segregation.aspatial.ConProf

class segregation.aspatial.ConProf(data, group_pop_var, total_pop_var, m=1000)[source]

Concentration Profile 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

mint

a numeric value indicating the number of thresholds to be used. A large value of m creates a smoother-looking graph and a more precise concentration profile value but slows down the calculation speed.

Notes

Based on Hong, Seong-Yun, and Yukio Sadahiro. “Measuring geographic segregation: a graph-based approach.” Journal of Geographical Systems 16.2 (2014): 211-231.

Reference: [HS14].

Examples

In this example, we will calculate the concentration profile (R) 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 ConProf

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.

>>> conprof_index = ConProf(df, 'tractid', 'pop10')
>>> conprof_index.statistic
0.06393365660089256

You can plot the profile curve with the plot method.

>>> conprof_index.plot()
Attributes
statisticfloat

Concentration Profile Index

core_dataa pandas DataFrame

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

Methods

plot()

Plot the Concentration Profile

__init__(data, group_pop_var, total_pop_var, m=1000)[source]

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

Methods

__init__(data, group_pop_var, total_pop_var)

Initialize self.

plot()

Plot the Concentration Profile