lmiranda
b6d210ec6b
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>
90 lines
2.4 KiB
SQL
90 lines
2.4 KiB
SQL
-- Mart: Neighbourhood Amenities Analysis
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-- Dashboard Tab: Amenities
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-- Grain: One row per neighbourhood per year
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with amenities as (
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select * from {{ ref('int_neighbourhood__amenity_scores') }}
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),
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-- City-wide averages for comparison
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city_avg as (
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select
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year,
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avg(parks_per_1000) as city_avg_parks,
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avg(schools_per_1000) as city_avg_schools,
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avg(transit_per_1000) as city_avg_transit,
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avg(total_amenities_per_1000) as city_avg_total_amenities
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from amenities
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group by year
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),
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final as (
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select
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a.neighbourhood_id,
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a.neighbourhood_name,
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a.geometry,
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a.population,
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a.land_area_sqkm,
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a.year,
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-- Raw counts
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a.parks_count,
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a.schools_count,
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a.transit_count,
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a.libraries_count,
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a.community_centres_count,
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a.recreation_count,
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a.total_amenities,
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-- Per 1000 population
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a.parks_per_1000,
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a.schools_per_1000,
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a.transit_per_1000,
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a.total_amenities_per_1000,
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-- Per square km
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a.amenities_per_sqkm,
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-- City averages
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round(ca.city_avg_parks::numeric, 3) as city_avg_parks_per_1000,
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round(ca.city_avg_schools::numeric, 3) as city_avg_schools_per_1000,
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round(ca.city_avg_transit::numeric, 3) as city_avg_transit_per_1000,
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-- Amenity index (100 = city average)
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case
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when ca.city_avg_total_amenities > 0
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then round(a.total_amenities_per_1000 / ca.city_avg_total_amenities * 100, 1)
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else null
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end as amenity_index,
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-- Category indices
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case
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when ca.city_avg_parks > 0
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then round(a.parks_per_1000 / ca.city_avg_parks * 100, 1)
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else null
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end as parks_index,
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case
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when ca.city_avg_schools > 0
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then round(a.schools_per_1000 / ca.city_avg_schools * 100, 1)
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else null
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end as schools_index,
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case
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when ca.city_avg_transit > 0
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then round(a.transit_per_1000 / ca.city_avg_transit * 100, 1)
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else null
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end as transit_index,
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-- Amenity tier (1 = best, 5 = lowest)
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ntile(5) over (
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partition by a.year
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order by a.total_amenities_per_1000 desc
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) as amenity_tier
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from amenities a
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left join city_avg ca on a.year = ca.year
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)
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select * from final
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