geosnap.analyze.regionalize

geosnap.analyze.regionalize(gdf, n_clusters=6, spatial_weights='rook', method=None, columns=None, threshold_variable='count', threshold=10, temporal_index='year', unit_index='geoid', scaler='std', weights_kwargs=None, region_kwargs=None, model_colname=None, return_model=False)

Create a spatial geodemographic typology by running a cluster analysis on the metro area’s neighborhood attributes and including a contiguity constraint.

Parameters:

gdfgeopandas.GeoDataFrame

long-form geodataframe holding neighborhood attribute and geometry data.

n_clustersint

the number of clusters to model. The default is 6).

spatial_weights[‘queen’, ‘rook’] or libpysal.weights.W object

spatial weights matrix specification`. By default, geosnap will calculate Rook weights, but you can also pass a libpysal.weights.W object for more control over the specification.

methodstr in [‘ward_spatial’, ‘kmeans_spatial’, ‘spenc’, ‘skater’, ‘azp’, ‘max_p’]

the clustering algorithm used to identify neighborhood types

columnsarray_like

subset of columns on which to apply the clustering

threshold_variablestr

for max-p, which variable should define p. The default is “count”, which will grow regions until the threshold number of polygons have been aggregated

thresholdnumeric

threshold to use for max-p clustering (the default is 10).

temporal_indexstr

which column on the dataframe defines time and or sequencing of the long-form data. Default is “year”

unit_indexstr

which column on the long-form dataframe identifies the stable units over time. In a wide-form dataset, this would be the unique index

weights_kwargsdict

If passing a libpysal.weights.W instance to spatial_weights, these additional keyword arguments that will be passed to the weights constructor

region_kwargs: dict

additional keyword arguments passed to the regionalization algorithm

scalerNone or scaler class from sklearn.preprocessing

a scikit-learn preprocessing class that will be used to rescale the data. Defaults to sklearn.preprocessing.StandardScaler

model_colnamestr

column name for storing cluster labels on the output dataframe. If no name is provided, the colun will be named after the clustering method. If there is already a column named after the clustering method, the name will be incremented with a number

return_model: bool

If True, also retun a dictional of fitted classes from the regionalization provider

Returns:

gdfgeopandas.GeoDataFrame

GeoDataFrame with a column of neighborhood cluster labels appended as a new column. If cluster method exists as a column on the DataFrame then the column will be incremented.

modelsdict of named tuples (only returned if return_model is True)

tab-completable dictionary of named tuples keyed on the Community’s time variable (e.g. year). The tuples store model results and have attributes X, columns, labels, instance, W, which store the input matrix, column labels, fitted model instance, and spatial weights matrix