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/transit_accessibility_bar.ipynb

@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transit Accessibility Bar Chart\n",
+    "\n",
+    "Shows transit stops per 1,000 residents across Toronto 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 | transit_per_1000, transit_index, transit_count |\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",
+    "    transit_per_1000,\n",
+    "    transit_index,\n",
+    "    transit_count,\n",
+    "    population,\n",
+    "    amenity_tier\n",
+    "FROM mart_neighbourhood_amenities\n",
+    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_amenities)\n",
+    "  AND transit_per_1000 IS NOT NULL\n",
+    "ORDER BY transit_per_1000 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. Sort by transit accessibility\n",
+    "2. Select top 20 for visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = df.head(20).to_dict('records')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[['neighbourhood_name', 'transit_per_1000', 'transit_index', 'transit_count']].head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_horizontal_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_horizontal_bar\n",
+    "\n",
+    "fig = create_horizontal_bar(\n",
+    "    data=data,\n",
+    "    name_column='neighbourhood_name',\n",
+    "    value_column='transit_per_1000',\n",
+    "    title='Top 20 Neighbourhoods by Transit Accessibility',\n",
+    "    color='#00BCD4',\n",
+    "    value_format='.2f',\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transit Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"City-wide Transit Statistics:\")\n",
+    "print(f\"  Total Transit Stops: {df['transit_count'].sum():,.0f}\")\n",
+    "print(f\"  Average per 1,000 pop: {df['transit_per_1000'].mean():.2f}\")\n",
+    "print(f\"  Median per 1,000 pop: {df['transit_per_1000'].median():.2f}\")\n",
+    "print(f\"  Best Access: {df['transit_per_1000'].max():.2f} per 1,000\")\n",
+    "print(f\"  Worst Access: {df['transit_per_1000'].min():.2f} per 1,000\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}