Published December 29, 2021 | Version v1
Journal article Open

A fuzzy approach for determining the cognitive spatial location of an object in geographical information system

Creators

  • 1. Odessa State Environmental University

Description

The work is devoted to the problem of interpretation of fuzzy semantics of cognitive descriptions of spatial relations in natural language and their visualization in a geographic information system (GIS). The solution to the problem of determining the fuzzy spatial location of an object based on vague descriptions of the observer in natural language is considered. The task is relevant in critical situations when there is no way to report the exact coordinates of the observed object, except by describing its location relative to the observer itself. Such a situation may be the result of a crime, terrorist act or natural disaster. An observer who finds itself at the scene transmits a text message, which is a description of the location of the object or place (for example, the crime scene, the location of dangerous objects, the crash site). The semantics of the spatial location of the object can be further extracted from the text message.

The proposed fuzzy approach is based on the formalization of the observer's phrases, with which it can describe spatial relations, in the form of a set of linguistic variables that determine the direction and distance to the object. Examples of membership functions for linguistic variables are given.

The spatial knowledge base is built on the basis of the phrases of observers and their corresponding fuzzy regions. Algorithms for constructing cognitive regions in GIS have been developed. Methods of their superposition to obtain the final fuzzy location of the object are proposed. An example of the implementation of a fuzzy model for identifying cognitive regions based on vague descriptions of several observers, performed using developed Python scripts integrated into ArcGIS 10.5, is considered.

Files

A fuzzy approach for determining the cognitive spatial location of an object in geographical information system.pdf

Files (684.7 kB)

