refactor: multi-dashboard structural migration

- Rename dbt project from toronto_housing to portfolio
- Restructure dbt models into domain subdirectories:
  - shared/ for cross-domain dimensions (dim_time)
  - staging/toronto/, intermediate/toronto/, marts/toronto/
- Update SQLAlchemy models for raw_toronto schema
- Add explicit cross-schema FK relationships for FactRentals
- Namespace figure factories under figures/toronto/
- Namespace notebooks under notebooks/toronto/
- Update Makefile with domain-specific targets and env loading
- Update all documentation for multi-dashboard structure

This enables adding new dashboard projects (e.g., /football, /energy)
without structural conflicts or naming collisions.

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

notebooks/toronto/safety/crime_rate_choropleth.ipynb

@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Crime Rate Choropleth Map\n",
+    "\n",
+    "Displays crime rates per 100,000 population 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_safety` | neighbourhood × year | crime_rate_per_100k, crime_index, safety_tier, geometry |\n",
+    "\n",
+    "### SQL Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\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_id,\n",
+    "    neighbourhood_name,\n",
+    "    geometry,\n",
+    "    year,\n",
+    "    crime_rate_per_100k,\n",
+    "    crime_index,\n",
+    "    safety_tier,\n",
+    "    total_incidents,\n",
+    "    population\n",
+    "FROM public_marts.mart_neighbourhood_safety\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_safety)\n",
+    "ORDER BY crime_rate_per_100k 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 year\n",
+    "2. Convert geometry to GeoJSON\n",
+    "3. Use reversed color scale (green=low crime, red=high crime)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "\n",
+    "gdf = gpd.GeoDataFrame(\n",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), 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[\n",
+    "    [\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"crime_rate_per_100k\",\n",
+    "        \"crime_index\",\n",
+    "        \"safety_tier\",\n",
+    "        \"total_incidents\",\n",
+    "    ]\n",
+    "].head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_choropleth_figure` from `portfolio_app.figures.toronto.choropleth`.\n",
+    "\n",
+    "**Key Parameters:**\n",
+    "- `color_column`: 'crime_rate_per_100k'\n",
+    "- `color_scale`: 'RdYlGn_r' (red=high crime, green=low crime)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.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=\"crime_rate_per_100k\",\n",
+    "    hover_data=[\"neighbourhood_name\", \"crime_index\", \"total_incidents\"],\n",
+    "    color_scale=\"RdYlGn_r\",\n",
+    "    title=\"Toronto Crime Rate per 100,000 Population\",\n",
+    "    zoom=10,\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Safety Tier Interpretation\n",
+    "\n",
+    "| Tier | Meaning |\n",
+    "|------|--------|\n",
+    "| 1 | Highest crime (top 20%) |\n",
+    "| 2-4 | Middle tiers |\n",
+    "| 5 | Lowest crime (bottom 20%) |"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}