Combines geographic boundary files for communities, cities and tracts in San Diego County.

sandiegodata.org-geography-2018-12

Resources | Packages | Documentation| Contacts| References

Resources

Direct access to individual CSV files.

Packages

Collections of all files in the dataset. ZIP packages have all files and documentation in a ZIP file and are compressed to load faster. Excel files have all data in worksheet tabs. CSV packages reference files stored on the net, and are best for use with analysis tools like Metapack.

Accessing Packages in Metapack

import metapack as mp
# ZIP Package
pkg = mp.open_package('http://library.metatab.org/sandiegodata.org-geography-2018-12.zip')
# CSV Package
pkg = mp.open_package('http://library.metatab.org/sandiegodata.org-geography-2018-12.csv')

resource = pkg.resource('resource_name') # Get a resource
df = resource.dataframe() # Create a pandas Dataframe
gdf = resource.geoframe() # Create a GeoPandas GeoDataFrame

Documentation

This package combines three SANGIS datasets for communities and cities in San Diego county into a single file, in the communities resource, with Census tract definitions for San Diego count. The source files are:

  • Municipal boundaries, of incorporated cities and the rest of the county
  • Communities in unincorporated county areas
  • Communities in San Diego.
  • Census tract boundaries for 2016
  • Census places and counties

In addition to simply combining these four boundary files, the dataset also links tracts into the other three regions, in the tracts_links and tracts_all_regions datasets. Using these datasets, you can get all of the tracts in Escondido, or all of the tracts in the San Diego community of Clairemont. The join is performed by containment of the Internal Point, which is defined by the census for each tract. Because tract boundaries are not always coincident with municipal boundaries, there are many cases where the collection of tracts for a city or community will have a different boundary than the actual region. Smaller, less densely populated regions, like San Marcos, or particuarly afected.

The tracts_links dataset is probably the most useful. It joins tracts to regions, and has two sets of columns, for city and community. This dataset includes every tract in the county, and each appears only once. If a tract is included in both a city and community, then there is a name and code for both the city columns and the community columns. Regions not in a city have a city value of “County” and a city code of “CN”.

The communities dataset, has a type field to distinguish the types of area, which is one of:

  • city
  • county_community
  • sd_community
  • community

The tracts_all_regions dataset may have more than one row for each tract; the tract will appear once for each of the four region types that it is in, but no tract is in more than 2 regions. For instance, a tract in a community of San Diego will appear twice, once for the community, and once for the City.

Versions

  1. Added county outline
  2. Broke out seperate files for non-tract geographies.

Contacts

References

Urls used in the creation of this data package.

  • censusgeo://2016/5/CA/140. Tract boundary shapes
  • file:data/community-acronyms.csv. Acronyms for communities
  • shape+file:data/Promise_Zone_SD.zip. Promise zone boundary
  • shape+file:data/Municipal_Boundaries.zip. Converted source of boundaries for cities in San Diego county
  • shape+file:data/Community_Plan_CN.ZIP. Converted source of boundaries for communities in unincorporated San Diego county
  • shape+file:data/Community_Plan_SD.zip. Converted source of boundaries for communities in the city of San Diego.
  • metapack+http://library.metatab.org/census.gov-counties-2017-2.csv#counties. County boundaries shapes
  • censusgeo://CA/place. Census Defined Places

Last Modified 2019-01-31T02:04:12