Spatial methods have a rich history of reforming city infrastructure. For example, John Snow's 1854 London Cholera map spurred cities to protect drinking water via sewer systems and to increase green spaces for public health. Today, geospatial data and mapping are among the technologies that cities use the most due to strategic (e.g., long-Term planning, land-use), tactical (e.g., property tax, site selection, asset tracking) and operational (e.g., E-911, situation awareness, gunshot location) use cases. Moreover, they (e.g., Google Maps) help citizens navigate, drones stay clear of restricted spaces (e.g., airports, NFL games), and sharing-economy (e.g., Uber) match consumers with nearby providers. Future spatial computing opportunities for smart cities are even more compelling. GIS promises to help re-imagine, redesign, see, and compare alternative infrastructure futures to address risks (e.g., climate change, rising inequality, population growth) and opportunities (e.g., autonomous vehicles, distributed energy production). This paper surveys recent spatial computing accomplishments and identifies research needs for smart-city use-cases.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Smart Cities Conference, ISC2 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Feb 28 2019|
|Event||2018 IEEE International Smart Cities Conference, ISC2 2018 - Kansas City, United States|
Duration: Sep 16 2018 → Sep 19 2018
|Name||2018 IEEE International Smart Cities Conference, ISC2 2018|
|Conference||2018 IEEE International Smart Cities Conference, ISC2 2018|
|Period||9/16/18 → 9/19/18|
Bibliographical noteFunding Information:
National Science Foundation under Grants No. 1541876, 1029711, IIS-1320580, 0940818 and IIS-1218168
This material is based upon work supported by the National Science Foundation under Grants No. 1541876, 1029711, IIS-1320580, 0940818 and IIS-1218168, the USDOD under Grants No. HM1582-08-1-0017 and HM0210-13-1-0005, ARPA-E under Grant No. DE-AR0000795, USDA under Grant No. 2017-51181-27222, NIH under Grant No. UL1 TR002494, KL2 TR002492 and TL1 TR002493 and the OVPR Infrastructure Investment Initiative and Minnesota Supercomputing Institute (MSI) at the University of Minnesota. We thank our NSF Smart and Connected Communities project parteners, namely, Hennepin County, and cities of Minneapolis, St. Paul and Tallahassee for sharing use-cases and datasets. We would like to thank Jamal Golmohammadi for helping with the figures and Kim Koffolt, Samantha Detor and the spatial computing research group for their helpful comments and refinement.
© 2018 IEEE.
- smart city
- spatial computing