API reference


Provides visualisations for the Geospatial Distribution Dynamics - giddy module. giddy provides a tool for space–time analytics that consider the role of space in the evolution of distributions over time.

Directional LISA analytics

dynamic_lisa_heatmap(rose[, p, ax])

Heatmap indicating significant transition of LISA values over time inbetween Moran Scatterplot quadrants

dynamic_lisa_rose(rose[, attribute, ax])

Plot dynamic LISA values in a rose diagram.

dynamic_lisa_vectors(rose[, ax, arrows])

Plot vectors of positional transition of LISA values in Moran scatterplot

dynamic_lisa_composite(rose, gdf[, p, figsize])

Composite visualisation for dynamic LISA values over two points in time.

dynamic_lisa_composite_explore(rose, gdf[, ...])

Interactive exploration of dynamic LISA values for different dates in a dataframe.


Provides visualisations for the esda subpackage. esda provides tools for exploratory spatial data analysis that consider the role of space in a distribution of attribute values.

Moran analytics

moran_scatterplot(moran[, zstandard, p, ...])

Moran Scatterplot

plot_moran_simulation(moran[, aspect_equal, ...])

Global Moran's I simulated reference distribution.

plot_moran(moran[, zstandard, aspect_equal, ...])

Global Moran's I simulated reference distribution and scatterplot.

plot_moran_bv_simulation(moran_bv[, ax, ...])

Bivariate Moran's I simulated reference distribution.

plot_moran_bv(moran_bv[, aspect_equal, ...])

Bivariate Moran's I simulated reference distribution and scatterplot.

lisa_cluster(moran_loc, gdf[, p, ax, ...])

Create a LISA Cluster map

plot_local_autocorrelation(moran_loc, gdf, ...)

Produce three-plot visualisation of Moran Scatteprlot, LISA cluster and Choropleth maps, with Local Moran region and quadrant masking

moran_facet(moran_matrix[, figsize, ...])

Moran Facet visualization.


Provides visualisations for all core components of Python Spatial Analysis Library in libpysal.

libpysal weights

plot_spatial_weights(w, gdf[, indexed_on, ...])

Plot spatial weights network.


Provides Choropleth visualizations and mapping utilities.

Value-by-Alpha maps

value_by_alpha_cmap(x, y[, cmap, ...])

Calculates Value by Alpha rgba values

vba_choropleth(x_var, y_var, gdf[, cmap, ...])

Value by Alpha Choropleth

vba_legend(rgb_bins, alpha_bins, cmap[, ax])

Creates Value by Alpha heatmap used as choropleth legend.

mapclassify_bin(y, classifier[, k, pct, ...])

Classify your data with pysal.mapclassify Note: Input parameters are dependent on classifier used.

Colormap utilities

shift_colormap(cmap[, start, midpoint, ...])

Function to offset the "center" of a colormap.

truncate_colormap(cmap[, minval, maxval, n])

Function to truncate a colormap by selecting a subset of the original colormap's values