846 lines
25 KiB
PL/PgSQL
846 lines
25 KiB
PL/PgSQL
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--For Longer term Dev
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--Break out table definitions to types
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--Automate type creation from a script, something like
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----CREATE OR REPLACE FUNCTION OBS_Get<%=tag_name%>(geom GEOMETRY)
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----RETURNS TABLE(
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----<%=get_dimensions_for_tag(tag_name)%>
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----AS $$
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----DECLARE
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----target_cols text[];
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----names text[];
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----vals NUMERIC[];-
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----q text;
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----BEGIN
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----target_cols := Array[<%=get_dimensions_for_tag(tag_name)%>],
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--Functions for augmenting specific tables
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--------------------------------------------------------------------------------
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-- Creates a table of demographic snapshot
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CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetDemographicSnapshotJ(geom geometry, time_span text default '2009 - 2013', geometry_level text default '"us.census.tiger".block_group')
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RETURNS SETOF JSON
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AS $$
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DECLARE
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target_cols text[];
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BEGIN
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target_cols := Array['total_pop',
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'male_pop',
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'female_pop',
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'median_age',
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'white_pop',
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'black_pop',
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'asian_pop',
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'hispanic_pop',
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'amerindian_pop',
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'other_race_pop',
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'two_or_more_races_pop',
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'not_hispanic_pop',
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--'not_us_citizen_pop',
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--'workers_16_and_over',
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--'commuters_by_car_truck_van',
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--'commuters_drove_alone',
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--'commuters_by_carpool',
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--'commuters_by_public_transportation',
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--'commuters_by_bus',
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--'commuters_by_subway_or_elevated',
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--'walked_to_work',
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--'worked_at_home',
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--'children',
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'households',
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--'population_3_years_over',
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--'in_school',
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--'in_grades_1_to_4',
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--'in_grades_5_to_8',
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--'in_grades_9_to_12',
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--'in_undergrad_college',
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'pop_25_years_over',
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'high_school_diploma',
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'less_one_year_college',
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'one_year_more_college',
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'associates_degree',
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'bachelors_degree',
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'masters_degree',
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--'pop_5_years_over',
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--'speak_only_english_at_home',
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--'speak_spanish_at_home',
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--'pop_determined_poverty_status',
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--'poverty',
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'median_income',
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'gini_index',
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'income_per_capita',
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'housing_units',
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'vacant_housing_units',
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'vacant_housing_units_for_rent',
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'vacant_housing_units_for_sale',
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'median_rent',
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'percent_income_spent_on_rent',
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'owner_occupied_housing_units',
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'million_dollar_housing_units',
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'mortgaged_housing_units',
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--'pop_15_and_over',
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--'pop_never_married',
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--'pop_now_married',
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--'pop_separated',
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--'pop_widowed',
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--'pop_divorced',
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'commuters_16_over',
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'commute_less_10_mins',
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'commute_10_14_mins',
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'commute_15_19_mins',
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'commute_20_24_mins',
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'commute_25_29_mins',
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'commute_30_34_mins',
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'commute_35_44_mins',
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'commute_45_59_mins',
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'commute_60_more_mins',
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'aggregate_travel_time_to_work',
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'income_less_10000',
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'income_10000_14999',
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'income_15000_19999',
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'income_20000_24999',
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'income_25000_29999',
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'income_30000_34999',
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'income_35000_39999',
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'income_40000_44999',
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'income_45000_49999',
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'income_50000_59999',
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'income_60000_74999',
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'income_75000_99999',
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'income_100000_124999',
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'income_125000_149999',
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'income_150000_199999',
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'income_200000_or_more',
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'land_area'];
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RETURN QUERY
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EXECUTE
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'select * from cdb_observatory._OBS_GetCensus($1, $2 )'
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USING geom, target_cols
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RETURN;
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END;
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$$ LANGUAGE plpgsql;
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-- CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetDemographicSnapshot(geom geometry, time_span text default '2009 - 2013', geometry_level text default '"us.census.tiger".block_group' )
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-- RETURNS TABLE(
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-- total_pop NUMERIC,
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-- male_pop NUMERIC,
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-- female_pop NUMERIC,
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-- median_age NUMERIC,
