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observatory-extension/release/observatory--0.0.1.sql

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--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
-- Complain if script is sourced in psql, rather than via CREATE EXTENSION
\echo Use "CREATE EXTENSION observatory" to load this file. \quit
-- Version number of the extension release
CREATE OR REPLACE FUNCTION cdb_observatory_version()
RETURNS text AS $$
SELECT '0.0.1'::text;
$$ language 'sql' STABLE STRICT;
-- Internal identifier of the installed extension instence
-- e.g. 'dev' for current development version
CREATE OR REPLACE FUNCTION _cdb_observatory_internal_version()
RETURNS text AS $$
SELECT installed_version FROM pg_available_extensions where name='observatory' and pg_available_extensions IS NOT NULL;
$$ language 'sql' STABLE STRICT;
-- Returns the table name with geoms for the given geometry_id
-- TODO probably needs to take in the column_id array to get the relevant
-- table where there is multiple sources for a column from multiple
-- geometries.
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GeomTable(
geom geometry,
geometry_id text
)
RETURNS TEXT
AS $$
DECLARE
result text;
BEGIN
EXECUTE '
SELECT tablename FROM observatory.OBS_table
WHERE id IN (
SELECT table_id
FROM observatory.OBS_table tab,
observatory.OBS_column_table coltable,
observatory.OBS_column col
WHERE type ILIKE ''geometry''
AND coltable.column_id = col.id
AND coltable.table_id = tab.id
AND col.id = $1
)
'
USING geometry_id, geom
INTO result;
return result;
END;
$$ LANGUAGE plpgsql;
-- A type for use with the OBS_GetColumnData function
CREATE TYPE cdb_observatory.OBS_ColumnData AS (colname text, tablename text, aggregate text);
-- A function that gets the column data for multiple columns
-- Old: OBS_GetColumnData
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetColumnData(
geometry_id text,
column_ids text[],
timespan text
)
RETURNS cdb_observatory.OBS_ColumnData[]
AS $$
DECLARE
result cdb_observatory.OBS_ColumnData[];
BEGIN
EXECUTE '
WITH geomref AS (
SELECT t.table_id id
FROM observatory.OBS_column_to_column c2c, observatory.OBS_column_table t
WHERE c2c.reltype = ''geom_ref''
AND c2c.target_id = $1
AND c2c.source_id = t.column_id
),
column_ids as (
select row_number() over () as no, a.column_id as column_id from (select unnest($2) as column_id) a
)
SELECT array_agg(ROW(colname, tablename, aggregate)::cdb_observatory.OBS_ColumnData order by column_ids.no)
FROM column_ids, observatory.OBS_column c, observatory.OBS_column_table ct, observatory.OBS_table t
WHERE column_ids.column_id = c.id
AND c.id = ct.column_id
AND t.id = ct.table_id
AND t.timespan = $3
AND t.id in (SELECT id FROM geomref)
'
USING geometry_id, column_ids, timespan
INTO result;
RETURN result;
END;
$$ LANGUAGE plpgsql;
--Gets the column id for a census variable given a human readable version of it
-- Old: OBS_LOOKUP_CENSUS_HUMAN
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_LookupCensusHuman(
column_names text[],
-- TODO: change variable name table_name to table_id
table_name text DEFAULT '"us.census.acs".extract_block_group_5yr_2013_69b156927c'
)
RETURNS text[] as $$
DECLARE
column_id text;
result text;
BEGIN
EXECUTE format('
WITH col_names AS (
select row_number() over() as no, a.column_name as column_name from(
select unnest($1) as column_name
) a
)
select array_agg(column_id order by col_names.no)
FROM observatory.OBS_column_table,col_names
where colname = col_names.column_name
and table_id = %L limit 1
', table_name)
INTO result
using column_names;
RETURN result;
END
$$ LANGUAGE plpgsql;
--Test point cause Stuart always seems to make random points in the water
CREATE OR REPLACE FUNCTION cdb_observatory._TestPoint()
RETURNS geometry
AS $$
BEGIN
-- new york city
RETURN CDB_LatLng(40.704512, -73.936669);
END;
$$ LANGUAGE plpgsql;
--Test polygon cause Stuart always seems to make random points in the water
-- TODO: remove as it's not used anywhere?
