  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.
