Hierarchical clustering for spatial data, which requires clustering results not only homogeneous in non-geographical features among samples but also geographically close to each other within a cluster. It modified typically used hierarchical agglomerative clustering algorithms for introducing the spatial homogeneity, by considering geographical locations as vertices and converting spatial adjacency into whether a shared edge exists between a pair of vertices (Tzeng & Hsu, 2022) <arXiv:2201.08302>. The constraints of the vertex links automatically enforce the spatial contiguity property at each step of iterations. In addition, methods to find an appropriate number of clusters and to report cluster members are also provided.
Package details |
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Author | ShengLi Tzeng [cre, aut], Hao-Yun Hsu [aut] |
Maintainer | ShengLi Tzeng <slt.cmu@gmail.com> |
License | LGPL-3 |
Version | 1.2.0 |
Package repository | View on CRAN |
Installation |
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