Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Wang, Ruci; Hou, Hao; Murayama, Yuji

doi:10.3390/su10082633

Cited by 30 publications

(17 citation statements)

References 65 publications

(52 reference statements)

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“…The CA-Markov model is a hybrid model combining Markov and cellular automata models. Because of its ability to allocate spatial information of landscape, it has been widely used in land cover modeling and landscape prediction [1,44]. It has already gained an excellent reputation and has a prospective future in several fields, for instance: computer science, mathematics, and geographical science [45,46].…”

Section: Cellular Automata-markov Modelmentioning

confidence: 99%

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

et al. 2020

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Rapid urbanization is one of the most concerning issues in the 21st century because of its significant impacts on various fields, including agriculture, forestry, ecology, and climate. The urban heat island (UHI) phenomenon, highly related to the rapid urbanization, has attracted considerable attention from both academic scholars and governmental policymakers because of its direct influence on citizens’ daily life. Land surface temperature (LST) is a widely used indicator to assess the intensity of UHI significantly affected by the local land use/cover (LULC). In this study, we used the Landsat time-series data to derive the LULC composition and LST distribution maps of Nanjing in 2000, 2014, and 2018. A correlation analysis was carried out to check the relationship between LST and the density of each class of LULC. We found out that cropland and forest in Nanjing are helping to cool the city with different degrees of cooling effects depending on the location and LULC composition. Then, a Cellar Automata (CA)-Markov model was applied to predict the LULC conditions of Nanjing in 2030 and 2050. Based on the simulated LULC maps and the relationship between LST and LULC, we delineated high- and moderate-LST related risk areas in the city of Nanjing. Our findings are valuable for the local government to reorganize the future development zones in a way to control the urban climate environment and to keep a healthy social life within the city.

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Section: Cellular Automata-markov Modelmentioning

confidence: 99%

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

et al. 2020

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“…These conflicts are mainly associated with the occupation of cropland, and ecological security should also receive more attention [114]. Scenario simulation is a good method to cope with the problem of urban sprawl, for it is based on historical land use data which can predict land use status under different development scenarios, and thereby propose how to optimize the quantity and spatial allocation of land resources under specific scenarios [115]. Therefore, GA-LUO can be extended to the field of urbanization research.…”

Section: Discussionmentioning

confidence: 99%

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Ding

Zheng

2021

Land

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Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources.

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“…[11] Zhu Lei et al (2020) made a sensitivity analysis of different parameters based on CA-Markov model for arable land in the oasis area of the Manas River basin [12] ; Wang Lixia et al (2020) used the Weihe River region as the study area, calculated interannual NDVI values based on MODIS data, and used the CA-Markov model to simulate the spatial and temporal variation of NDVI in the region using the rank of NDVI as the metacell type. [13]; Ruci Wang and Hao Hou et al (2019) monitored and simulated changes in farmland dynamics in Hangzhou from 1995 to 2035 [14] . Nanchang, the capital city of Jiangxi Province, is the core city of the Poyang Lake City Cluster, the economic, political and cultural centre of Jiangxi Province, and one of the important central cities in the middle reaches of the Yangtze River.…”

Section: Introductionmentioning

confidence: 99%

Study of urbanization trend prediction in Nanchang based on CA-Markov model

2022

E3S Web Conf.

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In order to promote the healthy development of Nanchang city in the future and maintain the virtuous cycle of urban ecosystem, this paper uses CA-Markov model to simulate and forecast the land use of Nanchang city in 2030 and explore the urbanization process of Nanchang city. Firstly, the land use changes in Nanchang from 2000 to 2020 are described, followed by land use simulations for Nanchang in 2020 as a verification of accuracy, and land use projections for Nanchang in 2030 based on accuracy requirements. The results show that: (1) the rate of urban expansion will continue to be maintained or even gradually increase from 2020 to 2030 under the expansion model based on the period 2000-2020. (2) The main factor in the change of land use types in the built-up area of Nanchang over the past two decades is socio-economic factors, with natural factors taking second place. (3) The area of land development intensity in Nanchang will reach its peak in 2030, and the expansion of the municipal districts will gradually slowdown in favour of Nanchang and Xinjian counties, which have a more extensive land area and are close to the central urban area.

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Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Cited by 30 publications

References 65 publications

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Study of urbanization trend prediction in Nanchang based on CA-Markov model

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