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-- white_pop NUMERIC,
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-- black_pop NUMERIC,
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-- asian_pop NUMERIC,
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-- hispanic_pop NUMERIC,
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-- amerindian_pop NUMERIC,
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-- other_race_pop NUMERIC,
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-- two_or_more_races_pop NUMERIC,
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-- not_hispanic_pop NUMERIC,
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-- --not_us_citizen_pop NUMERIC,
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-- --workers_16_and_over NUMERIC,
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-- --commuters_by_car_truck_van NUMERIC,
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-- --commuters_drove_alone NUMERIC,
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-- --commuters_by_carpool NUMERIC,
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-- --commuters_by_public_transportation NUMERIC,
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-- --commuters_by_bus NUMERIC,
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-- --commuters_by_subway_or_elevated NUMERIC,
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-- --walked_to_work NUMERIC,
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-- --worked_at_home NUMERIC,
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-- --children NUMERIC, -- TODO we should be able to get this at BG
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-- households NUMERIC,
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-- --population_3_years_over NUMERIC,
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-- --in_school NUMERIC,
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-- --in_grades_1_to_4 NUMERIC,
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-- --in_grades_5_to_8 NUMERIC,
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-- --in_grades_9_to_12 NUMERIC,
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-- --in_undergrad_college NUMERIC,
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-- pop_25_years_over NUMERIC,
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-- high_school_diploma NUMERIC,
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-- less_one_year_college NUMERIC,
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-- one_year_more_college NUMERIC,
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-- associates_degree NUMERIC,
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-- bachelors_degree NUMERIC,
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-- masters_degree NUMERIC,
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-- --pop_5_years_over NUMERIC,
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-- --speak_only_english_at_home NUMERIC,
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-- --speak_spanish_at_home NUMERIC,
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-- --pop_determined_poverty_status NUMERIC,
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-- --poverty NUMERIC,
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-- median_income NUMERIC,
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-- gini_index NUMERIC,
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-- income_per_capita NUMERIC,
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-- housing_units NUMERIC,
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-- vacant_housing_units NUMERIC,
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-- vacant_housing_units_for_rent NUMERIC,
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-- vacant_housing_units_for_sale NUMERIC,
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-- median_rent NUMERIC,
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-- percent_income_spent_on_rent NUMERIC,
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-- owner_occupied_housing_units NUMERIC,
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-- million_dollar_housing_units NUMERIC,
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-- mortgaged_housing_units NUMERIC,
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-- --pop_15_and_over NUMERIC,
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-- --pop_never_married NUMERIC,
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-- --pop_now_married NUMERIC,
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-- --pop_separated NUMERIC,
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-- --pop_widowed NUMERIC,
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-- --pop_divorced NUMERIC,
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-- commuters_16_over NUMERIC,
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-- commute_less_10_mins NUMERIC,
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-- commute_10_14_mins NUMERIC,
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-- commute_15_19_mins NUMERIC,
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-- commute_20_24_mins NUMERIC,
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-- commute_25_29_mins NUMERIC,
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-- commute_30_34_mins NUMERIC,
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-- commute_35_44_mins NUMERIC,
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-- commute_45_59_mins NUMERIC,
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-- commute_60_more_mins NUMERIC,
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-- aggregate_travel_time_to_work NUMERIC,
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-- income_less_10000 NUMERIC,
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-- income_10000_14999 NUMERIC,
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-- income_15000_19999 NUMERIC,
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-- income_20000_24999 NUMERIC,
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-- income_25000_29999 NUMERIC,
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-- income_30000_34999 NUMERIC,
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-- income_35000_39999 NUMERIC,
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-- income_40000_44999 NUMERIC,
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-- income_45000_49999 NUMERIC,
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-- income_50000_59999 NUMERIC,
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-- income_60000_74999 NUMERIC,
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-- income_75000_99999 NUMERIC,
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-- income_100000_124999 NUMERIC,
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-- income_125000_149999 NUMERIC,
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-- income_150000_199999 NUMERIC,
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-- income_200000_or_more NUMERIC,
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-- land_area NUMERIC)
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-- AS $$
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-- DECLARE
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-- target_cols text[];
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-- names text[];
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-- vals NUMERIC[];
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-- q text;
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-- BEGIN
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-- target_cols := Array['total_pop',
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-- 'male_pop',
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-- 'female_pop',
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-- 'median_age',
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-- 'white_pop',
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-- 'black_pop',
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-- 'asian_pop',
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-- 'hispanic_pop',
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-- 'amerindian_pop',
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-- 'other_race_pop',
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-- 'two_or_more_races_pop',
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-- 'not_hispanic_pop',
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-- --'not_us_citizen_pop',
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-- --'workers_16_and_over',
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-- --'commuters_by_car_truck_van',
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-- --'commuters_drove_alone',
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-- --'commuters_by_carpool',
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-- --'commuters_by_public_transportation',
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-- --'commuters_by_bus',
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-- --'commuters_by_subway_or_elevated',
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-- --'walked_to_work',
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-- --'worked_at_home',
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-- --'children',
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-- 'households',
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-- --'population_3_years_over',
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-- --'in_school',
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-- --'in_grades_1_to_4',
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-- --'in_grades_5_to_8',
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-- --'in_grades_9_to_12',
