Exploratory Spatial Data Analysis for Flow Data: Exploring The Error Term of Spatial Interaction Models
Abstract
In a number of problem domains there is an increasing interest in exploring flow data, which is defined as data that captures movement between places on a given network, including most branch of engineering, transportation, telecommunication, social system, and economic geography;.. However, the current state of the art in exploratory spatial data analysis (ESDA,); which is largely dominated by geo-statistical and lattice data analysis, lack techniques and methodologies for the exploration of flow data. Although the general underlying concepts are largely the same, flow data requires a different set of specific methods for data exploration. In this paper I extend the methods of spatial stat is tic for identifying spatial clusters and outliers to work with flow data, and demonstrate the methods to detect spatial error dependence and heteroskedasticity in the error term of spatial interaction models.Downloads
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Anselin, L. and I. Syabri. 2003. GeoDa 0.9 User's Guide. Urbana-Champaign, IL: Spatial Analysis Laboratory (SAL). Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign, IL.
Anselin, L. 1999a. Interactive techniques and exploratory spatial data analysis. In Geographical Information Systems. 2d. ed, ed. P. Longley, M. Goodchild, D. Maguire, and D. Rhind. New York: John Wiley & Sons.
Anselin, L. 1999b. Spatial econometrics. In Companion in Theoretical Econometrics, ed. B. Baltagi. Oxford: Basil Blackwell.
Anselin, L. 1998. Exploratory spatial data analysis in a geocomputational environment. In Geocomputation, a primer, eds. P. Longley, S. Brooks, R. McDonnell, and B. Macmillan. New York: John Wiley & Sons.
Anselin L. and A. Bera. 1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. In Handbook of Applied Economic Statictics, pp. 237-289 eds. Amman Ullah and David Giles. New York: Marcel Dekker.
Anselin, L. 1996. The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In Spatial analytical perspectives on GIS, eds. M. Fischer, H. Scholten, and D. Unwin. London: Taylor and Francis.
Anselin, L. 1995. Local Indicators of Spatial Association - LISA. Geographical Analysis, 27(2), 93-115.
Anselin, L. 1994. Exploratory spatial data analysis and geographic information systems. In New Tools for Spatial Analysis, ed.M. Painho. Luxembourg: Eurostat.
Black, W. R. 1992. Network autocorrelation in transport network and flow Systems. Geographical Analysis, 24(3), 207-222.
Bolduc, D. 1992. Spatial autoregressive error components in travel flow models. Regional Science and Urban Economics, 22(3), 371-385.
Bowman, J. L. and M. E. Ben-Akiva. 2001. Activity-based disaggregate travel demand model system with activity schedules. Transportation Research Part A: Policy and Practice, Volume 35, Issue 1, Pages 1-28.
Cliff, A.D., and J. K. Ord. 1981. Spatial Processes, Models, and Applications. London: Pion.
Fotheringham, A. S. 1983. A New Set of Spatial-Interaction Models: The Theory of Competing Destinations. Environment and Planning A 15: 15-36.
Fotheringam A. S. and M. E. O'Kelly. 1989. Spatial Interaction Models: Formulations and Applications. Dordrecht: Kluwer.
Getis, A. and J. K. Ord. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.
Jovicic G., and C. O. Hansen. 2003. A passenger travel demand model for Copenhagen. Transportation Research Part A: Policy and Practice, Volume 37, Issue 4, pp. 333-349.
Messner, S., L. Anselin, R. Baller, D. Hawkins, G. Deane, and S. Tolnay. 1999. The spatial patterning of county homicide rates: An application of exploratory spatial data analysis. Journal of Quatitative Criminology 15 (4): 423-450.
Munshi, K. 1999. Urban passenger travel demand estimation: A household activity approach. Transportation Research Part A: Policy and Practice, Volume 27, Issue 6, pp. 423-432.
Ord, J.K. and A. Getis. 1995. Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-305.
Tukey, John W. 1977. Exploratory Data Analysis. Reading, Massachusetts: Addison-Wesley.
Syabri, I. and L. Anselin. 2001. Visualizing Spatial Autocorrelation with Dynamically Linked Windows. Computing Science and Statistics 33.
Syabri, I., L. Anselin, and O. Smirnov. 2002. Visualizing multivariate spatial correlation with dynamically linked windows. In New Tools for Spatial Data Analysis: Proceedings of the Specialist Meeting, eds. L. Anselin and S. Rey. Santa Barbara: Center for Spatially Integrated Social Science (CSISS), University of California, Santa Barbara.
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