Spatial computing perspective on food energy and water nexus

  • Emre Eftelioglu
  • Zhe Jiang
  • Reem Ali
  • Shashi Shekhar


In the coming decades, the increasing world population is expected to grow the demand for food, energy, and water resources. In addition, these resources will be under stress due to the climate change and urbanization. Previously, more problems were caused by piecemeal approaches analyzing and planning those resources independent of each other. The goal of the food, energy, and water (FEW) nexus approach is to prevent such problems by understanding, appreciating, and visualizing the interconnections and interdependencies of FEW resources at local, regional, and global levels. The nexus approach seeks to use the FEW resources as an interrelated system of systems, but data and modeling constraints make it a challenging task. In addition, the lack of complete knowledge and observability of FEW interactions exacerbates the problem. Related work focused on physical science solutions (e.g., desalination, bio-pesticides). No doubt these are necessary and worthwhile for FEW resource security. Overlooked in these work is that spatial computing may help domain scientists achieve their goals for the FEW nexus. In this paper, we describe our vision of the spatial computing’s role in understanding the FEW nexus using a Nexus Dashboard analogy. From a spatial data lifecycle perspective, we provide more details on the spatial computing components behind the Nexus Dashboard vision. In each component, we list new technical challenges that are likely to drive future spatial computing research.


Food, energy, and water nexus Spatial computing System of systems Sustainability Precision agriculture 



This material is based upon work supported by the National Science Foundation under Grant Nos. 1029711, IIS-1320580, 0940818, and IIS-1218168; U.S. DoD under Grant No. HM1582-08-1-0017; and the University of Minnesota via U-Spatial. We would like to thank Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their comments.


