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/intermediate/toronto/_intermediate.yml

@@ -0,0 +1,87 @@
+version: 2
+
+models:
+  - name: int_rentals__annual
+    description: "Rental data enriched with time and zone dimensions"
+    columns:
+      - name: rental_id
+        tests:
+          - unique
+          - not_null
+      - name: zone_code
+        tests:
+          - not_null
+
+  - name: int_neighbourhood__demographics
+    description: "Combined census demographics with neighbourhood attributes"
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: census_year
+        description: "Census year"
+        tests:
+          - not_null
+      - name: income_quintile
+        description: "Income quintile (1-5, city-wide)"
+
+  - name: int_neighbourhood__housing
+    description: "Housing indicators combining census and rental data"
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: year
+        description: "Reference year"
+      - name: rent_to_income_pct
+        description: "Rent as percentage of median income"
+      - name: is_affordable
+        description: "Boolean: rent <= 30% of income"
+
+  - name: int_neighbourhood__crime_summary
+    description: "Aggregated crime with year-over-year trends"
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: year
+        description: "Statistics year"
+        tests:
+          - not_null
+      - name: crime_rate_per_100k
+        description: "Total crime rate per 100K population"
+      - name: yoy_change_pct
+        description: "Year-over-year change percentage"
+
+  - name: int_neighbourhood__amenity_scores
+    description: "Normalized amenities per capita and per area"
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: year
+        description: "Reference year"
+      - name: total_amenities_per_1000
+        description: "Total amenities per 1000 population"
+      - name: amenities_per_sqkm
+        description: "Total amenities per square km"
+
+  - name: int_rentals__neighbourhood_allocated
+    description: "CMHC rental data allocated to neighbourhoods via area weights"
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: year
+        description: "Survey year"
+        tests:
+          - not_null
+      - name: avg_rent_2bed
+        description: "Weighted average 2-bedroom rent"
+      - name: vacancy_rate
+        description: "Weighted average vacancy rate"

dbt/models/intermediate/toronto/int_census__toronto_cma.sql

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+-- Intermediate: Toronto CMA census statistics by year
+-- Provides city-wide averages for metrics not available at neighbourhood level
+-- Used when neighbourhood-level data is unavailable (e.g., median household income)
+-- Grain: One row per year
+
+with years as (
+    select * from {{ ref('int_year_spine') }}
+),
+
+census as (
+    select * from {{ ref('stg_toronto__census') }}
+),
+
+-- Census data is only available for 2016 and 2021
+-- Map each analysis year to the appropriate census year
+year_to_census as (
+    select
+        y.year,
+        case
+            when y.year <= 2018 then 2016
+            else 2021
+        end as census_year
+    from years y
+),
+
+-- Toronto CMA median household income from Statistics Canada
+-- Source: Census Profile Table 98-316-X2021001
+-- 2016: $65,829 (from Census Profile)
+-- 2021: $84,000 (from Census Profile)
+cma_income as (
+    select 2016 as census_year, 65829 as median_household_income union all
+    select 2021 as census_year, 84000 as median_household_income
+),
+
+-- City-wide aggregates from loaded neighbourhood data
+city_aggregates as (
+    select
+        census_year,
+        sum(population) as total_population,
+        avg(population_density) as avg_population_density,
+        avg(unemployment_rate) as avg_unemployment_rate
+    from census
+    where population is not null
+    group by census_year
+),
+
+final as (
+    select
+        y.year,
+        y.census_year,
+        ci.median_household_income,
+        ca.total_population,
+        ca.avg_population_density,
+        ca.avg_unemployment_rate
+    from year_to_census y
+    left join cma_income ci on y.census_year = ci.census_year
+    left join city_aggregates ca on y.census_year = ca.census_year
+)
+
+select * from final

