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).

Centrography

minimum_bounding_rectangle(points)	Find minimum bounding rectangle of a point array.
hull(points)	Find convex hull of a point array.
mean_center(points)	Find mean center of a point array.
weighted_mean_center(points, weights)	Find weighted mean center of a marked point pattern.
manhattan_median(points)	Find manhattan median of a point array.
std_distance(points)	Calculate standard distance of a point array.
euclidean_median(points)	Calculate the Euclidean median for a point pattern.
ellipse(points)	Calculate parameters of standard deviational ellipse for a point pattern.
skyum(points)	WARNING: This function is deprecated in favor of minimum_bounding_circle
dtot(coord, points)	Sum of Euclidean distances between event points and a selected point.

Density

plot_density(data, bandwidth[, kernel, ...])

Plot kernel density of a given point pattern

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, ...])	Quadrat analysis of point pattern.

Distance Based Statistics

f(coordinates[, support, distances, metric, ...])	Ripley's F function
g(coordinates[, support, distances, metric, ...])	Ripley's G function
k(coordinates[, support, distances, metric, ...])	Ripley's K function
j(coordinates[, support, distances, metric, ...])	Ripely's J function
l(coordinates[, support, permutations, ...])	Ripley's L function
f_test(coordinates[, support, distances, ...])	Ripley's F function
g_test(coordinates[, support, distances, ...])	Ripley's G function
k_test(coordinates[, support, distances, ...])	Ripley's K function
j_test(coordinates[, support, distances, ...])	Ripley's J function
l_test(coordinates[, support, distances, ...])	Ripley's L function

Window functions

Window(parts[, holes])	Geometric container for point patterns.
as_window(pysal_polygon)	Convert a libpysal polygon to a Window.
poly_from_bbox(bbox)
to_ccf(poly)

Random distributions

random.poisson(hull[, intensity, size])	Simulate a poisson random point process with a specified intensity.
random.normal(hull[, center, cov, size])	Simulate a multivariate random normal point cluster
random.cluster_poisson(hull[, intensity, ...])	Simulate a cluster poisson random point process with a specified intensity & number of seeds.
random.cluster_normal(hull[, cov, size, n_seeds])	Simulate a cluster poisson random point process with a specified intensity & number of seeds.

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[, ...])	Global Knox statistic for space-time interactions
KnoxLocal(s_coords, t_coords, delta, tau[, ...])	Local Knox statistics for space-time interactions
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.

Visualization

plot_density(data, bandwidth[, kernel, ...])

Plot kernel density of a given point pattern