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Optimal zoning systems for spatial interaction models

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  • S Openshaw

Abstract

The design of zoning systems for spatial interaction models is a major problem which affects both the interpretation and acceptability of these models. This paper demonstrates that zoning-system effects on parameter values and model performances are nontrivial, and that their magnitude is far larger than was previously thought likely. An approach which is most appropriate in an applied context, where there is also the problem of poor model performance, is to identify a zoning system which will approximately optimise model performance. The paper gives details of how this may be achieved. This method is demonstrated by a series of empirical studies. Finally, there is a brief discussion of the general implications for spatial model building.

Suggested Citation

  • S Openshaw, 1977. "Optimal zoning systems for spatial interaction models," Environment and Planning A, Pion Ltd, London, vol. 9(2), pages 169-184, February.
  • Handle: RePEc:pio:envira:v:9:y:1977:i:2:p:169-184
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    Cited by:

    1. Yoshiki Yamagata & Daisuke Murakami & Kazuhiro Minami & Nana Arizumi & Sho Kuroda & Tomoya Tanjo & Hiroshi Maruyama, 2016. "Electricity Self-Sufficient Community Clustering for Energy Resilience," Energies, MDPI, Open Access Journal, vol. 9(7), pages 1-13, July.
    2. Jesus Mur & Marcos Herrera & Manuel Ruiz, 2011. "Selecting the W Matrix. Parametric vs Nonparametric Approaches," ERSA conference papers ersa11p1055, European Regional Science Association.
    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. Alex Hagen-Zanker & Ying Jin, 2011. "Adaptive zoning and its effectiveness in spatial economic activity simulation," ERSA conference papers ersa10p1036, European Regional Science Association.
    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. 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.
    7. 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.
    8. Xiao Li & Steven Farber, 2016. "Spatial representation in the social interaction potential metric: an analysis of scale and parameter sensitivity," Journal of Geographical Systems, Springer, vol. 18(4), pages 331-357, October.
    9. Cörvers Frank & Hensen M. & Bongaerts D., 2006. "The Delimitation and Coherence of Functional and Administrative Regions," ROA Research Memorandum 002, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    10. 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.
    11. 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.
    12. Luis Martínez & José Viegas & Elisabete Silva, 2009. "A traffic analysis zone definition: a new methodology and algorithm," Transportation, Springer, vol. 36(5), pages 581-599, September.

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