With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

Clustering is an efficient way to group data into different classes on basis of the internal and
previously unknown schemes inherent of the data. With the development of the location …

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay
between space and time. Several available surveys capture STDM advances and report a …

Complex systems are very often organized under the form of networks where nodes and
edges are embedded in space. Transportation and mobility networks, Internet, mobile phone …

Generative Adversarial Networks (GANs) have shown remarkable success in producing
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …

Explosive growth in geospatial and temporal data as well as the emergence of new
technologies emphasize the need for automated discovery of spatiotemporal knowledge …

Learning more about people mobility is an important task for official decision makers and
urban planners. Mobility data sets characterize the variation of the presence of people in …

Emergency event prediction is a crucial topic since the events could involve human injuries
or even deaths. Many countries record a considerable number of emergency events (EVs) …

An increase in the size of data repositories of spatiotemporal data has opened up new
challenges in the fields of spatiotemporal data analysis and data mining. Foremost among …