CREATE OR REPLACE FUNCTION cdb_observatory._TestArea()
RETURNS geometry
AS $$
BEGIN
-- Buffer NYC point by 500 meters
RETURN ST_Buffer(cdb_observatory._TestPoint()::geography, 500)::geometry;
END;
$$ LANGUAGE plpgsql;
--Used to expand a column based response to a table based one. Give it the desired
--columns and it will return a partial query for rolling them out to a table.
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_BuildSnapshotQuery(names text[])
RETURNS TEXT
AS $$
DECLARE
q text;
i numeric;
BEGIN
q := 'SELECT ';
FOR i IN 1..array_upper(names,1)
LOOP
q = q || format(' vals[%s] As %I', i, names[i]);
IF i < array_upper(names, 1) THEN
q= q || ',';
END IF;
END LOOP;
RETURN q;
END;
$$ LANGUAGE plpgsql;
--For Longer term Dev
--Break out table definitions to types
--Automate type creation from a script, something like
----CREATE OR REPLACE FUNCTION OBS_Get<%=tag_name%>(geom GEOMETRY)
----RETURNS TABLE(
----<%=get_dimensions_for_tag(tag_name)%>
----AS $$
----DECLARE
----target_cols text[];
----names text[];
----vals NUMERIC[];-
----q text;
----BEGIN
----target_cols := Array[<%=get_dimensions_for_tag(tag_name)%>],
--Functions for augmenting specific tables
--------------------------------------------------------------------------------
-- Creates a table of demographic snapshot
CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetDemographicSnapshot(geom geometry, time_span text default '2009 - 2013', geometry_level text default '"us.census.tiger".block_group')
RETURNS json
AS $$
BEGIN
RETURN row_to_json(cdb_observatory._OBS_GetDemographicSnapshot(geom, time_span, geometry_level));
END;
$$ LANGUAGE plpgsql;
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' )
RETURNS TABLE(
total_pop NUMERIC,
male_pop NUMERIC,
female_pop NUMERIC,
median_age NUMERIC,
white_pop NUMERIC,
black_pop NUMERIC,
asian_pop NUMERIC,
hispanic_pop NUMERIC,
amerindian_pop NUMERIC,
other_race_pop NUMERIC,
two_or_more_races_pop NUMERIC,
not_hispanic_pop NUMERIC,
--not_us_citizen_pop NUMERIC,
--workers_16_and_over NUMERIC,
--commuters_by_car_truck_van NUMERIC,
--commuters_drove_alone NUMERIC,
--commuters_by_carpool NUMERIC,
--commuters_by_public_transportation NUMERIC,
--commuters_by_bus NUMERIC,
--commuters_by_subway_or_elevated NUMERIC,
--walked_to_work NUMERIC,
--worked_at_home NUMERIC,
--children NUMERIC, -- TODO we should be able to get this at BG
households NUMERIC,
--population_3_years_over NUMERIC,
--in_school NUMERIC,
--in_grades_1_to_4 NUMERIC,
--in_grades_5_to_8 NUMERIC,
--in_grades_9_to_12 NUMERIC,
--in_undergrad_college NUMERIC,
pop_25_years_over NUMERIC,
high_school_diploma NUMERIC,
less_one_year_college NUMERIC,
one_year_more_college NUMERIC,
associates_degree NUMERIC,
bachelors_degree NUMERIC,
masters_degree NUMERIC,
--pop_5_years_over NUMERIC,
--speak_only_english_at_home NUMERIC,
--speak_spanish_at_home NUMERIC,
--pop_determined_poverty_status NUMERIC,
--poverty NUMERIC,
median_income NUMERIC,
gini_index NUMERIC,
income_per_capita NUMERIC,
housing_units NUMERIC,
vacant_housing_units NUMERIC,
vacant_housing_units_for_rent NUMERIC,
vacant_housing_units_for_sale NUMERIC,
median_rent NUMERIC,
percent_income_spent_on_rent NUMERIC,
owner_occupied_housing_units NUMERIC,
million_dollar_housing_units NUMERIC,
mortgaged_housing_units NUMERIC,
--pop_15_and_over NUMERIC,
--pop_never_married NUMERIC,
--pop_now_married NUMERIC,
--pop_separated NUMERIC,
--pop_widowed NUMERIC,
--pop_divorced NUMERIC,
commuters_16_over NUMERIC,
commute_less_10_mins NUMERIC,
commute_10_14_mins NUMERIC,
commute_15_19_mins NUMERIC,
commute_20_24_mins NUMERIC,
commute_25_29_mins NUMERIC,
commute_30_34_mins NUMERIC,
commute_35_44_mins NUMERIC,
