feat: Implement Phase 4 dbt model restructuring

Create neighbourhood-centric dbt transformation layer:

Staging (5 models):
- stg_toronto__neighbourhoods - Neighbourhood dimension
- stg_toronto__census - Census demographics
- stg_toronto__crime - Crime statistics
- stg_toronto__amenities - Amenity counts
- stg_cmhc__zone_crosswalk - Zone-to-neighbourhood weights

Intermediate (5 models):
- int_neighbourhood__demographics - Combined census with quintiles
- int_neighbourhood__housing - Housing + affordability indicators
- int_neighbourhood__crime_summary - Aggregated crime with YoY
- int_neighbourhood__amenity_scores - Per-capita amenity metrics
- int_rentals__neighbourhood_allocated - CMHC via area weights

Marts (5 models):
- mart_neighbourhood_overview - Composite livability score
- mart_neighbourhood_housing - Affordability index
- mart_neighbourhood_safety - Crime rates per 100K
- mart_neighbourhood_demographics - Income/age indices
- mart_neighbourhood_amenities - Amenity index

Closes #60, #61, #62, #63

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

dbt/models/intermediate/_intermediate.yml

@@ -11,3 +11,77 @@ models:
       - 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/int_neighbourhood__amenity_scores.sql

@@ -0,0 +1,79 @@
+-- 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,
+
+        a.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/int_neighbourhood__crime_summary.sql

@@ -0,0 +1,81 @@
+-- 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.avg_rate_per_100k,
+        w.yoy_change_pct,
+
+        -- Crime rate per 100K population
+        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/int_neighbourhood__demographics.sql

@@ -0,0 +1,44 @@
+-- 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,
+
+        c.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/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) 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/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/marts/_marts.yml

@@ -9,3 +9,127 @@ models:
         tests:
           - unique
           - not_null
+
+  - name: mart_neighbourhood_overview
+    description: "Neighbourhood overview with composite livability score"
+    meta:
+      dashboard_tab: Overview
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: neighbourhood_name
+        description: "Official neighbourhood name"
+        tests:
+          - not_null
+      - name: geometry
+        description: "PostGIS geometry for mapping"
+      - name: livability_score
+        description: "Composite score: safety (30%), affordability (40%), amenities (30%)"
+      - name: safety_score
+        description: "Safety component score (0-100)"
+      - name: affordability_score
+        description: "Affordability component score (0-100)"
+      - name: amenity_score
+        description: "Amenity component score (0-100)"
+
+  - name: mart_neighbourhood_housing
+    description: "Housing and affordability metrics by neighbourhood"
+    meta:
+      dashboard_tab: Housing
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: neighbourhood_name
+        description: "Official neighbourhood name"
+        tests:
+          - not_null
+      - name: geometry
+        description: "PostGIS geometry for mapping"
+      - name: rent_to_income_pct
+        description: "Rent as percentage of median income"
+      - name: affordability_index
+        description: "100 = city average affordability"
+      - name: rent_yoy_change_pct
+        description: "Year-over-year rent change"
+
+  - name: mart_neighbourhood_safety
+    description: "Crime rates and safety metrics by neighbourhood"
+    meta:
+      dashboard_tab: Safety
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: neighbourhood_name
+        description: "Official neighbourhood name"
+        tests:
+          - not_null
+      - name: geometry
+        description: "PostGIS geometry for mapping"
+      - name: crime_rate_per_100k
+        description: "Total crime rate per 100K population"
+      - name: crime_index
+        description: "100 = city average crime rate"
+      - name: safety_tier
+        description: "Safety tier (1=safest, 5=highest crime)"
+        tests:
+          - accepted_values:
+              arguments:
+                values: [1, 2, 3, 4, 5]
+
+  - name: mart_neighbourhood_demographics
+    description: "Demographics and income metrics by neighbourhood"
+    meta:
+      dashboard_tab: Demographics
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: neighbourhood_name
+        description: "Official neighbourhood name"
+        tests:
+          - not_null
+      - name: geometry
+        description: "PostGIS geometry for mapping"
+      - name: median_household_income
+        description: "Median household income"
+      - name: income_index
+        description: "100 = city average income"
+      - name: income_quintile
+        description: "Income quintile (1-5)"
+        tests:
+          - accepted_values:
+              arguments:
+                values: [1, 2, 3, 4, 5]
+
+  - name: mart_neighbourhood_amenities
+    description: "Amenity access metrics by neighbourhood"
+    meta:
+      dashboard_tab: Amenities
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood identifier"
+        tests:
+          - not_null
+      - name: neighbourhood_name
+        description: "Official neighbourhood name"
+        tests:
+          - not_null
+      - name: geometry
+        description: "PostGIS geometry for mapping"
+      - name: total_amenities_per_1000
+        description: "Total amenities per 1000 population"
+      - name: amenity_index
+        description: "100 = city average amenities"
+      - name: amenity_tier
+        description: "Amenity tier (1=best, 5=lowest)"
+        tests:
+          - accepted_values:
+              arguments:
+                values: [1, 2, 3, 4, 5]

