journal article·2025-09-24Open access

Simulation of Urban Sprawl Factors in Medium-Scale Metropolitan Areas Using a Cellular Automata-Based Model: The Case of Erzurum, Turkey

Şennur Arınç Akkuş, Ahmet Tortum, Dilan Kılıç

Applied Sciences· Volume 15· Issue 19

Abstract

Urban development is the planned growth of cities that takes into account ecological issues, the needs of urban life, social and technical equipment standards, and quality of life. However, as a result of policies implemented by decision-makers and users, both planned and unplanned, urban space is expanding spatially outwards from the city, while also experiencing densification in vacant areas within the city and functional transformations in land use. This process, known as urban sprawl, has been intensely debated over the past century. Making the negative effects of urban sprawl measurable and understandable from a scientific perspective is critically important for sustainable urban planning and management. Transportation surfaces hold a significant share in the land use patterns of expanding cities in physical space, and accessibility is one of the main driving forces behind land use change. Therefore, the most significant consequence of urban sprawl is the increase in urban mobility, which is shaped by the needs of urban residents to access urban functions. This increase poses risk factors for the planning period in terms of time, cost, and especially environmental impact. Urban space has a dynamic and complex structure. Planning is based on being able to guess how this structure will change over time. At first, geometric models were used to study cities, but as time went on and the network of relationships became more complicated, more modern and technological methods were needed. Artificial Neural Networks, Support Vector Machines, Agent-Based Models, Markov Chain Models, and Cellular Automata, developed using computer-aided design technologies, can be cited as examples of these approaches. In this study, the temporal change in urban sprawl and its relationship with influencing factors will be revealed using the SLEUTH model, which is one of the cellular automata-based urban simulation models. Erzurum, one of the medium-sized metropolitan cities that gained importance after the conversion of provincial borders into municipal borders with the Metropolitan Law No. 6360, has been selected as the case study area for this research. The urban sprawl process and determining factors of Erzurum will be analyzed using the SLEUTH model. By creating a simulation model of the current situation within the specified time periods and generating future scenarios, the aim is to develop planning decisions with sustainable, ecological, and optimal size and density values.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Land Use and Ecosystem Services · Urban Design and Spatial Analysis · Remote Sensing and Land Use

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Civil Engineering, Atatürk University, 25000 Erzurum, Turkey

Funding

Scientific Research Projects Coordination Unit (BAP) of Ataturk University

References (39)

Show source-provided references
1. Batty, M. (2007). Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals, MIT Press.
3. Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal10.1016/S0198-9715(01)00014-X ↗
4. The size, scale, and shape of cities10.1126/science.1151419 ↗
5. Pacione, M. (2009). Urban Geography: A Global Perspective, Routledge.10.4324/9780203881927 ↗
6. Urban sprawl: Diagnosis and remedies10.1177/016001700761012710 ↗
7. Is Los Angeles-style sprawl desirable?10.1080/01944369708975728 ↗
8. Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore10.1080/136588198241617 ↗
9. Hensher, D.A., and Button, K.J. (2004). Overview of land use transport models. Transport Geography and Spatial Systems, Elsevier.10.1108/9781615832538 ↗
10. Alonso, W. (1964). Location and Land Use, Harvard University Press.10.4159/harvard.9780674730854 ↗
11. Anselin, L. (1988). Spatial Econometrics: Methods and Models, Kluwer Academic Publishers.10.1007/978-94-015-7799-1 ↗
12. GIS and transportation: Status and challenges
13. Statistical mechanics of cellular automata10.1103/RevModPhys.55.601 ↗
14. Cellular automata as models of complexity10.1038/311419a0 ↗
15. Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns10.1068/a251175 ↗
16. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area10.1068/b240247 ↗
17. Methods and techniques for rigorous calibration of cellular automata models of urbanization
18. Multi-agent systems for the simulation of land-use and land-cover change: A review10.1111/1467-8306.9302004 ↗
19. Neural-network-based cellular automata for simulating multiple land-use changes using GIS10.1080/13658810210137004 ↗
20. Calibration of cellular automata by using neural networks for the simulation of complex urban systems10.1068/a33210 ↗
21. Eastman, J.R. (2016). TerrSet Geospatial Monitoring and Modeling System (Software Manual), Clark Labs, Clark University.
22. Assessing the suitability of the SLEUTH cellular automata model for capturing heterogeneous urban growth in developing cities: A case study in Northern Nigeria10.1016/j.heliyon.2024.e36504 ↗
23. A user-friendly assessment of six commonly used urban growth models
24. (2025, September 18). World Cities Report 2016: Urbanization and Development—Emerging Futures. Available online: https://unhabitat.org/world-cities-report-2016.
25. Futures of global urban expansion: Uncertainties and implications for biodiversity conservation10.1088/1748-9326/8/1/014025 ↗
26. Turner, B.L., Clark, W.C., Kates, R.W., Richards, J.F., Mathews, J.T., and Meyer, W.B. (2015). Land-use/land-cover change. The Earth as Transformed by Human Action, Cambridge University Press.
27. Seto, K.C., and Reenberg, A. (2014). Rethinking Global Land Use in an Urban Era, MIT Press.10.7551/mitpress/9780262026901.001.0001 ↗
28. Deep learning for urban land use category classification: A review and experimental assessment10.1016/j.rse.2024.114290 ↗
29. Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore–Washington metropolitan area10.1068/b2983 ↗
30. Fujita, M., and Thisse, J.F. (2013). Economics of Agglomeration, Cambridge University Press.10.1017/CBO9781139051552 ↗
31. Paddison, R. (2001). Urbanization, Suburbanization, Counterurbanization and Reurbanization. Handbook of Urban Studies, SAGE Publications.10.4135/9781848608375 ↗
32. Batty, M. (2018). Inventing Future Cities, MIT Press.10.7551/mitpress/11923.001.0001 ↗
33. Chakraborty, A., Sikder, S., Omrani, H., and Teller, J. (2022). Cellular Automata in Modeling and Predicting Urban Densification: Revisiting the Literature since 1971. Land, 11.10.3390/land11071113 ↗
34. (2025, September 18). HGM, Available online: https://www.harita.gov.tr/.
35. (2025, September 18). TÜIK, Available online: https://data.tuik.gov.tr.
36. (2025, September 18). Project Gigalopolis. Available online: https://www.ncgia.ucsb.edu/projects/gig.
37. Machine Models of Self-Reproduction10.1090/psapm/014/9961 ↗
38. Land suitability and urban growth modeling: Development of SLEUTH-Suitability10.1016/j.compenvurbsys.2020.101475 ↗
39. Application of Geo-Spatial Techniques and Cellular Automata for Modelling Urban Growth of a Heterogeneous Urban Fringe