REDCAP
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Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) is developed by D. Guo (2008). Like SKATER, REDCAP starts from building a spanning tree in 4 different approaches (single-linkage, average-linkage, complete-linkage, and wards-linkage). Then, REDCAP provides 2 different approaches (firstāorder and full-order constraining) to prune the tree to find clusters. The REDCAP with first-order approach using a minimum spanning tree is exactly the same as SKATER. For more information, please read
Name
Type
Description
weights
WeightsResult
The weights object WeightsResult
k
Number
The number of clusters
vals
Array
The list of numeric vectors of the selected variable
method
String
The REDCAP method: {'single-linkage', 'average-linkage', 'complete-linkage', 'Ward-linkage'}.
min_bound
Number
The minimum value that the sum value of the bounding variable in each cluster should be greater than
bound_vals
Array
The numeric vector of the selected bounding variable
scale_method
String
The scaling method: {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}
distance_method
String
The distance method: {"euclidean", "manhattan"}
Type
Description
ClusteringResult
The Clustering object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}
Examples
Try it yourself in the playground (jsgeoda + deck.gl):