</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>1</td><td>Other Theft       </td><td>2020</td><td>Mount Pleasant           </td></tr>\n",
       "\t<tr><td>1</td><td>Theft of Bicycle  </td><td>2020</td><td>Renfrew-Collingwood      </td></tr>\n",
       "\t<tr><td>1</td><td>Other Theft       </td><td>2020</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>1</td><td>Other Theft       </td><td>2020</td><td>Central Business District</td></tr>\n",
       "\t<tr><td>1</td><td>Theft from Vehicle</td><td>2020</td><td>Riley Park               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " replicate & TYPE & YEAR & NEIGHBOURHOOD\\\\\n",
       " <int> & <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & Other Theft        & 2020 & Central Business District\\\\\n",
       "\t 1 & Other Theft        & 2020 & Mount Pleasant           \\\\\n",
       "\t 1 & Theft of Bicycle   & 2020 & Renfrew-Collingwood      \\\\\n",
       "\t 1 & Other Theft        & 2020 & Central Business District\\\\\n",
       "\t 1 & Other Theft        & 2020 & Central Business District\\\\\n",
       "\t 1 & Theft from Vehicle & 2020 & Riley Park               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 6 × 4\n",
       "\n",
       "| replicate &lt;int&gt; | TYPE &lt;chr&gt; | YEAR &lt;dbl&gt; | NEIGHBOURHOOD &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Other Theft        | 2020 | Central Business District |\n",
       "| 1 | Other Theft        | 2020 | Mount Pleasant            |\n",
       "| 1 | Theft of Bicycle   | 2020 | Renfrew-Collingwood       |\n",
       "| 1 | Other Theft        | 2020 | Central Business District |\n",
       "| 1 | Other Theft        | 2020 | Central Business District |\n",
       "| 1 | Theft from Vehicle | 2020 | Riley Park                |\n",
       "\n"
      ],
      "text/plain": [
       "  replicate TYPE               YEAR NEIGHBOURHOOD            \n",
       "1 1         Other Theft        2020 Central Business District\n",
       "2 1         Other Theft        2020 Mount Pleasant           \n",
       "3 1         Theft of Bicycle   2020 Renfrew-Collingwood      \n",
       "4 1         Other Theft        2020 Central Business District\n",
       "5 1         Other Theft        2020 Central Business District\n",
       "6 1         Theft from Vehicle 2020 Riley Park               "
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# Taking samples of sample size 500 from both the datasets\n",
    "\n",
    "set.seed(2)#Do not change\n",
    "sample_size_both <- 500\n",
    "all_theft_2018_sample <- rep_sample_n(all_theft_2018, size = sample_size_both)\n",
    "all_theft_2020_sample <- rep_sample_n(all_theft_2020, size = sample_size_both)\n",
    "head(all_theft_2018_sample)\n",
    "head(all_theft_2020_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a296ca6d-8b6e-4bbf-9dcb-c13f5e68f186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.05"
      ],
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       "0.05"
      ],
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       "0.05"
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       "[1] 0.05"