dbt/models/intermediate/toronto/int_neighbourhood__amenity_scores.sql

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+-- Intermediate: Normalized amenities per 1000 population
+-- Pivots amenity types and calculates per-capita metrics
+-- Grain: One row per neighbourhood per year
+
+with neighbourhoods as (
+    select * from {{ ref('stg_toronto__neighbourhoods') }}
+),
+
+amenities as (
+    select * from {{ ref('stg_toronto__amenities') }}
+),
+
+-- Aggregate amenity types
+amenities_by_year as (
+    select
+        neighbourhood_id,
+        amenity_year as year,
+        sum(case when amenity_type = 'Parks' then amenity_count else 0 end) as parks_count,
+        sum(case when amenity_type = 'Schools' then amenity_count else 0 end) as schools_count,
+        sum(case when amenity_type = 'Transit Stops' then amenity_count else 0 end) as transit_count,
+        sum(case when amenity_type = 'Libraries' then amenity_count else 0 end) as libraries_count,
+        sum(case when amenity_type = 'Community Centres' then amenity_count else 0 end) as community_centres_count,
+        sum(case when amenity_type = 'Recreation' then amenity_count else 0 end) as recreation_count,
+        sum(amenity_count) as total_amenities
+    from amenities
+    group by neighbourhood_id, amenity_year
+),
+
+amenity_scores as (
+    select
+        n.neighbourhood_id,
+        n.neighbourhood_name,
+        n.geometry,
+        n.population,
+        n.land_area_sqkm,
+
+        coalesce(a.year, 2021) as year,
+
+        -- Raw counts
+        a.parks_count,
+        a.schools_count,
+        a.transit_count,
+        a.libraries_count,
+        a.community_centres_count,
+        a.recreation_count,
+        a.total_amenities,
+
+        -- Per 1000 population
+        case when n.population > 0
+            then round(a.parks_count::numeric / n.population * 1000, 3)
+            else null
+        end as parks_per_1000,
+
+        case when n.population > 0
+            then round(a.schools_count::numeric / n.population * 1000, 3)
+            else null
+        end as schools_per_1000,
+
+        case when n.population > 0
+            then round(a.transit_count::numeric / n.population * 1000, 3)
+            else null
+        end as transit_per_1000,
+
+        case when n.population > 0
+            then round(a.total_amenities::numeric / n.population * 1000, 3)
+            else null
+        end as total_amenities_per_1000,
+
+        -- Per square km
+        case when n.land_area_sqkm > 0
+            then round(a.total_amenities::numeric / n.land_area_sqkm, 2)
+            else null
+        end as amenities_per_sqkm
+
+    from neighbourhoods n
+    left join amenities_by_year a on n.neighbourhood_id = a.neighbourhood_id
+)
+
+select * from amenity_scores

dbt/models/intermediate/toronto/int_neighbourhood__crime_summary.sql

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+-- Intermediate: Aggregated crime by neighbourhood with YoY change
+-- Pivots crime types and calculates year-over-year trends
+-- Grain: One row per neighbourhood per year
+
+with neighbourhoods as (
+    select * from {{ ref('stg_toronto__neighbourhoods') }}
+),
+
+crime as (
+    select * from {{ ref('stg_toronto__crime') }}
+),
+
+-- Aggregate crime types
+crime_by_year as (
+    select
+        neighbourhood_id,
+        crime_year as year,
+        sum(incident_count) as total_incidents,
+        sum(case when crime_type = 'Assault' then incident_count else 0 end) as assault_count,
+        sum(case when crime_type = 'Auto Theft' then incident_count else 0 end) as auto_theft_count,
+        sum(case when crime_type = 'Break and Enter' then incident_count else 0 end) as break_enter_count,
+        sum(case when crime_type = 'Robbery' then incident_count else 0 end) as robbery_count,
+        sum(case when crime_type = 'Theft Over' then incident_count else 0 end) as theft_over_count,
+        sum(case when crime_type = 'Homicide' then incident_count else 0 end) as homicide_count,
+        avg(rate_per_100k) as avg_rate_per_100k
+    from crime
+    group by neighbourhood_id, crime_year
+),
+
+-- Add year-over-year changes
+with_yoy as (
+    select
+        c.*,
+        lag(c.total_incidents, 1) over (
+            partition by c.neighbourhood_id
+            order by c.year
+        ) as prev_year_incidents,
+        round(
+            (c.total_incidents - lag(c.total_incidents, 1) over (
+                partition by c.neighbourhood_id
+                order by c.year
+            ))::numeric /
+            nullif(lag(c.total_incidents, 1) over (
+                partition by c.neighbourhood_id
+                order by c.year
+            ), 0) * 100,
+            2
+        ) as yoy_change_pct
+    from crime_by_year c
+),
+
+crime_summary as (
+    select
+        n.neighbourhood_id,
+        n.neighbourhood_name,
+        n.geometry,
+        n.population,
+
+        w.year,
+        w.total_incidents,
+        w.assault_count,
+        w.auto_theft_count,
+        w.break_enter_count,
+        w.robbery_count,
+        w.theft_over_count,
+        w.homicide_count,
+        w.yoy_change_pct,
+
+        -- Crime rate per 100K population (use source data avg, or calculate if population available)
+        coalesce(
+            w.avg_rate_per_100k,
+            case
+                when n.population > 0
+                then round(w.total_incidents::numeric / n.population * 100000, 2)
+                else null
+            end
+        ) as crime_rate_per_100k
+
+    from neighbourhoods n
+    inner join with_yoy w on n.neighbourhood_id = w.neighbourhood_id
+)
+
+select * from crime_summary

