Networks and Spatial Economics

, Volume 15, Issue 2, pp 337–365 | Cite as

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

  • Daniel A. Griffith
  • Yongwan Chun


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.


Geographic resolution Geographic scale Network autocorrelation Spatial interaction Spatial autocorrelation 


  1. Batty M (2013) Resilient cities, networks, and disruptions. Environ Plan B 40:571–573CrossRefGoogle Scholar
  2. Casella G, George E (1992) Explaining the Gibbs sampler. Am Stat 46:167–174Google Scholar
  3. Chun Y, Griffith D (2011) Modeling network autocorrelation in space-time migration flow data: An eigenvector spatial filtering approach. Ann AAG 101:523–536Google Scholar
  4. Ducruet C, Beauguitte L (2013) Spatial science and network science: Review and outcomes of a complex relationship. Netw Spat Econ. Online First, doi: 10.1007/s11067-013-9222-6, last accessed on 24 June 2014
  5. Fisch O (1980) Spatial equilibrium with locational interdependencies: the case of environmental spillovers. Reg Sci Urban Econ 10:201–209CrossRefGoogle Scholar
  6. Griffith D (1997) Using estimated missing spatial data in obtaining single facility location-allocation solutions. l'Espace Géographique 26:173–182CrossRefGoogle Scholar
  7. Griffith D (2003) Using estimated missing spatial data with the 2-median model. Ann Oper Res 122:233–247CrossRefGoogle Scholar
  8. Griffith D (2007) Spatial structure and spatial interaction: 25 years later. Rev Reg Stud 37(#1):28–38Google Scholar
  9. Griffith D (2009a) Spatial autocorrelation in spatial interaction: complexity-to-simplicity in journey-to-work flows. In: Nijkamp P, Reggiani A (eds) Complexity and Spatial Networks: In Search of Simplicity. Springer, Berlin, pp 221–237CrossRefGoogle Scholar
  10. Griffith D (2009b) Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows. J Geogr Syst 11:117–140CrossRefGoogle Scholar
  11. Griffith D (2011) Visualizing analytical spatial autocorrelation components latent in spatial interaction data: an eigenvector spatial filter approach. Comput Environ Urban Syst 35:140–149CrossRefGoogle Scholar
  12. Griffith D, Arbia G (2010) Detecting negative spatial autocorrelation in georeferenced random variables. Int J Geogr Inf Sci 24:417–437CrossRefGoogle Scholar
  13. Guo D (2009) Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans Vis Comput Graph 15:1041–1048CrossRefGoogle Scholar
  14. Hill T, Smith A (2014) Migrants: Where do they come from? Significance 11(4):24–29CrossRefGoogle Scholar
  15. Holmes J, Haggett P (1977) Graph theory interpretation of flow matrices: a note on maximization procedures for identifying significant links. Geogr Anal 9:388–399CrossRefGoogle Scholar
  16. Kim K, Lee S-I, Shin J, Choi E (2012) Developing a flow mapping module in a GIS environment. Cartogr J 49:164–175CrossRefGoogle Scholar
  17. LeSage J, Fischer M (2010) Spatial econometric modeling of origin–destination flows. In: Fischer M, Getis A (eds) Handbook of Applied Spatial Analysis. Springer, Berlin, pp 409–433CrossRefGoogle Scholar
  18. LeSage J, Pace R (2008) Spatial econometric modeling of origin–destination flows. J Reg Sci 48:941–967CrossRefGoogle Scholar
  19. Lesage J, Pace R (2009) Introduction to Spatial Econometrics. CRC Press, Boca Raton, FLCrossRefGoogle Scholar
  20. McMillen D (2003) Identifying sub-centers using contiguity matrices. Urban Stud 40:57–69CrossRefGoogle Scholar
  21. McMillen D (2004) Employment densities, spatial autocorrelation, and subcenters in large metropolitan areas. J Reg Sci 44:225–243CrossRefGoogle Scholar
  22. Novak D, Hodgdon C, Guo F, Aultman-Hall L (2011) Nationwide freight generation models: A spatial regression approach. Netw Spat Econ 11:23–41CrossRefGoogle Scholar
  23. Patuelli R, Reggiani A, Gorman S, Nijkamp P, Bade F-J (2007) Network analysis of commuting flows: A comparative static approach to German data. Netw Spat Econ 7:315–331CrossRefGoogle Scholar
  24. Perrings C (1998) Resilience in the Dynamics of Economy-Environment Systems. Environ Resour Econ 11:503–520CrossRefGoogle Scholar
  25. Reggiani A (2013) Network resilience for transport security: Some methodological considerations. Transp Policy 28:63–68CrossRefGoogle Scholar
  26. Reggiani A, Bucci P, Russo G (2011) Accessibility and network structures in the German Commuting. Netw Spat Econ 11:621–641CrossRefGoogle Scholar
  27. Reggiani A, de Graaff T, Nijkamp P (2002) Resilience: An evolutionary approach to spatial economic systems. Netw Spat Econ 2:211–229CrossRefGoogle Scholar
  28. Schelling T (1969) Models of segregation. Am Econ Rev 59:488–93Google Scholar
  29. Schelling T (1971) Dynamic models of segregation. J Math Sociol 1:143–86CrossRefGoogle Scholar
  30. Sinha P, Griffith D (2013) Spatial autocorrelation and the solution to the p-median problem, paper presented at Spatial Statistics 2013, The Ohio Union, The Ohio State University, June 4–7Google Scholar
  31. Tobler W (1987) Experiments in migration mapping by computer. Am Cartogr 14:155–63CrossRefGoogle Scholar
  32. Wilson A (1970) Entropy in Urban and Regional Modeling. Pion, LondonGoogle Scholar
  33. Wood J, Dykes J, Slingsby A (2010) Visualisation of origins, destinations and flows with OD maps. Cartogr J 47:117–129CrossRefGoogle Scholar
  34. Xiao N, Chun Y (2009) Visualizing migration flows using kriskograms. Cartogr Geogr Inf Sci 36:183–191CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of Texas at Dallas, School of EPPSRichardsonUSA

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