Additional details

References

  • Goodchild, M., Egenhofer, M. J., Fegeas, R., Kottman, C. (Eds.) (1999). Interoperating Geographic Information Systems. Springer, 509. doi: https://doi.org/10.1007/978-1-4615-5189-8
  • Zhang, J., Goodchild, M. F. (2002). Uncertainty in Geographical Information. CRC Press, 288. doi: https://doi.org/10.1201/b12624
  • Fisher, P., Cheng, T., Wood, J. (2007). Higher Order Vagueness in Geographical Information: Empirical Geographical Population of Type n Fuzzy Sets. GeoInformatica, 11 (3), 311–330. doi: https://doi.org/10.1007/s10707-006-0009-5
  • Xiang, J. (2021). An intelligent computing and control model of topological relation between spatial objects based on fuzzy theory. Journal of Physics: Conference Series, 1948 (1), 012012. doi: https://doi.org/10.1088/1742-6596/1948/1/012012
  • Liu, Y., Yuan, Y., Gao, S. (2019). Modeling the Vagueness of Areal Geographic Objects: A Categorization System. ISPRS International Journal of Geo-Information, 8 (7), 306. doi: https://doi.org/10.3390/ijgi8070306
  • Kuznichenko, S., Buchynska, I., Kovalenko, L., Gunchenko, Y. (2019). Suitable Site Selection Using Two-Stage GIS-Based Fuzzy Multi-criteria Decision Analysis. Advances in Intelligent Systems and Computing, 214–230. doi: https://doi.org/10.1007/978-3-030-33695-0_16
  • Kuznichenko, S., Kovalenko, L., Buchynska, I., Gunchenko, Y. (2018). Development of a multi­criteria model for making decisions on the location of solid waste landfills. Eastern-European Journal of Enterprise Technologies, 2 (3 (92)), 21–30. doi: https://doi.org/10.15587/1729-4061.2018.129287
  • Towards Platial Joins and Buffers in Place-Based GIS (2013). Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place - COMP '13. doi: https://doi.org/10.1145/2534848.2534856
  • Blaschke, T., Merschdorf, H., Cabrera-Barona, P., Gao, S., Papadakis, E., Kovacs-Györi, A. (2018). Place versus Space: From Points, Lines and Polygons in GIS to Place-Based Representations Reflecting Language and Culture. ISPRS International Journal of Geo-Information, 7 (11), 452. doi: https://doi.org/10.3390/ijgi7110452
  • Scheider, S., Hahn, J., Weiser, P., Kuhn, W. (2018). Computing with cognitive spatial frames of reference in GIS. Transactions in GIS, 22 (5), 1083–1104. doi: https://doi.org/10.1111/tgis.12318
  • Talmy, L. (1983). How Language Structures Space. Spatial Orientation, 225–282. doi: https://doi.org/10.1007/978-1-4615-9325-6_11
  • Li, T. J.-J., Sen, S., Hecht, B. (2014). Leveraging Advances in Natural Language Processing to Better Understand Tobler's First Law of Geography. Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 513–516. doi: https://doi.org/10.1145/2666310.2666493
  • VoPham, T., Hart, J. E., Laden, F., Chiang, Y.-Y. (2018). Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology. Environmental Health, 17 (1). doi: https://doi.org/10.1186/s12940-018-0386-x
  • Lipinski, J., Schneegans, S., Sandamirskaya, Y., Spencer, J. P., Schöner, G. (2012). A neurobehavioral model of flexible spatial language behaviors. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38 (6), 1490–1511. doi: https://doi.org/10.1037/a0022643
  • Hahn, J., Fogliaroni, P., Frank, A. U., Navratil, G. (2016). A Computational Model for Context and Spatial Concepts. Lecture Notes in Geoinformation and Cartography, 3–19. doi: https://doi.org/10.1007/978-3-319-33783-8_1
  • Shi, W., Liu, K. (2004). Modeling Fuzzy Topological Relations Between Uncertain Objects in a GIS. Photogrammetric Engineering & Remote Sensing, 70 (8), 921–929. doi: https://doi.org/10.14358/pers.70.8.921
  • Liu, K., Shi, W. (2009). Quantitative fuzzy topological relations of spatial objects by induced fuzzy topology. International Journal of Applied Earth Observation and Geoinformation, 11 (1), 38–45. doi: https://doi.org/10.1016/j.jag.2008.06.001
  • Yan, Y., Feng, C.-C., Wang, Y.-C. (2016). Utilizing fuzzy set theory to assure the quality of volunteered geographic information. GeoJournal, 82 (3), 517–532. doi: https://doi.org/10.1007/s10708-016-9699-x
  • Du, S., Qin, Q., Wang, Q., Li, B. (2005). Fuzzy Description of Topological Relations I: A Unified Fuzzy 9-Intersection Model. Advances in Natural Computation, 1261–1273. doi: ttps://doi.org/10.1007/11539902_161
  • Sozer, A., Yazici, A., Oguztuzun, H. (2015). Indexing Fuzzy Spatiotemporal Data for Efficient Querying: A Meteorological Application. IEEE Transactions on Fuzzy Systems, 23 (5), 1399–1413. doi: https://doi.org/10.1109/tfuzz.2014.2362121
  • Cheng, H. (2016). Modeling and querying fuzzy spatiotemporal objects. Journal of Intelligent & Fuzzy Systems, 31 (6), 2851–2858. doi: https://doi.org/10.3233/jifs-169167
  • Guo, J., Shao, X. (2017). A fine fuzzy spatial partitioning model for line objects based on computing with words and application in natural language spatial query. Journal of Intelligent & Fuzzy Systems, 32 (3), 2017–2032. doi: https://doi.org/10.3233/jifs-161616
  • Wang, X., Du, S., Feng, C.-C., Zhang, X., Zhang, X. (2018). Interpreting the Fuzzy Semantics of Natural-Language Spatial Relation Terms with the Fuzzy Random Forest Algorithm. ISPRS International Journal of Geo-Information, 7 (2), 58. doi: https://doi.org/10.3390/ijgi7020058
  • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning – II. Information Sciences, 8 (4), 301–357. doi: https://doi.org/10.1016/0020-0255(75)90046-8
  • Xu, J., Pan, X. (2020). A Fuzzy Spatial Region Extraction Model for Object's Vague Location Description from Observer Perspective. ISPRS International Journal of Geo-Information, 9 (12), 703. doi: https://doi.org/10.3390/ijgi9120703
  • Karpinski, M., Kuznichenko, S., Kazakova, N., Fraze-Frazenko, O., Jancarczyk, D. (2020). Geospatial Assessment of the Territorial Road Network by Fractal Method. Future Internet, 12 (11), 201. doi: https://doi.org/10.3390/fi12110201
  • Malczewski, J. (2000). On the Use of Weighted Linear Combination Method in GIS: Common and Best Practice Approaches. Transactions in GIS, 4 (1), 5–22. doi: https://doi.org/10.1111/1467-9671.00035