refactor: update app code for domain-scoped schema migration

- Update dbt model references to use new schema naming (stg_toronto, int_toronto, mart_toronto)
- Refactor figure factories to use consistent column naming from new schema
- Update callbacks to work with refactored data structures
- Add centralized design tokens module for consistent styling
- Streamline CLAUDE.md documentation

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

dbt/models/intermediate/toronto/_intermediate.yml

@@ -5,11 +5,11 @@ models:
     description: "Rental data enriched with time and zone dimensions"
     columns:
       - name: rental_id
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: zone_code
-        tests:
+        data_tests:
           - not_null
 
   - name: int_neighbourhood__demographics
@@ -17,11 +17,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: census_year
         description: "Census year"
-        tests:
+        data_tests:
           - not_null
       - name: income_quintile
         description: "Income quintile (1-5, city-wide)"
@@ -31,7 +31,7 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: year
         description: "Reference year"
@@ -45,11 +45,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: year
         description: "Statistics year"
-        tests:
+        data_tests:
           - not_null
       - name: crime_rate_per_100k
         description: "Total crime rate per 100K population"
@@ -61,7 +61,7 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: year
         description: "Reference year"
@@ -75,11 +75,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: year
         description: "Survey year"
-        tests:
+        data_tests:
           - not_null
       - name: avg_rent_2bed
         description: "Weighted average 2-bedroom rent"

dbt/models/intermediate/toronto/int_neighbourhood__crime_summary.sql

@@ -16,12 +16,12 @@ crime_by_year as (
         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,
+        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

dbt/models/intermediate/toronto/int_rentals__neighbourhood_allocated.sql

@@ -42,10 +42,10 @@ 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,
+        max(case when bedroom_type = '2bed' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_2bed,
+        max(case when bedroom_type = '1bed' 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 = '3bed' 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