segregation.aspatial.DensityCorrectedDissim

class segregation.aspatial.DensityCorrectedDissim(data, group_pop_var, total_pop_var, xtol=1e-05)[source]

Calculation of Density Corrected Dissimilarity index

Parameters
dataa pandas DataFrame
group_pop_varstring

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

total_pop_varstring

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

xtolfloat

The degree of tolerance in the optimization process of returning optimal theta_j

Notes

Based on Allen, Rebecca, et al. “More reliable inference for the dissimilarity index of segregation.” The econometrics journal 18.1 (2015): 40-66.

Reference: [ABDW15].

Examples

In this example, we will calculate the Dissimilarity with Density Correction (Ddc) for the Riverside County using the census tract data of 2010. The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.

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 DensityCorrectedDissim

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.

>>> density_corrected_dissim_index = DensityCorrectedDissim(df, 'tractid', 'pop10')
>>> density_corrected_dissim_index.statistic
0.29350643204887517
Attributes
statisticfloat

Dissimilarity with Density-Correction (density correction from Allen, Rebecca et al. (2015))

core_dataa pandas DataFrame

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

__init__(data, group_pop_var, total_pop_var, xtol=1e-05)[source]

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

Methods

__init__(data, group_pop_var, total_pop_var)

Initialize self.