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Explorations in Spatial Demography

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Population Change and Rural Society

Summary and Conclusions

In this chapter, we have discussed the role of geographic space in quantitative demography. A re-emerging interest in spatial demography is evidenced by an increasing number of demographers seeking to adopt the formal tools of spatial econometrics to improve on traditional regression models of demographic processes operating in space. The concept of spatial autocorrelation and ways to specify correctly multiple regression models in the presence of spatial autocorrelation are made more concrete through an illustration of spatial modeling of county-level growth in the U.S. Great Plains region during the 1990s.

It is our belief that we will have moved the science of spatial demography forward in very exciting ways as our own statistical models become more sophisticated, as spatial processes are brought into empirical demographic studies to correct for potential misspecification, and as ourwork begins to add significantly to the larger literature on spatial data analysis. The growing interest in the field of spatial econometrics among several disciplines in the social sciences, of which the re-emergence of interest in spatial demography is a part, suggests an exciting future for quantitative demographers.

Please direct all correspondence to Paul R. Voss at 316 Agriculture Hall, 1450 Linden Drive, Madison, WI, 53706, or voss@ssc.wisc.edu. The authors extend their appreciation to David Long and Nick Fisher for assistance and advice regarding the GIS applications and spatial modeling for the Great Plainsworking illustration, to Jeremy White for graphic support, and to Glenn Deane for extensive comments on earlier drafts. This research was supported in part by the U.S. Department of Agriculture, Hatch Grant WIS04536, by the National Institute for Child Health and Human Development, Center Grant HD05876 and Training Grant HD07014, and by the University of Wisconsin Center for Demography T and Ecology, through its Geographic Information and Analysis Core.

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References

  • Anselin, L. (1988). Spatial econometrics, methods, and models. Dordrecht, Netherlands: Kluwer.

    Google Scholar 

  • Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27, 93–115.

    Article  Google Scholar 

  • Anselin, L. (1996). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In M. Fischer, H.J. Scholten, & D. Unwin (Eds.), Spatial analytical perspectives on GIS (pp. 111–125). London: Taylor & Francis.

    Google Scholar 

  • Anselin, L. (2001). Rao’s score test in spatial econometrics. Journal of Statistical Planning and Inference, 97(1), 113–139.

    Article  Google Scholar 

  • Anselin, L. (2003). Spatial externalities, spatial multipliers, and spatial econometrics. International Regional Science Review, 26(2), 153–166.

    Article  Google Scholar 

  • Anselin, L., & Bera, A. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In A. Ullah & D. Giles (Eds.), Handbook of applied economic statistics (pp. 237–289). New York: Marcel Dekker.

    Google Scholar 

  • Bailey, T.C., & Gatrell, A.C. (1995). Interactive spatial data analysis. Harlow, UK: Longman Scientific & Technical.

    Google Scholar 

  • Brueckner, J.K. (2003). Strategic interaction among governments: An overview of empirical studies. International Regional Science Review, 26(2), 175–188.

    Article  Google Scholar 

  • Chou, Y.-H. (1997). Exploring spatial analysis in Geographic Information Systems. Santa Fe, NM: OnWord Press.

    Google Scholar 

  • Cliff, A.D., & Ord, J.K. (1973). Spatial autocorrelation. London: Pion.

    Google Scholar 

  • Cliff, A.D., & Ord, J.K. (1981). Spatial processes: models and applications. London: Pion.

    Google Scholar 

  • Cressie, N.A.C. (1993). Statistics for spatial data. New York: Wiley.

    Google Scholar 

  • Fotheringham, A.S., Brunsdon, C., & Charlton, M.E. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Chichester, UK: Wiley.

    Google Scholar 

  • Griffith, D.A. (1996). Some guidelines for specifying the geographic weights matrix contained in spatial statistical models. In S.L. Arlinghaus (Ed.), Practical handbook of spatial statistics (pp. 65–82). Boca Raton, FL: CRC Press.

    Google Scholar 

  • Haining, R. (2003). Spatial data analysis in the social and environmental sciences. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Hoaglin, D.C., Mosteller, F., & Tukey, J.W. (Eds.). (1983). Understanding robust and exploratory data analysis. New York: Wiley.

    Google Scholar 

  • Hoaglin, D.C., Mosteller, F., & Tukey, J.W. (Eds.). (1985). Exploring data tables, trends, and shapes. New York: Wiley.

    Google Scholar 

  • Lobao, L. (2004). Continuity and change in place stratification: Spatial inequality and middle-range territorial units. Rural Sociology, 69(1), 1–30.

    Article  Google Scholar 

  • Lobao, L., & Saenz, R. (2002). Spatial inequality and diversity as an emerging research area. Rural Sociology, 67(4), 497–511.

    Article  Google Scholar 

  • Martin, R.J. (1987). Some comments on correction techniques for boundary effects and missing value techniques. Geographical Analysis, 19, 273–282.

    Article  Google Scholar 

  • Messner, S.F., & Anselin, L. (2004). Spatial analyses of homicide with areal data. In M.F. Goodchild & D.G. Janelle (Eds.), Spatially integrated social science (pp. 127–144). Oxford: Oxford University Press.

    Google Scholar 

  • Moran, P.A.P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17–23.

    Article  Google Scholar 

  • Rogers, E.M. (1962). Diffusion of innovation. New York: Free Press.

    Google Scholar 

  • Stephan, F.F. (1934). Sampling errors and interpretations of social data ordered in time and space. Journal of the American Statistical Association, 29 (Suppl. 185), 165–166.

    Article  Google Scholar 

  • Tobler, W.R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.

    Article  Google Scholar 

  • Tolnay, S.E., Deane, G., & Beck, E.M. (1996). Vicarious violence: Spatial effects on Southern lynchings, 1890–1919. American Journal of Sociology, 102(3), 788–815.

    Article  Google Scholar 

  • Tukey, J.W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Voss, P.R. (1993). Applied demography and rural sociology. In D.L. Brown, D.R. Field, & J.J. Zuiches (Eds.), The demography of rural life (pp. 145–170). University Park, PA: Northeast Regional Center for Rural Development.

    Google Scholar 

  • Voss, P.R. (2004). Demography as a spatial social science. Paper presented at the Annual Meeting of the Southern Demographic Association, Hilton Head, NC.

    Google Scholar 

  • White, K.J.C. (2003). A century of population change in the U.S. Great Plains (Doctoral dissertation, University of Washington, 2003).

    Google Scholar 

  • Wrigley, N., Holt, T., Steel, D., & Tranmer, M. (1996). Analysing, modeling, and resolving the ecological fallacy. In P. Longley & M. Batty (Eds.), Spatial analysis: Modelling in a GIS environment (pp. 23–40). Cambridge: GeoInformation International.

    Google Scholar 

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Voss, P.R., Curtis White, K.J., Hammer, R.B. (2006). Explorations in Spatial Demography. In: Kandel, W.A., Brown, D.L. (eds) Population Change and Rural Society. The Springer Series on Demographic Methods and Population Analysis, vol 16. Springer, Dordrecht . https://doi.org/10.1007/1-4020-3902-6_19

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