l3ocho
62d1a52eed
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- 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>
79 lines
2.1 KiB
SQL
79 lines
2.1 KiB
SQL
-- Mart: Neighbourhood Safety Analysis
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-- Dashboard Tab: Safety
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-- Grain: One row per neighbourhood per year
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with crime as (
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select * from {{ ref('int_neighbourhood__crime_summary') }}
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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(crime_rate_per_100k) as city_avg_crime_rate,
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avg(assault_count) as city_avg_assault,
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avg(auto_theft_count) as city_avg_auto_theft,
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avg(break_enter_count) as city_avg_break_enter
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from crime
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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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c.neighbourhood_id,
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c.neighbourhood_name,
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c.geometry,
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c.population,
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c.year,
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-- Total crime
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c.total_incidents,
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c.crime_rate_per_100k,
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c.yoy_change_pct as crime_yoy_change_pct,
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-- Crime breakdown
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c.assault_count,
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c.auto_theft_count,
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c.break_enter_count,
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c.robbery_count,
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c.theft_over_count,
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c.homicide_count,
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-- Per 100K rates by type
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case when c.population > 0
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then round(c.assault_count::numeric / c.population * 100000, 2)
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else null
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end as assault_rate_per_100k,
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case when c.population > 0
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then round(c.auto_theft_count::numeric / c.population * 100000, 2)
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else null
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end as auto_theft_rate_per_100k,
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case when c.population > 0
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then round(c.break_enter_count::numeric / c.population * 100000, 2)
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else null
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end as break_enter_rate_per_100k,
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-- Comparison to city average
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round(ca.city_avg_crime_rate::numeric, 2) as city_avg_crime_rate,
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-- Crime index (100 = city average)
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case
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when ca.city_avg_crime_rate > 0
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then round(c.crime_rate_per_100k / ca.city_avg_crime_rate * 100, 1)
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else null
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end as crime_index,
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-- Safety tier based on crime rate percentile
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ntile(5) over (
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partition by c.year
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order by c.crime_rate_per_100k desc
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) as safety_tier
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from crime c
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left join city_avg ca on c.year = ca.year
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)
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select * from final
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