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/housing/tenure_breakdown_bar.ipynb

@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Housing Tenure Breakdown Bar Chart\n",
+    "\n",
+    "Shows the distribution of owner-occupied vs renter-occupied dwellings across neighbourhoods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Data Reference\n",
+    "\n",
+    "### Source Tables\n",
+    "\n",
+    "| Table | Grain | Key Columns |\n",
+    "|-------|-------|-------------|\n",
+    "| `mart_neighbourhood_housing` | neighbourhood × year | pct_owner_occupied, pct_renter_occupied, income_quintile |\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_name,\n",
+    "    pct_owner_occupied,\n",
+    "    pct_renter_occupied,\n",
+    "    income_quintile,\n",
+    "    total_rental_units,\n",
+    "    average_dwelling_value\n",
+    "FROM public_marts.mart_neighbourhood_housing\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_housing)\n",
+    "  AND pct_owner_occupied IS NOT NULL\n",
+    "ORDER BY pct_renter_occupied DESC\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query, engine)\n",
+    "print(f\"Loaded {len(df)} neighbourhoods with tenure data\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transformation Steps\n",
+    "\n",
+    "1. Filter to most recent year with tenure data\n",
+    "2. Melt owner/renter columns for stacked bar\n",
+    "3. Sort by renter percentage (highest first)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare for stacked bar\n",
+    "df_stacked = df.melt(\n",
+    "    id_vars=[\"neighbourhood_name\", \"income_quintile\"],\n",
+    "    value_vars=[\"pct_owner_occupied\", \"pct_renter_occupied\"],\n",
+    "    var_name=\"tenure_type\",\n",
+    "    value_name=\"percentage\",\n",
+    ")\n",
+    "\n",
+    "df_stacked[\"tenure_type\"] = df_stacked[\"tenure_type\"].map(\n",
+    "    {\"pct_owner_occupied\": \"Owner\", \"pct_renter_occupied\": \"Renter\"}\n",
+    ")\n",
+    "\n",
+    "data = df_stacked.to_dict(\"records\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Highest Renter Neighbourhoods:\")\n",
+    "df[\n",
+    "    [\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"pct_renter_occupied\",\n",
+    "        \"pct_owner_occupied\",\n",
+    "        \"income_quintile\",\n",
+    "    ]\n",
+    "].head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_stacked_bar` from `portfolio_app.figures.toronto.bar_charts`.\n",
+    "\n",
+    "**Key Parameters:**\n",
+    "- `x_column`: 'neighbourhood_name'\n",
+    "- `value_column`: 'percentage'\n",
+    "- `category_column`: 'tenure_type'\n",
+    "- `show_percentages`: True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.bar_charts import create_stacked_bar\n",
+    "\n",
+    "# Show top 20 by renter percentage\n",
+    "top_20_names = df.head(20)[\"neighbourhood_name\"].tolist()\n",
+    "data_filtered = [d for d in data if d[\"neighbourhood_name\"] in top_20_names]\n",
+    "\n",
+    "fig = create_stacked_bar(\n",
+    "    data=data_filtered,\n",
+    "    x_column=\"neighbourhood_name\",\n",
+    "    value_column=\"percentage\",\n",
+    "    category_column=\"tenure_type\",\n",
+    "    title=\"Housing Tenure Mix - Top 20 Renter Neighbourhoods\",\n",
+    "    color_map={\"Owner\": \"#4CAF50\", \"Renter\": \"#2196F3\"},\n",
+    "    show_percentages=True,\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### City-Wide Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# City-wide averages\n",
+    "print(f\"City Average Owner-Occupied: {df['pct_owner_occupied'].mean():.1f}%\")\n",
+    "print(f\"City Average Renter-Occupied: {df['pct_renter_occupied'].mean():.1f}%\")\n",
+    "\n",
+    "# By income quintile\n",
+    "print(\"\\nTenure by Income Quintile:\")\n",
+    "df.groupby(\"income_quintile\")[\n",
+    "    [\"pct_owner_occupied\", \"pct_renter_occupied\"]\n",
+    "].mean().round(1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}