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A variance-stabilizing coding scheme for spatial link matrices


  • M Tiefelsdorf
  • D A Griffith
  • B Boots


In spatial statistics and spatial econometrics two coding schemesare used predominately. Except for some initial work, the properties ofboth coding schemes have not been investigated systematically. In this paper we do so for significant spatial processes specifiedas either a simultaneous autoregressive or a moving average process. Resultsshow that the C -coding scheme emphasizes spatialobjects with relatively large numbers of connections, such as those inthe 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 thoseof the C -coding and the W -coding schemes. To compare these three coding schemes within and across thedifferent spatial processes, we find a set of autocorrelation parameters that makes the processes stochastically homologous via a methodbased on the exact conditional expectation of Moran's I . In the new S -coding scheme thetopology induced heterogeneity can be removed in toto for Moran's I as well as for moving average processes and it canbe 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, Pion Ltd, London, vol. 31(1), pages 165-180, January.
  • Handle: RePEc:pio:envira:v:31:y:1999:i:1:p:165-180

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    Cited by:

    1. Christoph Grimpe & Roberto Patuelli, 2011. "Regional knowledge production in nanomaterials: a spatial filtering approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(3), pages 519-541, June.
    2. Shanaka Herath & Johanna Choumert & Gunther Maier, 2015. "The value of the greenbelt in Vienna: a spatial hedonic analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 349-374, March.
    3. 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.
    4. Young, Jeffrey S. & Binkley, James K. & Florax, Raymond J.G.M., 2016. "A Follow-Up to Benirschka & Binkley’s “Land Price Volatility in a Geographically Dispersed Market”: Updates to Data and Methodology," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235538, Agricultural and Applied Economics Association.
    5. Gude, Alberto & Álvarez, Inmaculada C. & Orea, Luis, 2017. "Heterogeneous spillovers among Spanish provinces: A generalized spatial stochastic frontier model," Efficiency Series Papers 2017/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    6. repec:lrk:eeaart:35_2_6 is not listed on IDEAS
    7. Jens K. Perret, 2010. "A Core-Periphery Pattern in Russia – Twin Peaks or a Rat’s Tail," EIIW Discussion paper disbei178, Universitätsbibliothek Wuppertal, University Library.
    8. repec:eee:ecomod:v:299:y:2015:i:c:p:51-63 is not listed on IDEAS
    9. 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.

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