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