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

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.