Spatial big data: Case studies on volume, velocity, and variety

Spatial computing encompasses the ideas, solutions, tools, technologies, and systems that transform our lives and society by creating a new understanding of spaces, their locations, places, and properties; how we know, communicate, and visualize our relation to places in a space of interest; and how we navigate through those places. From virtual globes to consumer global navigation satellite system devices, spatial computing is transforming society. With the rise of new spatial big data (SBD), spatial computing researchers will be working to develop a compelling array of new geo-related capabilities. We believe that these data, which we call SBD, represent the next frontier in spatial computing. Examples of emerging SBD include temporally detailed (TD) road maps that provide traffic speed values every minute for every road in a city, global positioning system (GPS) trajectory data from cell phones, engine measurements of fuel consumption, and greenhouse gas (GHG) emissions. 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 [1]. Spatial data frequently demonstrate at least one of these core features, given the variety of data types in spatial computing such as points, lines, and polygons. In addition, spatial analytics have shown to be more computationally expensive than their nonspatial brethren [2] as they need to account for spatial autocorrelation and nonstationarity, among other things.