Source code for segregation.local.local_multi_location_quotient
"""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_location_quotient(data, groups):
"""
Calculation of Location Quotient index for each group and unit
Parameters
----------
data : a pandas DataFrame of n rows
groups : list of strings of length k.
The variables names in data of the groups of interest of the analysis.
Returns
-------
statistics : np.array(n,k)
Location Quotient values for each group and unit.
Column k has the Location Quotient of position k in groups.
core_data : a pandas DataFrame
A pandas DataFrame that contains the columns used to perform the estimate.
Notes
-----
Based on Isard, Walter. Methods of regional analysis. Vol. 4. Рипол Классик, 1967.
Reference: :cite:`isard1967methods`.
"""
core_data = data[groups]
df = np.array(core_data)
n = df.shape[0]
K = df.shape[1]
T = df.sum()
ti = df.sum(axis = 1)
Xk = df.sum(axis = 0)
multi_LQ = (df / np.repeat(ti, K, axis = 0).reshape(n,K)) / (Xk / T)
return multi_LQ, core_data, groups
[docs]class MultiLocationQuotient(MultiGroupIndex, SpatialImplicitIndex):
"""Multigroup Local Diversity 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
-----
Reference: :cite:`isard1967methods`.
"""
[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_location_quotient(self.data, self.groups)
self.statistics = aux[0]
self.data = aux[1]
self.groups = aux[2]
self._function = _multi_location_quotient