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Modeling population density across major US cities: a polycentric spatial regression approach

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Abstract

A common approach to modeling population density gradients across a city is to adjust the specification of a selected set of mathematical functions to achieve the best fit to an urban place’s empirical density values. In this paper, we employ a spatial regression approach that takes into account the spatial autocorrelation latent in urban population density. We also use a Minkowskian distance metric instead of Euclidean or network distance to better describe spatial separation. We apply our formulation to the 20 largest metropolitan areas in the US according to the 2000 census, using block group level data. The general model furnishes good descriptions for both monocentric and polycentric cities.

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Authors and Affiliations

  1. School of Social Sciences, University of Texas at Dallas, Dallas, USA

    Daniel A. Griffith

  2. Earth Systems and GeoInformation Sciences, George Mason University, Fairfax, USA

    David W. Wong

Authors
  1. Daniel A. Griffith
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  2. David W. Wong
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Correspondence to Daniel A. Griffith.

Additional information

D. A. Griffith is Ashbel Smith Professor at the School of Economic, Political and Policy Sciences.

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Griffith, D.A., Wong, D.W. Modeling population density across major US cities: a polycentric spatial regression approach. J Geograph Syst 9, 53–75 (2007). https://doi.org/10.1007/s10109-006-0032-y

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  • DOI: https://doi.org/10.1007/s10109-006-0032-y

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