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 language | English (US) |
|---|---|
| Pages (from-to) | 411-417 |
| Number of pages | 7 |
| Journal | Geographical Analysis |
| Volume | 41 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 1 2009 |
| Externally published | Yes |
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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 journal › Article
}
TY - JOUR
T1 - What Problem? Spatial autocorrelation and geographic information science
AU - Goodchild, Michael
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
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U2 - 10.1111/j.1538-4632.2009.00769.x
DO - 10.1111/j.1538-4632.2009.00769.x
M3 - Article
VL - 41
SP - 411
EP - 417
JO - Geographical Analysis
JF - Geographical Analysis
SN - 0016-7363
IS - 4
ER -