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-- --'in_undergrad_college',
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-- 'pop_25_years_over',
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-- 'high_school_diploma',
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-- 'less_one_year_college',
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-- 'one_year_more_college',
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-- 'associates_degree',
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-- 'bachelors_degree',
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-- 'masters_degree',
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-- --'pop_5_years_over',
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-- --'speak_only_english_at_home',
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-- --'speak_spanish_at_home',
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-- --'pop_determined_poverty_status',
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-- --'poverty',
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-- 'median_income',
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-- 'gini_index',
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-- 'income_per_capita',
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-- 'housing_units',
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-- 'vacant_housing_units',
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-- 'vacant_housing_units_for_rent',
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-- 'vacant_housing_units_for_sale',
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-- 'median_rent',
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-- 'percent_income_spent_on_rent',
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-- 'owner_occupied_housing_units',
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-- 'million_dollar_housing_units',
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-- 'mortgaged_housing_units',
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-- --'pop_15_and_over',
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-- --'pop_never_married',
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-- --'pop_now_married',
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-- --'pop_separated',
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-- --'pop_widowed',
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-- --'pop_divorced',
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-- 'commuters_16_over',
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-- 'commute_less_10_mins',
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-- 'commute_10_14_mins',
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-- 'commute_15_19_mins',
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-- 'commute_20_24_mins',
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-- 'commute_25_29_mins',
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-- 'commute_30_34_mins',
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-- 'commute_35_44_mins',
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-- 'commute_45_59_mins',
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-- 'commute_60_more_mins',
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-- 'aggregate_travel_time_to_work',
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-- 'income_less_10000',
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-- 'income_10000_14999',
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-- 'income_15000_19999',
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-- 'income_20000_24999',
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-- 'income_25000_29999',
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-- 'income_30000_34999',
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-- 'income_35000_39999',
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-- 'income_40000_44999',
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-- 'income_45000_49999',
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-- 'income_50000_59999',
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-- 'income_60000_74999',
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-- 'income_75000_99999',
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-- 'income_100000_124999',
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-- 'income_125000_149999',
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-- 'income_150000_199999',
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-- 'income_200000_or_more',
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-- 'land_area'];
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--
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-- q :=
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-- $query$
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-- WITH a As (
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-- SELECT
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-- array_agg(_OBS_GetCensusJ->>'name') As names,
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-- array_agg(_OBS_GetCensusJ->>'value') As vals
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-- FROM cdb_observatory._OBS_GetCensusJ($1,$2,$3,$4)
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-- )$query$ ||
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-- cdb_observatory._OBS_BuildSnapshotQuery(target_cols) ||
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-- ' FROM a'
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-- ;
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--
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-- RETURN QUERY
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-- EXECUTE
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-- q
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-- USING geom, target_cols, time_span, geometry_level;
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--
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-- RETURN;
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-- END;
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-- $$ LANGUAGE plpgsql;
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--Base functions for performing augmentation
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----------------------------------------------------------------------------------------
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--Returns arrays of values for the given census dimension names for a given
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--point or polygon
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CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetCensus(
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geom geometry,
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dimension_names text[],
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time_span text DEFAULT '2009 - 2013',
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geometry_level text DEFAULT '"us.census.tiger".block_group'
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)
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RETURNS SETOF JSON
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AS $$
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DECLARE
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ids text[];
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BEGIN
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ids := cdb_observatory._OBS_LookupCensusHuman(dimension_names);
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RETURN QUERY
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SELECT * FROM cdb_observatory._OBS_Get(geom, ids, time_span, geometry_level);
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END;
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$$ LANGUAGE plpgsql;
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CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetCensus(
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geom geometry,
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dimension_name text,
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time_span text DEFAULT '2009 - 2013',
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geometry_level text DEFAULT '"us.census.tiger".block_group'
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)
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RETURNS NUMERIC
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AS $$
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DECLARE
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ids Text[];
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result_json json;
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result Numeric;
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BEGIN
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ids := cdb_observatory._OBS_LookupCensusHuman(Array[dimension_name]);
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result_json := (SELECT a FROM cdb_observatory._OBS_Get(geom, ids, time_span, geometry_level) as a limit 1);
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EXECUTE
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format('select $1::numeric as "%s"', result_json->>'name')
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INTO result
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USING
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result_json->>'value';
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return result;
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END;
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$$ LANGUAGE plpgsql;
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-- Base augmentation fucntion.
