momepy.NeighborDistance#

momepy.NeighborDistance(gdf, spatial_weights, unique_id, verbose=True)[source]#

Calculate the mean distance to adjacent buildings (based on spatial_weights). If no neighbours are found, return np.nan.

\[\frac{1}{n}\sum_{i=1}^n dist_i=\frac{dist_1+dist_2+\cdots+dist_n}{n}\]

Adapted from [Schirmer and Axhausen, 2015].

Parameters:

gdfGeoDataFrame: A GeoDataFrame containing objects to analyse.
spatial_weightslibpysal.weights: A spatial weights matrix based on unique_id.
unique_idstr: The name of the unique ID column used as the spatial_weights index.
verbosebool (default True): If True, shows progress bars in loops and indication of steps.

Attributes:

seriesSeries: A Series containing resulting values.
gdfGeoDataFrame: The original GeoDataFrame.
swlibpysal.weights: The spatial weights matrix.
idSeries: A Series containing used unique ID.

Examples

>>> buildings_df['neighbour_distance'] = momepy.NeighborDistance(buildings_df,
...                                                              sw,
...                                                              'uID').series
100%|██████████| 144/144 [00:00<00:00, 345.78it/s]
>>> buildings_df['neighbour_distance'][0]
29.18589019096464