IDEAS home Printed from https://ideas.repec.org/a/pes/ierequ/v9y2014i4p113-131.html
   My bibliography  Save this article

The Modifiable Areal Unit Problem – Analysis Of Correlation And Regression

Author

Listed:
  • Michal Bernard Pietrzak

    (Nicolaus Copernicus University, Poland)

Abstract

The paper focuses on the issue of the modifiable areal unit problem, which means a possibility of obtaining various results for spatial economic analyses depending on the assumed composition of territorial units. The major research objective of the work is to examine the scale problem that constitutes one of the aspects of the modifiable areal unit problem. Analysis of the scale problem will be conducted for two research problems, namely, for the problem of the causal relationships between the level of investment outlays in enterprises per capita and the number of entities of the national economy per capita, and the issue of the dependence between the registered unemployment rate and the level of investment outlays per capita. The calculations based on the empirical values of those variables have showed that moving to a higher level of aggregation resulted in a change in the estimates of the parameters. The results obtained were the justification for undertaking the realisation of the objective. The scale problem was considered by means of a simulation analysis with a special emphasis laid on differentiating the variables expressed in absolute quantities and ones expressed in relative quantities. The study conducted allowed the identification of changes in basic properties as well as in correlation of the researched variables expressed in absolute and relative quantities. Based on the findings, it was stated that a correlation analysis and a regression analysis may lead to different conclusions depending on the assumed level of aggregation. The realisation of the research objective set in the paper also showed the need to consider the adequate character of variables in both spatial economic analyses and during the examination of the scale problem.

Suggested Citation

  • Michal Bernard Pietrzak, 2014. "The Modifiable Areal Unit Problem – Analysis Of Correlation And Regression," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(4), pages 113-131, December.
  • Handle: RePEc:pes:ierequ:v:9:y:2014:i:4:p:113-131
    DOI: 10.12775/EQUIL.2014.028
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.12775/EQUIL.2014.028
    Download Restriction: no

    File URL: https://libkey.io/10.12775/EQUIL.2014.028?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. S Openshaw, 1984. "Ecological Fallacies and the Analysis of Areal Census Data," Environment and Planning A, , vol. 16(1), pages 17-31, January.
    2. S Openshaw, 1977. "Optimal Zoning Systems for Spatial Interaction Models," Environment and Planning A, , vol. 9(2), pages 169-184, February.
    3. Michal Bernard Pietrzak, 2014. "Redefining The Modifiable Areal Unit Problem Within Spatial Econometrics, The Case Of The Scale Problem," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(2), pages 111-132, June.
    4. Duane F. Marble, 2000. "Some thoughts on the integration of spatial analysis and Geographic Information Systems," Journal of Geographical Systems, Springer, vol. 2(1), pages 31-35, March.
    5. Michal Bernard Pietrzak, 2014. "Redefining The Modifiable Areal Unit Problem Within Spatial Econometrics, The Case Of The Aggregation Problem," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(3), pages 131-151, September.
    6. S Openshaw & R S Baxter, 1977. "Algorithm 3: A Procedure to Generate Pseudo-Random Aggregations of N Zones into M Zones, Where M is Less Than N," Environment and Planning A, , vol. 9(12), pages 1423-1428, December.
    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. Bin Zhu & Chih-Wei Hsieh & Yue Zhang, 2018. "Incorporating Spatial Statistics into Examining Equity in Health Workforce Distribution: An Empirical Analysis in the Chinese Context," IJERPH, MDPI, vol. 15(7), pages 1-15, June.
    2. Michal Bernard Pietrzak, 2014. "Redefining The Modifiable Areal Unit Problem Within Spatial Econometrics, The Case Of The Scale Problem," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(2), pages 111-132, June.
    3. Michal Bernard Pietrzak, 2014. "Redefining The Modifiable Areal Unit Problem Within Spatial Econometrics, The Case Of The Aggregation Problem," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(3), pages 131-151, September.

