segregation.local.LocalDistortion

class segregation.local.LocalDistortion(data, groups=None, metric='euclidean', network=None, distance_matrix=None, normalize=True, n_seeds=4, **kwargs)

Multigroup Local Distortion Coefficients.

Parameters:

datapandas.DataFrame or geopandas.GeoDataFrame, required

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

groupslist, required

list of columns on dataframe holding population totals for each group

metricstr (optional; ‘euclidean’ by default)

Distance metric for calculating pairwise distances, Accepts any inputs to scipy.spatial.distance.pdist. Ignored if passing a network or distance matrix

network: pandarm.Network object (optional; None by default)

A pandarm Network object used to compute distance between observations

distance_matrix:

numpy array of distances between observations in the dataset

normalize: bool (optional; False by default)

If True, normalize coefficients by the maximum theoretical segregation value for this dataset

n_seeds: int (optional; 4 by default)

Number of corner positions used to build the maximally-segregated reference landscape. Raising this tightens the normalization constant at the cost of one extra divergence profile per seed. Ignored when normalize is False.

Attributes:

statisticspandas.Series

KL Divergence coefficients

core_dataa pandas DataFrame

DataFrame that contains the columns used to perform the estimate.

normalization_constantfloat or None

The maximal-segregation distortion coefficient used to normalize the coefficients, or None when normalize is False.

Notes

Olteanu et al. (2019): ‘Segregation through the multiscalar lens’. https://doi.org/10.1073/pnas.1900192116

__init__(data, groups=None, metric='euclidean', network=None, distance_matrix=None, normalize=True, n_seeds=4, **kwargs)

Init.

Methods

__init__(data[, groups, metric, network, ...])

Init.