  1. Achache J (2015) Modeling the Global Earth Observation System of Systems. Proc 2006 Inst Ind Eng Annu Conf 12:14946Google Scholar
  2. Allan JA (2003) Virtual water—the water, food, and trade nexus. Useful concept or misleading metaphor? Water Int 28:106–113. doi: 10.1080/02508060.2003.9724812 CrossRefGoogle Scholar
  3. Andrews-Speed P, Bleischwitz R, Boersma T, et al. (2012) The Global Resource Nexus. The Struggles for land, energy, food, water, and minerals. Routledge, LondonGoogle Scholar
  4. Bazilian M, Rogner H, Howells M, et al. (2011) Considering the energy, water and food nexus: towards an integrated modelling approach. Energy Policy 39:7896–7906. doi: 10.1016/j.enpol.2011.09.039 CrossRefGoogle Scholar
  5. Brown DG, Band LE, Green KO, et al. (2014) Advancing land change modeling: opportunities and research requirements. National Academies Press, Washington DCGoogle Scholar
  6. Butler D (2013) When Google got flu wrong. Nature 494:155–156. doi: 10.1038/494155a CrossRefGoogle Scholar
  7. Campbell JB, Wynne RH (2011) Introduction to remote sensing, Fifth edn. The Guilford Press, New York(ISBN: 160918176X)Google Scholar
  8. Consortium O et al. (1999) OpenGIS simple features specification for SQL. OpenGIS Proj Doc 99(49):49–99Google Scholar
  9. Ester M, Kriegel H, Sander J (1997) Spatial data mining : a database approach. In: Proceedings of the 5th International Symposium on Advances in Spatial Databases. Springer, Berlin Heidelberg, pp. 47–66CrossRefGoogle Scholar
  10. FEWSNET (2013) Nutrition|Famine Early Warning Systems Network. Accessed 8 Jan 2016
  11. Flammini A, Puri M, Pluschke L, Dubois O (2014) Walking the nexus talk: assessing the water-energy-food nexus in the context of the sustainable energy for all initiativeGoogle Scholar
  12. Geographical Sciences Committee. (2014) Advancing land change modeling: opportunities and research requirements. National Academies Press, Washington DCGoogle Scholar
  13. GEOSS (2015) GEOSS Portal. Website GEO (gr. Earth Obs.)Google Scholar
  14. GNSS (2011) GNSS Systems ReportsGoogle Scholar
  15. Google (2015) Google Earth Web Site. Accessed 1 Jan 2015
  16. Healy RW, Alley WM, Engle MA, McMahon PB, Bales JD (2015) The water-energy nexus: An Earth Science Perspective (No. 1407). US Geological Survey, ChicagoGoogle Scholar
  17. Heidemann J, Stojanovic M, Zorzi M (2012) Underwater sensor networks: applications, advances and challenges. Philos Trans R Soc A Math Phys Eng Sci 370:158–175. doi: 10.1098/rsta.2011.0214 CrossRefGoogle Scholar
  18. Hellegers P, Zilberman D, Steduto P, McCornick P (2008) Interactions between water, energy, food and environment: evolving perspectives and policy issues. Water Policy 10:1. doi: 10.2166/wp.2008.048 CrossRefGoogle Scholar
  19. Hoekstra a. Y, Hoekstra a. Y (2003) Virtual water trade. Int Expert Meet Virtual Water Trade 12:1–244Google Scholar
  20. Hoff H (2011) Understanding the nexus. Background paper for the Bonn2011 Conference: the water, energy and food security nexus. Security 1–52Google Scholar
  21. Kemp K (2008) Encyclopedia of geographic information science. SAGE Publications (ISBN 1412913136)Google Scholar
  22. Lillesand TM, Kiefer RW, Chipman JW (2009) Remote sensing and image interpretation. John Wiley & Sons, ChichesterGoogle Scholar
  23. Liu J, Mooney H, Hull V, et al. (2015) Systems integration for global sustainability. Science (80-) 347:1258832–1258832. doi: 10.1126/science.1258832 CrossRefGoogle Scholar
  24. Lundy J, Bowdish L (2014) The energy-water-food neuxs: insights for the business community. NEXUS Platf. - Water Energy Food Secur. Nexus - NEXUS ResourGoogle Scholar
  25. McBratney A, Whelan B, Ancev T, Bouma J (2005) Future directions of precision agriculture. Precis Agric 6:7–23. doi: 10.1007/s11119-005-0681-8 CrossRefGoogle Scholar
  26. Melo MT, Nickel S, Saldanha-da-Gama F (2009) Facility location and supply chain management—a review. Eur J Oper Res 196:401–412. doi: 10.1016/j.ejor.2008.05.007 CrossRefGoogle Scholar
  27. Min H, Zhou GG (2002) Supply chain modeling: past, present and future. Comput Ind Eng 43:231–249. doi: 10.1016/S0360-8352(02)00066-9 CrossRefGoogle Scholar
  28. Mohtar RH, Daher B (2012) Water, energy, and food: the ultimate nexus. Encyclopedia of Agricultural, Food, and Biological EngineeringGoogle Scholar
  29. MWater (2013) mWater. Accessed 9 Jan 2016
  30. NAP (1997) Precision agriculture in the 21st century. National Academies Press, Washington, D.C.Google Scholar
  31. NAS (2012) Global navigation satellite systems: report of a Joint Workshop of the National Academy of Engineering and the Chinese Academy of Engineering. The National Academy Press(ISBN 978–0-309-22275-4)Google Scholar
