Measuring Spatial Dynamics in Metropolitan Areas
This article introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying â€œneighborhoodsâ€ 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.
When requesting a correction, please mention this item's handle: RePEc:sae:ecdequ:v:25:y:2011:i:1:p:54-64. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (SAGE Publishing)
If references are entirely missing, you can add them using this form.