Measuring Spatial Dynamics in Metropolitan Areas

Sergio J. Rey, Luc Anselin, David C. Folch, Daniel Arribas-Bel, Myrna L. Sastré Gutiérrez and Lindsey Interlante
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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, srey@asu.edu
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, Department of Economic Analysis, Universidad de Zaragoza, Zaragoza, Spain
Myrna L. Sastré 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

Economic Development Quarterly, 2011, vol. 25, issue 1, 54-64

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 (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:sae:ecdequ:v:25:y:2011:i:1:p:54-64

DOI: 10.1177/0891242410383414

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