Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, Estonia
From 1990 to 2018, built-up areas in Tallinn, Estonia’s capital city, increased by 25.03%, while its population decreased by −10.19%. Investigating the factors affecting urban expansion and modeling it are critical steps to detect future expansion trends and plan for a more sustainable environment....
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Urban Science |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2413-8851/5/4/85 |
| _version_ | 1797500102221234176 |
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| author | Najmeh Mozaffaree Pour Tõnu Oja |
| author_facet | Najmeh Mozaffaree Pour Tõnu Oja |
| author_sort | Najmeh Mozaffaree Pour |
| collection | DOAJ |
| description | From 1990 to 2018, built-up areas in Tallinn, Estonia’s capital city, increased by 25.03%, while its population decreased by −10.19%. Investigating the factors affecting urban expansion and modeling it are critical steps to detect future expansion trends and plan for a more sustainable environment. Different models have been used to investigate, predict, and simulate urban expansion in recent years. In this paper, we coupled the cellular automata, agent-based, and Markov models (CA–Agent model) in a novel manner to address the complexity of the dynamic simulation, generate heterogeneity in space, define more complicated rules, and employ the suitability analysis. In the CA–Agent model, cells are dynamic agents, and the model’s outcome emerges from cellular agents’ interactions over time using the rules of behavior and their decisions concerning the adjacent neighboring cells and probabilities of spatial changes. We performed the CA–Agent model run two times for 2018 and 2030. The first simulated results were used to validate the performance of the model. Kappa showed 0.86, indicating a relatively high model fit, so we conducted the second 12-year run up to the year 2030. The results illustrated that using these model parameters, the overall built-up areas will reach 175.24 sq. km with an increase of 30.25% in total from 1990 to 2030. Thus, implementing the CA–Agent model in the study area illustrated the temporal changes of land conversion and represented the present spatial planning results requiring regulation of urban expansion encroachment on agricultural and forest lands. |
| first_indexed | 2024-03-10T03:57:04Z |
| format | Article |
| id | doaj.art-682d3d5ae76d4d2084de24d8ba4b72ac |
| institution | Directory Open Access Journal |
| issn | 2413-8851 |
| language | English |
| last_indexed | 2024-03-10T03:57:04Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Urban Science |
| spelling | doaj.art-682d3d5ae76d4d2084de24d8ba4b72ac2023-11-23T10:53:11ZengMDPI AGUrban Science2413-88512021-11-01548510.3390/urbansci5040085Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, EstoniaNajmeh Mozaffaree Pour0Tõnu Oja1Department of Geography, Institute of Ecology and Earth Sciences, Faculty of Science and Technology, University of Tartu, Vanemuise 46, 50410 Tartu, EstoniaDepartment of Geography, Institute of Ecology and Earth Sciences, Faculty of Science and Technology, University of Tartu, Vanemuise 46, 50410 Tartu, EstoniaFrom 1990 to 2018, built-up areas in Tallinn, Estonia’s capital city, increased by 25.03%, while its population decreased by −10.19%. Investigating the factors affecting urban expansion and modeling it are critical steps to detect future expansion trends and plan for a more sustainable environment. Different models have been used to investigate, predict, and simulate urban expansion in recent years. In this paper, we coupled the cellular automata, agent-based, and Markov models (CA–Agent model) in a novel manner to address the complexity of the dynamic simulation, generate heterogeneity in space, define more complicated rules, and employ the suitability analysis. In the CA–Agent model, cells are dynamic agents, and the model’s outcome emerges from cellular agents’ interactions over time using the rules of behavior and their decisions concerning the adjacent neighboring cells and probabilities of spatial changes. We performed the CA–Agent model run two times for 2018 and 2030. The first simulated results were used to validate the performance of the model. Kappa showed 0.86, indicating a relatively high model fit, so we conducted the second 12-year run up to the year 2030. The results illustrated that using these model parameters, the overall built-up areas will reach 175.24 sq. km with an increase of 30.25% in total from 1990 to 2030. Thus, implementing the CA–Agent model in the study area illustrated the temporal changes of land conversion and represented the present spatial planning results requiring regulation of urban expansion encroachment on agricultural and forest lands.https://www.mdpi.com/2413-8851/5/4/85urban expansionCA–Agent modelcellular agentadjacent neighborhoodMarkovian probability transition |
| spellingShingle | Najmeh Mozaffaree Pour Tõnu Oja Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, Estonia Urban Science urban expansion CA–Agent model cellular agent adjacent neighborhood Markovian probability transition |
| title | Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, Estonia |
| title_full | Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, Estonia |
| title_fullStr | Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, Estonia |
| title_full_unstemmed | Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, Estonia |
| title_short | Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, Estonia |
| title_sort | urban expansion simulated by integrated cellular automata and agent based models an example of tallinn estonia |
| topic | urban expansion CA–Agent model cellular agent adjacent neighborhood Markovian probability transition |
| url | https://www.mdpi.com/2413-8851/5/4/85 |
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