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CREATE OR REPLACE FUNCTION cdb_observatory._OBS_Get(
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geom geometry,
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column_ids text[],
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time_span text,
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geometry_level text
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)
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RETURNS SETOF JSON
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AS $$
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DECLARE
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results json[];
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geom_table_name text;
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names text[];
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query text;
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data_table_info json[];
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BEGIN
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geom_table_name := cdb_observatory._OBS_GeomTable(geom, geometry_level);
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IF geom_table_name IS NULL
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THEN
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RAISE NOTICE 'Point % is outside of the data region', geom;
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RETURN QUERY SELECT '{}'::text[], '{}'::NUMERIC[];
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END IF;
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execute'
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select array_agg( _obs_getcolumndata) from cdb_observatory._OBS_GetColumnData($1,
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$2,
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$3);'
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INTO data_table_info
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using geometry_level, column_ids, time_span;
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IF ST_GeometryType(geom) = 'ST_Point'
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THEN
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results := cdb_observatory._OBS_GetPoints(geom,
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geom_table_name,
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data_table_info);
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ELSIF ST_GeometryType(geom) IN ('ST_Polygon', 'ST_MultiPolygon')
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THEN
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-- RAISE EXCEPTION 'polygons not supported for now';
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results := cdb_observatory._OBS_GetPolygons(geom,
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geom_table_name,
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data_table_info);
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END IF;
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RETURN QUERY
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EXECUTE
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$query$
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SELECT unnest($1)
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$query$
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USING results;
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END;
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$$ LANGUAGE plpgsql;
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-- If the variable of interest is just a rate return it as such,
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-- otherwise normalize it to the census block area and return that
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CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetPoints(
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geom geometry,
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geom_table_name text,
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data_table_info json[]
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)
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RETURNS json[]
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AS $$
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DECLARE
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result NUMERIC[];
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json_result json[];
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query text;
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i int;
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geoid text;
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area NUMERIC;
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BEGIN
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-- TODO: does 'geoid' need to be generalized to geom_ref??