dbt/models/marts/mart_neighbourhood_amenities.sql

@@ -0,0 +1,89 @@
+-- Mart: Neighbourhood Amenities Analysis
+-- Dashboard Tab: Amenities
+-- Grain: One row per neighbourhood per year
+
+with amenities as (
+    select * from {{ ref('int_neighbourhood__amenity_scores') }}
+),
+
+-- City-wide averages for comparison
+city_avg as (
+    select
+        year,
+        avg(parks_per_1000) as city_avg_parks,
+        avg(schools_per_1000) as city_avg_schools,
+        avg(transit_per_1000) as city_avg_transit,
+        avg(total_amenities_per_1000) as city_avg_total_amenities
+    from amenities
+    group by year
+),
+
+final as (
+    select
+        a.neighbourhood_id,
+        a.neighbourhood_name,
+        a.geometry,
+        a.population,
+        a.land_area_sqkm,
+        a.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
+        a.parks_per_1000,
+        a.schools_per_1000,
+        a.transit_per_1000,
+        a.total_amenities_per_1000,
+
+        -- Per square km
+        a.amenities_per_sqkm,
+
+        -- City averages
+        round(ca.city_avg_parks::numeric, 3) as city_avg_parks_per_1000,
+        round(ca.city_avg_schools::numeric, 3) as city_avg_schools_per_1000,
+        round(ca.city_avg_transit::numeric, 3) as city_avg_transit_per_1000,
+
+        -- Amenity index (100 = city average)
+        case
+            when ca.city_avg_total_amenities > 0
+            then round(a.total_amenities_per_1000 / ca.city_avg_total_amenities * 100, 1)
+            else null
+        end as amenity_index,
+
+        -- Category indices
+        case
+            when ca.city_avg_parks > 0
+            then round(a.parks_per_1000 / ca.city_avg_parks * 100, 1)
+            else null
+        end as parks_index,
+
+        case
+            when ca.city_avg_schools > 0
+            then round(a.schools_per_1000 / ca.city_avg_schools * 100, 1)
+            else null
+        end as schools_index,
+
+        case
+            when ca.city_avg_transit > 0
+            then round(a.transit_per_1000 / ca.city_avg_transit * 100, 1)
+            else null
+        end as transit_index,
+
+        -- Amenity tier (1 = best, 5 = lowest)
+        ntile(5) over (
+            partition by a.year
+            order by a.total_amenities_per_1000 desc
+        ) as amenity_tier
+
+    from amenities a
+    left join city_avg ca on a.year = ca.year
+)
+
+select * from final

