Source code for segregation.local.local_multi_simpson_concentration
"""Multigroup dissimilarity index"""
__author__ = "Renan X. Cortes <renanc@ucr.edu>, Sergio J. Rey <sergio.rey@ucr.edu> and Elijah Knaap <elijah.knaap@ucr.edu>"
import numpy as np
from .._base import MultiGroupIndex, SpatialImplicitIndex
np.seterr(divide="ignore", invalid="ignore")
def _multi_local_simpson_concentration(data, groups):
"""
Calculation of Local Simpson concentration index for each unit
Parameters
----------
data : a pandas DataFrame of n rows
groups : list of strings.
The variables names in data of the groups of interest of the analysis.
Returns
-------
statistics : np.array(n)
Local Simpson concentration values for each unit
core_data : a pandas DataFrame
A pandas DataFrame that contains the columns used to perform the estimate.
Notes
-----
Based on the local version of Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
Simpson's concentration index can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to belong to the same group.
Higher values means lesser segregation.
Simpson's Concentration + Simpson's Interaction = 1
Reference: :cite:`reardon2002measures`.
"""
core_data = data[groups]
df = np.array(core_data)
ti = df.sum(axis=1)
pik = df / ti[:, None]
local_SC = np.nansum(pik * pik, axis=1)
return local_SC, core_data, groups
[docs]class MultiLocalSimpsonConcentration(MultiGroupIndex, SpatialImplicitIndex):
"""Multigroup Local Simpson's Concentration Index.
Parameters
----------
data : pandas.DataFrame or geopandas.GeoDataFrame, required
dataframe or geodataframe if spatial index holding data for location of interest
groups : list, required
list of columns on dataframe holding population totals for each group
w : libpysal.weights.KernelW, optional
lipysal spatial kernel weights object used to define an egohood
network : pandana.Network
pandana Network object representing the study area
distance : int
Maximum distance (in units of geodataframe CRS) to consider the extent of the egohood
decay : str
type of decay function to apply. Options include
precompute : bool
Whether to precompute the pandana Network object
Attributes
----------
statistic : float
Multigroup Dissimilarity Index value
core_data : a pandas DataFrame
DataFrame that contains the columns used to perform the estimate.
Notes
-----
Based on Eq. 6 of pg. 139 (individual unit case) of Reardon, Sean F., and David O’Sullivan. "Measures of spatial segregation." Sociological methodology 34.1 (2004): 121-162.
Reference: :cite:`reardon2004measures`.
"""
[docs] def __init__(
self,
data,
groups,
w=None,
network=None,
distance=None,
decay=None,
precompute=None,
function='triangular'
):
"""Init."""
MultiGroupIndex.__init__(self, data, groups)
if any([w, network, distance]):
SpatialImplicitIndex.__init__(self, w, network, distance, decay, function, precompute)
aux = _multi_local_simpson_concentration(self.data, self.groups)
self.statistics = aux[0]
self.data = aux[1]
self.groups = aux[2]
self._function = _multi_local_simpson_concentration