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EXECUTE
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format('SELECT geoid
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FROM observatory.%I
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WHERE ST_WITHIN($1, the_geom)',
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geom_table_name)
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USING geom
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INTO geoid;
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RAISE NOTICE 'geoid is %, geometry table is % ', geoid, geom_table_name;
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EXECUTE
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format('SELECT ST_Area(the_geom::geography) / (1000 * 1000)
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FROM observatory.%I
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WHERE geoid = %L',
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geom_table_name,
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geoid)
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INTO area;
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IF area IS NULL
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THEN
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RAISE NOTICE 'No geometry at %', ST_AsText(geom);
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END IF;
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query := 'SELECT Array[';
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FOR i IN 1..array_upper(data_table_info, 1)
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LOOP
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IF area is NULL OR area = 0
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THEN
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-- give back null values
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query := query || format('NULL::numeric ');
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ELSIF ((data_table_info)[i])->>'aggregate' != 'sum'
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|
THEN
|
|
-- give back full variable
|
|
query := query || format('%I ', ((data_table_info)[i])->>'colname');
|
|
ELSE
|
|
-- give back variable normalized by area of geography
|
|
query := query || format('%I/%s ',
|
|
((data_table_info)[i])->>'colname',
|
|
area);
|
|
END IF;
|
|
|
|
IF i < array_upper(data_table_info, 1)
|
|
THEN
|
|
query := query || ',';
|
|
END IF;
|
|
END LOOP;
|
|
|
|
query := query || format(' ]::numeric[]
|
|
FROM observatory.%I
|
|
WHERE %I.geoid = %L
|
|
',
|
|
((data_table_info)[1])->>'tablename',
|
|
((data_table_info)[1])->>'tablename',
|
|
geoid
|
|
);
|
|
|
|
EXECUTE
|
|
query
|
|
INTO result
|
|
USING geom;
|
|
|
|
EXECUTE
|
|
$query$
|
|
select array_agg(row_to_json(t)) from(
|
|
select values as value,
|
|
meta->>'name' as name,
|
|
meta->>'tablename' as tablename,
|
|
meta->>'aggregate' as aggregate,
|
|
meta->>'type' as type,
|
|
meta->>'description' as description
|
|
from (select unnest($1) as values, unnest($2) as meta) b
|
|
) t
|
|
$query$
|
|
INTO json_result
|
|
USING result, data_table_info;
|
|
|
|
RETURN json_result;
|
|
END;
|
|
$$ LANGUAGE plpgsql;
|
|
|
|
|
|
CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetMeasure(
|
|
geom GEOMETRY,
|
|
measure_id TEXT,
|
|
normalize TEXT DEFAULT 'area', -- TODO denominator, none
|
|
boundary_id TEXT DEFAULT NULL,
|
|
time_span TEXT DEFAULT NULL
|
|
)
|
|
RETURNS JSON
|
|
AS $$
|
|
DECLARE
|
|
result json;
|
|
BEGIN
|
|
|
|
IF boundary_id IS NULL THEN
|
|
-- TODO we should determine best boundary for this geom
|
|
boundary_id := '"us.census.tiger".block_group';
|
|
END IF;
|
|
|
|
