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/demographics/population_density_bar.ipynb

@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Population Density Bar Chart\n",
+    "\n",
+    "Shows population density (people per sq km) 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_demographics` | neighbourhood × year | population_density, population, land_area_sqkm |\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",
+    "    population_density,\n",
+    "    population,\n",
+    "    land_area_sqkm,\n",
+    "    median_household_income,\n",
+    "    pct_renter_occupied\n",
+    "FROM public_marts.mart_neighbourhood_demographics\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_demographics)\n",
+    "  AND population_density IS NOT NULL\n",
+    "ORDER BY population_density 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 population density\n",
+    "2. Select top 20 most dense neighbourhoods"
+   ]
+  },
+  {
+   "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\", \"population_density\", \"population\", \"land_area_sqkm\"]].head(\n",
+    "    10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_horizontal_bar` from `portfolio_app.figures.toronto.bar_charts`."
+   ]
+  },
+  {
+   "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_horizontal_bar\n",
+    "\n",
+    "fig = create_horizontal_bar(\n",
+    "    data=data,\n",
+    "    name_column=\"neighbourhood_name\",\n",
+    "    value_column=\"population_density\",\n",
+    "    title=\"Top 20 Most Dense Neighbourhoods\",\n",
+    "    color=\"#9C27B0\",\n",
+    "    value_format=\",.0f\",\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Density Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"City-wide Statistics:\")\n",
+    "print(f\"  Total Population: {df['population'].sum():,.0f}\")\n",
+    "print(f\"  Total Area: {df['land_area_sqkm'].sum():,.1f} sq km\")\n",
+    "print(f\"  Average Density: {df['population_density'].mean():,.0f} per sq km\")\n",
+    "print(f\"  Max Density: {df['population_density'].max():,.0f} per sq km\")\n",
+    "print(f\"  Min Density: {df['population_density'].min():,.0f} per sq km\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}