personal-portfolio/notebooks/demographics/income_choropleth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Median Income Choropleth Map\n",
    "\n",
    "Displays median household income across Toronto's 158 neighbourhoods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Reference\n",
    "\n",
    "### Source Tables\n",
    "\n",
    "| Table | Grain | Key Columns |\n",
    "|-------|-------|-------------|\n",
    "| `mart_neighbourhood_demographics` | neighbourhood × year | median_household_income, income_index, income_quintile, geometry |\n",
    "\n",
    "### SQL Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "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",
    "    neighbourhood_id,\n",
    "    neighbourhood_name,\n",
    "    geometry,\n",
    "    year,\n",
    "    median_household_income,\n",
    "    income_index,\n",
    "    income_quintile,\n",
    "    population,\n",
    "    unemployment_rate\n",
    "FROM public_marts.mart_neighbourhood_demographics\n",
    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_demographics)\n",
    "ORDER BY median_household_income DESC\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(query, engine)\n",
    "print(f\"Loaded {len(df)} neighbourhoods\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Steps\n",
    "\n",
    "1. Filter to most recent census year\n",
    "2. Convert geometry to GeoJSON\n",
    "3. Scale income to thousands for readability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import json\n",
    "\n",
    "df['income_thousands'] = df['median_household_income'] / 1000\n",
    "\n",
    "gdf = gpd.GeoDataFrame(\n",
    "    df,\n",
    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
    "    crs='EPSG:4326'\n",
    ")\n",
    "\n",
    "geojson = json.loads(gdf.to_json())\n",
    "data = df.drop(columns=['geometry']).to_dict('records')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['neighbourhood_name', 'median_household_income', 'income_index', 'income_quintile']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Visualization\n",
    "\n",
    "### Figure Factory\n",
    "\n",
    "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '../..')\n",
    "\n",
    "from portfolio_app.figures.choropleth import create_choropleth_figure\n",
    "\n",
    "fig = create_choropleth_figure(\n",
    "    geojson=geojson,\n",
    "    data=data,\n",
    "    location_key='neighbourhood_id',\n",
    "    color_column='median_household_income',\n",
    "    hover_data=['neighbourhood_name', 'income_index', 'income_quintile'],\n",
    "    color_scale='Viridis',\n",
    "    title='Toronto Median Household Income by Neighbourhood',\n",
    "    zoom=10,\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income Quintile Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby('income_quintile')['median_household_income'].agg(['count', 'mean', 'min', 'max']).round(0)"
   ]
  }
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