Classification of geospatial lattice data and their graphical representation

Koji Kurihara

Research output: Contribution to journalArticlepeer-review


Statistical analyses for spatial data are important problems in various types of fields. Lattice data are synoptic observations covering an entire spatial region, like cancer rates broken out by each county in a state. There are few approaches for cluster analysis of spatial data. But echelons are useful techniques to study the topological structure of such spatial data. In this paper, we explore cluster analysis for geospatial lattice data based on echelon analysis. We also provide new definitions of the neighbors and families of spatial data in order to support the clustering procedure. In addition, the spatial cluster structure is demonstrated by hierarchical graphical representation with several examples. Regional features are also shown in this dendrogram.
Original languageEnglish
Pages (from-to)251-258
Number of pages8
JournalClassification, Clustering, and Data Mining Applications (Edited by D. Banks et al.)
Publication statusPublished - 2004


  • Unemployment Rate Spatial Data Lattice Data Root Cluster Land Cover Pattern

ASJC Scopus subject areas

  • Social Sciences(all)


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