  32. NASA (2012) Landsat top ten—a shrinking sea, Aral Sea.
  33. NIC (2015a) Continuity of NASA Earth observations from space. National Academy Press, Washington, DCGoogle Scholar
  34. NIC (2015b) NASA Space Technology Roadmaps (2015 Draft)Google Scholar
  35. NRC (1997) Precision agriculture in the 21st century: geospatial and information technologies in crop management. The National Academies Press, Washington, DCGoogle Scholar
  36. NRC (2014) Opportunities to use remote sensing in undestanding permafrost and related ecological characteristics: report of a workshop. The National Academies Press(ISBN: 978–0-309-30121-3)Google Scholar
  37. NSF (2014a) Geospatial data project puts major issues on the map. Accessed 8 Jan 2016
  38. NSF (2014b) Food, Energy and Water: Transformative Research Opportunities in the Mathematical and Physical Sciences, Mathematical and Physical Sciences Advisory Committee - Subcommittee on Food Systems, July 2014.
  39. NSF (2015) Dear colleague letter: SEES: interactions of food systems with water and energy systems. \url{}
  40. Okolloh O (2009) Ushahidi, or “testimony”: Web 2.0 tools for crowdsourcing crisis information. Particip Learn Action 59:65–70Google Scholar
  41. Openshaw S (1984) The modifiable areal unit problem. Geo Books, NorwichGoogle Scholar
  42. Parkingson B, Spilker J (1996) Global positioning system: theory and applications. AiaaGoogle Scholar
  43. Pierre Guillibert (2015) FEW nexus resource platform. Accessed 8 Jan 2016
  44. Plant RE, Pettygrove GS, Reinert WR (2000) Precision agriculture can increase profits and limit environmental impacts. Calif Agric 54:66–71. doi: 10.3733/ca.v054n04p66 CrossRefGoogle Scholar
  45. Rabalais NN, Turner RE, Wiseman WJ (2002) Gulf of Mexico hypoxia, A.K.A. “The Dead Zone.”. Annu Rev Ecol Syst 33:235–263. doi: 10.1146/annurev.ecolsys.33.010802.150513 CrossRefGoogle Scholar
  46. Scott C, Pierce S a, Pasqualetti MJ, et al. (2011) Policy and institutional dimensions of the water-energy nexus. Energy Policy 39:6622–6630. doi: 10.1016/j.enpol.2011.08.013 CrossRefGoogle Scholar
  47. Scott CA, Kurian M, Wescoat JL Jr. (2015) The water-energy-food nexus: enhancing adaptive capacity to complex global challenges. Governing the nexus. Springer International Publishing, Switzerland, pp. 15–38.Google Scholar
  48. Shekar S, Chawla S (2003) Spatial databases: a tour. In: Spat. Databases A Tour. Accessed 8 Jan 2016
  49. Shekhar S, Chawla S, Ravada S, et al. (1999) Spatial databases—accomplishments and research needs. Knowl Data Eng IEEE Trans 11:45–55. doi: 10.1109/69.755614 CrossRefGoogle Scholar
  50. Shekhar S, Feiner S, Aref WG (2015a) From GPS and virtual globes to spatial computing—2020. Geoinformatica 19:799–832. doi: 10.1007/s10707-015-0235-9 CrossRefGoogle Scholar
  51. Shekhar S, Feiner SK, Aref WG (2015b) Spatial computing: accomplishments, opportunities, and research needs. Commun ACM 59:72–81. doi: 10.1145/2756547 CrossRefGoogle Scholar
  52. Tobler W (1970) A computer movie simulating urban growth in the Detroit region. Economic geography 234–240Google Scholar
  53. U.S. Dept.of State (2015) Fulbright Water-Energy-Food Nexus Workshop. Accessed 8 Jan 2016
  54. UNU-FLORES (2015) Nexus Observatory Platform Web SiteGoogle Scholar
  55. USGS (2011) USGS Groundwater Watch. Accessed 9 Jan 2016
  56. USGS (1997) U.S. GEOLOGICAL SURVEY Open-File Report 97–121Google Scholar
  57. Vatsavai R, Shekhar S, Burk T, Lime S (2006) UMN-MapServer: a high-performance, interoperable, and open source web mapping and geo-spatial analysis system. Geographic, Information Science, In, pp. 400–417Google Scholar
  58. Webber M (2015) Energy, water and food problems must be solved together. Sci Am 312:5CrossRefGoogle Scholar
  59. Wright BD (2009) The Food Price Crisis of 2007/2008 : FAO food outlook Glob Mark Anal Retrieved 1–11Google Scholar
  60. Zainuddin M, Kiyofuji H, Saitoh K, Saitoh SI (2006) Using multi-sensor satellite remote sensing and catch data to detect ocean hot spots for albacore (Thunnus alalunga) in the northwestern North Pacific. Deep Res Part II Top Stud Oceanogr 53:419–431. doi: 10.1016/j.dsr2.2006.01.007 CrossRefGoogle Scholar
  61. Zhao D, Jiang H, Yang T, et al (2012) Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds. Journal of environmental management 95(1):98–107Google Scholar
  62. Zimmerman DL, Stein M (2010) Classical geostatistical methods. Handbook of spatial statistics, pp. 29–44. Chapman & Hall/CRC, Boca-RatonGoogle Scholar

Copyright information

© AESS 2016

Authors and Affiliations

  • Emre Eftelioglu
    • 1
  • Zhe Jiang
    • 1
  • Reem Ali
    • 1
  • Shashi Shekhar
    • 1
  1. 1.University of MinnesotaMinneapolisUSA

Personalised recommendations