IF time_span IS NULL THEN
|
|
-- TODO we should determine latest timespan for this measure
|
|
time_span := '2009 - 2013';
|
|
END IF;
|
|
|
|
|
|
EXECUTE '
|
|
SELECT * FROM cdb_observatory._OBS_Get($1, ARRAY[$2], $3, $4) LIMIT 1
|
|
'
|
|
INTO result
|
|
USING geom, measure_id, time_span, boundary_id;
|
|
|
|
RETURN result;
|
|
END;
|
|
$$ LANGUAGE plpgsql;
|
|
|
|
|
|
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetPolygons(
|
|
geom geometry,
|
|
geom_table_name text,
|
|
data_table_info json[]
|
|
)
|
|
RETURNS json[]
|
|
AS $$
|
|
DECLARE
|
|
result numeric[];
|
|
json_result json[];
|
|
q_select text;
|
|
q_sum text;
|
|
q text;
|
|
i NUMERIC;
|
|
BEGIN
|
|
|
|
q_select := 'SELECT geoid, ';
|
|
q_sum := 'SELECT Array[';
|
|
|
|
FOR i IN 1..array_upper(data_table_info, 1)
|
|
LOOP
|
|
q_select := q_select || format( '%I ', ((data_table_info)[i])->>'colname');
|
|
|
|
IF ((data_table_info)[i])->>'aggregate' ='sum'
|
|
THEN
|
|
q_sum := q_sum || format('sum(overlap_fraction * COALESCE(%I, 0)) ',((data_table_info)[i])->>'colname',((data_table_info)[i])->>'colname');
|
|
ELSE
|
|
q_sum := q_sum || ' NULL::numeric ';
|
|
END IF;
|
|
|
|
IF i < array_upper(data_table_info,1)
|
|
THEN
|
|
q_select := q_select || format(',');
|
|
q_sum := q_sum || format(',');
|
|
END IF;
|
|
END LOOP;
|
|
|
|
q = format('
|
|
WITH _overlaps As (
|
|
SELECT ST_Area(
|
|
ST_Intersection($1, a.the_geom)
|
|
) / ST_Area(a.the_geom) As overlap_fraction,
|
|
geoid
|
|
FROM observatory.%I As a
|
|
WHERE $1 && a.the_geom
|
|
),
|
|
values As (
|
|
', geom_table_name);
|
|
|
|
q := q || q_select || format('FROM observatory.%I ', ((data_table_info)[1]->>'tablename'));
|
|
|
|
q := q || ' ) ' || q_sum || ' ]::numeric[] FROM _overlaps, values
|
|
WHERE values.geoid = _overlaps.geoid';
|
|
|
|
EXECUTE
|
|
q
|
|
INTO result
|
|
USING geom;
|
|
|
|
EXECUTE
|
|
$query$
|
|
select array_agg(row_to_json(t)) from(
|
|
select values as value,
|
|
meta->>'name' as name,
|
|
meta->>'tablename' as tablename,
|
|
meta->>'aggregate' as aggregate,
|
|
meta->>'type' as type,
|
|
meta->>'description' as description
|
|
from (select unnest($1) as values, unnest($2) as meta) b
|
|
) t
|
|
$query$
|
|
INTO json_result
|
|
USING result, data_table_info;
|
|
|
|
RETURN json_result;
|
|
END;
|
|
$$ LANGUAGE plpgsql;
|
|
|
|
|
|
|
|
CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetSegmentSnapshot(
|
|
geom geometry,
|
|
geometry_level text DEFAULT '"us.census.tiger".census_tract'
|
|
)
|
|
RETURNS JSON
|
|
AS $$
|
|
DECLARE
|
|
target_cols text[];
|
|
result json;
|
|
seg_name Text;
|
|
geom_id Text;
|
|
q Text;
|
|
segment_name Text;
|
|
BEGIN
|
|
target_cols := Array[
|
|
'"us.census.acs".B01001001_quantile',
|
|
'"us.census.acs".B01001002_quantile',
|
|
'"us.census.acs".B01001026_quantile',
|
|
'"us.census.acs".B01002001_quantile',
|
|
'"us.census.acs".B03002003_quantile',
|
|
'"us.census.acs".B03002004_quantile',
|
|
'"us.census.acs".B03002006_quantile',
|
|
'"us.census.acs".B03002012_quantile',
|
|
'"us.census.acs".B05001006_quantile',--
|
|
'"us.census.acs".B08006001_quantile',--
|
|
'"us.census.acs".B08006002_quantile',--
|
|
'"us.census.acs".B08006008_quantile',--
|
|
'"us.census.acs".B08006009_quantile',--
|
|
'"us.census.acs".B08006011_quantile',--
|
|
'"us.census.acs".B08006015_quantile',--
|
|
'"us.census.acs".B08006017_quantile',--
|
|
'"us.census.acs".B09001001_quantile',--
|
|
'"us.census.acs".B11001001_quantile',
|
|
'"us.census.acs".B14001001_quantile',--
|
|
'"us.census.acs".B14001002_quantile',--
|
|
'"us.census.acs".B14001005_quantile',--
|
|
'"us.census.acs".B14001006_quantile',--
|
|
'"us.census.acs".B14001007_quantile',--
|
|
'"us.census.acs".B14001008_quantile',--
|
|
'"us.census.acs".B15003001_quantile',
