momepy.Simpson#
- class momepy.Simpson(gdf, values, spatial_weights, unique_id, binning='HeadTailBreaks', gini_simpson=False, inverse=False, categorical=False, verbose=True, **classification_kwds)[source]#
Calculates the Simpson’s diversity index of values within neighbours defined in
spatial_weights
. Usesmapclassify.classifiers
under the hood for binning. Requiresmapclassify>=.2.1.0
dependency.\[\lambda=\sum_{i=1}^{R} p_{i}^{2}\]Adapted from [Feliciotti, 2018].
- Parameters:
- gdfGeoDataFrame
A GeoDataFrame containing morphological tessellation.
- valuesstr, list, np.array, pd.Series
The name of the dataframe column,
np.array
, orpd.Series
where character values are stored.- spatial_weightslibpysal.weights, optional
A spatial weights matrix. If
None
, Queen contiguity matrix of set order will be calculated based on objects.- unique_idstr
The name of the column with unique IDs used as the
spatial_weights
index.- binningstr (default ‘HeadTailBreaks’)
One of mapclassify classification schemes. For details see mapclassify API documentation.
- gini_simpsonbool (default False)
Return Gini-Simpson index instead of Simpson index (
1 - λ
).- inversebool (default False)
Return Inverse Simpson index instead of Simpson index (
1 / λ
).- categoricalbool (default False)
Treat values as categories (will not use
binning
).- verbosebool (default True)
If
True
, shows progress bars in loops and indication of steps.- **classification_kwdsdict
Keyword arguments for the classification scheme. For details see mapclassify documentation.
See also
momepy.simpson_diversity
Calculates the Simpson’s diversity index of data.
Examples
>>> sw = momepy.sw_high(k=3, gdf=tessellation_df, ids='uID') >>> tessellation_df['area_Simpson'] = mm.Simpson(tessellation_df, ... 'area', ... sw, ... 'uID').series 100%|██████████| 144/144 [00:00<00:00, 455.83it/s]
- 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.
- binningstr
The binning method used.
- binsmapclassify.classifiers.Classifier
The generated bins.
- classification_kwdsdict
Keyword arguments for the classification scheme. For details see mapclassify documentation.
- __init__(gdf, values, spatial_weights, unique_id, binning='HeadTailBreaks', gini_simpson=False, inverse=False, categorical=False, verbose=True, **classification_kwds)[source]#
Methods
__init__
(gdf, values, spatial_weights, unique_id)