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    {
     "data": {
      "text/html": [
       "0.068"
      ],
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       "0.068"
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       "0.068"
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       "[1] 0.068"
      ]
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     "metadata": {},
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    {
     "data": {
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       "0.018"
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       "0.018"
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       "[1] 0.018"
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     "metadata": {},
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    }
   ],
   "source": [
    "# Calculating sample proportions\n",
    "# p1: proportion of theft crimes in kitsilano in 2018 in sample\n",
    "# p2: proportion of theft crimes in kitsilano in 2020 in sample\n",
    "# p: difference in proportion of theft crimes in kitsilano in 2018 and 2020 in sample\n",
    "\n",
    "p1_hat <- nrow(filter(all_theft_2018_sample, NEIGHBOURHOOD == \"Kitsilano\"))/nrow(all_theft_2018_sample)\n",
    "p2_hat <- nrow(filter(all_theft_2020_sample, NEIGHBOURHOOD == \"Kitsilano\"))/nrow(all_theft_2020_sample)\n",
    "p_hat <- p2_hat-p1_hat\n",
    "\n",
    "p1_hat\n",
    "p2_hat\n",
    "p_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2cbefc8-e9b4-4c40-b8b4-a39533723593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>p1_hat</th><th scope=col>p2_hat</th><th scope=col>p_hat</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.05</td><td>0.068</td><td>0.018</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 3\n",
       "\\begin{tabular}{lll}\n",
       " p1\\_hat & p2\\_hat & p\\_hat\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.05 & 0.068 & 0.018\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 3\n",
       "\n",
       "| p1_hat &lt;dbl&gt; | p2_hat &lt;dbl&gt; | p_hat &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 0.05 | 0.068 | 0.018 |\n",
       "\n"
      ],
      "text/plain": [
       "  p1_hat p2_hat p_hat\n",
       "1 0.05   0.068  0.018"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating table containing the proportions in samples for both 2018 and 2020 and their difference \n",
    "\n",
    "sample_proportion_table <- tibble(p1_hat = p1_hat, p2_hat = p2_hat, p_hat = p_hat)\n",
    "sample_proportion_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cee2e66f-28d4-4e07-9157-8662ea7470e3",
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DRLCRay1RbzFKYpCidQKhWtshTWdHitZYm2qtxNY3St0NZck5jglfwWb/8SQaf+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\naW9uRGF0ZSAoRDoyMDI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PwdqnhXxVpcGtaBqcXk3VlcZ2uuQQQQ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\nEFSDnAYy/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       "<g style=\"fill:rgb(0%,0%,0%);fill-opacity:1;\">\n",
