Increasingly widespread sensor networks collect sequences of numerical observations at
fixedlocations over time, and generate vast amounts of geo-referenced time series. Decision …

Spatial data, such as latitude and longitude, refer to information associated with specific
locations. Having a temporal component refers to data that has a time element, like a date or …

As more and more real time spatio-temporal datasets become available at increasing spatial
and temporal resolutions, the provision of high quality, predictive information about spatio …

In the modern realm of pervasive, frequent, sizable and instant data capturing with
advancements in instrumentation, data generation and data gathering techniques, we can …

The growth of urban areas in recent years has motivated a large amount of new sensor
applications in smart cities. At the centre of many new applications stands the goal of …

In 2009, a book dedicated to the Turing Award winner Jim Gray introduced the idea of a
fourth paradigm of science (Hey et al. 2009). The concept behind this new paradigm was …

Forecasting Spatio-Temporal processes has been attracting a great deal of interest within
the research community. In this context, there is an increasing trend of proposing and …

The transformation from a data-poor to a data-rich environment is the most profound event in
the history of geography and earth science. For most of history, we invested a tremendous …

Sensor data on traffic events have prompted a wide range of research issues, related with
the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static …

With an ever-increasing number of sensors in modern society, spatio-temporal time series
forecasting has become a de facto tool to make informed decisions about the future. Most …