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Classification and Regression via Integer Optimization for Neighborhood Change
Author(s) -
Olson Alexander W.,
Zhang Kexin,
CalderonFigueroa Fernando,
Yakubov Ronen,
Sanner Scott,
Silver Daniel,
ArribasBel Dani
Publication year - 2021
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12252
Subject(s) - leverage (statistics) , computer science , cluster analysis , regression , artificial intelligence , machine learning , term (time) , cluster (spacecraft) , data mining , econometrics , mathematics , statistics , physics , quantum mechanics , programming language
This 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.