commute_45_59_mins NUMERIC,
commute_60_more_mins NUMERIC,
aggregate_travel_time_to_work NUMERIC,
income_less_10000 NUMERIC,
income_10000_14999 NUMERIC,
income_15000_19999 NUMERIC,
income_20000_24999 NUMERIC,
income_25000_29999 NUMERIC,
income_30000_34999 NUMERIC,
income_35000_39999 NUMERIC,
income_40000_44999 NUMERIC,
income_45000_49999 NUMERIC,
income_50000_59999 NUMERIC,
income_60000_74999 NUMERIC,
income_75000_99999 NUMERIC,
income_100000_124999 NUMERIC,
income_125000_149999 NUMERIC,
income_150000_199999 NUMERIC,
income_200000_or_more NUMERIC,
land_area NUMERIC)
AS $$
DECLARE
target_cols text[];
names text[];
vals NUMERIC[];
q text;
BEGIN
target_cols := Array['total_pop',
'male_pop',
'female_pop',
'median_age',
'white_pop',
'black_pop',
'asian_pop',
'hispanic_pop',
'amerindian_pop',
'other_race_pop',
'two_or_more_races_pop',
'not_hispanic_pop',
--'not_us_citizen_pop',
--'workers_16_and_over',
--'commuters_by_car_truck_van',
--'commuters_drove_alone',
--'commuters_by_carpool',
--'commuters_by_public_transportation',
--'commuters_by_bus',
--'commuters_by_subway_or_elevated',
--'walked_to_work',
--'worked_at_home',
--'children',
'households',
--'population_3_years_over',
--'in_school',
--'in_grades_1_to_4',
--'in_grades_5_to_8',
--'in_grades_9_to_12',
--'in_undergrad_college',
'pop_25_years_over',
'high_school_diploma',
'less_one_year_college',
'one_year_more_college',
'associates_degree',
'bachelors_degree',
'masters_degree',
--'pop_5_years_over',
--'speak_only_english_at_home',
--'speak_spanish_at_home',
--'pop_determined_poverty_status',
--'poverty',
'median_income',
'gini_index',
'income_per_capita',
'housing_units',
'vacant_housing_units',
'vacant_housing_units_for_rent',
'vacant_housing_units_for_sale',
'median_rent',
'percent_income_spent_on_rent',
'owner_occupied_housing_units',
'million_dollar_housing_units',
'mortgaged_housing_units',
--'pop_15_and_over',
--'pop_never_married',
--'pop_now_married',
--'pop_separated',
--'pop_widowed',
--'pop_divorced',
'commuters_16_over',
'commute_less_10_mins',
'commute_10_14_mins',
'commute_15_19_mins',
'commute_20_24_mins',
'commute_25_29_mins',
'commute_30_34_mins',
'commute_35_44_mins',
'commute_45_59_mins',
'commute_60_more_mins',
'aggregate_travel_time_to_work',
'income_less_10000',
'income_10000_14999',
'income_15000_19999',
'income_20000_24999',
'income_25000_29999',
'income_30000_34999',
'income_35000_39999',
'income_40000_44999',
'income_45000_49999',
'income_50000_59999',
'income_60000_74999',
'income_75000_99999',
'income_100000_124999',
'income_125000_149999',
'income_150000_199999',
'income_200000_or_more',
'land_area'];
q := 'WITH a As (
SELECT
dimension As names,
dimension_value As vals
FROM cdb_observatory._OBS_GetCensus($1,$2,$3,$4)
)' ||
cdb_observatory._OBS_BuildSnapshotQuery(target_cols) ||
' FROM a';
RETURN QUERY
EXECUTE
q
USING geom, target_cols, time_span, geometry_level;
RETURN;
END;
$$ LANGUAGE plpgsql;
--Base functions for performing augmentation
----------------------------------------------------------------------------------------
--Returns arrays of values for the given census dimension names for a given
--point or polygon
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetCensus(
geom geometry,
dimension_names text[],
time_span text DEFAULT '2009 - 2013',
geometry_level text DEFAULT '"us.census.tiger".block_group'
)
RETURNS TABLE(dimension text[], dimension_value NUMERIC[])
AS $$
DECLARE
ids text[];
BEGIN
ids := cdb_observatory._OBS_LookupCensusHuman(dimension_names);
RETURN QUERY
SELECT names, vals FROM cdb_observatory._OBS_Get(geom, ids, time_span, geometry_level);
END;
$$ LANGUAGE plpgsql;
-- Base augmentation fucntion.
