momepy.AverageCharacter#

momepy.AverageCharacter(gdf, values, spatial_weights, unique_id, rng=None, mode='all', verbose=True)[source]#

Calculates the average of a character within a set neighbourhood defined in spatial_weights. Can be set to mean, median or mode. mean is defined as:

\[\frac{1}{n}\left(\sum_{i=1}^{n} value_{i}\right)\]

Adapted from [Hausleitner and Berghauser Pont, 2017].

Parameters:

gdfGeoDataFrame: A GeoDataFrame containing a morphological tessellation.
valuesstr, list, np.array, pd.Series: The name of the dataframe column, np.array, or pd.Series where character values are stored.
unique_idstr: The name of the column with unique ID used as the spatial_weights index.
spatial_weightslibpysal.weights: A spatial weights matrix.
rngtuple, list, optional (default None): A two-element sequence containing floats between 0 and 100 (inclusive) that are the percentiles over which to compute the range. The order of the elements is not important.
modestr (default ‘all’): The mode of average calculation. It can be set to 'all', 'mean', 'median', or 'mode' or a list of any of the options.
verbosebool (default True): If True, shows progress bars in loops and indication of steps.

Attributes:

seriesSeries: A Series containing resulting mean values.
meanSeries: A Series containing resulting mean values.
medianSeries: A Series containing resulting median values.
modeSeries: A Series containing resulting mode values.
gdfGeoDataFrame: The original GeoDataFrame.
valuesGeoDataFrame: A Series containing used values.
swlibpysal.weights: The spatial weights matrix.
idSeries: A Series containing used unique ID.
rngtuple: The range.
modesstr: The mode.

Examples

>>> sw = libpysal.weights.DistanceBand.from_dataframe(tessellation,
...                                                   threshold=100,
...                                                   silence_warnings=True,
...                                                   ids='uID')
>>> tessellation['mean_area'] = momepy.AverageCharacter(tessellation,
...                                                     values='area',
...                                                     spatial_weights=sw,
...                                                     unique_id='uID').mean
100%|██████████| 144/144 [00:00<00:00, 1433.32it/s]
>>> tessellation.mean_area[0]
4823.1334436678835