dbt/models/marts/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

dbt/models/marts/mart_neighbourhood_housing.sql

@@ -0,0 +1,93 @@
+-- Mart: Neighbourhood Housing Analysis
+-- Dashboard Tab: Housing
+-- Grain: One row per neighbourhood per year
+
+with housing as (
+    select * from {{ ref('int_neighbourhood__housing') }}
+),
+
+rentals as (
+    select * from {{ ref('int_rentals__neighbourhood_allocated') }}
+),
+
+demographics as (
+    select * from {{ ref('int_neighbourhood__demographics') }}
+),
+
+-- Add year-over-year rent changes
+with_yoy as (
+    select
+        h.*,
+        r.avg_rent_bachelor,
+        r.avg_rent_1bed,
+        r.avg_rent_3bed,
+        r.total_rental_units,
+        d.income_quintile,
+
+        -- Previous year rent for YoY calculation
+        lag(h.avg_rent_2bed, 1) over (
+            partition by h.neighbourhood_id
+            order by h.year
+        ) as prev_year_rent_2bed
+
+    from housing h
+    left join rentals r
+        on h.neighbourhood_id = r.neighbourhood_id
+        and h.year = r.year
+    left join demographics d
+        on h.neighbourhood_id = d.neighbourhood_id
+        and h.year = d.census_year
+),
+
+final as (
+    select
+        neighbourhood_id,
+        neighbourhood_name,
+        geometry,
+        year,
+
+        -- Tenure mix
+        pct_owner_occupied,
+        pct_renter_occupied,
+
+        -- Housing values
+        average_dwelling_value,
+        median_household_income,
+
+        -- Rental metrics
+        avg_rent_bachelor,
+        avg_rent_1bed,
+        avg_rent_2bed,
+        avg_rent_3bed,
+        vacancy_rate,
+        total_rental_units,
+
+        -- Affordability
+        rent_to_income_pct,
+        is_affordable,
+
+        -- Affordability index (100 = city average)
+        round(
+            rent_to_income_pct / nullif(
+                avg(rent_to_income_pct) over (partition by year),
+                0
+            ) * 100,
+            1
+        ) as affordability_index,
+
+        -- Year-over-year rent change
+        case
+            when prev_year_rent_2bed > 0
+            then round(
+                (avg_rent_2bed - prev_year_rent_2bed) / prev_year_rent_2bed * 100,
+                2
+            )
+            else null
+        end as rent_yoy_change_pct,
+
+        income_quintile
+
+    from with_yoy
+)
+
+select * from final

dbt/models/marts/mart_neighbourhood_overview.sql

@@ -0,0 +1,110 @@
+-- Mart: Neighbourhood Overview with Composite Livability Score
+-- Dashboard Tab: Overview
+-- Grain: One row per neighbourhood per year
+
+with demographics as (
+    select * from {{ ref('int_neighbourhood__demographics') }}
+),
+
+housing as (
+    select * from {{ ref('int_neighbourhood__housing') }}
+),
+
+crime as (
+    select * from {{ ref('int_neighbourhood__crime_summary') }}
+),
+
+amenities as (
+    select * from {{ ref('int_neighbourhood__amenity_scores') }}
+),
+
+-- Compute percentile ranks for scoring components
+percentiles as (
+    select
+        d.neighbourhood_id,
+        d.neighbourhood_name,
+        d.geometry,
+        d.census_year as year,
+        d.population,
+        d.median_household_income,
+
+        -- Safety score: inverse of crime rate (higher = safer)
+        case
+            when c.crime_rate_per_100k is not null
+            then 100 - percent_rank() over (
+                partition by d.census_year
+                order by c.crime_rate_per_100k
+            ) * 100
+            else null
+        end as safety_score,
+
+        -- Affordability score: inverse of rent-to-income ratio
+        case
+            when h.rent_to_income_pct is not null
+            then 100 - percent_rank() over (
+                partition by d.census_year
+                order by h.rent_to_income_pct
+            ) * 100
+            else null
+        end as affordability_score,
+
+        -- Amenity score: based on amenities per capita
+        case
+            when a.total_amenities_per_1000 is not null
+            then percent_rank() over (
+                partition by d.census_year
+                order by a.total_amenities_per_1000
+            ) * 100
+            else null
+        end as amenity_score,
+
+        -- Raw metrics for reference
+        c.crime_rate_per_100k,
+        h.rent_to_income_pct,
+        h.avg_rent_2bed,
+        a.total_amenities_per_1000
+
+    from demographics d
+    left join housing h
+        on d.neighbourhood_id = h.neighbourhood_id
+        and d.census_year = h.year
+    left join crime c
+        on d.neighbourhood_id = c.neighbourhood_id
+        and d.census_year = c.year
+    left join amenities a
+        on d.neighbourhood_id = a.neighbourhood_id
+        and d.census_year = a.year
+),
+
+final as (
+    select
+        neighbourhood_id,
+        neighbourhood_name,
+        geometry,
+        year,
+        population,
+        median_household_income,
+
+        -- Component scores (0-100)
+        round(safety_score::numeric, 1) as safety_score,
+        round(affordability_score::numeric, 1) as affordability_score,
+        round(amenity_score::numeric, 1) as amenity_score,
+
+        -- Composite livability score: safety (30%), affordability (40%), amenities (30%)
+        round(
+            (coalesce(safety_score, 50) * 0.30 +
+             coalesce(affordability_score, 50) * 0.40 +
+             coalesce(amenity_score, 50) * 0.30)::numeric,
+            1
+        ) as livability_score,
+
+        -- Raw metrics
+        crime_rate_per_100k,
+        rent_to_income_pct,
+        avg_rent_2bed,
+        total_amenities_per_1000
+
+    from percentiles
+)
+
+select * from final

