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The Effect of Data Aggregation on a Poisson Regression Model of Canadian Migration

Author

Listed:
  • C G Amrhein

    (Department of Geography, University of Toronto, Toronto, Ontario M5S 1A1, Canada)

  • R Flowerdew

    (Department of Geography, University of Lancaster, Lancaster LA1 4YB, England)

Abstract

A Statistics Canada data set for Canadian migration data at the census division level incorporating information on income tax for 1986 has already been presented. This matrix of 260 × 260 flows was used to calibrate a set of Poisson regression models by utilizing flows for the aggregate population. In this paper, the relatively high spatial resolution is used to test for aggregation effects as the original 260 units are combined to form fewer, synthetic regions with larger areas. A series of simulation experiments are performed with three different aggregation algorithms to create 130, 65, and ultimately 10 (corresponding to the provinces) synthetic regions. Average results from the experiments are compared with the original model. Results are obtained that suggest that, in this case, obvious aggregation effects similar to those observed elsewhere (by Openshaw) are not observed.

Suggested Citation

  • C G Amrhein & R Flowerdew, 1992. "The Effect of Data Aggregation on a Poisson Regression Model of Canadian Migration," Environment and Planning A, , vol. 24(10), pages 1381-1391, October.
    • Handle: RePEc:sae:envira:v:24:y:1992:i:10:p:1381-1391
    DOI: 10.1068/a241381
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    References listed on IDEAS

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    1. Robin Flowerdew & Carl Amrhein, 1989. "Poisson Regression Models Of Canadian Census Division Migration Flows," Papers in Regional Science, Wiley Blackwell, vol. 67(1), pages 89-102, January.
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    Cited by:

    1. Tobias Ebert & Jochen E. Gebauer & Thomas Brenner & Wiebke Bleidorn & Samuel D. Gosling & Jeff Potter & P. Jason Rentfrow, 2019. "Are Regional Differences in Personality and their Correlates robust? Applying Spatial Analysis Techniques to Examine Regional Variation in Personality across the U.S. and Germany," Working Papers on Innovation and Space 2019-05, Philipps University Marburg, Department of Geography.
    2. Briant, A. & Combes, P.-P. & Lafourcade, M., 2010. "Dots to boxes: Do the size and shape of spatial units jeopardize economic geography estimations?," Journal of Urban Economics, Elsevier, vol. 67(3), pages 287-302, May.
    3. Gábor Békés & Péter Harasztosi, 2018. "Grid and shake: spatial aggregation and the robustness of regionally estimated elasticities," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 143-170, January.
    4. Juan Carlos Duque & Raul Ramos Lobo & Jordi Surinach Caralt, 2004. "Design of Homogeneous Territorial Units: A Methodological Proposal," Working Papers in Economics 115, Universitat de Barcelona. Espai de Recerca en Economia.
    5. Martin Bell & Dominic Brown, 2014. "Analysing spatial interactions: inter-regional migration flows," Chapters, in: Robert Stimson (ed.), Handbook of Research Methods and Applications in Spatially Integrated Social Science, chapter 19, pages 403-435, Edward Elgar Publishing.
    6. Tomoki Nakaya & A. Fotheringham & Kazumasa Hanaoka & Graham Clarke & Dimitris Ballas & Keiji Yano, 2007. "Combining microsimulation and spatial interaction models for retail location analysis," Journal of Geographical Systems, Springer, vol. 9(4), pages 345-369, December.
    7. Gustavo A. García & Edgar Julián Muñoz González & Stefany Gallego Ortiz, 2018. "Análisis Espacial de la Informalidad Laboral a Nivel Intra-urbano," Documentos de Trabajo de Valor Público 16499, Universidad EAFIT.
    8. Zuo, Chengchao & Birkin, Mark, 2019. "Modelling the economic and demographic impacts of major transport infrastructure provision: A case study of UK regions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 207-222.
    9. John Stillwell, 2005. "Inter-regional migration modelling - a review and assessment," ERSA conference papers ersa05p770, European Regional Science Association.

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