    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. Michal Bernard Pietrzak, 2014. "Redefining The Modifiable Areal Unit Problem Within Spatial Econometrics, The Case Of The Aggregation Problem," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(3), pages 131-151, September.
    2. Michal Bernard Pietrzak, 2014. "Redefining The Modifiable Areal Unit Problem Within Spatial Econometrics, The Case Of The Scale Problem," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(2), pages 111-132, June.
    3. Richard Connors & David Watling, 2015. "Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation," Networks and Spatial Economics, Springer, vol. 15(2), pages 367-395, June.
    4. Lansley, Guy & Longley, Paul, 2016. "Deriving age and gender from forenames for consumer analytics," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 271-278.
    5. Cabrera Delgado, Jorge & Bonnel, Patrick, 2016. "Level of aggregation of zoning and temporal transferability of the gravity distribution model: The case of Lyon," Journal of Transport Geography, Elsevier, vol. 51(C), pages 17-26.
    6. Feng Guo & Lisa Aultman-Hall, 2014. "A zone design methodology for national freight origin-destination data and transportation modeling," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(8), pages 738-756, December.
    7. Yolande Pottie-Sherman & Rima Wilkes, 2017. "Does Size Really Matter? On the Relationship between Immigrant Group Size and Anti-Immigrant Prejudice," International Migration Review, Wiley Blackwell, vol. 51(1), pages 218-250, March.
    8. Herrera Gómez, Marcos & Mur Lacambra, Jesús & Ruiz Marín, Manuel, 2012. "Selecting the Most Adequate Spatial Weighting Matrix:A Study on Criteria," MPRA Paper 73700, University Library of Munich, Germany.
    9. Lee, Jaeyoung & Abdel-Aty, Mohamed & Jiang, Ximiao, 2014. "Development of zone system for macro-level traffic safety analysis," Journal of Transport Geography, Elsevier, vol. 38(C), pages 13-21.
    10. Frank Corvers & Maud Hensen & Dion Bongaerts, 2009. "Delimitation and Coherence of Functional and Administrative Regions," Regional Studies, Taylor & Francis Journals, vol. 43(1), pages 19-31.
    11. Abdel-Aty, Mohamed & Lee, Jaeyoung & Siddiqui, Chowdhury & Choi, Keechoo, 2013. "Geographical unit based analysis in the context of transportation safety planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 62-75.
    12. Gandhi Pawitan, 2009. "Spatial distribution based on semivariogram model," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 1(1), pages 27-35, April.
    13. Emma Dean & Mark J. Taylor & Hulya Francis & Paulo Lisboa & Debbie Appleton & Mark Jones, 2017. "A Methodological Framework for Geographic Information Systems Development," Systems Research and Behavioral Science, Wiley Blackwell, vol. 34(6), pages 759-772, November.
    14. Ouassim Manout & Patrick Bonnel, 2019. "The impact of ignoring intrazonal trips in assignment models: a stochastic approach," Transportation, Springer, vol. 46(6), pages 2397-2417, December.
    15. Norton, Daniel & Hynes, Stephen, 2018. "Estimating the Benefits of the Marine Strategy Framework Directive in Atlantic Member States: A Spatial Value Transfer Approach," Ecological Economics, Elsevier, vol. 151(C), pages 82-94.
    16. DUJARDIN, Claire & lorant, VINCENT & THOMAS, Isabelle, 2013. "Self-assessed health of elderly people in Brussels: does the built environment matter?," LIDAM Discussion Papers CORE 2013048, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. Jesus Mur & Marcos Herrera & Manuel Ruiz, 2011. "Selecting the W Matrix. Parametric vs Nonparametric Approaches," ERSA conference papers ersa11p1055, European Regional Science Association.
    18. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    19. Herrera Gómez, Marcos & Mur Lacambra, Jesús & Ruiz Marín, Manuel, 2011. "¿Cuál matriz de pesos espaciales?. Un enfoque sobre selección de modelos [Which spatial weighting matrix? An approach for model selection]," MPRA Paper 37585, University Library of Munich, Germany.
    20. Li, Qingquan & Zhang, Tong & Wang, Handong & Zeng, Zhe, 2011. "Dynamic accessibility mapping using floating car data: a network-constrained density estimation approach," Journal of Transport Geography, Elsevier, vol. 19(3), pages 379-393.
    21. Keshkamat, S.S. & Kooiman, A. & van Maarseveen, M.F.A.M. & der Veen, A. van & Zuidgeest, M.H.P., 2012. "A boundary object for scale selection — Moderating differences and synergising understanding," Ecological Economics, Elsevier, vol. 76(C), pages 15-24.

    More about this item

    Keywords

    spatial econometrics; modifiable areal unit problem; scale problem;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    Statistics

    Access and download statistics

    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:pes:ierequ:v:9:y:2014:i:4:p:113-131. 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: Adam P. Balcerzak (email available below). General contact details of provider: https://edirc.repec.org/data/ibgtopl.html .

    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.