Dasymetric Spatial Interpolation in BigQuery

Mapping census population to H3 hexagons

  • A simple spatial interpolation based on area overlap
  • A dasymetric interpolation using Open Street Map data
  • Dasymetric interpolation using parcel information
Census block groups in Orange county, FL; Dark red indicates higher population
WITH 
county_polyfill AS(
SELECT
jslibs.h3.ST_H3_POLYFILLFROMGEOG(county_geom ,7) as geo
FROM `bigquery-public-data.geo_us_boundaries.counties`
WHERE geo_id = '12095'
),
h3_hexagons as (
SELECT
h3,
jslibs.h3.ST_H3_BOUNDARY(h3) as h3geo
FROM county_polyfill, UNNEST(geo) as h3
),
geom_overlap AS(
SELECT
h3,
h3geo,
geo_id,
total_pop,
ST_AREA(ST_INTERSECTION(h3geo, blockgroup_geom)) as intersection_h3,
ST_AREA(blockgroup_geom) as cbg_area
FROM h3_hexagons
JOIN `bigquery-public-data.geo_census_blockgroups.us_blockgroups_national` cbg_geo
ON ST_INTERSECTS(h3geo, blockgroup_geom)
JOIN `bigquery-public-data.census_bureau_acs.blockgroup_2018_5yr` cbg_pop
USING(geo_id)
)
SELECT
h3,
ANY_VALUE(h3geo) as hex_geo,
SUM(SAFE_DIVIDE(intersection_h3,cbg_area) * total_pop) as h3_pop
FROM geom_overlap
GROUP BY h3
Population interpolated from CBG to H3 zoom level 7; simple overlap
WITH buildings_overlap AS (
SELECT
osm.osm_way_id,
osm.geometry,
cbg_geo.geo_id,
cbg_geo.blockgroup_geom,
ST_AREA(ST_INTERSECTION(blockgroup_geom,osm.geometry)) as intersection_area,
FROM `my-project.mydataset.buildings_osm` osm
JOIN `bigquery-public-data.geo_census_blockgroups.us_blockgroups_national` cbg_geo
ON ST_INTERSECTS(blockgroup_geom, geometry)
WHERE CONCAT(state_fips_code, county_fips_code) = '12095'
),
--total area of all residential polygons for each CBG
building_area_per_cbg AS (
SELECT
geo_id,
SUM(intersection_area) as building_area_total
FROM buildings_overlap
GROUP BY geo_id
),
--population for each overlapping area
share_population_building AS (
SELECT
osm_way_id,
geometry,
geo_id,
SAFE_DIVIDE(intersection_area, building_area_total) * total_pop as building_pop_overlap
FROM buildings_overlap
JOIN building_area_per_cbg
USING(geo_id)
JOIN `bigquery-public-data.census_bureau_acs.blockgroup_2018_5yr`
USING(geo_id)
),
population_per_building as (
SELECT
osm_way_id,
ANY_VALUE(geometry) as building_geo,
SUM(building_pop_overlap) as building_pop_total
FROM share_population_building
GROUP BY osm_way_id
),
county_polyfill as (
SELECT
jslibs.h3.ST_H3_POLYFILLFROMGEOG(county_geom ,7) as geo
FROM `bigquery-public-data.geo_us_boundaries.counties`
WHERE geo_id = '12095'
),
h3_hexagons as (
SELECT
h3,
jslibs.h3.ST_H3_BOUNDARY(h3) as h3geo
FROM county_polyfill, UNNEST(geo) as h3
),
--overlap of each H3 hexagon with each building
geom_overlap as (
SELECT
h3,
h3geo,
osm_way_id,
ST_AREA(ST_INTERSECTION(building_geo, h3geo)) as intersection_h3,
ST_AREA(building_geo) as building_area,
building_pop_total
FROM h3_hexagons
JOIN population_per_building
ON ST_INTERSECTS(building_geo, h3geo)
)
SELECT
h3,
ANY_VALUE(h3geo) as hex_geo,
SUM(SAFE_DIVIDE(intersection_h3,building_area) * building_pop_total) as h3_pop
FROM geom_overlap
GROUP BY h3
Population interpolated from CBG to H3 zoom level 7 using OSM data for refinement
Population interpolated from CBG to H3 zoom level 7 using parcel data for refinement
Top Left: CBG polygons and population; Top Right: spatial interpolation- simple overlap; BottomLeft: spatial interpolation using OSM data; Bottom Right: spatial interpolation using parcel data
  • Simple overlap - assumption: population is equally distributed over space within each CBG
  • OSM building polygon approach- assumption: population increases linearly with building area within CBG
  • Parcel polygon approach- assumption: population increases linearly with parcel area within CBG

Sources:

Dasymetric maps: https://en.wikipedia.org/wiki/Dasymetric_map
H3: https://eng.uber.com/h3/
JS library: https://github.com/CartoDB/bigquery-jslibs
Carto: https://spatial-data-science-conference.com/
Open Street Map: https://www.openstreetmap.org/
BigQuery Geography functions: https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions
Kepler: https://kepler.gl/
BigQuery public datasets: https://cloud.google.com/bigquery/public-data
Geospatial data science: https://geographicdata.science/book/intro.html

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Economist turned Data Scientist. Creating human mobility insights at Unacast

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Mathias Schläffer

Economist turned Data Scientist. Creating human mobility insights at Unacast