REDCAP
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 (first‐order 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 https://geodacenter.github.io/workbook/9c_spatial3/lab9c.html#redcap
redcap()
Arguments
Name | Type | Description |
| WeightsResult | The weights object |
| Number | The number of clusters |
| Array | The list of numeric vectors of the selected variable |
| String | The REDCAP method: {'single-linkage', 'average-linkage', 'complete-linkage', 'Ward-linkage'}. |
| Number | The minimum value that the sum value of the bounding variable in each cluster should be greater than |
| Array | The numeric vector of the selected bounding variable |
| String | The scaling method: {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} |
| String | The distance method: {"euclidean", "manhattan"} |
Return
Type | Description |
ClusteringResult | The Clustering object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'} |
Examples
Try it yourself in the playground (jsgeoda + deck.gl):
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