Open Street Map Extract for Demographic Search

civicknowledge.com-osm-demosearch-2.1.1. Modified 2021-03-26T01:46:41

Resources | Packages | Documentation| Contacts| References| Data Dictionary

Resources

  • point_tags. Points converted to counts of tags per geohash
  • residential_roads. Residential roads per 4 digit geohash
  • nonres_roads. Non residential roads per 4 digit geohash
  • block_geo. Geographic shapes for blocs in block_osm
  • block_osm. Points from geohash_tags allocated to 2020 census blocks
  • utm_map. Map from census blocks to UTM zones
  • cbsa_map. Map from census blocks to CBSA Metros
  • business_clusters. High density business clusters in CBSAs
  • metro_points. Points grouped into major categories and linked to business clusters.
  • bus_densities. High-density business goehashes

Documentation

Contacts

Data Dictionary

residential_roads | nonres_roads | bus_densities | point_tags | block_geo | block_osm | utm_map | cbsa_map | business_clusters | metro_points

residential_roads

Column NameData TypeDescription
zonestring
epsginteger
us_stateinteger
cus_stateinteger
highwaystring
geometrystring

nonres_roads

Column NameData TypeDescription
zonestring
epsginteger
us_stateinteger
cus_stateinteger
highwaystring
geometrystring

bus_densities

Column NameData TypeDescription
geohashstring
countnumber
densitynumber

point_tags

Column NameData TypeDescription
geohashstring
geoidstring
amenityinteger
tourisminteger
shopinteger
leisureinteger
naturalinteger
parkinginteger
bankinteger
barinteger
bicycle_parkinginteger
cafeinteger
clothesinteger
convenienceinteger
fast_foodinteger
fitness_centreinteger
fuelinteger
grave_yardinteger
hotelinteger
laundryinteger
parkinteger
parking_spaceinteger
playgroundinteger
restaurantinteger
supermarketinteger
geometrystring

block_geo

Column NameData TypeDescription
geoidstring
alandinteger
awaterinteger
latnumber
lonnumber
geometrystring

block_osm

Column NameData TypeDescription
geoidstring
amenityinteger
tourisminteger
shopinteger
leisureinteger
naturalinteger
parkinginteger
bankinteger
barinteger
bicycle_parkinginteger
cafeinteger
clothesinteger
convenienceinteger
fast_foodinteger
fitness_centreinteger
fuelinteger
grave_yardinteger
hotelinteger
laundryinteger
parkinteger
parking_spaceinteger
playgroundinteger
restaurantinteger
supermarketinteger

utm_map

Column NameData TypeDescription
geoidstring
bandinteger
zonestring
epsginteger
cus_stateinteger

cbsa_map

Column NameData TypeDescription
cbsastring
blockstring

business_clusters

Column NameData TypeDescription
cluster_ninteger
geometrystring
cbsastring

metro_points

Column NameData TypeDescription
geoidstring
geohashstring
geometrystring
entertaininteger
casualinteger
shopinteger
activeinteger
travelinteger
cbsastring

References

Urls used in the creation of this data package.

  • metapack+http://library.metatab.org/civicknowledge.com-geohash-us.csv#us_geohashes. All 4 digit geohases in the continential US
  • north-america-latest. OSM North America extract
  • metapack+http://library.metatab.org/civicknowledge.com-mgrs.csv#utm_grid.
  • data/csv/points.csv. Points from the OSM file
  • data/csv/lines.csv. Lines from the OSM file
  • data/csv/multipolygons.csv. Polygons from the OSM file
  • data/csv/multilinestrings.csv. Lines from the OSM file
  • data/csv/other_relations.csv. Other geo data from the OSM file
  • censusgeo://2020/5/{st}/block. Block url template
  • censusgeo://2020/5/US/cbsa. Metro areas

Packages

Accessing Data in Vanilla Pandas

import pandas as pd


point_tags_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/point_tags.csv')
residential_roads_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/residential_roads.csv')
nonres_roads_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/nonres_roads.csv')
block_geo_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/block_geo.csv')
block_osm_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/block_osm.csv')
utm_map_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/utm_map.csv')
cbsa_map_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/cbsa_map.csv')
business_clusters_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/business_clusters.csv')
metro_points_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/metro_points.csv')
bus_densities_df =  pd.read_csv('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1/data/bus_densities.csv')

Accessing Package in Metapack

import metapack as mp
pkg = mp.open_package('http://library.metatab.org/civicknowledge.com-osm-demosearch-2.1.1.csv')

# Create Dataframes
point_tags_gdf = pkg.resource('point_tags').geoframe()
residential_roads_gdf = pkg.resource('residential_roads').geoframe()
nonres_roads_gdf = pkg.resource('nonres_roads').geoframe()
block_geo_gdf = pkg.resource('block_geo').geoframe()
block_osm_df = pkg.resource('block_osm').dataframe()
utm_map_df = pkg.resource('utm_map').dataframe()
cbsa_map_df = pkg.resource('cbsa_map').dataframe()
business_clusters_gdf = pkg.resource('business_clusters').geoframe()
metro_points_gdf = pkg.resource('metro_points').geoframe()
bus_densities_df = pkg.resource('bus_densities').dataframe()