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Spatial Autocorrelation in Spatial Interactions Models: Geographic Scale and Resolution Implications for Network Resilience and Vulnerability

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  • Daniel Griffith
  • Yongwan Chun

Abstract

This paper addresses the theme of spatial autocorrelation impacting spatial equilibria, and hence an understanding of economic network resilience and vulnerability. It exploits the notion that spatial autocorrelation in the geographic distribution of origin and destination attributes and network autocorrelation in the flows between origins and destinations constitute two spatial autocorrelation components contained in spatial interaction data. It illustrates that a spatial interaction model specification needs to incorporate both components in order to furnish sound implications about associated economic network resilience and vulnerability. Such models also need to undergo sensitivity analyses in terms of changes in geographic scale and resolution. And, it furnishes a novel 3-D visualization of geographic flows, such as journey-to-work trips, in order to achieve a better comprehension of economic network resilience and vulnerability. Copyright Springer Science+Business Media New York 2015

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  • Daniel Griffith & Yongwan Chun, 2015. "Spatial Autocorrelation in Spatial Interactions Models: Geographic Scale and Resolution Implications for Network Resilience and Vulnerability," Networks and Spatial Economics, Springer, vol. 15(2), pages 337-365, June.
    • Handle: RePEc:kap:netspa:v:15:y:2015:i:2:p:337-365
    DOI: 10.1007/s11067-014-9256-4
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    2. Victor Cantillo & Luis F. Macea & Miguel Jaller, 2019. "Assessing Vulnerability of Transportation Networks for Disaster Response Operations," Networks and Spatial Economics, Springer, vol. 19(1), pages 243-273, March.
    3. Daniel A. Griffith & Manfred M. Fischer & James LeSage, 2017. "The spatial autocorrelation problem in spatial interaction modelling: a comparison of two common solutions," Letters in Spatial and Resource Sciences, Springer, vol. 10(1), pages 75-86, March.
    4. Laurent, Thibault & Margaretic, Paula & Thomas-Agnan, Christine, "undated". "An open source software tool for spatial flow data analysis_ supprimé à la demande de T. Laurent," TSE Working Papers 125949, Toulouse School of Economics (TSE).
    5. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    6. T. Edward Yu & Bijay P. Sharma & Burton C. English, 2019. "Investigating Lock Delay on the Upper Mississippi River: a Spatial Panel Analysis," Networks and Spatial Economics, Springer, vol. 19(1), pages 275-291, March.
    7. Yingxia Pu & Xinyi Zhao & Guangqing Chi & Jin Zhao & Fanhua Kong, 2019. "A spatial dynamic panel approach to modelling the space-time dynamics of interprovincial migration flows in China," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(31), pages 913-948.
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    15. Klapka, Pavel & Kraft, Stanislav & Halás, Marián, 2020. "Network based definition of functional regions: A graph theory approach for spatial distribution of traffic flows," Journal of Transport Geography, Elsevier, vol. 88(C).

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