IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v24y2022i3d10.1007_s10109-021-00371-5.html
   My bibliography  Save this article

Scale and local modeling: new perspectives on the modifiable areal unit problem and Simpson’s paradox

Author

Listed:
  • A. Stewart Fotheringham

    (Arizona State University)

  • M. Sachdeva

    (Arizona State University)

Abstract

The concept of ‘spatial scale’, or simply ‘scale’ is implicit in any discussion of global versus local models. The raison d’etre of local models is that a global scale (where here ‘global’ simply refers to all locations within a predefined area of interest) might be the incorrect scale at which to undertake any analysis of spatial processes; the alternative being a local scale (where here ‘local’ refers to individual locations). Here we explore two well-known scale issues in the context of local modeling: the modifiable areal unit problem (MAUP) and Simpson’s paradox. In doing so, we highlight that scale effects play two very different roles in any consideration of local versus global modeling. First, we examine the sensitivity of global and local models to the MAUP and show how the effects of the MAUP in global models are a function of the degree to which processes vary over space. This generates a new insight into the MAUP: it results from the properties of processes rather than the properties of data. Then we highlight the extreme differences that can result when calibrating global and local models and how Simpson’s paradox can arise in this context. In the examination of the MAUP, scale is treated as a measure of the degree to which data are aggregated prior to any form of modeling; in the study of Simpson’s paradox, scale refers to the geographical entity for which a model is calibrated.

Suggested Citation

  • A. Stewart Fotheringham & M. Sachdeva, 2022. "Scale and local modeling: new perspectives on the modifiable areal unit problem and Simpson’s paradox," Journal of Geographical Systems, Springer, vol. 24(3), pages 475-499, July.
  • Handle: RePEc:kap:jgeosy:v:24:y:2022:i:3:d:10.1007_s10109-021-00371-5
    DOI: 10.1007/s10109-021-00371-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10109-021-00371-5
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10109-021-00371-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Furong Li & Huiyan Sang, 2019. "Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1050-1062, July.
    2. Ziqi Li & A. Stewart Fotheringham & Taylor M. Oshan & Levi John Wolf, 2020. "Measuring Bandwidth Uncertainty in Multiscale Geographically Weighted Regression Using Akaike Weights," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 110(5), pages 1500-1520, September.
    3. C G Amrhein, 1995. "Searching for the Elusive Aggregation Effect: Evidence from Statistical Simulations," Environment and Planning A, , vol. 27(1), pages 105-119, January.
    4. J. Keith Ord & Arthur Getis, 2001. "Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 411-432, August.
    5. Daniel A. Griffith & Ferenc Csillag, 1993. "Exploring Relationships Between Semi‐Variogram And Spatial Autoregressive Models," Papers in Regional Science, Wiley Blackwell, vol. 72(3), pages 283-295, July.
    6. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4, Fall.
    7. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    8. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
    9. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    10. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taylor M. Oshan & Levi J. Wolf & Mehak Sachdeva & Sarah Bardin & A. Stewart Fotheringham, 2022. "A scoping review on the multiplicity of scale in spatial analysis," Journal of Geographical Systems, Springer, vol. 24(3), pages 293-324, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Philip A. White & Durban G. Keeler & Daniel Sheanshang & Summer Rupper, 2022. "Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
    2. Guanyu Hu & Yishu Xue & Zhihua Ma, 2020. "Bayesian Clustered Coefficients Regression with Auxiliary Covariates Assistant Random Effects," Papers 2004.12022, arXiv.org, revised Aug 2021.
    3. Yu, Danlin & Murakami, Daisuke & Zhang, Yaojun & Wu, Xiwei & Li, Ding & Wang, Xiaoxi & Li, Guangdong, 2020. "Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 21-37.
    4. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    5. Hu, Guanyu, 2021. "Spatially varying sparsity in dynamic regression models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 23-34.
    6. Herrera Gómez, Marcos & Cid, Juan Carlos & Paz, Jorge Augusto, 2012. "Introducción a la econometría espacial: Una aplicación al estudio de la fecundidad en la Argentina usando R [Introduction to Spatial Econometrics: An application to the study of fertility in Argent," MPRA Paper 41138, University Library of Munich, Germany.
    7. Qinglin Jia & Tao Zhang & Long Cheng & Gang Cheng & Minjie Jin, 2022. "The Impact of the Neighborhood Built Environment on the Walking Activity of Older Adults: A Multi-Scale Spatial Heterogeneity Analysis," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    8. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    9. Feili Wei & Shuang Li & Ze Liang & Aiqiong Huang & Zheng Wang & Jiashu Shen & Fuyue Sun & Yueyao Wang & Huan Wang & Shuangcheng Li, 2021. "Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM 2.5 Concentrations in Major Chinese Cities between 2005 and 2015," Energies, MDPI, vol. 14(11), pages 1-20, June.
    10. Lambert, Dayton M. & Brown, Jason P. & Florax, Raymond J.G.M., 2010. "A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application," Regional Science and Urban Economics, Elsevier, vol. 40(4), pages 241-252, July.
    11. Yuan Yan & Hsin-Cheng Huang & Marc G. Genton, 2021. "Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 387-408, September.
    12. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
    13. Zhihua Ma & Yishu Xue & Guanyu Hu, 2019. "Heterogeneous Regression Models for Clusters of Spatial Dependent Data," Papers 1907.02212, arXiv.org, revised Apr 2020.
    14. Dongsheng Zhan & Qianyun Zhang & Xiaoren Xu & Chunshui Zeng, 2022. "Spatiotemporal Distribution of Continuous Air Pollution and Its Relationship with Socioeconomic and Natural Factors in China," IJERPH, MDPI, vol. 19(11), pages 1-17, May.
    15. Paul Voss & David Long & Roger Hammer & Samantha Friedman, 2006. "County child poverty rates in the US: a spatial regression approach," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 25(4), pages 369-391, August.
    16. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2006. "The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data," Tinbergen Institute Discussion Papers 06-049/3, Tinbergen Institute.
    17. Kim, Sunghoon & DeSarbo, Wayne S. & Chang, Won, 2021. "Note: A new approach to the modeling of spatially dependent and heterogeneous geographical regions," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 792-803.
    18. Elena Kotyrlo, 2013. "Stationarity conditions for the spatial first-order and serial second-order model," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 19-29, March.
    19. Zhang, Tonglin & Lin, Ge, 2007. "A decomposition of Moran's I for clustering detection," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6123-6137, August.
    20. Raymond J. G. M. Florax & Arno J. Van der Vlist, 2003. "Spatial Econometric Data Analysis: Moving Beyond Traditional Models," International Regional Science Review, , vol. 26(3), pages 223-243, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jgeosy:v:24:y:2022:i:3:d:10.1007_s10109-021-00371-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.