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Analysis of Changes in Topological Relations Between Spatial Objects at Different Times

  • Sergey EremeevEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1126)

Abstract

There are many problems with situations in which the relationships between elements change over time. The initial data can be images of some area for a different period of time or from different scales. The solution of these problems is necessary for a detailed analysis of the map. In the article the problem of analysis of topological relations between spatial objects for different periods of time is considered. It is proposed to use the methods of temporal graph theory to present information about the relations between objects taking into account time. A mathematical model for storing information about topological relations is demonstrated. The relationship matrix contains information about the topology of the map for different periods of time. An algorithm for the analysis of unchanged objects for a given period of time is developed. An algorithm to determine the areas of the map that have changed the maximum number of times is also developed. The results of experiments on the division of the map into 4 and 16 sectors are shown. Screenshots of map fragments and matrix of changes of topological connections of temporal graph are given. These algorithms can be used in the modeling of environmental disasters, environmental planning, for the analysis of real estate in municipal GIS.

Keywords

Topological relations Temporal graphs Spatial objects GIS 

Notes

Acknowledgment

The reported study was funded by RFBR and Vladimir region according to the research project No. 17-47-330387.

References

  1. 1.
    Sitanggang, I., Roseli, S., Syaufina, L.: Spatial co-location patterns on weather and forest fire data. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 10(9), 13–20 (2018).  https://doi.org/10.5815/ijitcs.2018.09.02CrossRefGoogle Scholar
  2. 2.
    Mamoria, P., Raj, D.: An analysis of fuzzy and spatial methods for edge detection. Int. J. Inf. Eng. Electron. Bus. (IJIEEB) 8(6), 62–68 (2016)Google Scholar
  3. 3.
    Eremeev, S., Seltsova, E.: Algorithms for topological analysis of spatial data. In: Hu, Z., Petoukhov, S., He, M. (eds.) Advances in Artificial Systems for Medicine and Education II, AIMEE2018 2018. AISC, vol. 902, pp. 81–92. Springer, Cham (2020)Google Scholar
  4. 4.
    Sitanggang, I., Shofiana, D., Sihombing, B.: Hotspot sequence patterns with an improvement in spatial feature. Int. J. Eng. Manuf. (IJEM) 8(6), 13–25 (2018)Google Scholar
  5. 5.
    Eremeev, S., Andrianov, D., Kovalev, Y. Kuptsov, K.: Algorithm for encoding nD spatial objects into GIS. In: Proceedings of the International Conference Information Technology and Nanotechnology. Session Image Processing and Earth Remote Sensing, ITNT-2018, Samara, Russia, pp. 149–155 (2018)Google Scholar
  6. 6.
    Kostakos, V.: Tempotal graphs. Phys. A 6, 1007–1023 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-RNN: deep learning on spatio-temporal graphs. In: CVPR (2016)Google Scholar
  8. 8.
    Chen, X., Liu, Y., Liu, H., Carbonell, J.: Learning spatial-temporal varying graphs with applications to climate data analysis. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI-10, pp. 425–430 (2010)Google Scholar
  9. 9.
    Sridhar, M., Cohn, A.G., Hogg, D.C.: Discovering an event taxonomy from video using qualitative spatio-temporal graphs. In: Coelho, H., Suder, R., Wooldridge, M. (eds.) 19th European Conference on Artificial Intelligence, ECAI 2010, pp. 1103–1104. IOS Press, Lisbon (2010)Google Scholar
  10. 10.
    Erlebach, T., Hoffmann, M., Kammer, F.: On temporal graph exploration. In: Halldórsson, M., Iwama, K., Kobayashi, N., Speckmann, B. (eds.) Automata, Languages, and Programming, ICALP 2015. LNCS, vol. 9134, pp. 444–455. Springer, Heidelberg (2015)Google Scholar
  11. 11.
    Mertzios, G., Michail, O., Spirakis, P.: Temporal network optimization subject to connectivity constraints. Algorithmica 81(4), 1416–1449 (2019)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ferreira, K., de Oliveira, A.G., Monteiro, A, de Almeida, D.B.F.C: Temporal GIS and spatiotemporal data sources. In: Proceedings XVI GEOINFO, pp. 1–13 (2015)Google Scholar
  13. 13.
    Choudhary, K., Boori, M., Kupriyanov, A.: Spatio-temporal analysis through remote sensing and GIS in Moscow region. In: Russia Proceedings of the International conference Information Technology and Nanotechnology. Session Image Processing, Geoinformation Technology and Information Security, pp. 42–46 (2017)Google Scholar
  14. 14.
    Dewan, A.M., Yamaguchi, Y.: Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl. Geogr. 29, 390–401 (2009)CrossRefGoogle Scholar
  15. 15.
    Nocerino, E., Menna, F., Remondino, F.: Multi-temporal analysis of landscapes and urban areas. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B4, pp. 85–90 (2012)CrossRefGoogle Scholar
  16. 16.
    Gebbert, S., Leppelt, T., Pebesma, E.: A topology based spatio-temporal map algebra for big data analysis. Data 4, 86 (2019)CrossRefGoogle Scholar
  17. 17.
    Yuan, L., Yu, Z., Chen, S., Luo, W., Wang, Y., Lü, G.: CAUSTA: clifford algebra based unified spatio temporal analysis. Trans. GIS 14(s1), 59–83 (2010)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Vladimir State UniversityVladimirRussia

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