explore_connectivity_map

explore_connectivity_map(
    gdf,
    *,
    w=None,
    entity=None,
    tiles='carto-positron',
    title=None,
)

Draw the spatial weights graph over the map and summarize its connectivity.

The figure overlays the neighbor graph (edges between adjacent centroids, one node per unit) on a light-grey polygon layer, and the companion histogram shows the neighbor-cardinality distribution — the standard visual audit of a W before any spatial statistic is computed on it.

Parameters

Name	Type	Description	Default
gdf	gpd.GeoDataFrame	Geometry frame (see :func:geometrics.read_gdf).	required
w	W | None	libpysal weights aligned to the gdf entity ids. None builds the default weights (queen contiguity for polygons, 6-nearest-neighbor otherwise) with a :class:~geometrics.GeometricsWarning.	None
entity	str | None	Entity id column of gdf; resolved automatically when None.	None
tiles	str | None	MapLibre basemap style (e.g. "carto-positron"). None draws a vector (tile-free) figure suitable for deterministic PNG export.	'carto-positron'
title	str | None	Figure title (a default naming the weights is used when None).	None

Returns

Name	Type	Description
	ConnectivityMapResult	The per-entity neighbor-cardinality frame, the graph figure (fig), the cardinality histogram (fig_hist), the connectivity scalars and w_spec.

Examples

Connectivity of a two-cell map (each unit has exactly one neighbor):

import geopandas as gpd
from shapely.geometry import box

from geometrics.weights import explore_connectivity_map, make_weights

gdf = gpd.GeoDataFrame(
    {"region": ["A", "B"]},
    geometry=[box(0, 0, 1, 1), box(1, 0, 2, 1)],
    crs="EPSG:4326",
)
res = explore_connectivity_map(gdf, w=make_weights(gdf), tiles=None)
(res.n_units, res.mean_neighbors)