personal-portfolio/notebooks/amenities/amenity_radar.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Amenity Radar Chart\n",
    "\n",
    "Spider/radar chart comparing amenity categories for selected neighbourhoods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Reference\n",
    "\n",
    "### Source Tables\n",
    "\n",
    "| Table | Grain | Key Columns |\n",
    "|-------|-------|-------------|\n",
    "| `mart_neighbourhood_amenities` | neighbourhood × year | parks_index, schools_index, transit_index |\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",
    "    neighbourhood_name,\n",
    "    parks_index,\n",
    "    schools_index,\n",
    "    transit_index,\n",
    "    amenity_index,\n",
    "    amenity_tier\n",
    "FROM public_marts.mart_neighbourhood_amenities\n",
    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\n",
    "ORDER BY amenity_index 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. Select top 5 and bottom 5 neighbourhoods by amenity index\n",
    "2. Reshape for radar chart format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select representative neighbourhoods\n",
    "top_5 = df.head(5)\n",
    "bottom_5 = df.tail(5)\n",
    "\n",
    "# Prepare radar data\n",
    "categories = ['Parks', 'Schools', 'Transit']\n",
    "index_columns = ['parks_index', 'schools_index', 'transit_index']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Top 5 Amenity-Rich Neighbourhoods:\")\n",
    "display(top_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])\n",
    "print(\"\\nBottom 5 Underserved Neighbourhoods:\")\n",
    "display(bottom_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Visualization\n",
    "\n",
    "### Figure Factory\n",
    "\n",
    "Uses `create_radar` from `portfolio_app.figures.radar`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "import sys\nsys.path.insert(0, '../..')\n\nfrom portfolio_app.figures.radar import create_comparison_radar\n\n# Compare top neighbourhood vs city average (100)\ntop_hood = top_5.iloc[0]\nmetrics = ['parks_index', 'schools_index', 'transit_index']\n\nfig = create_comparison_radar(\n    selected_data=top_hood.to_dict(),\n    average_data={'parks_index': 100, 'schools_index': 100, 'transit_index': 100},\n    metrics=metrics,\n    selected_name=top_hood['neighbourhood_name'],\n    average_name='City Average',\n    title=f\"Amenity Profile: {top_hood['neighbourhood_name']} vs City Average\",\n)\n\nfig.show()"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index Interpretation\n",
    "\n",
    "| Value | Meaning |\n",
    "|-------|--------|\n",
    "| < 100 | Below city average |\n",
    "| = 100 | City average |\n",
    "| > 100 | Above city average |"
   ]
  }
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