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_trend_line.ipynb

@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Crime Trend Line Chart\n",
+    "\n",
+    "Shows 5-year crime rate trends across Toronto."
+   ]
+  },
+  {
+   "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 | year, crime_rate_per_100k, crime_yoy_change_pct |\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",
+    "    year,\n",
+    "    AVG(crime_rate_per_100k) as avg_crime_rate,\n",
+    "    AVG(assault_rate_per_100k) as avg_assault_rate,\n",
+    "    AVG(auto_theft_rate_per_100k) as avg_auto_theft_rate,\n",
+    "    AVG(break_enter_rate_per_100k) as avg_break_enter_rate,\n",
+    "    SUM(total_incidents) as total_city_incidents,\n",
+    "    AVG(crime_yoy_change_pct) as avg_yoy_change\n",
+    "FROM public_marts.mart_neighbourhood_safety\n",
+    "WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.mart_neighbourhood_safety)\n",
+    "GROUP BY year\n",
+    "ORDER BY year\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query, engine)\n",
+    "print(f\"Loaded {len(df)} years of crime data\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transformation Steps\n",
+    "\n",
+    "1. Aggregate by year (city-wide)\n",
+    "2. Convert year to datetime\n",
+    "3. Melt for multi-line by crime type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"date\"] = pd.to_datetime(df[\"year\"].astype(str) + \"-01-01\")\n",
+    "\n",
+    "# Melt for multi-line\n",
+    "df_melted = df.melt(\n",
+    "    id_vars=[\"year\", \"date\"],\n",
+    "    value_vars=[\"avg_assault_rate\", \"avg_auto_theft_rate\", \"avg_break_enter_rate\"],\n",
+    "    var_name=\"crime_type\",\n",
+    "    value_name=\"rate_per_100k\",\n",
+    ")\n",
+    "\n",
+    "df_melted[\"crime_type\"] = df_melted[\"crime_type\"].map(\n",
+    "    {\n",
+    "        \"avg_assault_rate\": \"Assault\",\n",
+    "        \"avg_auto_theft_rate\": \"Auto Theft\",\n",
+    "        \"avg_break_enter_rate\": \"Break & Enter\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[[\"year\", \"avg_crime_rate\", \"total_city_incidents\", \"avg_yoy_change\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_price_time_series` (reused for any numeric trend)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.time_series import create_price_time_series\n",
+    "\n",
+    "data = df_melted.to_dict(\"records\")\n",
+    "\n",
+    "fig = create_price_time_series(\n",
+    "    data=data,\n",
+    "    date_column=\"date\",\n",
+    "    price_column=\"rate_per_100k\",\n",
+    "    group_column=\"crime_type\",\n",
+    "    title=\"Toronto Crime Trends by Type (5 Years)\",\n",
+    ")\n",
+    "\n",
+    "# Remove dollar sign formatting since this is rate data\n",
+    "fig.update_layout(yaxis_tickprefix=\"\", yaxis_title=\"Rate per 100K\")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Overall Trend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Total crime rate trend\n",
+    "total_data = (\n",
+    "    df[[\"date\", \"avg_crime_rate\"]]\n",
+    "    .rename(columns={\"avg_crime_rate\": \"total_rate\"})\n",
+    "    .to_dict(\"records\")\n",
+    ")\n",
+    "\n",
+    "fig2 = create_price_time_series(\n",
+    "    data=total_data,\n",
+    "    date_column=\"date\",\n",
+    "    price_column=\"total_rate\",\n",
+    "    title=\"Toronto Overall Crime Rate Trend\",\n",
+    ")\n",
+    "fig2.update_layout(yaxis_tickprefix=\"\", yaxis_title=\"Rate per 100K\")\n",
+    "fig2.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}