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       "  <use xlink:href=\"#glyph2-2\" x=\"41.625\" y=\"156.796875\"/>\n",
       "  <use xlink:href=\"#glyph2-1\" x=\"41.625\" y=\"153.796875\"/>\n",
       "  <use xlink:href=\"#glyph2-4\" x=\"41.625\" y=\"146.796875\"/>\n",
       "</g>\n",
       "<g style=\"fill:rgb(0%,0%,0%);fill-opacity:1;\">\n",
       "  <use xlink:href=\"#glyph2-1\" x=\"41.625\" y=\"97.542969\"/>\n",
       "  <use xlink:href=\"#glyph2-2\" x=\"41.625\" y=\"90.542969\"/>\n",
       "  <use xlink:href=\"#glyph2-1\" x=\"41.625\" y=\"87.542969\"/>\n",
       "  <use xlink:href=\"#glyph2-5\" x=\"41.625\" y=\"80.542969\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot with title “Proportion of theft crimes in Kitsilano\n",
       "in 2018 and 2020 (Sample)”"
      ]
     },
     "metadata": {
      "application/pdf": {
       "height": 300,
       "width": 300
      },
      "image/jpeg": {
       "height": 300,
       "width": 300
      },
      "image/png": {
       "height": 300,
       "width": 300
      },
      "image/svg+xml": {
       "height": 300,
       "isolated": true,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the proportion of theft-related crimes in Kitsilano in samples for both 2018 and 2020 using barplot\n",
    "\n",
    "barplot(c(p1_hat, p2_hat), main = \"Proportion of theft crimes in Kitsilano\\nin 2018 and 2020 (Sample)\", names.arg = c(\"p1_hat\", \"p2_hat\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "768b777a-b11e-4f56-86d0-e2b88b098abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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gXqWaZPp0ueXT+rsKFEfKB85UBRMwumgpbqpnYq112TS1kbW4vSUTE6qBh75myd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",
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K+w/wDgnt/zP3/cP/8AbmgD7Cr8q/jL/wAlf8c/9h2+\n/wDSh6/VSvyr+Mv/ACV/xz/2Hb7/ANKHoA46iiigAooooAKKKKACiiigAooooAKKKKACvsP/\nAIJ7f8z9/wBw/wD9ua+PK+w/+Ce3/M/f9w//ANuaAPsKvyr+Mv8AyV/xz/2Hb7/0oev1Ur8q\n/jL/AMlf8c/9h2+/9KHoA46iiigAooooAKKKKACiiigAooooAKKKKACvsP8A4J7f8z9/3D//\nAG5r48r7D/4J7f8AM/f9w/8A9uaAPsKrGn/8f1v/AL4/nVerGn/8f1v/AL4/nQB11FFFABRR\nRQAUUUUAFFcH8edZv/D3wX8a6lpcz21/baVcPFcRffhOw5kHoVGWz7VyVh4R0X4Z/GPwPY+E\n7SPTrPV9K1BNRhtidt0sIt2ink/vSBnI8w8nzSCTmgD13V/+QdP9B/MVytY2oeP/ABlJ8QJf\nDr/Da9i8LmXyz4sOqWxhCbN2/wAjPmY3fJjGe/Sul+yW3/P6n/fDUAU6/Kv4y/8AJX/HP/Yd\nvv8A0oev1g+yW3/P6n/fDV8T/ED9h/xp4q8e+JNas9f8NR2mpanc3kKT3FysipJKzqGAgIBw\nwzgkZ7mgD5Gor6a/4YB8e/8AQw+Ff/Am6/8Akej/AIYB8e/9DD4V/wDAm6/+R6APmWivpr/h\ngHx7/wBDD4V/8Cbr/wCR6P8AhgHx7/0MPhX/AMCbr/5HoA+ZaK+mv+GAfHv/AEMPhX/wJuv/\nAJHo/wCGAfHv/Qw+Ff8AwJuv/kegD5lor6cb/gn34/SNHbxB4VCvnaftV1zj/t3pv/DAPj3/\nAKGHwr/4E3X/AMj0AfMtFfTX/DAPj3/oYfCv/gTdf/I9H/DAPj3/AKGHwr/4E3X/AMj0AfMt\nFfTX/DAPj3/oYfCv/gTdf/I9H/DAPj3/AKGHwr/4E3X/AMj0AfMtfYf/AAT2/wCZ+/7h/wD7\nc1y3/DAPj3/oYfCv/gTdf/I9e+fsr/s9a18Ef+En/t/V9Juf7T+y+R/ZrzSbfL87du3xpj/W\nLjGe/TuAe31+Vfxl/wCSv+Of+w7ff+lD1+sH2S2/5/U/74avif4gfsP+NPFXj3xJrVnr/hqO\n01LU7m8hSe4uVkVJJWdQwEBAOGGcEjPc0AfI1FfTX/DAPj3/AKGHwr/4E3X/AMj0f8MA+Pf+\nhh8K/wDgTdf/ACPQB8y0V9Nf8MA+Pf8AoYfCv/gTdf8AyPR/wwD49/6GHwr/AOBN1/8AI9AH\nzLRX01/wwD49/wChh8K/+BN1/wDI9H/DAPj3/oYfCv8A4E3X/wAj0AfMtFfTj/8ABPzx/GiM\n3iDwqFcZU/arrn/yXpv/AAwD49/6GHwr/wCBN1/8j0AfMtFfTX/DAPj3/oYfCv8A4E3X/wAj\n0f8ADAPj3/oYfCv/AIE3X/yPQB8y0V9Nf8MA+Pf+hh8K/wDgTdf/ACPR/wAMA+Pf+hh8K/8A\ngTdf/I9AHzLX2H/wT2/5n7/uH/8AtzXLf8MA+Pf+hh8K/wDgTdf/ACPXvn7K/wCz1rXwR/4S\nf+39X0m5/tP7L5H9mvNJt8vzt27fGmP9YuMZ79O4B7fX5V/GX/kr/jn/ALDt9/6UPX6wfZLb\n/n9T/vhq+J/iB+w/408VePfEmtWev+Go7TUtTubyFJ7i5WRUklZ1DAQEA4YZwSM9zQB8jUV9\nNf8ADAPj3/oYfCv/AIE3X/yPR/wwD49/6GHwr/4E3X/yPQB8y0V9Nf8ADAPj3/oYfCv/AIE3\nX/yPR/wwD49/6GHwr/4E3X/yPQB8y0V9Nf8ADAPj3/oYfCv/AIE3X/yPR/wwD49/6GHwr/4E\n3X/yPQB8y0V9OP8A8E+/H8aozeIPCoDjK/6Vdcj/AMB6b/wwD49/6GHwr/4E3X/yPQB8y0V9\nNf8ADAPj3/oYfCv/AIE3X/yPR/wwD49/6GHwr/4E3X/yPQB8y0V9Nf8ADAPj3/oYfCv/AIE3\nX/yPR/wwD49/6GHwr/4E3X/yPQB8y19h/wDBPb/mfv8AuH/+3Nct/wAMA+Pf+hh8K/8AgTdf\n/I9e+fsr/s9a18Ef+En/ALf1fSbn+0/svkf2a80m3y/O3bt8aY/1i4xnv07gHt9flX8Zf+Sv\n+Of+w7ff+lD1+sH2S2/5/U/74avif4gfsP8AjTxV498Sa1Z6/wCGo7TUtTubyFJ7i5WRUklZ\n1DAQEA4YZwSM9zQB8jUV9Nf8MA+Pf+hh8K/+BN1/8j0f8MA+Pf8AoYfCv/gTdf8AyPQB8y0V\n9Nf8MA+Pf+hh8K/+BN1/8j0f8MA+Pf8AoYfCv/gTdf8AyPQB8y0V9Nf8MA+Pf+hh8K/+BN1/\n8j0f8MA+Pf8AoYfCv/gTdf8AyPQB8y0V9OSf8E+/H8QQt4g8KjeNw/0q65H/AID03/hgHx7/\nANDD4V/8Cbr/AOR6APmWivpr/hgHx7/0MPhX/wACbr/5Ho/4YB8e/wDQw+Ff/Am6/wDkegD5\nlor6a/4YB8e/9DD4V/8AAm6/+R6P+GAfHv8A0MPhX/wJuv8A5HoA+Za+w/8Agnt/zP3/AHD/\nAP25rlv+GAfHv/Qw+Ff/AAJuv/kevfP2V/2eta+CP/CT/wBv6vpNz/af2XyP7NeaTb5fnbt2\n+NMf6xcYz36dwD2+rGn/APH9b/74/nUn2S2/5/U/74aprO1gW7hK3auwcYUIeeaAOkooooAK\nKKKACiiigCG7tIL+0mtbmFLi2nRo5YZVDI6EYKkHgggkYrmfCHws8MeBLyS70bTngumgFqJr\ni7muWigByIozK7eXHnB2JheBxwK6yigCnq//ACDp/oP5iuVrqtX/AOQdP9B/MVytABRRRQAU\nUUUAFFFFABRRRQBcuv8AkH2X0f8A9CqnVy6/5B9l9H/9CqnQAUUUUAFFFFABRRRQAUUUUAFF\nFFABRRRQAUUUUAXLz/jzsv8Acb/0KqdXLz/jzsv9xv8A0KqdABRRRQAUUUUAFFFFABRRRQAU\nUUUAFFFFABRRRQBcvv8Aj3s/+uf9TVOrl9/x72f/AFz/AKmqdABRRRQAUUUUAFFFFABRRRQA\nUUUUAFFFFABRRRQBc1D7lp/1xH8zVOrmofctP+uI/map0AFFFFABRRRQAUUUUAFWNP8A+P63\n/wB8fzqvVjT/APj+t/8AfH86AOuooooAKKKKACiiigAooooAhu7f7VbPFu27h1xnFZX/AAjf\n/Tz/AOOf/XrbooAxP+Eb/wCnn/xz/wCvR/wjf/Tz/wCOf/XrbrzPxZ8a5fCPiKy0ifwF4pvJ\nL+7eysLi1+wGK7kWN5Ds3XSsBsjdvnVenrxQB2P/AAjf/Tz/AOOf/Xo/4Rv/AKef/HP/AK9c\nivxv0vT/ABTLpPiSGLwbCmk2upGfXr+CFleae4iEDAM0e4fZ92VkbO7GBiut1zx14b8MQ2ku\ns+IdK0mK8/49nvr2KFZ+n3CzDd1HT1FAC/8ACN/9PP8A45/9ej/hG/8Ap5/8c/8Ar1wnjT9o\njw34J8fp4cvr/SIIINMfVL+7utVjheFACY4oocEyysMNsyvykEZyAervvib4etPh1c+N49Tt\nbnw/FaNeLdR3CeXIBn5A+du4sNmM/e460AaH/CN/9PP/AI5/9ej/AIRv/p5/8c/+vXOeAPjD\nofjDw3oV9eatoOn6nq0aSQ6ba6zHdH94zrGgbCFnJjkUgLw8cignYTXR6f448Oavrdzo1j4g\n0u91e2z5+n297HJcRYODujDFlwfUUAcf4X8aW/jH4geNvBcdvLbT+Dns45btyGW5+1QeeCq9\nV2jg5JzXYf8ACN/9PP8A45/9esPTfGnw3tdT1XVNP13wtDqF7BDeahe215bLLcRKqrFLM6nL\nKFZQrMSAGABwRXSx+ILO90BtY0uRdbszA08B02RJvtIAJAjbdtYnGByBnuKAK3/CN/8ATz/4\n5/8AXo/4Rv8A6ef/ABz/AOvWPN8WNBh+FQ+IO+eTQm09dQVEQGdgwBWILnHmFiE25+8cZrUg\n8e+HpdZg0WTWtPtvEEqB/wCxpryIXi5XdgxBicgemR70ASf8I3/08/8Ajn/16P8AhG/+nn/x\nz/69c54N+Mmg+J7y60+7vtO0jWU1a+0y30ybUIzcXQtriSHzUQ7WIbyycAHHIycZrVX4iaNp\n+j3Opa9rGiaNaRX89itw+qxtCWjlZArSNtCyfL80fJRsrk4zQBe/4Rv/AKef/HP/AK9H/CN/\n9PP/AI5/9esm3+JVlN441LQ2+zpp1nolrrf9rm6XynjmlnTHTAUCDdv3YIboMZO54d8WaJ4v\ns3u9C1nT9atUcxtPp10lwit/dLISAfagCH/hG/8Ap5/8c/8Ar0f8I3/08/8Ajn/1626KAMT/\nAIRv/p5/8c/+vR/wjf8A08/+Of