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Knowledge-Based Multicriteria Spatial Decision Support System (MC-SDSS) for Trends Assessment of Settlements Suitability

  • Waleed Lagrab
  • Noura Aknin
Conference paper
First Online:
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)

Abstract

Spatial data mining is the discovery of interesting hidden patterns and characteristics that may be implicitly in spatial databases. This paper aims to produce a descriptive model for examining the suitability in settlements by applying various machine learning techniques to figure out the knowledge discovery in spatial databases (KDSD). The study illustrates the unique hallmark that characterizes the spatial data mining by conducting the data mining algorithms. Moreover, the study presents the importance of spatial data mining and discussed multiple data sets preprocessing, classification functions, clustering and outlier detection in directions supervised learning for extracting classification rules and assessing the local amenity based on rules reliability. The classification accuracy among the three methods of the classifier algorithms (Decision Tree, Rule-Based, and Bayesian) is also compared, thereby determining the most suitable classifier by experiments performance evaluation of the training and test set.

Keywords

SDSS Data mining Classification Suitability analysis Knowledge discovery Educational facility 
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References

  1. 1.
    Liao, S.-H., Chu, P.-H., Hsiao, P.-Y.: Data mining techniques and applications–a decade review from 2000 to 2011. Expert Syst. Appl. 39, 11303–11311 (2012)CrossRefGoogle Scholar
  2. 2.
    Lagrab, W., Aknin, N.: Analysis of educational services distribution-based geographic information system (GIS). Int. J. Sci. Technol. Res. 4, 63–91 (2015)Google Scholar
  3. 3.
    Lagrab, W., Aknin, N.: A suitability analysis of elementary schools - based geographic information system (GIS). J. Theor. Appl. Inf. Technol. 95, 731–742 (2017)Google Scholar
  4. 4.
    CA Dept. of Education: School Site Selection and Approval Guide - Facility Design (California Dept of Education) (2015). http://www.cde.ca.gov/ls/fa/sf/schoolsiteguide.asp
  5. 5.
    Moore, D.P.: Guide for Planning Educational Facilities. Planning Guide. Council of Educational Facility Planners International, Columbus (1991)Google Scholar
  6. 6.
    Kumar, A., Kakkar, A., Majumdar, R., Baghel, A.S.: Spatial data mining: recent trends and techniques. In: 2015 International Conference on Computer and Computational Sciences (ICCCS), pp. 39–43 (2015)Google Scholar
  7. 7.
    Mennis, J., Guo, D.: Spatial data mining and geographic knowledge discovery—an introduction. Comput. Environ. Urban Syst. 33, 403–408 (2009)CrossRefGoogle Scholar
  8. 8.
    Koperski, K., Adhikary, J., Han, J.: Spatial data mining: progress and challenges survey paper. In: Proceedings of ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada, pp. 1–10. Citeseer (1996)Google Scholar
  9. 9.
    Shunzhi, Z., Wenxing, H., Qunyong, W., Maoqing, L.: Research on data mining model of GIS-based urban underground pipeline network. In: 2009 IEEE International Conference on Control and Automation, pp. 1515–1520 (2009)Google Scholar
  10. 10.
    Wang, Y., Chen, X.: Study on land use of changping district with spatial data mining method. In: Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, pp. 218–222 (2011)Google Scholar
  11. 11.
    Kaundinya, D.P., Balachandra, P., Ravindranath, N.H., Ashok, V.: A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: a case study of Tumkur district, India. Energy 52, 77–88 (2013)CrossRefGoogle Scholar
  12. 12.
    Ruiz, M.C., Romero, E., Pérez, M.A., Fernández, I.: Development and application of a multi-criteria spatial decision support system for planning sustainable industrial areas in Northern Spain. Autom. Constr. 22, 320–333 (2012)CrossRefGoogle Scholar
  13. 13.
    Ferretti, V., Montibeller, G.: Key challenges and meta-choices in designing and applying multi-criteria spatial decision support systems. Decis. Support Syst. 84, 41–52 (2016)CrossRefGoogle Scholar
  14. 14.
    Mason, S.O., Baltsavias, E.P., Bishop, I.: Spatial decision support systems for the management of informal settlements. Comput. Environ. Urban Syst. 21, 189–208 (1997)CrossRefGoogle Scholar
  15. 15.
    Bottero, M., Comino, E., Duriavig, M., Ferretti, V., Pomarico, S.: The application of a multicriteria spatial decision support system (MCSDSS) for the assessment of biodiversity conservation in the Province of Varese (Italy). Land Use Policy 30, 730–738 (2013)CrossRefGoogle Scholar
  16. 16.
    Ochola, W.O., Kerkides, P.: An integrated indicator-based spatial decision support system for land quality assessment in Kenya. Comput. Electron. Agric. 45, 3–26 (2004)CrossRefGoogle Scholar
  17. 17.
    Palmisano, G.O., Govindan, K., Boggia, A., Loisi, R.V., De Boni, A., Roma, R.: Local action groups and rural sustainable development. A spatial multiple criteria approach for efficient territorial planning. Land Use Policy 59, 12–26 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Computer Science, Operational Research and Applied Statistics Laboratory, Information Technology and Modeling Systems Research UnitAbdelmalek Essaâdi UniversityTetouanMorocco

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