An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen,China |
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| Institution: | 1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, 210023, China;2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu, 210023, China;3. College of Civil Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China;1. New York University, New York, New York, 10003, Canada;2. University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada;3. George Mason University, Fairfax, VA, 22030, USA;1. Department of Geographical & Sustainability Sciences, University of Iowa, Iowa City, IA 52242, USA;2. Department of Geography, University of North Carolina, Chapel Hill, NC 27599, USA;1. School of Environmental and Rural Studies, Pontificia Universidad Javeriana, Transv. 4° No. 42 - 00, Edificio J. Rafael Arboleda, SJ. Piso 8, Bogotá, Colombia;2. Department of Industrial Engineering, Universidad de Los Andes, Cra 1 Este No 19A – 40, Edificio Mario Laserna, Bogotá, Colombia;3. Department of Civil Engineering, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy;4. Department of Chemistry and Biology, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy;5. Department of Civil Engineering, Pontificia Universidad Javeriana, Cra. 7 No. 40-62, Edificio José Gabriel Maldonado, S.J., Bogotá, Colombia |
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| Abstract: | A forward-looking urban land use plan is crucial to a city’s sustainability, which requires a deep understanding of human-environment interactions between different domains, and modelling them soundly. One of the key challenges of modelling these interactions is to understand and model how human individuals make and develop their location decisions by learning that then shape urban land-use patterns. To investigate this issue, we have constructed an extended experience-weighted attraction learning model to represent the human agents’ learning when they make location decisions. Consequently, we propose and have developed an agent-based learning-embedded model (ABM-learning) for residential land growth simulation that incorporates a learning model, a decision-making model, a land use conversion model and the constraint of urban land use master plan. The proposed model was used for a simulation of the residential land growth in Shenzhen city, China. By validating the model against empirical data, the results showed that the site-specific accuracy of the model has been improved when embedding learning model. The analysis on the simulation accuracies has proved the argument that modelling individual-level learning matters in the agent’s decision model and the agent-based models. We also applied the model to predict residential land growth in Shenzhen from 2015 to 2035, and the result can be a reference for land-use allocation in detailed planning of Shenzhen. The ABM-learning is applicable to studying the past urban growth trajectory, aiding in the formulation of detailed residential land and public service facility planning and assessing the land use planning effectiveness. |
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| Keywords: | Agent-Based modelling Experience-Weighted attraction learning model Residential land growth ABM-learning Shenzhen city |
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