Classification and Regression via Integer Optimization for Neighborhood Change
2020; Wiley; Volume: 53; Issue: 2 Linguagem: Inglês
10.1111/gean.12252
ISSN1538-4632
AutoresAlexander W Olson, Kexin Zhang, Fernando Calderón-Figueroa, Ronen Yakubov, Scott Sanner, Daniel Silver, Daniel Arribas‐Bel,
Tópico(s)Spatial and Panel Data Analysis
ResumoThis article applies a method we term “predictive clustering” to cluster neighborhoods. Much of the literature in this direction is based on groupings built using intrinsic characteristics of each observation. Our approach departs from this framework by delineating clusters based on how the neighborhood’s features respond to a particular outcome of interest (e.g., income change). To do so, we leverage a classification and regression via integer optimization (CRIO) method that groups neighborhoods according to their predictive characteristics and consistently outperforms traditional clustering methods along several metrics. The CRIO methodology contributes a novel methodological and conceptual capability to the literature on neighborhood dynamics that can provide useful insights for policymaking.
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