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_Get(
geom geometry,
column_ids text[],
time_span text,
geometry_level text
)
RETURNS TABLE(names text[], vals NUMERIC[])
AS $$
DECLARE
results NUMERIC[];
geom_table_name text;
names text[];
query text;
data_table_info cdb_observatory.OBS_ColumnData[];
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', geom;
RETURN QUERY SELECT '{}'::text[], '{}'::NUMERIC[];
END IF;
data_table_info := cdb_observatory._OBS_GetColumnData(geometry_level,
column_ids,
time_span);
names := (SELECT array_agg((d).colname)
FROM unnest(data_table_info) As d);
IF ST_GeometryType(geom) = 'ST_Point'
THEN
results := cdb_observatory._OBS_GetPoints(geom,
geom_table_name,
data_table_info);
ELSIF ST_GeometryType(geom) IN ('ST_Polygon', 'ST_MultiPolygon')
THEN
results := cdb_observatory._OBS_GetPolygons(geom,
geom_table_name,
data_table_info);
END IF;
IF results IS NULL
THEN
results := Array[]::numeric[];
END IF;
RETURN QUERY SELECT names, results;
END;
$$ LANGUAGE plpgsql;
-- If the variable of interest is just a rate return it as such,
-- otherwise normalize it to the census block area and return that
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetPoints(
geom geometry,
geom_table_name text,
data_table_info cdb_observatory.OBS_ColumnData[]
)
RETURNS NUMERIC[]
AS $$
DECLARE
result NUMERIC[];
query text;
i int;
geoid text;
area NUMERIC;
BEGIN
-- TODO: does 'geoid' need to be generalized to geom_ref??
EXECUTE
format('SELECT geoid
FROM observatory.%I
WHERE ST_WITHIN($1, the_geom)',
geom_table_name)
USING geom
INTO geoid;
RAISE NOTICE 'geoid is %, geometry table is % ', geoid, geom_table_name;
EXECUTE
format('SELECT ST_Area(the_geom::geography) / (1000 * 1000)
FROM observatory.%I
WHERE geoid = %L',
geom_table_name,
geoid)
INTO area;
IF area IS NULL
THEN
RAISE NOTICE 'No geometry at %', ST_AsText(geom);
END IF;
query := 'SELECT Array[';
FOR i IN 1..array_upper(data_table_info, 1)
LOOP
IF area is NULL OR area = 0
THEN
-- give back null values
query := query || format('NULL::numeric ');
ELSIF ((data_table_info)[i]).aggregate != 'sum'
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;
RETURN result;
END;
$$ LANGUAGE plpgsql;
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetPolygons(
geom geometry,
geom_table_name text,
data_table_info cdb_observatory.OBS_ColumnData[]
)
RETURNS NUMERIC[]
AS $$
DECLARE
result NUMERIC[];
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;
RETURN result;
END;
$$ LANGUAGE plpgsql;
CREATE OR REPLACE FUNCTION OBS_GetSegmentSnapshot(geom geometry, geometry_level text default '"us.census.tiger".census_tract')
RETURNS json
AS $$
BEGIN
RETURN row_to_json(cdb_observatory._OBS_GetSegmentSnapshot(geom, geometry_level));
END;
$$ LANGUAGE plpgsql;
CREATE OR REPLACE FUNCTION _OBS_GetSegmentSnapshot(
geom geometry,
geometry_level text DEFAULT '"us.census.tiger".census_tract'
)
RETURNS TABLE(
segment_name TEXT,
total_pop_quantile NUMERIC,
male_pop_quantile NUMERIC,
female_pop_quantile NUMERIC,
median_age_quantile NUMERIC,
white_pop_quantile NUMERIC,
black_pop_quantile NUMERIC,
asian_pop_quantile NUMERIC,
hispanic_pop_quantile NUMERIC,
not_us_citizen_pop_quantile NUMERIC,
workers_16_and_over_quantile NUMERIC,
