Spatial Big Data [chapter]

Michael Evans, Dev Oliver, Xun Zhou, Shashi Shekhar
2014 Big Data  
Increasingly, the size, variety, and update rate of spatial datasets exceed the capacity of commonly used spatial computing and spatial database technologies to learn, manage, and process data with reasonable effort. We believe that this data, which we call Spatial Big Data (SBD), represents the next frontier in spatial computing. Examples of emerging SBD include temporally detailed roadmaps that provide traffic speed values every minute for every road in a city, GPS trajectory data from
more » ... ones, and engine measurements of fuel consumption, greenhouse gas emissions, etc. A 2011 McKinsey Global Institute report defines traditional big data as data featuring one or more of the 3 "V's": Volume, Velocity, and Variety. This chapter discusses Spatial Big Data through case-studies on real-world datasets that feature one or more of the 3 "V's": a study on change detection in climate data illustrates volume, a study on finding anomalies in real-time highway traffic sensors shows Velocity, and two studies on Variety demonstrate both variety in input and output. Spatial data has traditionally challenged traditional data querying and mining algorithms, requiring new and interesting algorithms to be developed. Spatial Big Data highlights these challenges and provides for a rich area of research.
doi:10.1201/b16524-9 fatcat:t3pam3wjvraz7pun5cnjkilg54