METHODS FOR CALCULATION THE SPATIAL DISTRIBUTION OF THE TERRITORY MEMBERSHIP TO THE URBANIZED BASED ON THE HYBRID NEURAL NETWORK

Authors

  • Bogdan Gats Bukovinian University, Faculty of Information Technologies and Economics, Department of Computer Systems and Technologies, Chernivtsi
  • Yaroslav Vyklyuk Bukovinian University, Faculty of Information Technologies and Economics, Department of Computer Systems and Technologies, Chernivtsi; Wenzhou University, College of Mechanical and Electrical Engineering, Institute of Laser and Optoelectronic Intelligent Manufacturing, Wenzhou
  • Artur Horbovyy National University of the State Fiscal Service of Ukraine, Institute of Information Technologies, Irpin

DOI:

https://doi.org/10.2298/IJGI2002115G

Keywords:

spatial distribution of the territory membership to the urbanized, GIS, fuzzy logic

Abstract

This paper presents the method for calculation of the spatial distribution of the territory membership to the urbanized (TMU)—the value that puts the degree of suitability for development (tourism development) in accordance with each node of the grid of the study area within (0, 1). The method is based on a hybrid neural network training. It allows us to assess all kinds of territories for tourist infrastructure development capabilities with relation to attractiveness and provide results visualization on the geographical information system (GIS) maps. The developed algorithm of picking and conversion of geospatial data from GIS for knowledge base generation uses attractors coordinates (vectors of roads, a city center, a railway station) and random points of the explored area, makes possible distance calculation between them (on the road to the attractor and Manhattan distance) and following conversion to the ASCII file, that allows unifying input parameters of the set of models for forecasting development of tourist infrastructure objects. The paper studied typical tourist towns of the Ukrainian Carpathians.

Article metrics

References

Batty, M., & Longley, P. (1994). Fractal Cities: A Geometry of Form and Function. London, UK: Academic Press.

Chen, W., Panahi, M., Khosravi, K., Pourghasemi, H. R., Rezaie, F., & Parvinnezhad, D. (2019). Spatial Prediction of Groundwater Potentiality Using ANFIS Ensembled With Teaching-Learning-Based and Biogeography-Based Optimization. Journal of Hydrology, 572, 435–448. https://doi.org/10.1016/j.jhydrol.2019.03.013

Crols, T., Vanderhaegen, S., Canters, F., Engelen, G., Poelmans, L., Uljee, I., & White, R. (2017). Downdating High-Resolution Population Density Maps Using Sealed Surface Cover Time Series. Landscape and Urban Planning, 160, 96–106. https://doi.org/10.1016/j.landurbplan.2016.12.009

Deng, Z., Zhu, X., He, Q., & Tang, L. (2019). Land Use/Land Cover Classification Using Time Series Landsat 8 Images in a Heavily Urbanized Area. Advances in Space Research, 63(7), 2144–2154. https://doi.org/10.1016/j.asr.2018.12.005

El-Hallaq, M. A., & Habboub, M. O. (2015). Using Cellular Automata-Markov Analysis and Multi Criteria Evaluation for Predicting the Shape of the Dead Sea. Advances in Remote Sensing, 4(1), 83–95. https://doi.org/10.4236/ars.2015.41008

Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The Spatiotemporal Form of Urban Growth: Measurement, Analysis and Modeling. Remote Sensing of Environment, 86(3), 286–302. https://doi.org/10.1016/S0034-4257(03)00075-0

Koohpayma, J., Tahooni, A., Jelokhani-Niaraki, M., & Jokar Arsanjani, J. (2019). Spatial Analysis of Curb-Park Violations and Their Relationship with Points of Interest: A Case Study of Tehran, Iran. Sustainability, 11(22), 6336. https://doi.org/10.3390/su11226336

Lu, D., Li, L., Li, G., Fan, P., Ouyang, Z., & Moran, E. (2018). Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles. Remote Sensing, 10(7), 1101. https://doi.org/10.3390/rs10071101

Munteanu, C., Radeloff, V., Griffiths, P., Halada, L., Kaim, D., Knorn, J., . . . Stych, P. (2017). Land Change in the Carpathian Region Before and After Major Institutional Changes. In G. Gutman & V. Radeloff (Eds.), Land-Cover and Land-Use Changes in Eastern Europe after the Collapse of the Soviet Union in 1991 (pp. 57–90). https://doi.org/10.1007/978-3-319-42638-9_4

