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/overview/top_bottom_10_bar.ipynb

@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Top & Bottom 10 Neighbourhoods Bar Chart\n",
+    "\n",
+    "Horizontal bar chart showing the highest and lowest scoring neighbourhoods by livability."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Data Reference\n",
+    "\n",
+    "### Source Tables\n",
+    "\n",
+    "| Table | Grain | Key Columns |\n",
+    "|-------|-------|-------------|\n",
+    "| `mart_neighbourhood_overview` | neighbourhood × year | neighbourhood_name, livability_score |\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",
+    "    livability_score,\n",
+    "    safety_score,\n",
+    "    affordability_score,\n",
+    "    amenity_score\n",
+    "FROM public_marts.mart_neighbourhood_overview\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\n",
+    "  AND livability_score IS NOT NULL\n",
+    "ORDER BY livability_score DESC\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query, engine)\n",
+    "print(f\"Loaded {len(df)} neighbourhoods with scores\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transformation Steps\n",
+    "\n",
+    "1. Sort by livability_score descending\n",
+    "2. Take top 10 and bottom 10\n",
+    "3. Pass to ranking bar figure factory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The figure factory handles top/bottom selection internally\n",
+    "# Just prepare as list of dicts\n",
+    "data = df.to_dict(\"records\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Top 5:\")\n",
+    "display(df.head(5))\n",
+    "print(\"\\nBottom 5:\")\n",
+    "display(df.tail(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_ranking_bar` from `portfolio_app.figures.toronto.bar_charts`.\n",
+    "\n",
+    "**Key Parameters:**\n",
+    "- `data`: List of dicts with all neighbourhoods\n",
+    "- `name_column`: 'neighbourhood_name'\n",
+    "- `value_column`: 'livability_score'\n",
+    "- `top_n`: 10 (green bars)\n",
+    "- `bottom_n`: 10 (red bars)"
+   ]
+  },
+  {
+   "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_ranking_bar\n",
+    "\n",
+    "fig = create_ranking_bar(\n",
+    "    data=data,\n",
+    "    name_column=\"neighbourhood_name\",\n",
+    "    value_column=\"livability_score\",\n",
+    "    title=\"Top & Bottom 10 Neighbourhoods by Livability\",\n",
+    "    top_n=10,\n",
+    "    bottom_n=10,\n",
+    "    color_top=\"#4CAF50\",  # Green for top performers\n",
+    "    color_bottom=\"#F44336\",  # Red for bottom performers\n",
+    "    value_format=\".1f\",\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation\n",
+    "\n",
+    "- **Green bars**: Highest livability scores (best combination of safety, affordability, and amenities)\n",
+    "- **Red bars**: Lowest livability scores (areas that may need targeted investment)\n",
+    "\n",
+    "The ranking bar chart provides quick context for which neighbourhoods stand out at either extreme."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}