Spatial Computing : Challenges and Opportunities


Shashi Shekhar : Biography , Homepage , Picture


Computer Science Department, University of Minnesota.





Spatial computing includes computations about geographic phenomena (e.g. climate), and instrumented physical environment (e.g. roads or building, immersive environments), as well as computations using distributed device-collections with distance-dependent interaction ability (e.g. geo-sensor networks, ant colonies). It is important for current applications in sustainable development, energy, mobility, public safety, security, and health, as well as emerging application such as augmented reality, local advertisement, and m-commerce.

However, traditional computation models often abstract out physical locations in space and time. This leads to blind-spots, semantic gaps, inadequacies and inefficiencies. For example, a prominent e-commerce company claimed that geography is dead in Internet era only to discover logistics and distribution challenges. Dynamic programming is a popular algorithm design paradigm, however, its assumption about stationarity of candidate ranking may not be reasonable for spatio-temporal problems. Sorting is often used to speed-up searches in relational databases, but is not intuitive in spatial computing. Data-types (e.g. numbers, text) in common programming languages have a semantic gap with spatial computing needs (e.g. maps, gps-tracks). Independence assumption may simplify data mining and statistical reasoning, but is often inappropriate for spatial datasets. Graph models provide a simple shortest path algorithm (e.g. Dijktra's, A*), but may not be straightforward for geometric questions about turns, lane changes, etc.

This talk briefly introduces the fundamental ideas underlying the emerging spatial sciences, systems and services to address new challenges. Representative sciences include spatial cognition, spatial statistics, geographic information science, computational geometry, vision, robotics, graphics, spatial databases, spatial data mining, etc. Representing services and systems include web-service (e.g. mapquest, Google Earth), geographic information systems (e.g. ESRI Arc family), databases (e.g. Oracle SDO, IBM DB2 SDC, Postgres postgis), spatial data mining (e.g. R, Splus), etc.

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, the National Aeronautics and Space Administration, the Federal Highway Authority, and the University of Minnesota (e.g. Center for Transportation Studies).


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