Knowledge-Based Approach to Planning: A Case Study-Based Approach

  • S. SenEmail author
  • J. Shah
  • M. Sohoni


Planning is an anticipatory decision-making process that involves either a risk mitigation or resource allocation problem. Policy developers and enablers are required to interact with resource providers such as civil engineers and water resource engineers and consumers such as agriculturists and farmers in different projects. Such projects can benefit from a knowledge-based approach that uses ideas used in past and other projects based on a hierarchy of concepts and established facts. Domain knowledge extracted from artifacts such as planning documents and data models used in previous projects thus provide useful ontological structures for information sharing and foster evolution of a scientific process in developmental planning. In this paper, we review some knowledge-based paradigms used in spatial planning, identify gaps and redundancies in the current practices, and propose the development of a tool to aid the development and use of ontologies in water resource planning. We hypothesize that such ontologies have a spatial basis and outline a use case-based approach to identify upper-level concepts. The proposed tool and approach are aligned to benefit all stakeholders in the planning process by facilitating information sharing across information communities as well as assimilate knowledge baselines for future projects.


  1. 1.
    Barbier, E.B.: The concept of sustainable economic development. Environ. Conserv. 14(2), 101–110 (1987)CrossRefGoogle Scholar
  2. 2.
    Lintz, G.: The aspect of space in the concept of sustainable development: overview and consequences for research. In: ERSA Conference Papers. European Regional Science Association (1998)Google Scholar
  3. 3.
    Persson, Å., Weitz, N., Nilsson, M.: Follow-up and review of the sustainable development goals: alignment versus internalization. Rev. Eur. Comp. Int. Environ. Law 25(1), 59–68 (2016)CrossRefGoogle Scholar
  4. 4.
    Davoudi, S.: Planning as practice of knowing. Plan. Theor. 14(3), 316–331 (2015)CrossRefGoogle Scholar
  5. 5.
    Friedmann, J.: Planning in the Public Domain: From Knowledge to Action. Princeton University Press (1987)Google Scholar
  6. 6.
    Alexander, E.R.: There is no planning—only planning practices: notes for spatial planning theories. Plan. Theor. 15(1), 91–103 (2016)CrossRefGoogle Scholar
  7. 7.
    Mark, D.M., Smith, B., Tversky, B.: Ontology and geographic objects: an empirical study of cognitive categorization. In: International Conference on Spatial Information Theory. Springer (1999)Google Scholar
  8. 8.
    Demetriou, D., See, L., Stillwell, J.: Expert systems for planning and spatial decision support. In: GeoComputation. CRC Press (2014)Google Scholar
  9. 9.
    Hotelling, H.: Stability in competition. Econ. J. 39(153), 41–57 (1929)CrossRefGoogle Scholar
  10. 10.
    Lösch, A.: The Economics of Location: Translated from the Second rev. German ed. by William H. Woglom with the Assistance of Wolfgang F. Stolper. Yale University Press (1954)Google Scholar
  11. 11.
    Friedmann, J.: A general theory of polarized development. Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL) (1967)Google Scholar
  12. 12.
    Porter, M.E.: Clusters and the New Economics of Competition, vol. 76, Harvard Business Review Boston (1998)Google Scholar
  13. 13.
    Krugman, P.: The Self-Organizing Economy (1996)Google Scholar
  14. 14.
    Fan, W., Treyz, F., Treyz, G.: An evolutionary new economic geography model. J. Reg. Sci. 40(4), 671–695 (2000)CrossRefGoogle Scholar
  15. 15.
    Paulk, M.C., et al.: Capability maturity model, version 1.1. IEEE Softw. 10(4), 18–27 (1993)CrossRefGoogle Scholar
  16. 16.
    Sen, S.: Representing Geospatial Concepts: Activities or Entities? Universal Ontology of Geographic Space: Semantic Enrichment for Spatial Data: Semantic Enrichment for Spatial Data, p. 124 (2012)Google Scholar
  17. 17.
    Du, H., et al.: A method for matching crowd-sourced and authoritative geospatial data. Trans. GIS 21(2), 406–427 (2017)CrossRefGoogle Scholar
  18. 18.
    Tambassi, T.: From a geographical perspective: spatial turn, taxonomies and geo-ontologies. In: The Philosophy of Geo-Ontologies, pp. 27–36. Springer (2018)Google Scholar
  19. 19.
    Sen, S., et al.: Framework of semantic interoperability using geospatial ontologies. J. Geomatics, India, 71–76 (2007)Google Scholar
  20. 20.
    Murgante, B., Scorza, F.: Ontology and spatial planning. In: International Conference on Computational Science and Its Applications. Springer (2011)Google Scholar