dbt/models/intermediate/toronto/int_neighbourhood__demographics.sql

@@ -0,0 +1,45 @@
+-- Intermediate: Combined census demographics by neighbourhood
+-- Joins neighbourhoods with census data for demographic analysis
+-- Grain: One row per neighbourhood per census year
+
+with neighbourhoods as (
+    select * from {{ ref('stg_toronto__neighbourhoods') }}
+),
+
+census as (
+    select * from {{ ref('stg_toronto__census') }}
+),
+
+demographics as (
+    select
+        n.neighbourhood_id,
+        n.neighbourhood_name,
+        n.geometry,
+        n.land_area_sqkm,
+
+        -- Use census_year from census data, or fall back to dim_neighbourhood's year
+        coalesce(c.census_year, n.census_year, 2021) as census_year,
+        c.population,
+        c.population_density,
+        c.median_household_income,
+        c.average_household_income,
+        c.median_age,
+        c.unemployment_rate,
+        c.pct_bachelors_or_higher as education_bachelors_pct,
+        c.average_dwelling_value,
+
+        -- Tenure mix
+        c.pct_owner_occupied,
+        c.pct_renter_occupied,
+
+        -- Income quintile (city-wide comparison)
+        ntile(5) over (
+            partition by c.census_year
+            order by c.median_household_income
+        ) as income_quintile
+
+    from neighbourhoods n
+    left join census c on n.neighbourhood_id = c.neighbourhood_id
+)
+
+select * from demographics

dbt/models/intermediate/toronto/int_neighbourhood__housing.sql

@@ -0,0 +1,56 @@
+-- Intermediate: Housing indicators by neighbourhood
+-- Combines census housing data with allocated CMHC rental data
+-- Grain: One row per neighbourhood per year
+
+with neighbourhoods as (
+    select * from {{ ref('stg_toronto__neighbourhoods') }}
+),
+
+census as (
+    select * from {{ ref('stg_toronto__census') }}
+),
+
+allocated_rentals as (
+    select * from {{ ref('int_rentals__neighbourhood_allocated') }}
+),
+
+housing as (
+    select
+        n.neighbourhood_id,
+        n.neighbourhood_name,
+        n.geometry,
+
+        coalesce(r.year, c.census_year, 2021) as year,
+
+        -- Census housing metrics
+        c.pct_owner_occupied,
+        c.pct_renter_occupied,
+        c.average_dwelling_value,
+        c.median_household_income,
+
+        -- Allocated rental metrics (weighted average from CMHC zones)
+        r.avg_rent_2bed,
+        r.vacancy_rate,
+
+        -- Affordability calculations
+        case
+            when c.median_household_income > 0 and r.avg_rent_2bed > 0
+            then round((r.avg_rent_2bed * 12 / c.median_household_income) * 100, 2)
+            else null
+        end as rent_to_income_pct,
+
+        -- Affordability threshold (30% of income)
+        case
+            when c.median_household_income > 0 and r.avg_rent_2bed > 0
+            then r.avg_rent_2bed * 12 <= c.median_household_income * 0.30
+            else null
+        end as is_affordable
+
+    from neighbourhoods n
+    left join census c on n.neighbourhood_id = c.neighbourhood_id
+    left join allocated_rentals r
+        on n.neighbourhood_id = r.neighbourhood_id
+        and r.year = c.census_year
+)
+
+select * from housing

dbt/models/intermediate/toronto/int_rentals__annual.sql

@@ -0,0 +1,57 @@
+-- Intermediate: Annual rental data enriched with dimensions
+-- Joins rentals with time and zone dimensions for analysis
+
+with rentals as (
+    select * from {{ ref('stg_cmhc__rentals') }}
+),
+
+time_dim as (
+    select * from {{ ref('stg_dimensions__time') }}
+),
+
+zone_dim as (
+    select * from {{ ref('stg_dimensions__cmhc_zones') }}
+),
+
+enriched as (
+    select
+        r.rental_id,
+
+        -- Time attributes
+        t.date_key,
+        t.full_date,
+        t.year,
+        t.month,
+        t.quarter,
+
+        -- Zone attributes
+        z.zone_key,
+        z.zone_code,
+        z.zone_name,
+
+        -- Bedroom type
+        r.bedroom_type,
+
+        -- Metrics
+        r.rental_universe,
+        r.avg_rent,
+        r.median_rent,
+        r.vacancy_rate,
+        r.availability_rate,
+        r.turnover_rate,
+        r.year_over_year_rent_change,
+        r.reliability_code,
+
+        -- Calculated metrics
+        case
+            when r.rental_universe > 0 and r.vacancy_rate is not null
+            then round(r.rental_universe * (r.vacancy_rate / 100), 0)
+            else null
+        end as vacant_units_estimate
+
+    from rentals r
+    inner join time_dim t on r.date_key = t.date_key
+    inner join zone_dim z on r.zone_key = z.zone_key
+)
+
+select * from enriched

