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

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 …

As the world becomes increasingly connected, it is also more exposed to a myriad of cyber
threats. We need to use multiple types of tools and techniques to learn and understand the …

The geospatial sciences face grand information technology (IT) challenges in the twenty-first
century: data intensity, computing intensity, concurrent access intensity and spatiotemporal …

Spatio-temporal clustering is a process of grouping objects based on their spatial and
temporal similarity. It is relatively new subfield of data mining which gained high popularity …

Urban vitality provides an important basis for evaluating urban development and spatial
balance. In the era of big data, the quantitative analysis of urban vitality has become a …

Spatial and time-dependent data is of interest in many applications. This task is difficult due
to its complex spatial dependency, long-range temporal dependency, data non-stationarity …

With the advancement of remote sensing technology, wide usage of GPS devices in vehicles
and cell phones, popularity of mobile applications, crowd sourcing, and geographic …

Spatial time series is a common type of data dealt with in many domains, such as economic
statistics and environmental science. There have been many studies focusing on finding and …

Correlations between two time series, including the linear Pearson correlation and the
nonlinear transfer entropy, have attracted significant attention. In this work, we studied the …