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Evaluating Local Non-Stationarity when Considering the Spatial Variation of Large-scale Autocorrelation

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Abstract

Multi-scale effects of spatial autocorrelation may be present in datasets. Given the importance of detecting local non-stationarity in many theoretical as well as applied studies, it is necessary to “remove” the impact of large-scale autocorrelation before common techniques for local pattern analysis are applied. It is proposed in this paper to employ the regionalized range to define spatially varying sub-regions within which the impact of large-scale autocorrelation is minimized and the local patterns can be investigated. A case study is conducted on crime data to detect crime hot spots and cold spots in San Antonio, Texas. The results confirm the necessity of treating the non-stationarity of large-scale spatial autocorrelation prior to any action aiming at detecting local autocorrelation.
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Document Type: Research Article

Affiliations: Department of Geography, Texas State University-San Marcos

Publication date: 2006-03-01

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