Multi-group Segregation Indices¶

[1]:

%load_ext watermark
%watermark -a 'eli knaap' -v -d -u -p segregation,geopandas,libpysal,pandana

Author: eli knaap

Last updated: 2021-05-09

Python implementation: CPython
Python version       : 3.9.2
IPython version      : 7.23.1

segregation: 2.0.0
geopandas  : 0.9.0
libpysal   : 4.3.0
pandana    : 0.6.1

Classes for computing multigroup segregation indices are in the multigroup module

[2]:

import geopandas as gpd
import matplotlib.pyplot as plt

from libpysal.examples import load_example
from segregation.multigroup import MultiDissim, MultiInfoTheory

[3]:

sacramento = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
sacramento = sacramento.to_crs(sacramento.estimate_utm_crs())

Aspatial Segregation Indices¶

[4]:

multi_dissim = MultiDissim(sacramento, groups=['WHITE', 'BLACK', 'HISP'])

[5]:

multi_dissim.statistic

[5]:

0.42469982288295693

[6]:

multi_info = MultiInfoTheory(sacramento, groups=['WHITE', 'BLACK', 'HISP'])

[7]:

multi_info.statistic

[7]:

0.1800803002655424

Spatial Segregation Indices¶

As with single group measures, generalized spatial versions of multigroup indices can be created by passing a distance parameter or a W/Network object.

[8]:

spatial_multi_dissim = MultiDissim(sacramento, groups=['WHITE', 'BLACK', 'HISP'], distance=2000)

[9]:

spatial_multi_dissim.statistic

[9]:

0.3776841098505291

[10]:

from pandana import Network

[11]:

net = Network.from_hdf5("../40900.h5")

[12]:

net_multi_dissim  = MultiDissim(sacramento, groups=['WHITE', 'BLACK', 'HISP'], distance=2000, network=net, decay='linear')

[13]:

net_multi_dissim.statistic

[13]:

0.3997196467720179

Batch-Computing Multi-Group Measures¶

To compute all single group indices in one go, the package provides a wrapper function in the batch module similar to single-group indices

[14]:

from segregation.batch import batch_compute_multigroup

[16]:

all_multigroup = batch_compute_multigroup(sacramento, groups=['WHITE', 'BLACK', 'HISP'],)

[17]:

all_multigroup

[17]:

	Statistic
MultiDissim	0.424700
MultiDivergence	0.131709
MultiDiversity	0.731390
MultiGini	0.556467
MultiInfoTheory	0.180080
MultiNormExposure	0.191362
MultiRelativeDiversity	0.168574
MultiSquaredCoefVar	0.145315
SimpsonsConcentration	0.587698
SimpsonsInteraction	0.412302

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