# Beat Populations¶

Link census tract populations, total and by race, into police beats. Attributes population from tracts to beats by the areas of the overlaps. The basic procedure is to find the overlaps between beats and Census tracts, then addign a portion of the population of the tract to the beat, based on the raio of the size of overlap to the size of the tract.

In [1]:
import seaborn as sns
import metapack as mp
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
import utm

%matplotlib inline
sns.set_context('notebook')

In [2]:
#pkg = mp.jupyter.open_package()
pkg = mp.jupyter.open_source_package()
pkg

Out[2]:

# San Diego Police Regions and Demographics

sandiego.gov-police_regions-2.1.2 Last Update: 2021-02-25T19:07:59

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

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.

## Notes

• Version 2.1.1 Updates to 2019 Census, better EPSG ( UTM 11N ) for area calc

## References

• tracts, censusgeo://2019/5/CA/tract. Census tracts from 2016 5 year ACS, for San Diego county
• race, census://2019/5/CA/tract/B03002. Race, by tract, in San Diego county
• pd_beats_source, shape+http://seshat.datasd.org/sde/pd/pd_beats_datasd.zip. Police beats
• pd_divisions_source, shape+http://seshat.datasd.org/sde/pd/pd_divisions_datasd.zip. Police Divisions
• pd_neighborhoods_source, shape+http://seshat.datasd.org/sde/pd/pd_divisions_datasd.zip. Police Neighborhoods
In [3]:
# Convert to EPSG:26911, ( A randomly selected UTM Zone 11N CRS) so area calculations
# will be in square meters, rather than square degrees
beats = pkg.resource('pd_beats').geoframe()

Out[3]:
objectid beat div serv name geometry
0 3 935 9 930 NORTH CITY MULTIPOLYGON (((-117.20425 32.96202, -117.2043...
1 7 0 0 0 SAN DIEGO MULTIPOLYGON (((-117.22526 32.70267, -117.2264...
2 8 511 5 510 MULTIPOLYGON (((-117.22529 32.70261, -117.2246...
3 9 722 7 720 NESTOR POLYGON ((-117.09042 32.58383, -117.08714 32.5...
4 10 314 3 310 BIRDLAND POLYGON ((-117.15149 32.80650, -117.14943 32.7...
In [4]:
# Figure out the UTM zone, and from that the CRS EPSG for the UTM zone
xmin, ymin, xmax, ymax = beats.to_crs(4326).envelope.total_bounds
band = utm.from_latlon((ymax-ymin)/2+ymin, (xmax-xmin)/2+xmin )[2]
epsg = int('326'+str(band))

assert(epsg == 32611) # It's always San Diego, so we already know what it is ...

In [5]:
# Convert to a UTM EPSG, so that we can do calculations in meters.
beats = beats.to_crs(epsg)

# There are  beats that are way off in east county. Get rid of them.
#rightmost_centroid = beats.centroid.x.sort_values(ascending=False).iloc[:6].max()
#beats = beats[beats.centroid.x <rightmost_centroid]

# It looks like the dataset has multiple rows per beat, one feature per row. We need
# it to have one row per beat, with multiple features combined together.
beats = beats.dissolve(by='beat').reset_index()

beats['beat_area'] = beats.area / 1_000_000

beats.plot()

Out[5]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fd8f861d650>
In [6]:
tracts = pkg.reference('tracts').geoframe().to_crs(epsg)

tracts['tract_area'] = tracts.area / 1_000_000

tracts.plot()

Out[6]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fd8f8803910>
In [ ]:


In [7]:
# White, black, asian, etc are all non hispanic.
col_map = {
'b03002_001':'total',
'b03002_003':'white',
'b03002_004':'black',
'b03002_005':'aian',
'b03002_006':'asian',
'b03002_007':'nhopi',
'b03002_012':'hisp'

}

for k,v in list(col_map.items()):
col_map[k+'_m90'] = col_map[k]+'_m90'

t = pkg.reference('race').dataframe()

race_tracts = t[t.county=='073'].rename(columns=col_map).reset_index().rename(columns={'GEOID':'geoid'})

