momepy.Range#

momepy.Range(gdf, values, spatial_weights, unique_id, rng=(0, 100), verbose=True, **kwargs)[source]#

Calculates the range of values within neighbours defined in spatial_weights. Uses scipy.stats.iqr under the hood.

Adapted from [Dibble et al., 2017].

Parameters:

gdfGeoDataFrame: A GeoDataFrame containing morphological tessellation.
valuesstr, list, np.array, pd.Series: The name of the dataframe column, np.array, or pd.Series where character values are stored.
spatial_weightslibpysal.weights: A spatial weights matrix.
unique_idstr: The name of the column with unique IDs used as the spatial_weights index.
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.
**kwargsdict: Optional arguments for scipy.stats.iqr.
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.
valuesSeries: A Series containing used values.
swlibpysal.weights: The spatial weights matrix.
idSeries: A Series containing used unique ID.
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.
kwargsdict: Optional arguments for scipy.stats.iqr.

Examples

>>> sw = momepy.sw_high(k=3, gdf=tessellation_df, ids='uID')
>>> tessellation_df['area_IQR_3steps'] = mm.Range(tessellation_df,
...                                               'area',
...                                               sw,
...                                               'uID',
...                                               rng=(25, 75)).series
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