personal-portfolio/notebooks/safety/crime_trend_line.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crime Trend Line Chart\n",
    "\n",
    "Shows 5-year crime rate trends across Toronto."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Reference\n",
    "\n",
    "### Source Tables\n",
    "\n",
    "| Table | Grain | Key Columns |\n",
    "|-------|-------|-------------|\n",
    "| `mart_neighbourhood_safety` | neighbourhood × year | year, crime_rate_per_100k, crime_yoy_change_pct |\n",
    "\n",
    "### SQL Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "import os\n",
    "\n",
    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT\n",
    "    year,\n",
    "    AVG(crime_rate_per_100k) as avg_crime_rate,\n",
    "    AVG(assault_rate_per_100k) as avg_assault_rate,\n",
    "    AVG(auto_theft_rate_per_100k) as avg_auto_theft_rate,\n",
    "    AVG(break_enter_rate_per_100k) as avg_break_enter_rate,\n",
    "    SUM(total_incidents) as total_city_incidents,\n",
    "    AVG(crime_yoy_change_pct) as avg_yoy_change\n",
    "FROM mart_neighbourhood_safety\n",
    "WHERE year >= (SELECT MAX(year) - 5 FROM mart_neighbourhood_safety)\n",
    "GROUP BY year\n",
    "ORDER BY year\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(query, engine)\n",
    "print(f\"Loaded {len(df)} years of crime data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Steps\n",
    "\n",
    "1. Aggregate by year (city-wide)\n",
    "2. Convert year to datetime\n",
    "3. Melt for multi-line by crime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['year'].astype(str) + '-01-01')\n",
    "\n",
    "# Melt for multi-line\n",
    "df_melted = df.melt(\n",
    "    id_vars=['year', 'date'],\n",
    "    value_vars=['avg_assault_rate', 'avg_auto_theft_rate', 'avg_break_enter_rate'],\n",
    "    var_name='crime_type',\n",
    "    value_name='rate_per_100k'\n",
    ")\n",
    "\n",
    "df_melted['crime_type'] = df_melted['crime_type'].map({\n",
    "    'avg_assault_rate': 'Assault',\n",
    "    'avg_auto_theft_rate': 'Auto Theft',\n",
    "    'avg_break_enter_rate': 'Break & Enter'\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['year', 'avg_crime_rate', 'total_city_incidents', 'avg_yoy_change']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Visualization\n",
    "\n",
    "### Figure Factory\n",
    "\n",
    "Uses `create_price_time_series` (reused for any numeric trend)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '../..')\n",
    "\n",
    "from portfolio_app.figures.time_series import create_price_time_series\n",
    "\n",
    "data = df_melted.to_dict('records')\n",
    "\n",
    "fig = create_price_time_series(\n",
    "    data=data,\n",
    "    date_column='date',\n",
    "    price_column='rate_per_100k',\n",
    "    group_column='crime_type',\n",
    "    title='Toronto Crime Trends by Type (5 Years)',\n",
    ")\n",
    "\n",
    "# Remove dollar sign formatting since this is rate data\n",
    "fig.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total crime rate trend\n",
    "total_data = df[['date', 'avg_crime_rate']].rename(columns={'avg_crime_rate': 'total_rate'}).to_dict('records')\n",
    "\n",
    "fig2 = create_price_time_series(\n",
    "    data=total_data,\n",
    "    date_column='date',\n",
    "    price_column='total_rate',\n",
    "    title='Toronto Overall Crime Rate Trend',\n",
    ")\n",
    "fig2.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\n",
    "fig2.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}