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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4. 💠 Local Spatial Autocorrelation -Multivariate

The multivariate local spatial autocorrelation is a class of statistics that assesses the match between the distance among observations in attribute space and geographical space. They are used to identify observations that are both close in multiattribute space and close in geographical space. For more information, please read https://geodacenter.github.io/workbook/6c_local_multi/lab6c.html

Contents

  • 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

Previous3.5 Quantile LISANext4.1 Local Neighbor Match Test

Last updated 3 years ago

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