|
|
'"us.census.acs".B15003017_quantile',
|
|
'"us.census.acs".B15003022_quantile',
|
|
'"us.census.acs".B15003023_quantile',
|
|
'"us.census.acs".B16001001_quantile',--
|
|
'"us.census.acs".B16001002_quantile',--
|
|
'"us.census.acs".B16001003_quantile',--
|
|
'"us.census.acs".B17001001_quantile',--
|
|
'"us.census.acs".B17001002_quantile',--
|
|
'"us.census.acs".B19013001_quantile',
|
|
'"us.census.acs".B19083001_quantile',
|
|
'"us.census.acs".B19301001_quantile',
|
|
'"us.census.acs".B25001001_quantile',
|
|
'"us.census.acs".B25002003_quantile',
|
|
'"us.census.acs".B25004002_quantile',
|
|
'"us.census.acs".B25004004_quantile',
|
|
'"us.census.acs".B25058001_quantile',
|
|
'"us.census.acs".B25071001_quantile',
|
|
'"us.census.acs".B25075001_quantile',
|
|
'"us.census.acs".B25075025_quantile'
|
|
];
|
|
|
|
EXECUTE
|
|
$query$
|
|
SELECT (_OBS_GetCategories)->>'name'
|
|
FROM cdb_observatory._OBS_GetCategories(
|
|
$1,
|
|
Array['"us.census.spielman_singleton_segments".X10'],
|
|
$2)
|
|
LIMIT 1
|
|
$query$
|
|
INTO segment_name
|
|
USING geom, geometry_level;
|
|
|
|
q :=
|
|
format($query$
|
|
WITH a As (
|
|
SELECT
|
|
array_agg(_OBS_GET->>'name') As names,
|
|
array_agg(_OBS_GET->>'value') As vals
|
|
FROM cdb_observatory._OBS_Get($1,
|
|
$2,
|
|
'2009 - 2013',
|
|
$3)
|
|
|
|
), percentiles As (
|
|
%s
|
|
FROM a)
|
|
SELECT row_to_json(r) FROM
|
|
( SELECT $4 as segment_name, percentiles.*
|
|
FROM percentiles) r
|
|
$query$, cdb_observatory._OBS_BuildSnapshotQuery(target_cols)) results;
|
|
|
|
|
|
EXECUTE
|
|
q
|
|
into result
|
|
USING geom, target_cols, geometry_level, segment_name;
|
|
|
|
return result;
|
|
|
|
END;
|
|
$$ LANGUAGE plpgsql;
|
|
|
|
--Get categorical variables from point
|
|
|
|
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetCategories(
|
|
geom geometry,
|
|
dimension_names text[],
|
|
geometry_level text DEFAULT '"us.census.tiger".block_group',
|
|
time_span text DEFAULT '2009 - 2013'
|
|
)
|
|
RETURNS SETOF JSON as $$
|
|
DECLARE
|
|
geom_table_name text;
|
|
geoid text;
|
|
names text[];
|
|
results text[];
|
|
query text;
|
|
data_table_info json[];
|
|
BEGIN
|
|
|
|
geom_table_name := cdb_observatory._OBS_GeomTable(geom, geometry_level);
|
|
|
|
IF geom_table_name IS NULL
|
|
THEN
|
|
RAISE NOTICE 'Point % is outside of the data region', ST_AsText(geom);
|
|
RETURN QUERY SELECT '{}'::text[], '{}'::text[];
|
|
END IF;
|
|
|
|
execute'
|
|
select array_agg( _obs_getcolumndata) from cdb_observatory._OBS_GetColumnData($1,
|
|
$2,
|
|
$3);'
|
|
INTO data_table_info
|
|
using geometry_level, dimension_names, time_span;
|
|
|
|
|
|
EXECUTE
|
|
format('SELECT geoid
|
|
FROM observatory.%I
|
|
WHERE the_geom && $1',
|
|
geom_table_name)
|
|
USING geom
|
|
INTO geoid;
|
|
|
|
query := 'SELECT ARRAY[';
|
|
FOR i IN 1..array_upper(data_table_info, 1)
|
|
LOOP
|
|
query = query || format('%I ', lower(((data_table_info)[i])->>'colname'));
|
|
IF i < array_upper(data_table_info, 1)
|
|
THEN
|
|
query := query || ',';
|
|
END IF;
|
|
END LOOP;
|
|
|
|
query := query || format(' ]::text[]
|
|
FROM observatory.%I
|
|
WHERE %I.geoid = %L
|
|
',
|
|
((data_table_info)[1])->>'tablename',
|
|
((data_table_info)[1])->>'tablename',
|
|
geoid
|
|
);
|
|
|
|
EXECUTE
|
|
query
|
|
INTO results
|
|
USING geom;
|
|
|
|
RETURN QUERY
|
|
EXECUTE
|
|
$query$
|
|
select row_to_json(t) from(
|
|
select categories as category,
|
|
meta->>'name' as name,
|
|
meta->>'tablename' as tablename,
|
|
meta->>'aggregate' as aggregate,
|
|
meta->>'type' as type,
|
|
meta->>'description' as description
|
|
from (select unnest($1) as categories, unnest($2) as meta) b
|
|
) t
|
|
$query$
|
|
USING results, data_table_info;
|
|
RETURN;
|
|
|
|
END;
|
|
$$ LANGUAGE plpgsql;
|