Abstract
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 explicitlygeographical 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.
Levi John Wolf
Senior Lecturer/Associate Professor
I work in spatial data science, building new methods and software to learn new things about social and natural processes.
Elijah Knaap
Associate Director & Senior Research Scientist
My research interests include urban inequality, neighborhood dynamics, housing markets, spatial data science, regional science, and housing & land policy.
Sergio Rey
Director and Professor
My research interests include geographic information science, spatial inequality dynamics, regional science, spatial econometrics, and spatial data science.