segregation.singlegroup.PARDissim

class segregation.singlegroup.PARDissim(data, group_pop_var, total_pop_var, w=None, standardize=True, **kwargs)

Perimeter-Area Ratio Dissimilarity Index.

Parameters:

datapandas.DataFrame or geopandas.GeoDataFrame, required

dataframe or geodataframe if spatial index holding data for location of interest

group_pop_varstr, required

name of column on dataframe holding population totals for focal group

total_pop_varstr, required

name of column on dataframe holding total overall population

standardizebool

A condition for row standardisation of the weights matrices. If True, the values of cij in the formulas gets row standardized. For the sake of comparison, the seg R package of Hong, Seong-Yun, David O’Sullivan, and Yukio Sadahiro. “Implementing spatial segregation measures in R.” PloS one 9.11 (2014): e113767. works by default with row standardization.

Attributes:

statisticfloat

PARDissim Index

core_dataa pandas DataFrame

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

Notes

Originally based on Wong, David WS. “Spatial indices of segregation.” Urban studies 30.3 (1993): 559-572.

However, Tivadar, Mihai. “OasisR: An R Package to Bring Some Order to the World of Segregation Measurement.” Journal of Statistical Software 89.1 (2019): 1-39. points out that in Wong’s original there is an issue with the formula which is an extra division by 2 in the spatial interaction component. This function follows the formula present in the first Appendix of Tivadar, Mihai. “OasisR: An R Package to Bring Some Order to the World of Segregation Measurement.” Journal of Statistical Software 89.1 (2019): 1-39.

References: [Wong, 1993] and [Tivadar, 2019].

__init__(data, group_pop_var, total_pop_var, w=None, standardize=True, **kwargs)

Init.

Methods

__init__(data, group_pop_var, total_pop_var)

Init.