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.