Analyzing Time-Dependent Infrastructure Optimization Based on Geographic Information System Technologies

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 600)


There exist different reasons for infrastructure providers to think about upcoming changes and necessary adaptations. This paper covers the experiences made during a three-year research project (called SinOptiKom) during the development of a geographic information system supported tool for analyzing time-dependent infrastructure optimization results. Beside the data preparation and requirements for the successful implementation of such a tool, the resulting design decisions are presented. Examples for the use and combination of common techniques (such as semantic zooming or highlighting) as well as important usability aspects are explained and will greatly support future research in the domain of infrastructure optimization.


Geographical information Systems Infrastructure Optimization analysis Transformation visualization Time-Dependent visualization 



The work in this paper has been funded by the German Federal Ministry of Education and Research (BMBF, project “SinOptiKom”, 033W009A).


  1. 1.
    Baron, S., Kaufmann Alves, I., Schmitt, T.G., Thielen, C.: Optimization of transformation processes of drainage systems in rural areas. In: 13th International Conference on Urban Drainage (2014)Google Scholar
  2. 2.
    Baron, S., Hoek, J., Kaufmann Alves, I., Herz, S.: Comprehensive scenario management of sustainable spatial planning and urban water services. Water Sci. Technol. 73(5), 1041–1051 (2016)Google Scholar
  3. 3.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of 1996 IEEE Symposium on Visual Languages, pp. 336–343 (1996)Google Scholar
  4. 4.
    Baron, S., Kaufmann Alves I., Schmitt, T.G., Schöffel, S., Schwank, J.: Cross-sectoral optimization and visualization of transformation processes in urban water infrastructures in rural areas. In: Proceedings IWA World Water Congress 2015. IWA Publishing (2015)Google Scholar
  5. 5.
    Roberts, J.C.: State of the art: coordinated and multiple views in exploratory visualization. In: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization, Zurich, pp. 61–71 (2007)Google Scholar
  6. 6.
    Jern, M., Franzen, J.: “GeoAnalytics’’ - exploring spatio-temporal and multivariate data. In: Conference on Information Visualisation, IV 2006, London, pp. 25–31 (2006)Google Scholar
  7. 7.
    Chambers, J., Cleveland, W., Kleiner, B., Tukey, P.: Graphical Methods for Data Analysis, pp. 158–162. Wadsworth, Pacific Grove (1983)MATHGoogle Scholar
  8. 8.
    Saary, M.: Radar plots: a useful way for presenting multivariate health care data. J. Clin. Epidemiol. 61(4), 311–317 (2008)CrossRefGoogle Scholar
  9. 9.
    Perlin, K., Fox, D.: Pad: an alternative approach to the computer interface. In: Proceedings of SIGGRAPH 1993, pp. 57–64. ACM, New York (1993)Google Scholar
  10. 10.
  11. 11.
    National Geographic Society: Geographic Information System (GIS). Accessed 9 Mar 2017
  12. 12.
    Thapa, R.B., Murayama, Y.: Land evaluation for peri-urban agriculture using analytical hierarchical process and geographic information system techniques: a case study of Hanoi. Land Use Pol. 25(2), 225–239 (2008)CrossRefGoogle Scholar
  13. 13.
    Weng, Q.: Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J. Environ. Manag. 64(3), 273–284 (2002)CrossRefGoogle Scholar
  14. 14.
    McKee, K.T., Shields, T.M., Jenkins, P.R., Zenilman, J.M., Glass, G.E.: Application of a geographic information system to the tracking and control of an outbreak of Shigellosis. Clin. Infect. Dis. 31(3), 728–733 (2000)CrossRefGoogle Scholar
  15. 15.
    Aloquili, O., Elbanna, A., Al-Azizi, A.: Automatic vehicle location tracking system based on GIS environment. IET Softw. 3(4), 255–263 (2009)CrossRefGoogle Scholar
  16. 16.
    Truong, L.T., Somenahalli, S.V.: Using GIS to identify pedestrian-vehicle crash hot spots and unsafe bus stops. J. Public Transp. 14(1), 6 (2011)CrossRefGoogle Scholar
  17. 17.
    Li, L., Zhu, L., Sui, D.Z.: A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes. J. Transp. Geogr. 15(4), 274–285 (2007)CrossRefGoogle Scholar
  18. 18.
    Tran, P., Shaw, R., Chantry, G., Norton, J.: GIS and local knowledge in disaster management: a case study of flood risk mapping in Viet Nam. Disasters 33(1), 152–169 (2009)CrossRefGoogle Scholar
  19. 19.
    Zerger, A., Smith, D.I.: Impediments to using GIS for real-time disaster decision support. Comput. Environ. Urban Syst. 27(2), 123–141 (2003)CrossRefGoogle Scholar
  20. 20.
    Cova, T.J.: GIS in emergency management. Geogr. Inf. Syst. 2, 845–858 (1999)Google Scholar
  21. 21.
    Broesamle, H., Mannstein, H., Schillings, C., Trieb, F.: Assessment of solar electricity potentials in North Africa based on satellite data and a geographic information system. Sol. Energy 70(1), 1–12 (2001)CrossRefGoogle Scholar
  22. 22.
    Smith, T.M., Lakshmanan, V.: Utilizing Google Earth as a GIS platform for weather applications. In: 22nd International Conference on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology (2006)Google Scholar
  23. 23.
    Schmitt, T.G., Worreschk, S., Kaufmann Alves, I., Herold, F., Thielen, C.: An optimization and decision support tool for long-term strategies in the transformation of urban water infrastructure. In: Conference on Hydro informatics, NYC, USA (2014)Google Scholar
  24. 24.
  25. 25.
  26. 26.
  27. 27.
  28. 28.
  29. 29.
  30. 30.
    GRASS – Geographic Resources Analysis Support System.
  31. 31.
    GeoServer – Open Source Server for sharing geospatial data.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Computer Graphics and HCIUniversity of KaiserslauternKaiserslauternGermany

Personalised recommendations