geosnap.Community.cluster

Community.cluster(self, n_clusters=6, method=None, best_model=False, columns=None, verbose=False, scaler='std', pooling='fixed', **kwargs)[source]

Create a geodemographic typology by running a cluster analysis on the study area’s neighborhood attributes.

Parameters
n_clustersint, required

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

methodstr in [‘kmeans’, ‘ward’, ‘affinity_propagation’, ‘spectral’, ‘gaussian_mixture’, ‘hdbscan’], required

the clustering algorithm used to identify neighborhood types

best_modelbool, optional

if using a gaussian mixture model, use BIC to choose the best n_clusters. (the default is False).

columnsarray_like, required

subset of columns on which to apply the clustering

verbosebool, optional

whether to print warning messages (the default is False).

scalerNone or scaler from sklearn.preprocessing, optional

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

pooling[“fixed”, “pooled”, “unique”], optional (default=’fixed’)

How to treat temporal data when applying scaling. Options include:

  • fixed : scaling is fixed to each time period

  • pooled : data are pooled across all time periods

  • unique : if scaling, apply the scaler to each time period, then generate clusters unique to each time period.

Returns
geosnap.Community

a copy of input Community with neighborhood cluster labels appended as a new column. If the cluster is already present, the name will be incremented