Abstract
The MAUP (Modifiable Areal Unit Problem) is a particular form of the more general Modifiable Unit Problem (MUP) that has a long tradition in statistics, see Yule and Kendall (1950), whose spatial manifestation has been treated at length by Openshaw and Taylor (1979); Arbia (1989) among others. The MAUP presents two facets. The first is known as the “scale problem” and refers to the indeterminacy of any statistical measure with respect to changes in the level of data aggregation (e.g. from NUTS-3 to NUTS-2 in the EUROSTAT 2012). The second is referred to as the “aggregation (or zoning) problem” and concerns the indeterminacy of any statistical measure with respect to changes in the aggregation criterion at a given spatial scale (e.g. two alternative partitions of the same area at a given spatial scale). In this paper we explicitly aim to study the effects of scale on non linear spatial interaction models.
This paper has been previously published in the Journal of Geographical Systems. Special Issue on “Advances in the Statistical Modelling of Spatial Interaction Data”, Vol. 15, Number 3/July 2013, © Springer-Verlag Berlin Heidelberg, pp. 249–264.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arbia G (1989) Spatial data configuration in the statistical analysis of regional economics and related problems. Kluwer, Dordrecht
Arbia G, Petrarca F (2011) Effects of MAUP on spatial econometric models. Lett Spat Resour Sci 4(3):173–185
Cliff AD, Ord JK (1981) Spatial processes: models and applications. Pion, London
Cramer JS (1964) Efficient grouping, regression and correlation in Engel curve analysis. J Am Stat Assoc 59:233–50
EUROSTAT (2012) http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction
Griffith DA (2009) Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows. J Geogr Syst 11(2):117–140
Haitovsky J (1973) Regression estimation from grouped observations. Griffin’s statistical courses and monographs, vol 33. Hafner Press, New York
LeSage J, Fisher M (2010) Spatial econometric methods for modeling origin-destination flows. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Dordrecht, pp 409–433
Openshaw S, Taylor PJ (1979) A million or so of correlated coefficients: three experiments on the modifiable areal unit problem. In: Wrigley N (ed) Statistical applications in the spatial sciences. Pion, London, pp 120–438
Orcutt G, Watts HW, Edwards JB (1968) Data aggregation and information loss. Am Econ Rev 58:773–87
Pesaran MH, Pierse RG, Kumar MS (1989) Econometric analysis of aggregation in the context of linear prediction models. Econometrica 57:861–888
Prais S, Aitchinson J (1954) The grouping of observations in regression analysis. Rev Int Stat Inst 22:1–22
Theil H (1954) Linear aggregation on economic relations. North-Holland Publishing, Amsterdam
Yule U, Kendall MS (1950) An introduction to the theory of statistics. Griffin, London
Zellner A (1962) An efficient method of estimating seemingly unrelated regression equations and tests for aggregation bias. J Am Stat Assoc 57:348–368
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Arbia, G., Petrarca, F. (2016). Effects of Scale in Spatial Interaction Models. In: Patuelli, R., Arbia, G. (eds) Spatial Econometric Interaction Modelling. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-30196-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-30196-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30194-5
Online ISBN: 978-3-319-30196-9
eBook Packages: Economics and FinanceEconomics and Finance (R0)