segregation.aspatial.SimpsonsConcentration

class segregation.aspatial.SimpsonsConcentration(data, groups)[source]

Calculation of Simpson’s Concentration index

Parameters
dataa pandas DataFrame
groupslist of strings.

The variables names in data of the groups of interest of the analysis.

Notes

Based on Simpson, Edward H. “Measurement of diversity.” nature 163.4148 (1949): 688.

Simpson’s concentration index (Lambda) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to belong to the same group.

Higher values means higher segregation.

Simpson’s Concentration + Simpson’s Interaction = 1

Reference: [Sim49].

Examples

In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.

Firstly, we need to perform some import the modules and the respective function.

>>> import libpysal
>>> import geopandas as gpd
>>> from segregation.multigroup_aspatial import SimpsonsConcentration

Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.

>>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
>>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']

The value is estimated below.

>>> index = SimpsonsConcentration(input_df, groups_list)
>>> index.statistic
0.49182413151957904
Attributes
statisticfloat

Simpson’s Concentration Index

core_dataa pandas DataFrame

A pandas DataFrame that contains the columns used to perform the estimate.

__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(data, groups)

Initialize self.