In [8]:
race_tracts = race_tracts[['geoid', 'total', 'white', 'black', 'aian', 'asian', 'nhopi', 'hisp']]

Out[8]:
0 1 2 3 4
GEOID 14000US06073000100 14000US06073000201 14000US06073000202 14000US06073000300 14000US06073000400
total 3093 1891 4542 5239 3801
white 2389 1569 3390 3820 2148
black 0 10 4 266 228
aian 0 11 0 0 0
asian 112 75 379 146 430
nhopi 0 0 3 7 0
hisp 489 140 616 871 884
In [9]:
t = gpd.sjoin(beats, tracts)

ax = t.plot()
beats.centroid.plot(ax=ax, color='red')

t = t[['geoid', 'beat']].drop_duplicates()\
.merge(tracts[['geoid','geometry', 'tract_area']],on='geoid')\
.merge(beats[['beat','geometry', 'beat_area']],on='beat')

In [35]:
# merge by finding intersections. Each record in intr will be an area that is entirely
# in both one tract and one beat.
intr = gpd.overlay(beats, tracts, how='intersection')[['beat','geoid','geometry']]
print(len(beats), len(tracts), len(intr))
intr['intr_area'] = (intr.geometry.area/1_000_000.0).astype(float)

# Get rid of really small intersections
intr = intr[intr.intr_area >= .01]

# Merge back with beats and tracts to get beat and tract areas.
merged = intr[['beat','geoid', 'intr_area']]\
.merge(tracts[['geoid', 'tract_area']],on='geoid')\
.merge(beats[['beat', 'beat_area']],on='beat')\
.merge(race_tracts, on='geoid')

merged = merged.drop_duplicates(subset=['beat','geoid'])

merged['tract_overlap_proportion'] = merged.intr_area/merged.tract_area
merged['beat_overlap_proportion'] = merged.intr_area/merged.beat_area

# The intersection areas must be smaller than both of the areas being intersected
assert(not any(merged.intr_area > merged.beat_area))
assert(not any(merged.intr_area > merged.tract_area))

# Check that all of the areas of the beats are accounted for
assert(all(merged.groupby('beat').beat_overlap_proportion.sum().round(1) == 1))

# Multiply by the overlap portion to get the final values.
merged.loc[:,'total':'hisp'] = merged.loc[:,'total':'hisp'].multiply(merged.tract_overlap_proportion, axis=0)


127 8057 1011

Out[35]:
0 1 2 3 4
beat 0 511 0 511 0
geoid 14000US06073003800 14000US06073003800 14000US06073011300 14000US06073011300 14000US06073009902
intr_area 0.0367429 1.77045 0.0511633 1.91826 17.1586
tract_area 1.81432 1.81432 10.7308 10.7308 20.1302
beat_area 18.2217 6.79885 18.2217 6.79885 18.2217
total 129.043 6217.91 11.7624 441.007 0
white 59.6613 2874.76 5.74055 215.23 0
black 25.3348 1220.75 1.99775 74.9015 0
aian 1.2151 58.5491 0.0572148 2.14515 0
asian 11.2599 542.555 0.505397 18.9488 0
nhopi 0.931574 44.8876 0.0333753 1.25134 0
hisp 27.3599 1318.33 3.28508 123.167 0
tract_overlap_proportion 0.0202516 0.975818 0.0047679 0.178763 0.85238
beat_overlap_proportion 0.00201643 0.260404 0.00280781 0.282145 0.941655
In [11]:
beat_demographics = merged.groupby('beat').sum()[['total', 'white', 'black', 'aian', 'asian', 'nhopi', 'hisp']].round()

In [14]:
# Sanity checks. SD population is about 1.3M

assert beat_demographics.sum().total > 1_300_000

In [24]:
rel_err = np.abs(beat_demographics.loc[:,'white':].sum().sum() - beat_demographics.total.sum()) / beat_demographics.total.sum()
assert rel_err < 0.05

In [33]:
merged.loc[:,'total':'hisp'].multiply(merged.tract_overlap_proportion, axis=0)

