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Measuring Spatial Dynamics in Metropolitan Areas

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  • Sergio J. Rey

    ()
    (GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA)

  • Luc Anselin

    (GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA)

  • David C. Folch

    (GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA)

  • Daniel Arribas-Bel

    (GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA and Department of Economic Analysis, Universidad de Zaragoza, Zaragoza, Spain)

  • Myrna L Sastre Gutiérrez

    (GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA)

  • Lindsey Interlante

    (GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA)

Abstract

This article introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods” a priori and then studying how resident attributes change over time, this approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both aspects of a neighborhood transform from one period to the next. The approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. The authors also develop indicators of spatial change at both the macro (city) level and the local (neighborhood) scale. The authors illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the United States for the period 1990-2000. !Keywords: neighborhood change; regionalization

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Bibliographic Info

Article provided by in its journal Economic Development Quarterly.

Volume (Year): 25 (2011)
Issue (Month): 1 (February)
Pages: 54-64

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Handle: RePEc:sae:ecdequ:v:25:y:2011:i:1:p:54-64

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  1. Sergio Rey & Myrna Sastr�-Guti�rrez, 2010. "Interregional Inequality Dynamics in Mexico," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(3), pages 277-298.
  2. Bond, Eric W. & Coulson, N. Edward, 1989. "Externalities, filtering, and neighborhood change," Journal of Urban Economics, Elsevier, vol. 26(2), pages 231-249, September.
  3. Clapp, John M. & Wang, Yazhen, 2006. "Defining neighborhood boundaries: Are census tracts obsolete?," Journal of Urban Economics, Elsevier, vol. 59(2), pages 259-284, March.
  4. Mingche M. Li & H. James Brown, 1980. "Micro-Neighborhood Externalities and Hedonic Housing Prices," Land Economics, University of Wisconsin Press, vol. 56(2), pages 125-141.
  5. Galster, George, 2002. "An economic efficiency analysis of deconcentrating poverty populations," Journal of Housing Economics, Elsevier, vol. 11(4), pages 303-329, December.
  6. Juan Carlos Duque & Raúl Ramos & Jordi Suriñach, 2007. "Supervised Regionalization Methods: A Survey," International Regional Science Review, , vol. 30(3), pages 195-220, July.
  7. Durlauf, S.N., 1992. "A Theory of Persistent Income Inequality," Papers 47, Stanford - Institute for Thoretical Economics.
  8. Juan C. Duque & Luc Anselin & Sergio J. Rey, 2012. "The Max-P-Regions Problem," Journal of Regional Science, Wiley Blackwell, vol. 52(3), pages 397-419, 08.
  9. Flowerdew, Robin & Manley, David J. & Sabel, Clive E., 2008. "Neighbourhood effects on health: Does it matter where you draw the boundaries?," Social Science & Medicine, Elsevier, vol. 66(6), pages 1241-1255, March.
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