8A1626gv7630uxuL28nS2tLeNppppW2pGijLMT2AAJzQBl\n/wDCN/8ATz/45/8AXo/4Rv8A6ef/ABz/AOvXDf8ADQ2kx6ZFrdz4b8TWPhSXay+I7mwRbQRs\nfllZPM85IzkHe0QGCCSBzUvij9oHwxo0euQaXfWet6vo15Y2l3p8V4iuguLmGAvkbiQhnGeP\nvDaSCeADtP8AhG/+nn/xz/69H/CN/wDTz/45/wDXpNJ8deG9e0+9v9M8Q6VqNjZEi6ubS9jl\nigIGSHZWIXA55xUdh8QfC2qaRJqtl4l0e70uOZbd76C/ieBJGYKqFw20MWZVAzklgO9AHJ+D\nvGdv478YeNPDMVvLaS+ELuCyluHIZbkywiYMoH3cBsc56V2P/CN/9PP/AI5/9eqNtc6FY6Rq\nXinwvpNtrkmpFbiaXw+Lczaky4jDeaXRJCoGMs/AXGe1c74S+NUvjDxBe6Tb+AvFNm9heLY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      ],
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "application/pdf": {
       "height": 300,
       "width": 300
      },
      "image/jpeg": {
       "height": 300,
       "width": 300
      },
      "image/png": {
       "height": 300,
       "width": 300
      },
      "image/svg+xml": {
       "height": 300,
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the total number of crimes for each type of theft-related crimes in the 2018 sample\n",
    "\n",
    "sample_kitsilano_theft_plot_type_2018 <- all_theft_2018_sample %>%\n",
    "        filter(NEIGHBOURHOOD == \"Kitsilano\") %>%\n",
    "        group_by(TYPE) %>%\n",
    "        summarise(count = n()) %>%\n",
    "        ggplot() +\n",
    "        geom_bar(aes(x = reorder(TYPE, -count), y = count), stat = \"identity\") +\n",
    "        xlab(\"TYPE\") +\n",
    "        ggtitle(\"Sample Kitsilano Theft TYPE Plot 2018\")\n",
    "\n",
    "sample_kitsilano_theft_plot_type_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7821d12a-b306-4285-adba-8a8f746b737a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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rtxVnFThePB6hWDJa08mIra6ls6mVx3TS7hbfx8kkXKyUHF2Fu6tZT25m3No3tc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\n05wof0KuiCtkRawi1+RZo6ckvUR9Tf41+gvKC8wL6kdgPzX1X2P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      "text/plain": [
       "plot without title"
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   "source": [
    "# Plotting the total number of crimes for each type of theft-related crimes in the 2020 sample\n",
    "\n",
    "sample_kitsilano_theft_plot_type_2020 <- all_theft_2020_sample %>%\n",
    "        filter(NEIGHBOURHOOD == \"Kitsilano\") %>%\n",
    "        group_by(TYPE) %>%\n",
    "        summarise(count = n()) %>%\n",
    "        ggplot() +\n",
    "        geom_bar(aes(x = reorder(TYPE, -count), y = count), stat = \"identity\") +\n",
    "        xlab(\"TYPE\") +\n",
    "        ggtitle(\"Sample Kitsilano Theft TYPE Plot 2020\")\n",
    "\n",
    "sample_kitsilano_theft_plot_type_2020"
   ]
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  {
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   "id": "3ea26d94-cb37-4299-a318-bd668bfa509a",
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   "source": [
    "## METHODS: PLAN"
   ]
  },
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    "Our report is trustworthy because we've sampled from the population in a random, unbiased fashion (using rep_sample_n). Our samples are sufficiently large (500 each) so they're representative of the population. We've also set the seed, making our report reproducible.\n",
