Citation: Department of Housing and Urban Development (2016). Miami-Dade County Public Housing Developments [gis dataset]. Office of Policy Development and Research. https://hudgis-hud.opendata.arcgis.com/datasets/HUD::public-housing-developments-1/about. last updated: 2025-08-15 (update frequency: 3 months). Abstract: Denotes the general position of Public Housing Developments by providing the location of the building with the largest number of units in a Development’s community. HUD furnishes technical and professional assistance in planning, developing and managing these developments. Public Housing Developments are depicted as a distinct address chosen to represent the general location of an entire Public Housing Development, which may be comprised of several buildings scattered across a community. The building with the largest number of units is selected to represent the location of the development. Key words: United States, HUD, Property, Housing, Public Housing, Public Housing Development, Development, Infrastructure Open Geospatial Consortium Standards: dynamic vector tiles | | WMS (coming soon) | WFS (coming soon) ESRI Standards: ArcGIS MapServer | ArcGIS FeatureServer | Open in ArcGIS Portal for GDSC Analytical Functions: gdsc_get_attributes(tablename=mdc_public_housing,lat=25.718,lng=-80.279,identifier=project_name,normal='') ETL documentation

ETL for mdc_public_housing

######### # GDSC ETL is performed in two steps: shell script to ETL data into postgres # and then additional transformations with SQL in postGIS (if needed). #### ######### # Step 1 - bash script (pseudo code) #### # create directory structure and move into it mkdir -p /data/mdc_public_housing/{download,etl} && cd /data/mdc_public_housing # set update flag based on last update and update frequency do_update=0 if date() < last_update + update_frequency else do_update = 1 # download if update flag is set: if do_update == 1 then wget -O download/mdc_public_housing.json 'https://services.arcgis.com/VTyQ9soqVukalItT/arcgis/rest/services/Public_Housing_Developments/FeatureServer/0/query?outFields=*&where=COUNTY_LEVEL%3D12086&outSR=4326&f=geojson' # replace column names Shape__Length and Shape__Area to avoid conflict when # providing as service on ArcServer. sed -i 's/Shape__Length/shape_length/gI; s/Shape__Area/shape_area/gI' download/mdc_public_housing.json # load into postGIS with: ogr2ogr -lco GEOMETRY_NAME=geom -f PostgreSQL PG:"" download/mdc_public_housing.json -nlt point -nln mdc_public_housing ######### # Step 2 - SQL script (pseudo code): #### # remove duplicate points and make geometries valid: UPDATE mdc_public_housing SET geom=ST_MakeValid(ST_RemoveRepeatedPoints(geom)); # add local geometry column and reproject existing geometries into local EPSG: SELECT AddGeometryColumn ( 'mdc_public_housing', 'geom_local', 2236, 'point', 2 ); UPDATE mdc_public_housing SET geom_local=ST_MakeValid(ST_RemoveRepeatedPoints(ST_Transform(geom,2236))); CREATE INDEX mdc_public_housing_geom_local_idx ON mdc_public_housing USING GIST (geom_local); NOTIFY pgrst, 'reload schema'; # Move into corrrect directory and create derivative directory in data package on osgeo cd /data/mdc_public_housing/ mkdir -p derived # Move into correct directory cd /data/mdc_public_housing/ # Create pg_dump of table SQL in derivate directory on postgis. pg_dump -h postgis-p5hguvsgv3a84juh -t mdc_public_housing > derived/mdc_public_housing.sql # Create downloadable tarfile of SQL in derivative directory on postgis rm -f derived/mdc_public_housing.sql.tar.gz tar -czf derived/mdc_public_housing.sql.tar.gz meta_dcat_mdc_public_housing.json -C derived mdc_public_housing.sql