A Nonparametric Analysis of Employment Density in a Polycentric City

Abstract

Nonparametric estimation procedures offer distinct advantages in modeling polycentric cities because they are flexible enough to account for functional form misspecification and incorrect subcenter sites. This paper presents locally weighted (LW) regression estimates of employment density in suburban Chicago. LW regression estimates are more accurate than OLS regression and capture the effects of missing variables. The results demonstrate that Chicago is indeed a polycentric city: although the traditional city center continues to affect employment density patterns in the suburbs, local peaks have developed around secondary employment centers.