This article proposes a novel method for data-driven identification of spatiotemporal homogeneous regions and their dynamics, enabling the exploration of their composition and extents. Using a simple network representation, the method enables temporal regionalization without the need for geographical harmonization. To allow for a transparent corroboration of our method, we use it as a basis for an interactive and intuitive interface for the progressive exploration of the results. The interface guides the user through the original data, enabling both experts and nonexperts to characterize broad patterns of stability and change and identify detailed local processes. The proposed methodology is suitable for any region-based data, and we validate our method with illustrative scenarios from Chicago and Toronto, with results that match the established literature. The system is publicly available, with demographic data for over forty regions in the USA and Canada between 1970 and 2010.

中文翻译：

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纵向数据不协调的邻域动力学

本文提出了一种新的数据驱动的时空同质区域及其动态识别方法，可以探索其组成和程度。使用简单的网络表示，该方法可以实现时间区域划分，而无需进行地理协调。为了透明地证实我们的方法，我们将其用作交互式和直观界面的基础，以逐步探索结果。该界面可指导用户浏览原始数据，从而使专家和非专家都可以表征稳定和变更的广泛模式，并确定详细的本地流程。所提出的方法学适用于任何基于区域的数据，并且我们以芝加哥和多伦多为例，对我们的方法进行了验证，其结果与现有文献相符。该系统是公开可用的，其中包含1970年至2010年期间美国和加拿大40多个地区的人口统计数据。