Developing effective visual analytics systems demands care in characterization of domain
problems and integration of visualization techniques and computational models. Urban …

Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …

Due to great advances in wireless communication, the connected Internet of vehicles
(CIoVs) has become prevalent. Naturally, internal connections among active vehicles are an …

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

Grid visualizations are widely used in many applications to visually explain a set of data and
their proximity relationships. However, existing layout methods face difficulties when dealing …

Transportation has become one of the fastest-growing fields for greenhouse gas emissions.
It is important to promote the coordinated development of cities and transportation. To …

Geoparsing, the task of extracting toponyms from texts and associating them with
geographic locations, has witnessed remarkable progress over the past years. However …

With deep learning (DL) outperforming conventional methods for different tasks, much effort
has been devoted to utilizing DL in various domains. Researchers and developers in the …

Deep learning (DL) approaches are being increasingly used for time-series forecasting, with
many efforts devoted to designing complex DL models. Recent studies have shown that the …

Deep learning algorithms increasingly support automated systems in areas such as human
activity recognition and purchase recommendation. We identify a current trend in which data …