segregation.aspatial.Exposure

class segregation.aspatial.Exposure(data, group_pop_var, total_pop_var)[source]

Classic Exposure Index

Parameters
dataa pandas DataFrame
group_pop_varstring

The name of variable in data that contains the population size of the group of interest (X)

total_pop_varstring

The name of variable in data that contains the total population of the unit

Notes

The group of interest is labelled as group X, whilst Y is the complementary group. Groups X and Y are mutually excludent.

Based on Massey, Douglas S., and Nancy A. Denton. “The dimensions of residential segregation.” Social forces 67.2 (1988): 281-315.

Reference: [MD88].

Examples

In this example, we will calculate the Exposure Index (xPy) for the Riverside County using the census tract data of 2010. The group of interest (X) is non-hispanic black people which is the variable nhblk10 in the dataset and the Y group is the other part of the population.

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

>>> import pandas as pd
>>> import geopandas as gpd
>>> import segregation
>>> from segregation.aspatial import Exposure

Secondly, we need to read the data:

>>> # This example uses all census data that the user must provide your own copy of the external database.
>>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
>>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
>>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
>>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")

Then, we filter only for the desired county (in this case, Riverside County):

>>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]

The value is estimated below.

>>> exposure_index = Exposure(df, 'tractid', 'pop10')
>>> exposure_index.statistic
0.886785172226587

The interpretation of this number is that if you randomly pick a X member of a specific area, there is 88.68% of probability that this member shares a unit with a Y member.

Attributes
statisticfloat

Exposure Index

core_dataa pandas DataFrame

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

__init__(data, group_pop_var, total_pop_var)[source]

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

Methods

__init__(data, group_pop_var, total_pop_var)

Initialize self.