Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model

Liang, Xun; Liu, Xiaoping; Li, Dan; Zhao, Hui; Chen, Guangzhao

doi:10.1080/13658816.2018.1502441

Cited by 228 publications

(108 citation statements)

References 57 publications

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“…The CA model is a dynamic model with discrete time, space and state [41], and is a powerful method for describing, recognizing and simulating the behavior of complex systems [42]. It includes four basic elements: cellular space, grid size, cell neighborhood and transition rule [43]. The cellular space represents the collection of all cells, each cell having its own attributes (such as land use/cover type, etc.…”

Section: Ca-based Flus Modelmentioning

confidence: 99%

Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

Gong

Guldmann

et al. 2020

Remote Sensing

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Land use/cover change (LUCC) has an important impact on the terrestrial carbon cycle. The spatial distribution of regional carbon reserves can provide the scientific basis for the management of ecosystem carbon storage and the formulation of ecological and environmental policies. This paper proposes a method combining the CA-based FLUS model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to assess the temporal and spatial changes in ecosystem carbon storage due to land-use changes over 1990–2015 in the Qinghai Lake Basin (QLB). Furthermore, future ecosystem carbon storage is simulated and evaluated over 2020–2030 under three scenarios of natural growth (NG), cropland protection (CP), and ecological protection (EP). The long-term spatial variations in carbon storage in the QLB are discussed. The results show that: (1) Carbon storage in the QLB decreased at first (1990–2000) and increased later (2000–2010), with total carbon storage increasing by 1.60 Tg C (Teragram: a unit of mass equal to 1012 g). From 2010 to 2015, carbon storage displayed a downward trend, with a sharp decrease in wetlands and croplands as the main cause; (2) Under the NG scenario, carbon reserves decrease by 0.69 Tg C over 2020–2030. These reserves increase significantly by 6.77 Tg C and 7.54 Tg C under the CP and EP scenarios, respectively, thus promoting the benign development of the regional ecological environment. This study improves our understanding on the impact of land-use change on carbon storage for the QLB in the northeastern Qinghai–Tibetan Plateau (QTP).

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Section: Ca-based Flus Modelmentioning

confidence: 99%

Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

Gong

Guldmann

et al. 2020

Remote Sensing

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“…Compared with other models that depend on historical data of land use over multiple periods, the CA-based FLUS model is based on one phase of the data, which is combined with the various driving factors of natural effects and human interventions. Then, the suitability probability of each land type in the study area can be obtained, the dependence on the multiphase data and the error propagation can be avoided, and the complexity of various land use types under natural effects and human interventions can be processed effectively [38].…”

Section: Introductionmentioning

confidence: 99%

The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China

Liu

Zhang

2019

Sustainability

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The development and evolution of an urban green space system is affected by both natural effects and human intervention. The simulation and prediction of an urban green space system can enhance the foresight of urban planning. In this study, several land use change scenarios of the main urban area of Xuchang City were simulated from 2014 to 2030 based on high-resolution land use data. The layout of each scenario was evaluated using landscape indexes. A Cellular Automata–based method (i.e., future land use simulation, FLUS) was applied to develop the urban green space system, which we combined with urban land use evolution. Using recent data, the FLUS model effectively dealt with the uncertainty and complexity of various land use types under natural and human effects and solved the dependence and error transmission of multiperiod data in the traditional land use simulation process. The root mean square error (RMSE) of probability of the suitability occurrence module and the Kappa coefficient of the overall model simulation accuracy verification index both met accuracy requirements. It was feasible to combine the evolution of the urban green space system with urban land development. Moreover, under the Baseline Scenario, the urban land use layout was relatively scattered, and the urban green space system showed a disordered development trend. The Master Plan Scenario had a compact urban land use layout, and the green space system was characterized by networking and systematization, but it did not consider the service capacity of the green space. The Planning Guidance Scenario introduced constraint conditions (i.e., a spatial development strategy, green space accessibility, and ecological sensitivity), which provided a more intensive and efficient urban space and improved the service function of the green space system layout. Managers and planners can evaluate the urban future land use development mode under different constraints. Moreover, they would be able to adjust the urban planning in the implementation process. This work has transformed the technical nature of the planning work from “static results” to a “dynamic process”.

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“…The methodological innovation of the classical logistic regression model is tested by statistical and spatial analysis methods, and the results verify that the modified regression model can be used more accurately to investigate the driving mechanism of urban expansion in the past and simulate the spatial pattern of urban evolution in the future. 2 of 21 changes [7], economic development [16], industrial level [17], traffic systems [18], resident incomes [19], and land use policy [1,20], which are given close attention universally, generally play more crucial roles in affecting urban growth in the context of rapid urbanization and industrialization.Many studies have focused on analyzing the spatial drivers of urban expansion worldwide from different perspectives and at different spatial scales [21][22][23][24][25][26][27][28][29][30][31][32][33]. Some researchers have collected social and economic panel data of administrative divisions of a city or country and developed quantitative models to analyze the driving forces of urban expansion at the regional scale [21][22][23][24][25].…”

mentioning

confidence: 99%

“…With the development of geographic information systems and remote sensing techniques, other researchers have extracted geospatial data through spatial analysis software and used spatial analysis models to determine the driving forces of urban land expansion at the land patch scale [26]. Some classical methods, such as logistic regression analysis [27], cost-benefit framework analysis [28], space syntax-based analysis [29], and cellular automata (CA)-based analysis [30], are used in related research on drivers of urban land use change. In China, rapid urbanization in cities is partly reflected in statistical data on population increase, gross domestic product (GDP) growth, and transportation and real estate construction, which are led by a series of socioeconomic development planning schemes of the city.…”

mentioning

confidence: 99%

“…Many studies have focused on analyzing the spatial drivers of urban expansion worldwide from different perspectives and at different spatial scales [21][22][23][24][25][26][27][28][29][30][31][32][33]. Some researchers have collected social and economic panel data of administrative divisions of a city or country and developed quantitative models to analyze the driving forces of urban expansion at the regional scale [21][22][23][24][25].…”

mentioning

confidence: 99%

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Analysis of the Driving Forces of Urban Expansion Based on a Modified Logistic Regression Model: A Case Study of Wuhan City, Central China

et al. 2019

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Urban development policies and planning schemes are essential drivers of urban expansion in the contemporary world. However, they are usually investigated by qualitative analysis and it is difficult to use them in spatial analysis models. Within the advancement of technology regarding the geostatistical dataset, this study uses a field strength model to quantify policy-oriented factors and designs a modified logistic regression model to analyze the main drivers of urban expansion by selecting natural environment, socioeconomic development, and especially policy-oriented variables. Wuhan City in central China is taken as an example: the modified model is applied and compared with the classical model, and the driving mechanism of urban expansion in Wuhan from 2006 to 2013 is determined through spatial analysis. The results show that the urban system planning in combination with various anthropologic and environmental factors can be comprehensively quantified and described by the urban field strength. The methodological innovation of the classical logistic regression model is tested by statistical and spatial analysis methods, and the results verify that the modified regression model can be used more accurately to investigate the driving mechanism of urban expansion in the past and simulate the spatial pattern of urban evolution in the future.

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Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model

Cited by 228 publications

References 57 publications

Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China

Analysis of the Driving Forces of Urban Expansion Based on a Modified Logistic Regression Model: A Case Study of Wuhan City, Central China

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