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.

中文翻译：

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通过整数优化对邻域变化进行分类和回归

本文将我们称为“预测性聚类”的方法应用于聚类邻域。这个方向上的许多文献都是基于使用每个观测的内在特征建立的分组。我们的方法通过基于邻域的特征如何响应特定利益结果（例如收入变化）描绘聚类来描绘集群，从而偏离了此框架。为此，我们利用整数优化（CRIO）方法进行分类和回归，该方法根据邻域的预测特征对邻域进行分组，并在多个指标上始终优于传统的聚类方法。CRIO方法论为有关邻里动态的文献提供了一种新颖的方法论和概念上的能力，可以为政策制定提供有用的见解。