PostGeoDa
  • PostGeoDa (beta)
  • 0. 🖥️ Installation
  • 1. 🗺️ Choropleth Mapping
    • 1.1 Basic Mapping
    • 1.2 Rate Mapping
    • 1.3 Spatial Rate Mapping
  • 2. 🌐 Spatial Weights
    • 2.1 Contiguity-Based Weights
    • 2.2 Distance-Based Weights
    • 2.3 Kernel Weights
  • 3. 💠 Local Spatial Autocorrelation
    • 3.1 Local Moran
    • 3.2 Local Geary
    • 3.3 Local Getis-Ord G
    • 3.4 Local Join Count
    • 3.5 Quantile LISA
  • 4. 💠 Local Spatial Autocorrelation -Multivariate
    • 4.1 Local Neighbor Match Test
    • 4.2 Multivariate Local Geary
    • 4.3 Bivariate Local Join Count
    • 4.4 Multivariate Local Join Count
    • 4.5 Multivariate Quantile LISA
  • 5. 🌌 Spatial Clustering
    • 5.1 SKATER
    • 5.2 REDCAP
  • 6. ✨ Cluster Analysis
    • 6.1 HDBScan
    • 6.2 Fast K-Medoids
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5. 🌌 Spatial Clustering

Spatial Constrained Clustering methods are used to divide an original set of n spatial units into p internally connected regions that maximize within similarity.

PostGeoDa offers two popular spatial constrained clustering algorithms: SKATER (Assunção et al. 2006) and REDCAP (Guo 2008; Guo and Wang 2011). SKATER stands for Spatial `K’luster Analysis by Tree Edge Removal, and obtains regionalization through a graph partitioning approach. REDCAP stands for REgionalization with Dynamically Constrained Agglomerative clustering and Partitioning, and consists of a family of six hierarchical regionalization methods.

Contents

  • 5.1 SKATER

  • 5.2 REDCAP

Previous4.5 Multivariate Quantile LISANext5.1 SKATER

Last updated 3 years ago

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