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()

function redcap(
    WeightResult w,
    Number k, 
    Array vals,
    String method,
    Number min_bound, 
    Array bound_vals,
    String scale_method,
    String distance_type)

Arguments

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"}

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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