Out[33]:
total white black aian asian nhopi hisp
0 2.613335 1.208237 5.130699e-01 2.460767e-02 2.280311e-01 1.886588e-02 0.554083
1 6067.555950 2805.244794 1.191229e+03 5.713330e+01 5.294352e+02 4.380219e+01 1286.451364
2 0.056082 0.027370 9.525060e-03 2.727941e-04 2.409681e-03 1.591299e-04 0.015663
3 78.835542 38.475068 1.338958e+01 3.834724e-01 3.387340e+00 2.236923e-01 22.017709
4 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000
... ... ... ... ... ... ... ...
551 5564.298683 358.122985 9.225883e+01 0.000000e+00 1.332297e+03 0.000000e+00 3561.389244
552 0.000113 0.000047 1.249278e-05 6.104937e-07 6.060266e-06 2.799337e-06 0.000043
553 0.000020 0.000010 3.396700e-07 9.520676e-07 9.917371e-09 1.239671e-08 0.000009
554 3.493589 0.466302 1.833630e-01 3.595353e-03 7.612770e-01 9.066543e-03 1.683407
555 7150.583811 5565.303536 1.306223e+02 0.000000e+00 7.748280e+02 1.880170e+01 340.409747

556 rows × 7 columns

In [31]:
beat_demographics

Out[31]:
total white black aian asian nhopi hisp
beat
0 542.0 264.0 62.0 2.0 34.0 3.0 161.0
111 27069.0 13222.0 1530.0 0.0 5210.0 191.0 6035.0
112 12344.0 8237.0 282.0 2.0 1245.0 22.0 2098.0
113 12721.0 8210.0 1.0 0.0 784.0 0.0 3108.0
114 16373.0 8843.0 339.0 3.0 2183.0 0.0 4318.0
... ... ... ... ... ... ... ...
933 7391.0 5722.0 136.0 0.0 821.0 19.0 360.0
934 45788.0 25358.0 428.0 177.0 13754.0 12.0 4013.0
935 9513.0 5535.0 25.0 6.0 2731.0 16.0 706.0
936 7762.0 3479.0 102.0 63.0 2777.0 30.0 808.0
937 9451.0 5558.0 113.0 49.0 2568.0 0.0 763.0

127 rows × 7 columns

In [34]:
merged

Out[34]:
beat geoid intr_area tract_area beat_area total white black aian asian nhopi hisp tract_overlap_proportion beat_overlap_proportion
0 0 14000US06073003800 0.036743 1.814322 18.221746 129.043281 59.661253 25.334768 1.215097 11.259897 0.931574 27.359930 0.020252 0.002016
1 511 14000US06073003800 1.770449 1.814322 6.798852 6217.914965 2874.761062 1220.748842 58.549105 542.555041 44.887647 1318.330684 0.975818 0.260404
2 0 14000US06073011300 0.051163 10.730782 18.221746 11.762402 5.740548 1.997749 0.057215 0.505397 0.033375 3.285081 0.004768 0.002808
3 511 14000US06073011300 1.918262 10.730782 6.798852 441.007122 215.230067 74.901493 2.145150 18.948827 1.251338 123.167372 0.178763 0.282145
4 0 14000US06073009902 17.158599 20.130225 18.221746 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.852380 0.941655
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
551 833 14000US06073002601 0.831085 0.834416 0.833758 5586.604632 359.558615 92.628674 0.000000 1337.637729 0.000000 3575.666006 0.996007 0.996793
552 9 14000US06073021302 0.046602 381.905937 0.333563 0.929342 0.388039 0.102379 0.005003 0.049664 0.022941 0.356069 0.000122 0.139710
553 9 14000US06073021100 0.058278 1170.397788 0.333563 0.398244 0.192798 0.006822 0.019121 0.000199 0.000249 0.170989 0.000050 0.174713
554 9 14000US06073013314 0.219781 17.578573 0.333563 279.424806 37.295816 14.665770 0.287564 60.888577 0.725162 134.642523 0.012503 0.658890
555 933 14000US06073008324 5.483780 5.512622 5.609425 7188.193001 5594.574790 131.309366 0.000000 778.903283 18.900591 342.200165 0.994768 0.977601

556 rows × 14 columns

In [ ]: