San Diego Police Regions and Demographics

Boundary shapes for San Diego neighborhoods, beats and divisions, with ACS 2019 estimates for populations, by race.

sandiego.gov-police_regions-2.1.2. Modified 2021-02-25T19:08:44

Resources

pd_beats. Police beats
pd_divisions. Police Divisions
pd_neighborhoods. Police Neighborhoods
beat_demographics. Counts of people in the beat, by race.

Documentation

This package links shapefiles for San Diego police beats to Census tracts and merges in ACS estimates for population, by race, from the 2016 5 year ACS. When a police beat boundry crosses a tract, the tract population is allocated to beats by the proportion of the overlap by area. See the Jupyter notebook that performs the procedure for details.

For the race/ethicty statistics, Hispanic (‘hisp’) refers to Hispanics of any race, while all other races refer to non-Hispanics of that race.

Documentation Links

Documentation Page
Police Divisions Repository Page Data repository page that links to original files.

Contacts

Wrangler
Eric Busboom, Civic Knowledge

Data Dictionary

beat_demographics | pd_beats | pd_divisions | pd_neighborhoods

beat_demographics

Column Name	Data Type	Description
beat	integer	Beat number
total	integer	Total population of beat
white	integer	White population of beat
black	integer	Black population of beat
aian	integer	American Indian population of beat
asian	integer	Asian population of beat
nhopi	integer	Hawaiian population of beat
hisp	integer	Hispanic population of beat

pd_beats

Column Name	Data Type	Description
objectid	integer
beat	integer
div	integer
serv	integer
name	string
geometry	string

pd_divisions

Column Name	Data Type	Description
objectid	integer
div_num	integer
div_name	string
geometry	string

pd_neighborhoods

Column Name	Data Type	Description
objectid	integer
div_num	integer
div_name	string
geometry	string

References

Urls used in the creation of this data package.

censusgeo://2019/5/CA/tract. Census tracts from 2016 5 year ACS, for San Diego county
census://2019/5/CA/tract/B03002. Race, by tract, in San Diego county
shape+http://seshat.datasd.org/sde/pd/pd_beats_datasd.zip. Police beats
shape+http://seshat.datasd.org/sde/pd/pd_divisions_datasd.zip. Police Divisions
shape+http://seshat.datasd.org/sde/pd/pd_divisions_datasd.zip. Police Neighborhoods

Packages

s3 s3://library.metatab.org/sandiego.gov-police_regions-2.1.2.csv
csv http://library.metatab.org/sandiego.gov-police_regions-2.1.2.csv
source https://github.com/metatab-packages/sandiego.gov-police_regions.git

Accessing Data in Vanilla Pandas

import pandas as pd


pd_beats_df =  pd.read_csv('http://library.metatab.org/sandiego.gov-police_regions-2.1.2/data/pd_beats.csv')
pd_divisions_df =  pd.read_csv('http://library.metatab.org/sandiego.gov-police_regions-2.1.2/data/pd_divisions.csv')
pd_neighborhoods_df =  pd.read_csv('http://library.metatab.org/sandiego.gov-police_regions-2.1.2/data/pd_neighborhoods.csv')
beat_demographics_df =  pd.read_csv('http://library.metatab.org/sandiego.gov-police_regions-2.1.2/data/beat_demographics.csv')

Accessing Package in Metapack

import metapack as mp
pkg = mp.open_package('http://library.metatab.org/sandiego.gov-police_regions-2.1.2.csv')

# Create Dataframes
pd_beats_gdf = pkg.resource('pd_beats').geoframe()
pd_divisions_gdf = pkg.resource('pd_divisions').geoframe()
pd_neighborhoods_gdf = pkg.resource('pd_neighborhoods').geoframe()
beat_demographics_df = pkg.resource('beat_demographics').dataframe()