Which spatial statistics techniques should be converted to GIS functions?
A fundamental difference between spatial and aspatial data is that observations for geo-referenced data are correlated strictly due to their relative locational positions. This self-correlation activates complications in the statistical analysis of geo-referenced data that lie dormant in the statistical analysis of traditional data comprised of independent observations. Seeking a remedy for this problem has helped motivate the development of an array of procedures labelled spatial statistics. This contribution seeks to identify existing spatial statistical tools that would be routinely of value to the GIS user community — a major analyzer of geo-referenced data — and that currently are theoretically and conceptually ready to be made available through GIS packages (e.g., ARC/INFO, IDRISI). Technical and computational implementation issues are addressed. Current expert opinion, gleaned from two recent international conferences, is summarized. Gateways are identified for spatial statistical tools to be introduced into a GIS. The principal conclusion is that there is evidence from a growing number of initiatives, as well as testimony from spatial scientists and geographic analysis practitioners, supporting the need to convert spatial statistics techniques into GIS functions.
KeywordsAutocorrelation Kriging Glean Gridding
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