docs: Complete Phase 6 notebooks and Phase 7 documentation review

Phase 6 - Jupyter Notebooks (15 total):
- Overview tab: livability_choropleth, top_bottom_10_bar, income_safety_scatter
- Housing tab: affordability_choropleth, rent_trend_line, tenure_breakdown_bar
- Safety tab: crime_rate_choropleth, crime_breakdown_bar, crime_trend_line
- Demographics tab: income_choropleth, age_distribution, population_density_bar
- Amenities tab: amenity_index_choropleth, amenity_radar, transit_accessibility_bar

Phase 7 - Documentation:
- Updated CLAUDE.md with Sprint 9 completion status
- Added notebooks directory to application structure
- Expanded figures directory listing

Closes #71, #72, #73, #74, #75, #76, #77

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

notebooks/amenities/amenity_radar.ipynb

@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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 mart_neighbourhood_amenities\n",
+    "WHERE year = (SELECT MAX(year) FROM 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\n",
+    "sys.path.insert(0, '../..')\n",
+    "\n",
+    "from portfolio_app.figures.radar import create_radar_figure\n",
+    "\n",
+    "# Compare top neighbourhood vs city average (100)\n",
+    "top_hood = top_5.iloc[0]\n",
+    "\n",
+    "data = [\n",
+    "    {\n",
+    "        'name': top_hood['neighbourhood_name'],\n",
+    "        'values': [top_hood['parks_index'], top_hood['schools_index'], top_hood['transit_index']],\n",
+    "        'categories': categories\n",
+    "    },\n",
+    "    {\n",
+    "        'name': 'City Average',\n",
+    "        'values': [100, 100, 100],\n",
+    "        'categories': categories\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "fig = create_radar_figure(\n",
+    "    data=data,\n",
+    "    title=f\"Amenity Profile: {top_hood['neighbourhood_name']} vs City Average\",\n",
+    ")\n",
+    "\n",
+    "fig.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 |"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}