dbt/models/marts/mart_neighbourhood_safety.sql

@@ -0,0 +1,78 @@
+-- Mart: Neighbourhood Safety Analysis
+-- Dashboard Tab: Safety
+-- Grain: One row per neighbourhood per year
+
+with crime as (
+    select * from {{ ref('int_neighbourhood__crime_summary') }}
+),
+
+-- City-wide averages for comparison
+city_avg as (
+    select
+        year,
+        avg(crime_rate_per_100k) as city_avg_crime_rate,
+        avg(assault_count) as city_avg_assault,
+        avg(auto_theft_count) as city_avg_auto_theft,
+        avg(break_enter_count) as city_avg_break_enter
+    from crime
+    group by year
+),
+
+final as (
+    select
+        c.neighbourhood_id,
+        c.neighbourhood_name,
+        c.geometry,
+        c.population,
+        c.year,
+
+        -- Total crime
+        c.total_incidents,
+        c.crime_rate_per_100k,
+        c.yoy_change_pct as crime_yoy_change_pct,
+
+        -- Crime breakdown
+        c.assault_count,
+        c.auto_theft_count,
+        c.break_enter_count,
+        c.robbery_count,
+        c.theft_over_count,
+        c.homicide_count,
+
+        -- Per 100K rates by type
+        case when c.population > 0
+            then round(c.assault_count::numeric / c.population * 100000, 2)
+            else null
+        end as assault_rate_per_100k,
+
+        case when c.population > 0
+            then round(c.auto_theft_count::numeric / c.population * 100000, 2)
+            else null
+        end as auto_theft_rate_per_100k,
+
+        case when c.population > 0
+            then round(c.break_enter_count::numeric / c.population * 100000, 2)
+            else null
+        end as break_enter_rate_per_100k,
+
+        -- Comparison to city average
+        round(ca.city_avg_crime_rate::numeric, 2) as city_avg_crime_rate,
+
+        -- Crime index (100 = city average)
+        case
+            when ca.city_avg_crime_rate > 0
+            then round(c.crime_rate_per_100k / ca.city_avg_crime_rate * 100, 1)
+            else null
+        end as crime_index,
+
+        -- Safety tier based on crime rate percentile
+        ntile(5) over (
+            partition by c.year
+            order by c.crime_rate_per_100k desc
+        ) as safety_tier
+
+    from crime c
+    left join city_avg ca on c.year = ca.year
+)
+
+select * from final

