segregation.dynamics.compute_multiscalar_profile(gdf, segregation_index=None, groups=None, group_pop_var=None, total_pop_var=None, distances=None, network=None, decay='linear', function='triangular', precompute=True, **kwargs)[source]

Compute multiscalar segregation profile.

This function calculates several Spatial Information Theory indices with increasing distance parameters.


geodataframe with rows as observations and columns as population variables. Note that if using a network distance, the coordinate system for this gdf should be 4326. If using euclidian distance, this must be projected into planar coordinates like state plane or UTM.

segregation_indexSpatialImplicit SegregationIndex Class

a class from the library such as MultiInformationTheory, or MinMax


list of population groups for calculating multigroup indices


name of population group on gdf for calculating single group indices


bame of total population on gdf for calculating single group indices


list of floats representing bandwidth distances that define a local environment.

networkpandana.Network (optional)

A pandana.Network likely created with

decaystr (optional)

decay type to be used in pandana accessibility calculation options are {‘linear’, ‘exp’, ‘flat’}. The default is ‘linear’.

function: ‘str’ (optional)

which weighting function should be passed to libpysal.weights.Kernel must be one of: ‘triangular’,’uniform’,’quadratic’,’quartic’,’gaussian’

precompute: bool

Whether the pandana.Network instance should precompute the range queries. This is True by default


additional keyword arguments passed to each index (e.g. for setting a random seed in indices like ModifiedGini or ModifiedDissm)


Series with distances as index and index statistics as values


Based on Sean F. Reardon, Stephen A. Matthews, David O’Sullivan, Barrett A. Lee, Glenn Firebaugh, Chad R. Farrell, & Kendra Bischoff. (2008). The Geographic Scale of Metropolitan Racial Segregation. Demography, 45(3), 489–514.

Reference: [Sean F. Reardon et al., 2008].