16.1 Topics Covered

• Understand the concept of a Local Indicator of Spatial Autocorrelation
• Identify clusters with the Local Moran cluster map and significance map
• Interpret the spatial footprint of spatial clusters
• Assess the significance by means of a randomization approach
• Assess the sensitivity of different significance cut-off values
• Interpret significance by means of Bonferroni bounds and the False Discovery Rate (FDR)
• Assess potential interaction effects by means of conditional cluster maps

GeoDa Functions

• Space > Univariate Local Moran’s I
  • significance map and cluster map
  • permutation inference
  • setting the random seed
  • selecting the significance filter
  • saving LISA statistics
  • select all cores and neighbors
  • local conditional map

Toolbar Icons

Figure 16.1: Moran Scatter Plot | Spatial Correlogram | Cluster Maps