dbt/models/staging/_sources.yml

@@ -41,3 +41,59 @@ sources:
         columns:
           - name: event_id
             description: "Primary key"
+
+      - name: fact_census
+        description: "Census demographics by neighbourhood and year"
+        columns:
+          - name: id
+            description: "Primary key"
+          - name: neighbourhood_id
+            description: "Foreign key to dim_neighbourhood"
+          - name: census_year
+            description: "Census year (2016, 2021, etc.)"
+          - name: population
+            description: "Total population"
+          - name: median_household_income
+            description: "Median household income"
+
+      - name: fact_crime
+        description: "Crime statistics by neighbourhood, year, and type"
+        columns:
+          - name: id
+            description: "Primary key"
+          - name: neighbourhood_id
+            description: "Foreign key to dim_neighbourhood"
+          - name: year
+            description: "Statistics year"
+          - name: crime_type
+            description: "Type of crime"
+          - name: count
+            description: "Number of incidents"
+          - name: rate_per_100k
+            description: "Rate per 100,000 population"
+
+      - name: fact_amenities
+        description: "Amenity counts by neighbourhood and type"
+        columns:
+          - name: id
+            description: "Primary key"
+          - name: neighbourhood_id
+            description: "Foreign key to dim_neighbourhood"
+          - name: amenity_type
+            description: "Type of amenity (parks, schools, transit)"
+          - name: count
+            description: "Number of amenities"
+          - name: year
+            description: "Reference year"
+
+      - name: bridge_cmhc_neighbourhood
+        description: "CMHC zone to neighbourhood mapping with area weights"
+        columns:
+          - name: id
+            description: "Primary key"
+          - name: cmhc_zone_code
+            description: "CMHC zone code"
+          - name: neighbourhood_id
+            description: "Neighbourhood ID"
+          - name: weight
+            description: "Proportional area weight (0-1)"

dbt/models/staging/_staging.yml

@@ -40,3 +40,90 @@ models:
         tests:
           - unique
           - not_null
+
+  - name: stg_toronto__neighbourhoods
+    description: "Staged Toronto neighbourhood dimension (158 official boundaries)"
+    columns:
+      - name: neighbourhood_id
+        description: "Neighbourhood primary key"
+        tests:
+          - unique
+          - not_null
+      - name: neighbourhood_name
+        description: "Official neighbourhood name"
+        tests:
+          - not_null
+      - name: geometry
+        description: "PostGIS geometry (POLYGON)"
+
+  - name: stg_toronto__census
+    description: "Staged census demographics by neighbourhood"
+    columns:
+      - name: census_id
+        description: "Census record identifier"
+        tests:
+          - unique
+          - not_null
+      - name: neighbourhood_id
+        description: "Neighbourhood foreign key"
+        tests:
+          - not_null
+      - name: census_year
+        description: "Census year (2016, 2021)"
+        tests:
+          - not_null
+
+  - name: stg_toronto__crime
+    description: "Staged crime statistics by neighbourhood"
+    columns:
+      - name: crime_id
+        description: "Crime record identifier"
+        tests:
+          - unique
+          - not_null
+      - name: neighbourhood_id
+        description: "Neighbourhood foreign key"
+        tests:
+          - not_null
+      - name: crime_type
+        description: "Type of crime"
+        tests:
+          - not_null
+
+  - name: stg_toronto__amenities
+    description: "Staged amenity counts by neighbourhood"
+    columns:
+      - name: amenity_id
+        description: "Amenity record identifier"
+        tests:
+          - unique
+          - not_null
+      - name: neighbourhood_id
+        description: "Neighbourhood foreign key"
+        tests:
+          - not_null
+      - name: amenity_type
+        description: "Type of amenity"
+        tests:
+          - not_null
+
+  - name: stg_cmhc__zone_crosswalk
+    description: "Staged CMHC zone to neighbourhood crosswalk with area weights"
+    columns:
+      - name: crosswalk_id
+        description: "Crosswalk record identifier"
+        tests:
+          - unique
+          - not_null
+      - name: cmhc_zone_code
+        description: "CMHC zone code"
+        tests:
+          - not_null
+      - name: neighbourhood_id
+        description: "Neighbourhood foreign key"
+        tests:
+          - not_null
+      - name: area_weight
+        description: "Proportional area weight (0-1)"
+        tests:
+          - not_null

