personal-portfolio/notebooks/safety/crime_breakdown_bar.ipynb

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 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crime Type Breakdown Bar Chart\n",
    "\n",
    "Stacked bar chart showing crime composition by Major Crime Indicator (MCI) categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Reference\n",
    "\n",
    "### Source Tables\n",
    "\n",
    "| Table | Grain | Key Columns |\n",
    "|-------|-------|-------------|\n",
    "| `mart_neighbourhood_safety` | neighbourhood \u00d7 year | assault_count, auto_theft_count, break_enter_count, robbery_count, etc. |\n",
    "\n",
    "### SQL Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "from dotenv import load_dotenv\n",
    "import os\n",
    "\n",
    "# Load .env from project root\n",
    "load_dotenv('../../.env')\n",
    "\n",
    "engine = create_engine(os.environ['DATABASE_URL'])\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT\n",
    "    neighbourhood_name,\n",
    "    assault_count,\n",
    "    auto_theft_count,\n",
    "    break_enter_count,\n",
    "    robbery_count,\n",
    "    theft_over_count,\n",
    "    homicide_count,\n",
    "    total_incidents,\n",
    "    crime_rate_per_100k\n",
    "FROM public_marts.mart_neighbourhood_safety\n",
    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_safety)\n",
    "ORDER BY total_incidents DESC\n",
    "LIMIT 15\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(query, engine)\n",
    "print(f\"Loaded top {len(df)} neighbourhoods by crime volume\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Steps\n",
    "\n",
    "1. Select top 15 neighbourhoods by total incidents\n",
    "2. Melt crime type columns into rows\n",
    "3. Pass to stacked bar figure factory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_melted = df.melt(\n",
    "    id_vars=['neighbourhood_name', 'total_incidents'],\n",
    "    value_vars=['assault_count', 'auto_theft_count', 'break_enter_count', \n",
    "                'robbery_count', 'theft_over_count', 'homicide_count'],\n",
    "    var_name='crime_type',\n",
    "    value_name='count'\n",
    ")\n",
    "\n",
    "# Clean labels\n",
    "df_melted['crime_type'] = df_melted['crime_type'].str.replace('_count', '').str.replace('_', ' ').str.title()\n",
    "\n",
    "data = df_melted.to_dict('records')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['neighbourhood_name', 'assault_count', 'auto_theft_count', 'break_enter_count', 'total_incidents']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Visualization\n",
    "\n",
    "### Figure Factory\n",
    "\n",
    "Uses `create_stacked_bar` from `portfolio_app.figures.bar_charts`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '../..')\n",
    "\n",
    "from portfolio_app.figures.bar_charts import create_stacked_bar\n",
    "\n",
    "fig = create_stacked_bar(\n",
    "    data=data,\n",
    "    x_column='neighbourhood_name',\n",
    "    value_column='count',\n",
    "    category_column='crime_type',\n",
    "    title='Crime Type Breakdown - Top 15 Neighbourhoods',\n",
    "    color_map={\n",
    "        'Assault': '#d62728',\n",
    "        'Auto Theft': '#ff7f0e',\n",
    "        'Break Enter': '#9467bd',\n",
    "        'Robbery': '#8c564b',\n",
    "        'Theft Over': '#e377c2',\n",
    "        'Homicide': '#1f77b4'\n",
    "    },\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCI Categories\n",
    "\n",
    "| Category | Description |\n",
    "|----------|------------|\n",
    "| Assault | Physical attacks |\n",
    "| Auto Theft | Vehicle theft |\n",
    "| Break & Enter | Burglary |\n",
    "| Robbery | Theft with force/threat |\n",
    "| Theft Over | Theft > $5,000 |\n",
    "| Homicide | Murder/manslaughter |"
   ]
  }
 ],
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