Collaborative optimization of rural residential land consolidation and urban construction land expansion: A case study of Huangpi in Wuhan, China

Abstract The synergetic development of urban and rural construction land is always an important issue. We propose a collaborative optimization model (COMRU) of rural residential land consolidation and urban construction land expansion, which is a coupling model of cellular automata (CA), genetic algorithms (GA), and the Lewis turning point theory. This model regards the rural population transfer as a scenario and generates a new quantity and space allocation system for the population and the land-use types in the study area. The optimized result will balance the development of urban and rural construction lands to ultimately reduce the income gap between urban and rural areas and promote the rationality of the spatial distribution of urban and rural construction lands. We applied COMRU to Huangpi District in the city of Wuhan, the capital of Hubei Province, People's Republic of China and obtained three important results: (1) After optimization, the scattered rural settlements were effectively consolidated and large amounts of land resources were released, thereby supplementing cultivated and urban construction lands; (2) The urban–rural income ratio decreased significantly, indicating a considerable reduction in the income gap between the urban and rural areas; (3) The structure and function of the construction lands were improved, leading to the improved equity of urban and rural public services. The final space optimization allocation program generated by COMRU provides a reference for the sequence of rural settlement consolidation and urban spatial planning.

[1]  Hong S. He,et al.  Imperviousness Change Analysis Tool (I-CAT) for simulating pixel-level urban growth , 2014 .

[2]  Mark A. Friedl,et al.  Urbanization and the loss of prime farmland: a case study in the Calgary–Edmonton corridor of Alberta , 2015, Regional Environmental Change.

[3]  Yan-sui Liu,et al.  Community-based rural residential land consolidation and allocation can help to revitalize hollowed villages in traditional agricultural areas of China: Evidence from Dancheng County, Henan Province , 2014 .

[4]  Yongnian Zeng,et al.  Simulating multi-objective land use optimization allocation using Multi-agent system—A case study in Changsha, China , 2016 .

[5]  Yaolin Liu,et al.  Urban Growth Modeling Based on a Game between Farmers and Governments: Case Study of Urban Fringe in Wuhan, Hubei Province in China , 2016 .

[6]  Xia Li,et al.  Combining system dynamics and hybrid particle swarm optimization for land use allocation , 2013 .

[7]  Paul H. Calamai,et al.  Evolutionary Multi-objective Optimization for landscape system design , 2011, J. Geogr. Syst..

[8]  Chen Zeng,et al.  Simultaneously simulate vertical and horizontal expansions of a future urban landscape: a case study in Wuhan, Central China , 2017, Int. J. Geogr. Inf. Sci..

[9]  Xiaoping Liu,et al.  A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas , 2012, Int. J. Geogr. Inf. Sci..

[10]  H. Jianhua,et al.  Rural land use spatial allocation in the semiarid loess hilly area in China: Using a Particle Swarm Optimization model equipped with multi-objective optimization techniques , 2012 .

[11]  M. Küchler,et al.  Urban growth along motorways in Switzerland , 2010 .

[12]  Wei Tang,et al.  A game-theory based agent-cellular model for use in urban growth simulation: A case study of the rapidly urbanizing Wuhan area of central China , 2015, Comput. Environ. Urban Syst..

[13]  Lu Tian,et al.  Identifying the urban-rural fringe using wavelet transform and kernel density estimation: A case study in Beijing City, China , 2016, Environ. Model. Softw..

[14]  Thomas Blaschke,et al.  Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS , 2013 .

[15]  W. Adams,et al.  Managing Tragedies: Understanding Conflict over Common Pool Resources , 2003, Science.

[16]  Liu Yansui,et al.  The process and driving forces of rural hollowing in China under rapid urbanization , 2010 .

[17]  Yanfang Liu,et al.  A Land-use Spatial Allocation Model Based on Modified Ant Colony Optimization , 2014 .

[18]  M. Batty,et al.  Modeling urban dynamics through GIS-based cellular automata , 1999 .

[19]  Wei Tang,et al.  A land-use spatial optimization model based on genetic optimization and game theory , 2015, Comput. Environ. Urban Syst..

[20]  Shaowen Wang,et al.  Sustainable land use optimization using Boundary-based Fast Genetic Algorithm , 2012, Comput. Environ. Urban Syst..

[21]  Daniel G. Brown,et al.  Knowledge-informed Pareto simulated annealing for multi-objective spatial allocation , 2007, Comput. Environ. Urban Syst..

[22]  B. Pijanowski,et al.  Urban expansion and its consumption of high-quality farmland in Beijing, China , 2015 .

