Main Article Content

Юрий Анатольевич Цыпкин Алексей Львович Шепелев


The article presents discussions on the significance of the sprawling city as a dynamically developing system in the manifestation of urbanization and suburbanization trends. Given the complexity of describing urban dynamics, there is a need for a multifactorial research of urban sprawl development. The transformation of the urban environment requires the involvement of various stakeholders, so it is necessary to touch upon foreign experience in order to develop a relevant approach to multifactor research, and in turn, a model of spatial development of suburbanized areas. Within the review of foreign experiences, the first study presented is the Munich study, which reflects the assessment of complex urban dynamism by engaging economic, social, cultural and environmental dynamism. In turn, Hungarian researchers divided the factors into six classes, calling them personal preference factors, and assigned them a digital form. In this study, the statistical treatment is based on assigning to the forms relative values of scores expressing the accessibility of functional objects in relationship to personal preference factors. Researchers from China developed a model of HUBs that reflects a general conceptual hierarchy of boundaries at the scale of urban settlements. They also proposed a rank-based urban clustering algorithm (RUC), which takes into account the area of urbanized areas and spatial proximity between them. The mentioned studies allow us to interpret the meaning of foreign scientific approach in multifactor study of urban areas and take advantage of foreign scientific experimental methods in preparing adaptable spatial development tools, which together are the first step in developing a model of spatial development of suburbanized areas. Our collective has also introduced a new concept of «ethical urbanism», which reflects the worldview position of «man-city-nature».

Article Details

Agricultural economics and policies


1. Gao, C.; Li, S.; Sun, M.; Zhao, X.; Liu, D. Exploring the Relationship between Urban Vibrancy and Built Environment Using Multi-Source Data: Case Study in Munich. Remote Sens. 2024, 16, 1107.
2. Bennett, M.M.; Smith, L.C. Advances in Using Multitemporal Night-Time Lights Satellite Imagery to Detect, Estimate, and Monitor Socioeconomic Dynamics. Remote Sens. Environ. 2017, 192, pp. 176-197.
3. Wu, C.; Ye, X.; Ren, F.; Wan, Y.; Ning, P.; Du, Q. Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China. PLoS ONE. 2016, 11, e0164553.
4. Yue, Y.; Zhuang, Y.; Yeh, A.G.; Xie, J.Y.; Ma, C.L.; Li, Q.Q. Measurements of POI-Based Mixed Use and Their Relationships with Neighbourhood Vibrancy. Int. J. Geogr. Inf. Sci. 2017, 31, pp. 658-675.
5. Chen, L.; Zhao, L.; Xiao, Y.; Lu, Y. Investigating the Spatiotemporal Pattern between the Built Environment and Urban Vibrancy Using Big Data in Shenzhen, China. Comput. Environ. Urban Syst. 2022, 95, 101827.
6. Huang, H.; Yao, X.A.; Krisp, J.M.; Jiang, B. Analytics of Location-Based Big Data for Smart Cities: Opportunities, Challenges, and Future Directions. Comput. Environ. Urban Syst. 2021, 90, 101712.
7. Open Data Portal of the Ministry of Culture of Russian Federation [Electronic resource]. – Access mode: https://opendata.mkrf.ru/. – Extracted from the screen. – 22, May 2024.
8. Chen, Y.; Yu, B.; Shu, B.; Yang, L.; Wang, R. Exploring the Spatiotemporal Patterns and Correlates of Urban Vitality: Temporal and Spatial Heterogeneity. Sustain. Cities Soc. 2023, 91, 104440.
9. Peter Gyenizce, Gabor Pirisi, Trocsanyi Andras, Szabolcs Czigany. A multi-factor model developed on residents’ opinions for the classification of urban residential areas. ResearchGate. Article in Geografie. 2016, 121, pp. 1-31.
10. Angel, S., Blei, A.M., Parent, J., Lamson-Hall, P., Galarza-Sanchez, N., Civco, D.L., Thom, K., 2016. Atlas of urban expansion – 2016 edition. The NYU Urbanization Project, New York, NY, USA.
11. Zhibang Xu, Limin Jiao, Ting Lan, Zhengzi Zhou, Hao Cui, Chengpeng Li, Gang Xu, Yaolin Liu. Mapping hierarchical urban boundaries for global urban settlements. ScienceDirect. International Journal of Applied Earth Observations and Geoinformation. 2021, 103, 102480.
12. Arino, O., Gross, D., Ranera, F., Leroy, M., Bicheron, P., Brockman, C., Defourny, P., Vancutsem, C., Achard, F., Durieux, L., Bourg, L., Latham, J., Di Gregorio, A., Witt, R., Herold, M., Sambale, J., Plummer, S., Weber, J.-L., 2007. GlobCover: ESA service for global land cover from MERIS. In: 2007 IEEE International Geoscience and Remote Sensing Symposium. Presented at the 2007 IEEE International Geoscience and Remote Sensing Symposium, pp. 2412-2415.
13. He, C., Liu, Z., Gou, S., Zhang, Q., Zhang, J., Xu, L. Detecting global urban expansion over the last three decades using a fully convolutional network. Environ. Res. Lett. 2019, 14, 034008.
14. Schneider, A., Friedl, M.A., Potere, D. Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’. Remote Sens. Environ. 2010, 114, pp. 1733-1746.