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

Spatial methods have a rich history of reforming city infrastructure. For example, John
Snow's 1854 London Cholera map spurred cities to protect drinking water via sewer systems …

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 …

Recent developments in data mining and machine learning approaches have brought lots of
excitement in providing solutions for challenging tasks (eg, computer vision). However …

Recent years have seen the emergence of many new and valuable spatial datasets such as
trajectories of cell-phones and Global Positioning System (GPS) devices, vehicle engine …

In-use, spatiotemporal NOX emissions were measured from a conventional powertrain
transit bus and a series electric hybrid bus over gradients of route kinetic intensity and …

This chapter provides an overview of spatial and spatiotemporal big data science. This
chapter starts with the unique characteristics of spatial and spatiotemporal data, and their …

Increasingly, location-aware sensors in urban transportation networks are generating a wide
variety of data which has spatio-temporal network semantics. Examples include temporally …

Spatial data have tremendous value and are necessary components in many important
societal applications. In recent years, our world has been witnessing a revolution brought by …

Given the increasing proliferation of location enabled sensors, we are able to collect a wide
variety of data which has spatio-temporal semantics. Many of these datasets have network …