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A Variance-Stabilizing Coding Scheme for Spatial Link Matrices

Author

Listed:
  • M Tiefelsdorf

    (Department of Geography, Centre for Earth Observation Science, University of Manitoba, 211 Isbister Building, Winnipeg, Manitoba R3T 2N2, Canada)

  • D A Griffith

    (Department of Geography, Syracuse University, 144 Eggers Hall, Syracuse, NY 13244, USA)

  • B Boots

    (Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, Ontario, N2L 3C5, Canada)

Abstract

In spatial statistics and spatial econometrics two coding schemes are used predominately. Except for some initial work, the properties of both coding schemes have not been investigated systematically. In this paper we do so for significant spatial processes specified as either a simulta-neous autoregressive or a moving average process. Results show that the C -coding scheme emphasizes spatial objects with relatively large numbers of connections, such as those in the interior of a study region. In contrast, the W -coding scheme assigns higher leverage to spatial objects with few connections, such as those on the periphery of a study region. To address this topology-induced heterogeneity, we design a novel S -coding scheme whose properties lie in between those of the C -coding and the W -coding schemes. To compare these three coding schemes within and across the different spatial processes, we find a set of autocorrelation parameters that makes the processes stochastically homologous via a method based on the exact conditional expectation of Moran's I . In the new S -coding scheme the topology induced heterogeneity can be removed in toto for Moran's I as well as for moving average processes and it can be substantially alleviated for autoregressive processes.

Suggested Citation

  • M Tiefelsdorf & D A Griffith & B Boots, 1999. "A Variance-Stabilizing Coding Scheme for Spatial Link Matrices," Environment and Planning A, , vol. 31(1), pages 165-180, January.
    • Handle: RePEc:sae:envira:v:31:y:1999:i:1:p:165-180
    DOI: 10.1068/a310165
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    References listed on IDEAS

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    Cited by:

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    8. Pace, R. Kelley & LeSage, James P., 2021. "A simple closed-form relation between spatial weight matrices with different scalings," Economics Letters, Elsevier, vol. 207(C).
    9. Orea, Luis & Álvarez, Inmaculada C., 2019. "Spatial Production Economics," Efficiency Series Papers 2019/06, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    10. Roberto Patuelli & Andrea Vaona & Christoph Grimpe, 2010. "The German East‐West Divide In Knowledge Production: An Application To Nanomaterial Patenting," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 101(5), pages 568-582, December.
    11. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Advances in Spatial Science, in: Esteban Fernández Vázquez & Fernando Rubiera Morollón (ed.), Defining the Spatial Scale in Modern Regional Analysis, edition 127, chapter 0, pages 173-192, Springer.
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    13. Alberto Gude & Inmaculada Álvarez & Luis Orea, 2018. "Heterogeneous spillovers among Spanish provinces: a generalized spatial stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 50(3), pages 155-173, December.
    14. Benoit Faye, 2021. "Methodological discussion of Airbnb's hedonic study: A review of the problems and some proposals tested on Bordeaux City data," Post-Print hal-03407540, HAL.
    15. Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach," Environment and Planning A, , vol. 39(5), pages 1193-1221, May.
    16. Jesús Crespo Cuaresma & Martin Feldkircher, 2013. "Spatial Filtering, Model Uncertainty And The Speed Of Income Convergence In Europe," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 720-741, June.
    17. Jens K. Perret, 2019. "Regional Convergence in the Russian Federation: Spatial and Temporal Dynamics," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(1), pages 11-39, March.
    18. 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.
    19. Schmidt, Sebastian & Kinne, Jan & Lautenbach, Sven & Blaschke, Thomas & Lenz, David & Resch, Bernd, 2022. "Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining," ZEW Discussion Papers 22-006, ZEW - Leibniz Centre for European Economic Research.
    20. Nicoletta D’Angelo & Antonino Abbruzzo & Giada Adelfio, 2021. "Spatio-Temporal Spread Pattern of COVID-19 in Italy," Mathematics, MDPI, vol. 9(19), pages 1-14, October.
    21. Li, Yan & Jiao, Yan, 2015. "Modeling spatial patterns of rare species using eigenfunction-based spatial filters: An example of modified delta model for zero-inflated data," Ecological Modelling, Elsevier, vol. 299(C), pages 51-63.
    22. Efthymiou, D. & Antoniou, C., 2013. "How do transport infrastructure and policies affect house prices and rents? Evidence from Athens, Greece," Transportation Research Part A: Policy and Practice, Elsevier, vol. 52(C), pages 1-22.
    23. Álvarez, Inmaculada C. & Gude, Alberto & Orea, Luis, 2019. "Effects of inter-industry and spatial spillovers on regional productivity: Evidence from Spanish panel data," Efficiency Series Papers 2019/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    24. Juan Armando Torres Munguía & Florina Cristina Badarau & Luis Rodrigo Díaz Pavez & Inmaculada Martinez-Zarzoso & Konstantin M. Wacker, 2022. "A global dataset of pandemic- and epidemic-prone disease outbreaks," Post-Print hal-04029973, HAL.

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