pygeoda.maxp_greedy

pygeoda.maxp_greedy(w, data, bound_variable, min_bound, **kwargs)[source]

A greedy algorithm to solve the max-p-region problem

The max-p-region problem is a special case of constrained clustering where a finite number of geographical areas are aggregated into the maximum number of regions (max-p-regions), such that each region is geographically connected and the clusters could maximize internal homogeneity.

Parameters

  • w (Weight) – an instance of Weight class

  • data (list or dataframe) – A list of numeric vectors of selected variable or a data frame of selected variables e.g. guerry[[‘Crm_prs’, ‘Literacy’]]

  • bound_variable (tuple) – A numeric vector of selected bounding variable

  • min_bound (float) – A minimum value that the sum value of bounding variable int each cluster should be greater than

  • iterations (int, optional) – The number of iterations of greedy algorithm. Defaults to 99.

  • init_regions (tuple, optional) – The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to “grow” clusters

  • scale_method (str, optional) – One of the scaling methods {‘raw’, ‘standardize’, ‘demean’, ‘mad’, ‘range_standardize’, ‘range_adjust’} to apply on input data. Default is ‘standardize’ (Z-score normalization).

  • distance_method (str, optional) – The distance method used to compute the distance betwen observation i and j. Defaults to “euclidean”. Options are “euclidean” and “manhattan”

  • random_seed (int, optional) – The seed for random number generator. Defaults to 123456789. It is the same as GeoDa software

  • cpu_threads (int, optional) – The number of cpu threads used for parallel computation

Returns

A dict with keys {“Clusters”, “TotalSS”, “Within-clusterSS”, “TotalWithin-clusterSS”, “Ratio”}

Return type

dict