    "(Note: We'll pretend we don't have access to the entire population of crimes in Vancouver).\n",
    "\n",
    "We see an increase of 0.018 (1.8%) in our sample proportion from 2018 to 2020. However, this increase could result from sampling variability and may not indicate a real difference in population proportion. Therefore, we want to verify whether this increase is statistically significant.\n",
    "\n",
    "We intend to explore this using hypothesis testing. Since we have 2 samples from 2 independent populations (2018 and 2020), we'll use 2-sample z-testing (Krishnamoorthy & Thomson, 2002). We'll also construct a 90% confidence interval of the difference in proportions and check if it contains 0.\n",
    "\n",
    "We'll use a significance level of 10% (rather than 5%) because we feel like a type 2 error would be worse than a type 1 error in our report.\n",
    "\n",
    "As described above, we intend to use asymptotics for our analysis. However, if we get time, we will also use the bootstrapping method to validate our results."
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    "#### Hypotheses:\n",
    "Our null hypothesis would be that the difference in the population proportion between 2020 and 2018 is 0, while our alternative hypothesis would be that this difference is greater than 0.\n",
    "\n",
    "H0: p2020-p2018 = 0\n",
    "\n",
    "Ha: p2020-p2018 > 0\n",
    "\n",
    "We'll use a right-tailed test."
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    "#### REFLECTION:\n",
    "\n",
    "1. We expect to reject the null hypothesis because the SFU study states an increase in theft within wealthy neighbourhoods like Kitsilano.\n",
    "\n",
    "2. Such findings could help us understand theft rates in Kitsilano before and after the pandemic, leading to better theft prevention policies.\n",
    "\n",
    "3. We could later explore the pandemic's impact on theft within poorer neighbourhoods, or explore a different type of crime.\n",
    "\n",
    "4. A drawback of this study is that we cannot establish a causal relationship between the the commencement of the pandemic and the increase in the theft crimes since this is a retrospective observational study."
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    "## REFERENCES"
   ]
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    "- Krishnamoorthy, K., &amp; Thomson, J. (2002). Hypothesis testing about proportions in two finite populations. The American Statistician, 56(3), 215–222. https://doi.org/10.1198/000313002164 \n",
    "\n",
    "- Andresen, M. A., &amp; Hodgkinson, T. (2022, January 9). In a world called catastrophe: The impact of covid-19 on neighbourhood level crime in Vancouver, Canada. Journal of experimental criminology. Retrieved November 4, 2022, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742714/ \n",
    "\n",
    "- Data is from: VPD open data. (n.d.). Retrieved November 4, 2022, from https://geodash.vpd.ca/opendata/ "
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