TITLE:

Spatial Computing Education: A Perspective

PRESENTER:

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

AFFILIATION:

Computer Science Department, University of Minnesota.

URL:

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

SLIDES:

ABSTRACT:

Spatial computing has enriched billions of lives via pervasive services (e.g., navigation and ride-sharing apps), ubiquitous systems (e.g., Global Positioning System, Geographic Information System), and pioneering scientific methods (e.g., spatial statistics, spatial cognition). It is only a beginning and major growth is projected by a 2019 National Academy report, the 2011 McKinsey Big Report, etc. motivating the need to create and enrich pathways for learning and education, and workforce development.

This presentation looks at the recent educational initiatives such as the current AAG Encoding Geography, and the UCGIS Call for Bringing the Geospatial Perspective to Data Science Degrees and Curricula. It points out that the programming languages rise and fall often affecting careers. Thus, students should learn computational thinking skills beyond programming to prepare for changes during their careers. It also discusses the relationship between computational thinking and programming using an analogy of the relationship between geometry and land surveying with illustrations from geography.

This perspective is based on spatial computing educational activities such as creation and teaching of spatial computing campus and massively open online courses, co-authoring a textbook, co-editing an Encyclopedia, leading an NSF IGERT project, and service on a national academy committee on GEOINT workforce.

KEYWORDS: Spatial Computing, Spatial Data Science, Computational Thinking, Computer Programming, Geographic Information Systems.

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

REFERENCES

  1. Spatial Computing ( html , short video , tweet ), Communications of the ACM, 59(1), January, 2016 (With S. Feiner, and W. Aref).
  2. A UCGIS Call to Action: Bringing the Geospatial Perspective to Data Science Degrees and Curricula, University Consortium for Geographic Information Science, Summer 2018.
  3. Encoding Geography , Amercican Association of Geographers.
  4. J. Wing, Computational Thinking, Viewpoint, Communications of the ACM, 49(3):33, 2006. Also see related Wikipedia article for the debate since.
  5. Personal location data, chapter 3e (pp. 85-96) in Big data: The next frontier for innovation, competition, and productivity , McKinsey Global Institute, 2011.
  6. National Research Council, Future U.S. Workforce for Geospatial Intelligence , National Academy Press, 2013 ( DOI: https://doi.org/10.17226/18265 ).
  7. National Research Council, Learning to Think Spatially: GIS as a Support System in the K-12 Curriculum , National Academy Press, 2006 ( DOI: https://doi.org/10.17226/11019 ).
  8. S. Shekhar, H. Xiong and X. Zhou (Co-EIC), Encyclopedia of GIS Springer, 2nd Ed. in 2017 (isbn 978-3-319-17884-4), 1st Ed. in 2008 (isbn 978-0-387-30858-6) .
  9. S. Shekhar and S. Chawla, Spatial Databases: A Tour , Prentice Hall 2003, ISBN 0-13-017480-7.
  10. Transdisciplinary Foundations of Geospatial Data Science ( html , pdf ) ISPRS International Journal of Geo-Informatics, 6(12), 2017. doi:10.3390/ijgi6120395. (with Y. Xie, E. Eftelioglu, R. Ali, X. Tang, Y. Li, and R. Doshi)
  11. Parallel Processing over Spatial-Temporal Datasets from Geo, Bio, Climate and Social Science Communities: A Research Roadmap, IEEE BigData Congress, 2017, pages 232-250 (w/ S. Prasad et al.),
  12. Transforming Smart Cities With Spatial Computing, Proc. IEEE International Smart Cities Conference , 2018 (with Y. Xie, J. Gupta, Y. Li).
  13. Enabling Spatial Big Data via CyberGIS: Challenges and Opportunities , in CyberGIS for Geospatial Discovery and Innovation (Ed. S. Wang and M. Goodchild), Springer, 2019, isbn 978-94-024-1529-2 (w/ M. Evans, D. Oliver, K. Yang, X. Zhou, and R. Ali) .
  14. Spatiotemporal Data Mining: A Computational Perspective, ISPRS International Journal on Geo-Information, 4(4):2306-2338, 2015, DOI: 10.3390/ijgi4042306, (w/ Z. Jiang, R. Ali, E. Efteliglu, X. Tang, V. Gunturi, and X. Zhou).
  15. Identifying patterns in spatial information: a survey of methods ( pdf ), Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 193-214, 1(3), May/June 2011, DOI: 10.1002/widm.25, (w/ M. R. Evans, J. M. Kang and P. Mohan).