commuters_by_car_truck_van_quantile NUMERIC,
commuters_by_public_transportation_quantile NUMERIC,
commuters_by_bus_quantile NUMERIC,
commuters_by_subway_or_elevated_quantile NUMERIC,
walked_to_work_quantile NUMERIC,
worked_at_home_quantile NUMERIC,
children_quantile NUMERIC,
households_quantile NUMERIC,
population_3_years_over_quantile NUMERIC,
in_school_quantile NUMERIC,
in_grades_1_to_4_quantile NUMERIC,
in_grades_5_to_8_quantile NUMERIC,
in_grades_9_to_12_quantile NUMERIC,
in_undergrad_college_quantile NUMERIC,
pop_25_years_over_quantile NUMERIC,
high_school_diploma_quantile NUMERIC,
bachelors_degree_quantile NUMERIC,
masters_degree_quantile NUMERIC,
pop_5_years_over_quantile NUMERIC,
speak_only_english_at_home_quantile NUMERIC,
speak_spanish_at_home_quantile NUMERIC,
pop_determined_poverty_status_quantile NUMERIC,
poverty_quantile NUMERIC,
median_income_quantile NUMERIC,
gini_index_quantile NUMERIC,
income_per_capita_quantile NUMERIC,
housing_units_quantile NUMERIC,
vacant_housing_units_quantile NUMERIC,
vacant_housing_units_for_rent_quantile NUMERIC,
vacant_housing_units_for_sale_quantile NUMERIC,
median_rent_quantile NUMERIC,
percent_income_spent_on_rent_quantile NUMERIC,
owner_occupied_housing_units_quantile NUMERIC,
million_dollar_housing_units_quantile NUMERIC
)
AS $$
DECLARE
target_cols text[];
seg_name Text;
geom_id Text;
q 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 (categories)[1]
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
names As names,
vals As vals
FROM cdb_observatory._OBS_Get($1,
$2,
'2009 - 2013',
$3)
), percentiles As (
%s
FROM a)
SELECT $4, percentiles.*
FROM percentiles
$query$, cdb_observatory._OBS_BuildSnapshotQuery(target_cols));
RETURN QUERY
EXECUTE
q
USING geom, target_cols, geometry_level, segment_name;
END;
$$ LANGUAGE plpgsql;
--Get categorical variables from point
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetCategories(
geom geometry,
dimension_names text[],
geometry_level text DEFAULT '"us.census.tiger".block_group',
time_span text DEFAULT '2009 - 2013'
)
RETURNS TABLE(names text[], categories text[]) as $$
DECLARE
geom_table_name text;
geoid text;
names text[];
results text[];
query text;
data_table_info cdb_observatory.OBS_ColumnData[];
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;
data_table_info := cdb_observatory._OBS_GetColumnData(geometry_level,
dimension_names,
time_span);
names := (SELECT array_agg((d).colname)
FROM unnest(data_table_info) As d);
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
SELECT names,results
RETURN;
END;
$$ LANGUAGE plpgsql;
-- Placeholder for permission tweaks at creation time.
-- Make sure by default there are no permissions for publicuser
-- NOTE: this happens at extension creation time, as part of an implicit transaction.
-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_observatory FROM PUBLIC, publicuser CASCADE;
-- Grant permissions on the schema to publicuser (but just the schema)
-- GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser;
-- Revoke execute permissions on all functions in the schema by default
-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_observatory FROM PUBLIC, publicuser;
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