BIG Geospatial Data

WHAT IS SPATIAL BIG DATA?  Defined in part by the context, use-case  Data too big, complex for traditional desktop GIS  Often seen as relating to user experience  Three data attributes of unsatisfactory user experience

VOLUME  Massive  Globally distributed  Unacceptable response time  Example: Kriging crowd-sourced temperature data

VELOCITY  Frequent data  Real time  Example: monitoring of smart phones, tweets  Data loss  System failure

VARIETY  Multi-dimensional  Large human effort to accomplish task  Fusion of multiple data sources  Example: mapping post-disaster situation on the ground

EXAMPLES OF BIG SPATIAL DATA  Raster  Global Climate Models  Unmanned aerial vehicle data (drones)  LiDAR  Vector  Volunteered Geographic information (OpenStreetMap)  GPS Trace Data (tied to eco-routing)

 Graph  Spatial-Temporal Engine Measurement Data (vehicle sensors sensing elevation)  Historical Speed Profiles (dynamic road routing)

Traditional Spatial DataBig Spatial Data Simple Use Cases Map of 2012 election voter preferences Real time maps of tweets, traffic ExamplesPoint, line, raster graph dataCheck-ins, drone videos, GPS tracks in phones Volume10 6 crime reports/year, gigabytes of roadmaps GPS traces VarietyRaster, vector, graphMoving objects, time-series VelocityLimited velocity (waiting for next Census) High velocity (real-time map of tweets)

SOURCES  Directed  surveillance  Automated  inherent  Volunteered  gifted

DATA PROCESSING  A need to utilize data  Integration  Open data analytics

APPLICATIONS OF BIG SPATIAL DATA  Eco-Routing  UPS routes avoid left-turns to limit idling, save fuel  Eco-routing could be extended across industries, help save fuel  Climate Change models  With more years of historical models, long-range climate models will be more robust  CartoDB earth observation  Disaster response  Red Cross detected tornado in Texas by following tweets, seeing hotspot

IT CHALLENGES  Data Intensity  Lots of data  Coming in fast!  Formatting, structure, organization  Computing Intensity  Earth phenomena is complex  Complex algorithms and models needed  Often beyond standard computing capacity

 Concurrent Intensity  Allow use to millions of people at the same time,  Emergency response capabilities  Spatiotemporal Intensity  Data must be intense across space and time  Geographic, atmospheric, oceanic

OTHER ISSUES  Trustworthy  Privacy  Ethical  Technocracy  Corporatization and technology lock-in

REFERENCES  Evans, M. R., Oliver, D., Yang, K., & Shekhar, S. (2013). Enabling Spatial Big Data via CyberGIS: Challenges and Opportunities. CyberGIS: Fostering a New Wave of Geospatial Innovation and Discovery. Springer Book.  Yang, C., Goodchild, M., Huang, Q., Nebert, D., Raskin, R., Xu, Y., & Fay, D. (2011). Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?. International Journal of Digital Earth, 4(4),