Ontiveros-Robles, E., Melin, P., & Castillo, O. (2019). Comparative Analysis of Type-1 Fuzzy Inference Systems with Different Sugeno Polynomial Orders Applied to Diagnosis Problems. In R. B. Kearfott, I. Batyrshin, M. Reformat, M. Ceberio, & V. Kreinovich (Eds.), Fuzzy Techniques: Theory and Applications (pp. 453–465). https://doi.org/10.1007/978-3-030-21920-8_41

Ragagnin Pimentel, M., & Pereira, M. de L. (2020). Tourism Spatial Distribution in Brazil: A Recent Assessment Using Thematic Cartography. Journal of Hospitality and Tourism Insights, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JHTI-03-2019-0051

Romão, J., Kourtit, K., Neuts, B., & Nijkamp, P. (2018). The Smart City as a Common Place for Tourists and Residents: A Structural Analysis of the Determinants of Urban Attractiveness. Cities, 78, 67–75. https://doi.org/10.1016/j.cities.2017.11.007

Serasinghe Pathiranage, I. S., Kantakumar, L. N., & Sundaramoorthy, S. (2018). Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning. Chinese Geographical Science, 28(2), 274–286. https://doi.org/10.1007/s11769-018-0946-6

Shafizadeh-Moghadam, H., Tayyebi, A., & Helbich, M. (2017). Transition Index Maps for Urban Growth Simulation: Application of Artificial Neural Networks, Weight of Evidence and Fuzzy Multi-Criteria Evaluation. Environmental Monitoring and Assessment, 189(6), 300. https://doi.org/10.1007/s10661-017-5986-3

Vyklyuk, Y. (2009). Crystal's fractal growth patterns in the fuzzy potential field for prognostication of socio-economic processes. Information Technology for Economics & Management, 5(1), 1. Retrieved from http://www.item.woiz.polsl.pl/issue5.1/pdf/Crystalsfractalgrowthpatterninthefuzzypotentialfieldforprognosticationofsocioeconomicprocesses.pdf

White, R., & Engelen, G. (1993). Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns. Environment and Planning A, 25(8), 1175–1199. https://doi.org/10.1068/a251175

Witten, T. A., & Sander, L. M. (1983). Diffusion-limited Aggregation. Physical Review B, 27(9), 5686–5697. https://doi.org/10.1103/PhysRevB.27.5686

Wu, F. (2002). Calibration of Stochastic Cellular Automata: The Application to Rural-Urban Land Conversions. International Journal of Geographical Information Science, 16(8), 795–818. https://doi.org/10.1080/13658810210157769

Yao, Y., Li, X., Liu, X., Liu, P., Liang, Z., Zhang, J., & Mai, K. (2017). Sensing Spatial Distribution of Urban Land Use by Integrating Points-Of-Interest and Google Word2Vec Model. International Journal of Geographical Information Science, 31(4), 825–848. https://doi.org/10.1080/13658816.2016.1244608

Yu, W., Ai, T., He, Y., & Shao, S. (2017). Spatial Co-Location Pattern Mining of Facility Points-Of-Interest Improved by Network Neighborhood and Distance Decay Effects. International Journal of Geographical Information Science, 31(2), 280–296. https://doi.org/10.1080/13658816.2016.1194423

Downloads

Published

2020-10-10

How to Cite

Gats, B., Vyklyuk, Y., & Horbovyy, A. (2020). METHODS FOR CALCULATION THE SPATIAL DISTRIBUTION OF THE TERRITORY MEMBERSHIP TO THE URBANIZED BASED ON THE HYBRID NEURAL NETWORK. Journal of the Geographical Institute “Jovan Cvijić” SASA, 70(2), 115–128. https://doi.org/10.2298/IJGI2002115G

Issue

Vol. 70 No. 2 (2020): Journal of the Geographical Institute “Jovan Cvijić” SASA

Section

Original scientific article

License

Copyright (c) 2020 Journal of the Geographical Institute “Jovan Cvijić” SASA

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.