From GPS, Google Maps & Uber to Spatial Computing and Spatial Big-Data Science


Shashi Shekhar : Biography ( 100 words , 350 words ), Homepage , Picture


Computer Science Department, University of Minnesota.






Spatial computing has already enriched billions of lives via pervasive services (e.g., navigation, ride-sharing and delivery apps), ubiquitous systems (e.g., global positioning system, geographical information system, spatial database management system), and pioneering scientific methods (e.g., spatial statistics). With 2 billion receivers in use for location and time services, the GPS has become a critical infrastructure for the world economy for use cases ranging from precision agriculture to navigation to ride sharing to smart cities. Moreover, billions of weather forecasts are used every day around the world and global agriculture is monitored to anticipate and prevent food shortage leveraging spatial big data from Earth Observation satellites.

These success stories are only a beginning and many transformative opportunities lie ahead. For example, a 2019 U.S. national academy report projects $1.6 trillion in savings for energy generation and use from earth observation data by 2035. Earlier, the 2011 Mckinsey Big Data report estimated that location trace data will generate about $600 billion annually by 2020. Furthermore, government and industry have recently started major initiatives such as NASA Earth Exchange, Amazon Earth on AWS, Google Earth Engine, Microsoft AI for Earth, and NSF Navigating the New Arctic for meeting grand challenges facing our changing planet such as climate change and environmental sustainability.

However, many spatial computing questions need to be probed to realize the transformative potential. For example, how may modern economy survive wide-spread GPS-jamming (or spoofing)? How may one continuously monitor our changing planet at high spatial resolution even during nights? How may spatial big data (e.g., smart-phone trajectories) be mined without violating privacy ? How may machine learning methods be generalized to address spatial challenges (e.g., auto-correlation, multi-scale, modifiable areal unit problem such as Gerrymandering)? How may we address spatial bias in data even when social feedback loops increase it? How may algorithms scale up to spatial big data to learn unbiased models? How may we leverage vehicle big data (e.g., on-board diagnostics with high spatio-temporal resolution) for eco-routing to model edge dependence of energy-use and emissions?

This presentation shares a perspective on the spatial computing accomplishments and opportunities such as those related to the growing spatial big data.

KEYWORDS: Spatial Computing, Spatial Big Data, Spatial Data Science, Spatial Data Mining, Spatial Databases, Geographic Information Systems.

ACKNOWLEDGMENTS: This work was supported in part by the National Science Foundation, the U.S. Department of Defense, and the University of Minnesota.


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