Geosilhouettes: geographical measures of cluster fit

Levi Wolf, Elijah Knaap, Sergio Rey

Regionalization, under various guises and descriptions, is a longstanding and pervasive interest of urban studies. With an increasingly large number of studies on urban place detection in language, behavior, pricing, and demography, recent critiques of longstanding regional science perspectives on place detection have focused on the arbitrariness and non-geographical nature of measures of best fit. In this paper, we develop new explicitly-geographical measures of cluster fit. These hybrid spatial-social measures, called geosilhouettes, are demonstrated to capture the “core” of geographical clusters in racial data on census blocks in Brooklyn neighborhoods. These new geosilhouettes are also useful in a variety of boundary analysis and outlier detection uses. These new measures are defined, demonstrated, and new directions are suggested.