TITLE:

From GPS, Google Maps & Uber to Spatial Computing

PRESENTER:

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

AFFILIATION:

Computer Science Department, University of Minnesota.

URL:

http://www.cs.umn.edu/~shekhar

VIDEOS:

SLIDES:

ABSTRACT:

Since public availability of Global Positioning System in the 1990s, spatial computing has enriched billions of lives via pervasive services (e.g., Google Maps, Uber, geo-tagging, check-in), ubiquitous systems (e.g., geographical information system, spatial database management system), and pioneering scientific methods (e.g., spatial statistics). These accomplishment are just the tip of the iceberg and there is a strong potential for a compelling array of new breakthroughs such as Earth dashboards for continuous monitoring of environmental hazards, localization indoors and underground, accurate spatio-temporal predictive models, time-travel (and depth) in virtual globes, etc. For example, a McKinsey report projected an annual $600B saving from leveraging spatial big data (e.g., smart-phone trajectories) for novel eco-routing services to reduce wasted fuel, greenhouse gas emission and pollution exposure during unnecessary waits at traffic lights and in congestion.

However, many fundamental research questions need to be investigated to realize the transformative potential. For example, how may location-based services survive GPS-jamming (or spoofing)? How may spatial big data (e.g., smart-phone trajectories) be mined without violating privacy ? How can machine learning algorithms be generalized to address spatio-temporal challenges (e.g., auto-correlation, non-stationarity, heterogeneity, multi-scale), to scale up to spatial big data and to model geographic concepts (e.g., context, hot-spots, hot-features, doughnut-hole patterns)? How can eco-routing address the new challenges, e.g., waits at traffic-signals violate the sub-path optimality assumption in popular A* and Dijktra's algorithms?

This presentation shares a perspective on the societal accomplishments, opportunities, and research needs in spatial computing based on a recent community report following the Computing Community Consortium workshop titled From GPS and Virtual Globes to Spatial Computing -- 2020 held at the National Academies.

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

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

REFERENCES

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