What Problem? Spatial autocorrelation and geographic information science

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

In retrospect it is the word "problem" in the title that seems most remarkable about the Cliff and Ord article. Spatial autocorrelation is indeed a problem in standard inferential statistics, which was developed to handle controlled experiments, when these methods are used to generalize from natural experiments. From the perspective of geographic information science, however, spatial dependence is a defining characteristic of geographic data that makes many of the functions of geographic information systems possible. The almost universal presence of spatial heterogeneity in such data also argues against generalization and is made explicit in the recent development of place-based analytic techniques. The final section argues for a new approach to the teaching of quantitative methods in the environmental and social sciences that treats natural experiments, spatial dependence, and spatial heterogeneity as the norm.

Original languageEnglish (US)
Pages (from-to)411-417
Number of pages7
JournalGeographical Analysis
Volume41
Issue number4
DOIs
StatePublished - Dec 1 2009
Externally publishedYes

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information science
autocorrelation
experiment
inferential statistics
quantitative method
cliff
teaching
information system
social science
Teaching
science
method
geographic information system
statistics
norm
environmental science

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

Cite this

What Problem? Spatial autocorrelation and geographic information science. / Goodchild, Michael.

In: Geographical Analysis, Vol. 41, No. 4, 01.12.2009, p. 411-417.

Research output: Contribution to journalArticle

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