The impact of spatial aggregation on urban development analyses

This paper illustrates the impacts of spatial data aggregation on the analysis of urban development. Spatial econometric methods are used to control for spatial autocorrelation in the data and existing weighting methods are used to overcome aggregation dependencies that are due to differences in siz...

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Bibliographic Details
Published in:Applied geography (Sevenoaks) 2014-02, Vol.47, p.46-56
Main Authors: Jacobs-Crisioni, Chris, Rietveld, Piet, Koomen, Eric
Format: Article
Language:English
Subjects:
Agglomeration
Approximation
Autocorrelation
Econometric analysis
Geography
Impact analysis
Land use
MAUP
Scale dependencies
Spatial econometrics
Urban development
Weighting methods
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Summary:This paper illustrates the impacts of spatial data aggregation on the analysis of urban development. Spatial econometric methods are used to control for spatial autocorrelation in the data and existing weighting methods are used to overcome aggregation dependencies that are due to differences in sizes of areal units. The analyses show that shape dependencies can be partially removed by the used weighting methods, and that even regularly latticed areal units need such weighting in practice. Aggregating to coarser resolutions does not affect the order of magnitude of coefficients estimated for variables that are aggregated by averaging, if the aggregation process maintains sufficient variance within variables. We argue that small-sized areal units approximating the true characteristics of the studied process are to be preferred in urban development analyses, because such micro-data allows the exploration of highly local factors alongside higher scale linkages. We demonstrate that spatial autocorrelation and scale dependencies interact and that spatial econometric methods can help explain variance in analyses of small-grained land-use data. •We look into scale and shape dependencies in spatial land-use pattern analyses.•Spatial econometric and weighting methods are used to lessen aggregation impacts.•Scale has a limited effect on explanatory analyses with factors on various scales.•Spatial econometric methods are needed in particular with fine resolution data.•Shape dependencies can partially be reduced with weighting methods.
ISSN:0143-6228
1873-7730
DOI:10.1016/j.apgeog.2013.11.014