  21. 21.
    Zhong, B., et al.: An ontological approach for technical plan definition and verification in construction. Autom. Constr. 55, 47–57 (2015)CrossRefGoogle Scholar
  22. 22.
    Murgante, B., Garramone, V.: Web 3.0 and knowledge management: opportunities for spatial planning and decision making. In: International Conference on Computational Science and Its Applications. Springer (2013)Google Scholar
  23. 23.
    Lanucara, S., et al.: Interoperable sharing and visualization of geological data and instruments: a proof of concept. In: International Conference on Computational Science and Its Applications. Springer (2017)Google Scholar
  24. 24.
    Prasad, P., Mishra, V., Sohoni, M.: Reforming rural drinking water schemes. Econ. Political Wkl 49(19), 59 (2014)Google Scholar
  25. 25.
    Barnes, J., Alatout, S.: Water worlds: introduction to the special issue of social studies of science. Soc. Stud. Sci. 42(4), 483–488 (2012)CrossRefGoogle Scholar
  26. 26.
    Yates, J.S., Harris, L.M., Wilson, N.J.: Multiple ontologies of water: politics, conflict and implications for governance. Environ. Plan. D Soc. Space 35(5), 797–815 (2017)CrossRefGoogle Scholar
  27. 27.
    Honkalaskar, V., Sohoni, M., Bhandarkar, U.: Selection of development agenda with the community by the generation of a shared understanding. J. Rural Commun. Dev. 12(1) (2017)Google Scholar
  28. 28.
    Bittner, T., Donnelly, M., Smith, B.: A spatio-temporal ontology for geographic information integration. Int. J. Geogr. Inf. Sci. 23(6), 765–798 (2009)CrossRefGoogle Scholar
  29. 29.
    Choudhary, V., et al.: Redesigning khardi rural piped water network scheme for sustainability. Technical Report No. TR-CSE-2013-56 (2013). Department of Computer Science and Engineering, IIT BombayGoogle Scholar
  30. 30.
    Hooda, N., Damani, O.: A system for optimal design of pressure constrained branched piped water networks. Proced. Eng. 186, 349–356 (2017)CrossRefGoogle Scholar
  31. 31.
    Storme, T., Witlox, F.: Location‐allocation models. The international encyclopedia of geographyGoogle Scholar
  32. 32.
    Jabareen, Y.: Planning the resilient city: concepts and strategies for coping with climate change and environmental risk. Cities 31, 220–229 (2013)CrossRefGoogle Scholar
  33. 33.
    Aeron, A., et al.: Advancements in flood risk mapping systems: a brief review. i-Manager’s J. Civil Eng. 2(1), 40 (2011)Google Scholar
  34. 34.
    Horrocks, I., et al.: Using semantic technology to tame the data variety challenge. IEEE Int. Comput. 20(6), 62–66 (2016)CrossRefGoogle Scholar
  35. 35.
    Sen, S., et al.: Schema matching as a step towards interoperability: experiments in the Indian NSDI. In: Beyond Spatial Data Infrastructures International Workshop at the GIScience (2006)Google Scholar
  36. 36.
    Baader, F., et al.: Introduction to Description Logic. Cambridge University Press (2017)Google Scholar
  37. 37.
    Elag, M., Goodall, J.L.: An ontology for component-based models of water resource systems. Water Resour. Res. 49(8), 5077–5091 (2013)CrossRefGoogle Scholar
  38. 38.
    Agresta, A., et al.: An ontology framework for flooding forecasting. In: International Conference on Computational Science and Its Applications. Springer (2014)Google Scholar
  39. 39.
    Yang, C., et al.: Utilizing cloud computing to address big geospatial data challenges. Comput. Environ. Urban Syst. 61, 120–128 (2017)CrossRefGoogle Scholar
  40. 40.
    Milz, D., et al.: Reconsidering scale: using geographic information systems to support spatial planning conversations. Plan. Pract. Res., 1–18 (2017)Google Scholar
  41. 41.
    Mark, D., et al.: Ontological foundations for geographic information science. Res. Chall. Geogr. Inf. Sci., 335–350 (2004)Google Scholar
  42. 42.
    Gyawali, B., et al.: Mapping natural language to description logic. In: European Semantic Web Conference. Springer (2017)Google Scholar
  43. 43.
    Wan, S., Angryk, R.A.: Measuring semantic similarity using wordnet-based context vectors. In: IEEE International Conference on Systems, Man and Cybernetics, ISIC. IEEE (2007)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.GISE Lab, Department of Computer Science and EngineeringIndian Institute of Technology BombayMumbaiIndia
  2. 2.Department of Computer Science and EngineeringCentre for Technology Alternatives for Rural Development (CTARA), Indian Institute of Technology BombayMumbaiIndia

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