Enabling Spatial Big Data via CyberGIS: Challenges and Opportunities Michael R. Evans Dev Oliver KwangSoo Yang Xun Zhou Shashi Shekhar 1 Introduction Recent years have seen the emergence of many new and valuable spatial datasets. Examples include trajectories of cell-phones and Global Position- ing System (GPS) devices, vehicle engine measurements, global climate mod- els (GCM) simulation data, volunteered geographic information (VGI), geo- social media, tweets, etc. The value of these datasets is already evident. For example, while mon- itoring tweets, the American Red Cross learned of a tornado touchdown in Texas before news reports [27]. Google has been able to estimate flu activity from search terms [23]. Everyday citizens around the world shape pop culture globally via crowd-sourced talent identification (e.g., Justin Bieber and Psy’s breakthrough via YouTube). However, these location-aware datasets are of a volume, variety, and ve- locity that exceed the capability of current CyberGIS technologies. We refer to these datasets as Spatial Big Data (SBD). 1.1 Defining Spatial Big Data Whether spatial data is defined as “Big” depends on the context. Spatial big data cannot be defined without reference to value proposition (use-case) and user experience, elements which in turn depend on the computational plat- form, use-case, and dataset at hand. User experience may be unsatisfactory due to computational reasons that often stem from workloads exceeding the capacity of the platform (Table 1). For example, users may experience unac- ceptable response times, which may be caused by high data volume during Computer Science Department, University of Minnesota, Minneapolis, MN 1