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>

dbt/models/marts/toronto/mart_neighbourhood_demographics.sql

@@ -0,0 +1,81 @@
+-- Mart: Neighbourhood Demographics Analysis
+-- Dashboard Tab: Demographics
+-- Grain: One row per neighbourhood per census year
+
+with demographics as (
+    select * from {{ ref('int_neighbourhood__demographics') }}
+),
+
+-- City-wide averages for comparison
+city_avg as (
+    select
+        census_year,
+        avg(median_household_income) as city_avg_income,
+        avg(median_age) as city_avg_age,
+        avg(unemployment_rate) as city_avg_unemployment,
+        avg(education_bachelors_pct) as city_avg_education,
+        avg(population_density) as city_avg_density
+    from demographics
+    group by census_year
+),
+
+final as (
+    select
+        d.neighbourhood_id,
+        d.neighbourhood_name,
+        d.geometry,
+        d.census_year as year,
+
+        -- Population
+        d.population,
+        d.land_area_sqkm,
+        d.population_density,
+
+        -- Income
+        d.median_household_income,
+        d.average_household_income,
+        d.income_quintile,
+
+        -- Income index (100 = city average)
+        case
+            when ca.city_avg_income > 0
+            then round(d.median_household_income / ca.city_avg_income * 100, 1)
+            else null
+        end as income_index,
+
+        -- Demographics
+        d.median_age,
+        d.unemployment_rate,
+        d.education_bachelors_pct,
+
+        -- Age index (100 = city average)
+        case
+            when ca.city_avg_age > 0
+            then round(d.median_age / ca.city_avg_age * 100, 1)
+            else null
+        end as age_index,
+
+        -- Housing tenure
+        d.pct_owner_occupied,
+        d.pct_renter_occupied,
+        d.average_dwelling_value,
+
+        -- Diversity index (using tenure mix as proxy - higher rental = more diverse typically)
+        round(
+            1 - (
+                power(d.pct_owner_occupied / 100, 2) +
+                power(d.pct_renter_occupied / 100, 2)
+            ),
+            3
+        ) * 100 as tenure_diversity_index,
+
+        -- City comparisons
+        round(ca.city_avg_income::numeric, 2) as city_avg_income,
+        round(ca.city_avg_age::numeric, 1) as city_avg_age,
+        round(ca.city_avg_unemployment::numeric, 2) as city_avg_unemployment
+
+    from demographics d
+    left join city_avg ca on d.census_year = ca.census_year
+)
+
+select * from final