API reference

Point Pattern

PointPattern(points[, window, names, …])

Planar Point Pattern Class 2-D.

Point Processes

PointProcess(window, n, samples[, asPP])

Point Process base class.

PoissonPointProcess(window, n, samples[, …])

Poisson point process including \(N\)-conditioned CSR process and \(\lambda\)-conditioned CSR process.

PoissonClusterPointProcess(window, n, …[, …])

Poisson cluster point process (Neyman Scott).



Find minimum bounding rectangle of a point array.


Find convex hull of a point array.


Find mean center of a point array.

weighted_mean_center(points, weights)

Find weighted mean center of a marked point pattern.


Find manhattan median of a point array.


Calculate standard distance of a point array.


Calculate the Euclidean median for a point pattern.


Calculate parameters of standard deviational ellipse for a point pattern.

skyum(points[, not_hull])

Implements Skyum (1990)’s algorithm for the minimum bounding circle in R^2.

dtot(coord, points)

Sum of Euclidean distances between event points and a selected point.

_circle(p, q, r[, dmetric])

Returns (radius, (center_x, center_y)) of the circumscribed circle by the triangle pqr.

Quadrat Based Statistics

RectangleM(pp[, count_column, count_row, …])

Rectangle grid structure for quadrat-based method.

HexagonM(pp, lh)

Hexagon grid structure for quadrat-based method.

QStatistic(pp[, shape, nx, ny, lh, realizations])

Quadrat analysis of point pattern.

Distance Based Statistics


Abstract Base Class for distance statistics.

G(pp[, intervals, dmin, dmax, d])

Estimates the nearest neighbor distance distribution function G for a point pattern.

F(pp[, n, intervals, dmin, dmax, d])

Estimates the empty space distribution function for a point pattern: F(d).

J(pp[, n, intervals, dmin, dmax, d])

Estimates the J function for a point pattern [LB96]

K(pp[, intervals, dmin, dmax, d])

Estimates the K function for a point pattern.

L(pp[, intervals, dmin, dmax, d])

Estimates the \(L\) function for a point pattern [OSullivanU10].

Envelopes(*args, **kwargs)

Abstract base class for simulation envelopes.

Genv(pp[, intervals, dmin, dmax, d, pct, …])

Simulation envelope for G function.

Fenv(pp[, n, intervals, dmin, dmax, d, pct, …])

Simulation envelope for F function.

Jenv(pp[, n, intervals, dmin, dmax, d, pct, …])

Simulation envelope for J function.

Kenv(pp[, intervals, dmin, dmax, d, pct, …])

Simulation envelope for K function.

Lenv(pp[, intervals, dmin, dmax, d, pct, …])

Simulation envelope for L function.

Window functions

Window(parts[, holes])

Geometric container for point patterns.


Convert a libpysal polygon to a Window.



Space-Time Interaction Tests

SpaceTimeEvents(path, time_col[, …])

Method for reformatting event data stored in a shapefile for use in calculating metrics of spatio-temporal interaction.

knox(s_coords, t_coords, delta, tau[, …])

Knox test for spatio-temporal interaction.

mantel(s_coords, t_coords[, permutations, …])

Standardized Mantel test for spatio-temporal interaction.

jacquez(s_coords, t_coords, k[, permutations])

Jacquez k nearest neighbors test for spatio-temporal interaction.

modified_knox(s_coords, t_coords, delta, tau)

Baker’s modified Knox test for spatio-temporal interaction.