Spatio-Temporal Data Mining: A Survey of Problems and Methods

TLDR

Spatio‑temporal data, increasingly collected across domains such as climate science, neuroscience, and transportation, differs from relational data by incorporating spatial and temporal attributes, which introduces additional challenges that have been studied for over a decade. This article surveys the emerging field of spatio‑temporal data mining, outlining its key problems and methods. The authors classify spatio‑temporal mining into six categories—clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining—by discussing data types, relevant questions, and problem forms within each category.

Abstract

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data mining community. In this article we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data mining problems in each of these categories.