dbt/models/staging/stg_cmhc__zone_crosswalk.sql

@@ -0,0 +1,18 @@
+-- Staged CMHC zone to neighbourhood crosswalk
+-- Source: bridge_cmhc_neighbourhood table
+-- Grain: One row per zone-neighbourhood intersection
+
+with source as (
+    select * from {{ source('toronto_housing', 'bridge_cmhc_neighbourhood') }}
+),
+
+staged as (
+    select
+        id as crosswalk_id,
+        cmhc_zone_code,
+        neighbourhood_id,
+        weight as area_weight
+    from source
+)
+
+select * from staged

dbt/models/staging/stg_toronto__amenities.sql

@@ -0,0 +1,19 @@
+-- Staged amenity counts by neighbourhood
+-- Source: fact_amenities table
+-- Grain: One row per neighbourhood per amenity type per year
+
+with source as (
+    select * from {{ source('toronto_housing', 'fact_amenities') }}
+),
+
+staged as (
+    select
+        id as amenity_id,
+        neighbourhood_id,
+        amenity_type,
+        count as amenity_count,
+        year as amenity_year
+    from source
+)
+
+select * from staged

dbt/models/staging/stg_toronto__census.sql

@@ -0,0 +1,27 @@
+-- Staged census demographics by neighbourhood
+-- Source: fact_census table
+-- Grain: One row per neighbourhood per census year
+
+with source as (
+    select * from {{ source('toronto_housing', 'fact_census') }}
+),
+
+staged as (
+    select
+        id as census_id,
+        neighbourhood_id,
+        census_year,
+        population,
+        population_density,
+        median_household_income,
+        average_household_income,
+        unemployment_rate,
+        pct_bachelors_or_higher,
+        pct_owner_occupied,
+        pct_renter_occupied,
+        median_age,
+        average_dwelling_value
+    from source
+)
+
+select * from staged

dbt/models/staging/stg_toronto__crime.sql

@@ -0,0 +1,20 @@
+-- Staged crime statistics by neighbourhood
+-- Source: fact_crime table
+-- Grain: One row per neighbourhood per year per crime type
+
+with source as (
+    select * from {{ source('toronto_housing', 'fact_crime') }}
+),
+
+staged as (
+    select
+        id as crime_id,
+        neighbourhood_id,
+        year as crime_year,
+        crime_type,
+        count as incident_count,
+        rate_per_100k
+    from source
+)
+
+select * from staged

dbt/models/staging/stg_toronto__neighbourhoods.sql

@@ -0,0 +1,25 @@
+-- Staged Toronto neighbourhood dimension
+-- Source: dim_neighbourhood table
+-- Grain: One row per neighbourhood (158 total)
+
+with source as (
+    select * from {{ source('toronto_housing', 'dim_neighbourhood') }}
+),
+
+staged as (
+    select
+        neighbourhood_id,
+        name as neighbourhood_name,
+        geometry,
+        population,
+        land_area_sqkm,
+        pop_density_per_sqkm,
+        pct_bachelors_or_higher,
+        median_household_income,
+        pct_owner_occupied,
+        pct_renter_occupied,
+        census_year
+    from source
+)
+
+select * from staged

dbt/package-lock.yml

@@ -0,0 +1,11 @@
+packages:
+  - name: dbt_utils
+    package: dbt-labs/dbt_utils
+    version: 1.3.3
+  - name: dbt_expectations
+    package: calogica/dbt_expectations
+    version: 0.10.4
+  - name: dbt_date
+    package: calogica/dbt_date
+    version: 0.10.1
+sha1_hash: 51a51ab489f7b302c8745ae3c3781271816b01be