Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

TL;DR

This survey comprehensively reviews the challenges, open problems, and recent advances in spatiotemporal data mining, highlighting issues in data representation, modeling, and diverse applications.

Contribution

It provides a detailed analysis of the current state, identifies gaps, and discusses open research problems in various aspects of spatiotemporal data mining.

Findings

Identifies key challenges in spatiotemporal relationships and data characteristics.

Highlights limitations in current data representation and visualization methods.

Outlines open problems across multiple application domains.

Abstract

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.