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Effects of Scale in Spatial Interaction Models

In: Spatial Econometric Interaction Modelling

Author

Listed:
  • Giuseppe Arbia

    (Catholic University of the Sacred Heart)

  • Francesca Petrarca

    (Roma Tre University)

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.

Suggested Citation

  • Giuseppe Arbia & Francesca Petrarca, 2016. "Effects of Scale in Spatial Interaction Models," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 85-101, Springer.
    • Handle: RePEc:spr:adspcp:978-3-319-30196-9_5
    DOI: 10.1007/978-3-319-30196-9_5
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    References listed on IDEAS

    as
    1. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, June.
    2. Pesaran, M Hashem & Pierse, Richard G & Kumar, Mohan S, 1989. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," Econometrica, Econometric Society, vol. 57(4), pages 861-888, July.
    3. Daniel Griffith, 2009. "Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows," Journal of Geographical Systems, Springer, vol. 11(2), pages 117-140, June.
    4. Daniel A. Griffith, 2009. "Spatial Autocorrelation in Spatial Interaction," Advances in Spatial Science, in: Aura Reggiani & Peter Nijkamp (ed.), Complexity and Spatial Networks, chapter 0, pages 221-237, Springer.
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    Cited by:

    1. Olga Demidova & Tatiana Bukina & Natalia Sverchkova, 2015. "Dependence of spatial effects on the level of regional aggregation, weights matrix, and estimation method," ERSA conference papers ersa15p322, European Regional Science Association.
    2. Nan Dong & Xiaohuan Yang & Hongyan Cai & Liming Wang, 2015. "A Novel Method for Simulating Urban Population Potential Based on Urban Patches: A Case Study in Jiangsu Province, China," Sustainability, MDPI, vol. 7(4), pages 1-20, April.
    3. Zhijiao Qin & Yan Yu & Dianfeng Liu, 2019. "The Effect of HOPSCA on Residential Property Values: Exploratory Findings from Wuhan, China," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    4. Juan C Duque & Henry Laniado & Adriano Polo, 2018. "S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-25, November.

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    More about this item

    Keywords

    Gravity models; Modifiable areal unit problem; Spatial autoregressive random fields; Spatial interaction models;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R19 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Other

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