[23]  K. Seto,et al.  From Bangalore to the Bay Area: Comparing transportation and activity accessibility as drivers of urban growth , 2009 .

[24]  Yaolin Liu,et al.  Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi, Central China , 2016 .

[25]  F. Creutzig,et al.  Future urban land expansion and implications for global croplands , 2016, Proceedings of the National Academy of Sciences.

[26]  Wenwu Tang,et al.  Optimal rural land use allocation in central China: Linking the effect of spatiotemporal patterns and policy interventions , 2017 .

[27]  Keith C. Clarke,et al.  Loose-Coupling a Cellular Automaton Model and GIS: Long-Term Urban Growth Prediction for San Francisco and Washington/Baltimore , 1998, Int. J. Geogr. Inf. Sci..

[28]  Yaolin Liu,et al.  Regional land-use allocation with a spatially explicit genetic algorithm , 2014, Landscape and Ecological Engineering.

[29]  Roger White,et al.  Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns , 1993 .

[30]  J. Gareth Polhill,et al.  Agent-based land-use models: a review of applications , 2007, Landscape Ecology.

[31]  A. Rompaey,et al.  Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flanders–Brussels region , 2009 .

[32]  Lin Xiao,et al.  Simulation of urban expansion and encroachment using cellular automata and multi-agent system model—A case study of Tianjin metropolitan region, China , 2016 .

[33]  Yan-sui Liu,et al.  Urban-rural transformation in relation to cultivated land conversion in China : Implications for optimizing land use and balanced regional development , 2015 .

[34]  Theodor J. Stewart,et al.  A genetic algorithm approach to multiobjective land use planning , 2004, Comput. Oper. Res..

[35]  Suzana Dragicevic,et al.  Modeling-in-the-middle: bridging the gap between agent-based modeling and multi-objective decision-making for land use change , 2011, Int. J. Geogr. Inf. Sci..

[36]  N. Grimm,et al.  Global Change and the Ecology of Cities , 2008, Science.

[37]  G. Heuvelink,et al.  Using Linear Integer Programming for Multi-Site Land-Use Allocation , 2003 .

[38]  Francisco F. Rivera,et al.  High performance genetic algorithm for land use planning , 2013, Comput. Environ. Urban Syst..

[39]  Jay M. Rosenberger,et al.  Mixed integer linear programming approaches for land use planning that limit urban sprawl , 2016, Comput. Ind. Eng..

[40]  Andrés Manuel García,et al.  Cellular automata models for the simulation of real-world urban processes: A review and analysis , 2010 .

[41]  Yan-sui Liu,et al.  Rural restructuring in China , 2016 .

[42]  Jean-Christophe Castella,et al.  Combining top-down and bottom-up modelling approaches of land use/cover change to support public policies: Application to sustainable management of natural resources in northern Vietnam , 2007 .

[43]  Yaolin Liu,et al.  Regional land-use allocation using a coupled MAS and GA model: from local simulation to global optimization, a case study in Caidian District, Wuhan, China , 2014 .

[44]  Xin Li,et al.  An improved Genetic Algorithm for spatial optimization of multi-objective and multi-site land use allocation , 2016, Comput. Environ. Urban Syst..

[45]  Suzana Dragicevic,et al.  Modeling urban growth using a variable grid cellular automaton , 2009, Comput. Environ. Urban Syst..

[46]  Yansui Liu,et al.  Progress of research on urban-rural transformation and rural development in China in the past decade and future prospects , 2016, Journal of Geographical Sciences.

[47]  Yan-sui Liu,et al.  Accelerated restructuring in rural China fueled by 'increasing vs. decreasing balance' land-use policy for dealing with hollowed villages , 2012 .

[48]  Wenli Chen,et al.  Optimal land use allocation of urban fringe in Guangzhou , 2012, Journal of Geographical Sciences.

[49]  K. Overmars,et al.  Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model , 2009, Landscape Ecology.

[50]  Yan-sui Liu,et al.  Key issues of land use in China and implications for policy making , 2014 .

[51]  Jing Ma,et al.  A new approach for urban-rural fringe identification: Integrating impervious surface area and spatial continuous wavelet transform , 2018, Landscape and Urban Planning.

[52]  Y. Wei,et al.  Modeling spatial variations of urban growth patterns in Chinese cities: The case of Nanjing , 2009 .

[53]  Yuheng Li,et al.  Urban‐rural interaction in China: historic scenario and assessment , 2011 .

[54]  T. Ramachandra,et al.  Urban sprawl: metrics, dynamics and modelling using GIS , 2004 .