dbt/models/intermediate/toronto/int_rentals__neighbourhood_allocated.sql

@@ -0,0 +1,73 @@
+-- Intermediate: CMHC rentals allocated to neighbourhoods via area weights
+-- Disaggregates zone-level rental data to neighbourhood level
+-- Grain: One row per neighbourhood per year
+
+with crosswalk as (
+    select * from {{ ref('stg_cmhc__zone_crosswalk') }}
+),
+
+rentals as (
+    select * from {{ ref('int_rentals__annual') }}
+),
+
+neighbourhoods as (
+    select * from {{ ref('stg_toronto__neighbourhoods') }}
+),
+
+-- Allocate rental metrics to neighbourhoods using area weights
+allocated as (
+    select
+        c.neighbourhood_id,
+        r.year,
+        r.bedroom_type,
+
+        -- Weighted average rent (using area weight)
+        sum(r.avg_rent * c.area_weight) as weighted_avg_rent,
+        sum(r.median_rent * c.area_weight) as weighted_median_rent,
+        sum(c.area_weight) as total_weight,
+
+        -- Weighted vacancy rate
+        sum(r.vacancy_rate * c.area_weight) / nullif(sum(c.area_weight), 0) as vacancy_rate,
+
+        -- Weighted rental universe
+        sum(r.rental_universe * c.area_weight) as rental_units_estimate
+
+    from crosswalk c
+    inner join rentals r on c.cmhc_zone_code = r.zone_code
+    group by c.neighbourhood_id, r.year, r.bedroom_type
+),
+
+-- Pivot to get 2-bedroom as primary metric
+pivoted as (
+    select
+        neighbourhood_id,
+        year,
+        max(case when bedroom_type = 'Two Bedroom' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_2bed,
+        max(case when bedroom_type = 'One Bedroom' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_1bed,
+        max(case when bedroom_type = 'Bachelor' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_bachelor,
+        max(case when bedroom_type = 'Three Bedroom +' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_3bed,
+        avg(vacancy_rate) as vacancy_rate,
+        sum(rental_units_estimate) as total_rental_units
+    from allocated
+    group by neighbourhood_id, year
+),
+
+final as (
+    select
+        n.neighbourhood_id,
+        n.neighbourhood_name,
+        n.geometry,
+
+        p.year,
+        round(p.avg_rent_bachelor::numeric, 2) as avg_rent_bachelor,
+        round(p.avg_rent_1bed::numeric, 2) as avg_rent_1bed,
+        round(p.avg_rent_2bed::numeric, 2) as avg_rent_2bed,
+        round(p.avg_rent_3bed::numeric, 2) as avg_rent_3bed,
+        round(p.vacancy_rate::numeric, 2) as vacancy_rate,
+        round(p.total_rental_units::numeric, 0) as total_rental_units
+
+    from neighbourhoods n
+    inner join pivoted p on n.neighbourhood_id = p.neighbourhood_id
+)
+
+select * from final

dbt/models/intermediate/toronto/int_rentals__toronto_cma.sql

@@ -0,0 +1,25 @@
+-- Intermediate: Toronto CMA rental metrics by year
+-- Aggregates rental data to city-wide averages by year
+-- Source: StatCan CMHC data at CMA level
+-- Grain: One row per year
+
+with rentals as (
+    select * from {{ ref('stg_cmhc__rentals') }}
+),
+
+-- Pivot bedroom types to columns
+yearly_rentals as (
+    select
+        year,
+        max(case when bedroom_type = 'bachelor' then avg_rent end) as avg_rent_bachelor,
+        max(case when bedroom_type = '1bed' then avg_rent end) as avg_rent_1bed,
+        max(case when bedroom_type = '2bed' then avg_rent end) as avg_rent_2bed,
+        max(case when bedroom_type = '3bed' then avg_rent end) as avg_rent_3bed,
+        -- Use 2-bedroom as standard reference
+        max(case when bedroom_type = '2bed' then avg_rent end) as avg_rent_standard,
+        max(vacancy_rate) as vacancy_rate
+    from rentals
+    group by year
+)
+
+select * from yearly_rentals

dbt/models/intermediate/toronto/int_year_spine.sql

@@ -0,0 +1,11 @@
+-- Intermediate: Year spine for analysis
+-- Creates a row for each year from 2014-2025
+-- Used to drive time-series analysis across all data sources
+
+with years as (
+    -- Generate years from available data sources
+    -- Crime data: 2014-2024, Rentals: 2019-2025
+    select generate_series(2014, 2025) as year
+)
+
+select year from years