Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.     

Calibration of cellular automata models using differential evolution to simulate present and future land use

A key issue in cellular automata (CA) modeling is the minimization of the differences between the actual and simulated patterns, which can be mathematically formulated as an objective function. We develop a new hybrid model (termed DE‐CA) by integrating differential evolution (DE) into CA to solve the objective function and retrieve the optimal CA parameters. Constrained relations among factors were applied in DE to generate different sets of CA parameters for prediction of future scenarios. The DE‐CA model was calibrated using historical spatial data to simulate 2016 land use in Kunming and predict multiple scenarios to the year 2026. Assessment of quantitative accuracy shows that DE‐CA yields 92.4% overall accuracy, where 6.8% is the correctly captured urban growth; further, the model reported only 5.0% false alarms and 2.6% misses. Regarding the simulation ability, our new CA model performs as well as the widely applied genetic algorithm‐based CA model, and outperforms both the logistic regression‐based CA model and a no‐change NULL model. We projected three possible scenarios for the year 2026 using DE‐CA to adequately address the baseline urban growth, environmental protection and urban planning to show the strong prediction ability of the new model.     

A Patch‐based Cellular Automaton for Simulating Land‐use Changes at Fine Spatial Resolution

While cellular automata have become popular tools for modeling land‐use changes, there is a lack of studies reporting their application at very fine spatial resolutions (e.g. 5 m resolution). Traditional cell‐based CA do not generate reliable results at such resolutions because single cells might only represent components of land‐use entities (i.e. houses or parks in urban residential areas), while recently proposed entity‐based CA models usually ignore the internal heterogeneity of the entities. This article describes a patch‐based CA model designed to deal with this problem by integrating cell and object concepts. A patch is defined as a collection of adjacent cells that might have different attributes, but that represent a single land‐use entity. In this model, a transition probability map was calculated at each cell location for each land‐use transition using a weight of evidence method; then, land‐use changes were simulated by employing a patch‐based procedure based on the probability maps. This CA model, along with a traditional cell‐based model were tested in the eastern part of the Elbow River watershed in southern Alberta, Canada, an area that is under considerable pressure for land development due to its proximity to the fast growing city of Calgary. The simulation results for the two models were compared to historical data using visual comparison, K_simulation indices, and landscape metrics. The results reveal that the patch‐based CA model generates more compact and realistic land‐use patterns than the traditional cell‐based CA. The K_simulation values indicate that the land‐use maps obtained with the patch‐based CA are in higher agreement with the historical data than those created by the cell‐based model, particularly regarding the location of change. The landscape metrics reveal that the patch‐based model is able to adequately capture the land‐use dynamics as observed in the historical data, while the cell‐based CA is not able to provide a similar interpretation. The patch‐based approach proposed in this study appears to be a simple and valuable solution to take into account the internal heterogeneity of land‐use classes at fine spatial resolutions and simulate their transitions over time.     

A multi‐label cellular automata model for land change simulation

The use of cellular automata (CA) has for some time been considered among the most appropriate approaches for modeling land‐use changes. Each cell in a traditional CA model has a state that evolves according to transition rules, taking into consideration its own and its neighbors’ states and characteristics. Here, we present a multi‐label CA model in which a cell may simultaneously have more than one state. The model uses a multi‐label learning method—a multi‐label support vector machine, Rank‐SVM—to define the transition rules. The model was used with a multi‐label land‐use dataset for Luxembourg, built from vector‐based land‐use data using a method presented here. The proposed multi‐label CA model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land‐use patterns. Applied to historical land use data, the proposed model estimated the land use change with an accuracy of 87.2% exact matching and 98.84% when including cells with a misclassification of a single label, which is comparably better than a classical multi‐class model that achieved 83.6%. The multi‐label cellular automata outperformed a model combining CA and artificial neural networks. All model goodness‐of‐fit comparisons were quantified using various performance metrics for predictive models.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models

This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern.     

Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata

This study evaluates the effects of cellular automata (CA) with different neighborhood sizes on the predictive performance of the Land Transformation Model (LTM). Landsat images were used to extract urban footprints and the driving forces behind urban growth seen for the metropolitan areas of Tehran and Isfahan in Iran. LTM, which uses a back-propagation neural network, was applied to investigate the relationships between urban growth and the associated drivers, and to create the transition probability map. To simulate urban growth, the following two approaches were implemented: (a) the LTM using a top-down approach for cell allocation grounding on the highest values in the transition probability map and (b) a CA with varying spatial neighborhood sizes. The results show that using the LTM-CA approach increases the accuracy of the simulated land use maps when compared with the use of the LTM top-down approach. In particular, the LTM-CA with a 7 × 7 neighborhood size performed well and improved the accuracy. The level of agreement between simulated and actual urban growth increased from 58% to 61% for Tehran and from 39% to 43% for Isfahan. In conclusion, even though the LTM-CA outperforms the LTM with a top-down approach, more studies have to be carried out within other geographical settings to better evaluate the effect of CA on the allocation phase of the urban growth simulation.     

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

This article proposes a grey wolf optimizer (GWO) and cellular automata (CA) integrated model for the simulation and spatial optimization of urban growth. A new grey wolf‐inspired approach is put forward to determine the urban growth rules of CA cells by using the GWO algorithm, which is suitable for solving optimization problems. The inspiration for GWO comes from the social leadership of wolf groups, as well as their hunting behavior. The GWO‐optimized urban growth rules for CA describe the relationship between the spatial variables and the urban land‐use status for each cell in the formation of “if–then.” The GWO algorithm and CA model are then integrated as the GWO–CA model for urban growth simulation and optimization. By taking Nanjing City as an example, the simulation accuracy in terms of urban cells is 86.6%, and the kappa coefficient is 0.715, indicating that the GWO algorithm is efficient at obtaining urban growth rules from spatial variables. The validation of the GWO–CA model also illustrates that it performs well in terms of the simulation and spatial optimization of urban growth, and can further contribute to urban planning and management.     

Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng,China

Simulations of intra-urban land use changes have gradually attracted more attention as these approaches are extremely helpful in regard to decision making and policy formulation. While prior studies mostly focused on methods of developing intra-urban level simulations, very little research has been conducted explain the factors driving intra-urban land use change. Urban planners are highly concerned with how inner-city structures are formed and how they function. Here, to simulate multiple intra-urban land use changes and to identify the contribution of different driving factors, we developed a random forests (RF) algorithm-based cellular automata (CA) simulation model. In this study, the model applied diverse categories of spatial variables, including traffic location factors, environmental factors, public services, and population density, as the driving factors to enhance our understanding of the dynamics of internal urban land use. The CA model was tested using data from the Huicheng district of Huizhou city in the Guangdong province of China. The Model was validated using actual historical land use data from 2000 to 2010. By applying the validated model, multiple intra-urban land use maps were simulated for 2015. Simultaneously, spatial variable importance measures (VIMs) were calculated by using the out-of-bag (OOB) error estimation approach of the RF algorithm. Based on the calculation results, we assessed and analysed the significance of each intra-urban land use driver for this region. This study provides urban planners and relevant scholars with detailed and targeted information that can aid in the formulation of specific planning strategies for different intra-urban land uses and support the future evolution of this area.     

Enhancing a GIS Cellular Automata Model of Land Use Change: Bayesian Networks, Influence Diagrams and Causality

Cellular Automata (CA) models at present do not adequately take into account the relationship and interactions between variables. However, land use change is influenced by multiple variables and their relationships. The objective of this study is to develop a novel CA model within a geographic information system (GIS) that consists of Bayesian Network (BN) and Influence Diagram (ID) sub‐models. Further, the proposed model is intended to simplify the definition of parameter values, transition rules and model structure. Multiple GIS layers provide inputs and the CA defines the transition rules by running the two sub‐models. In the BN sub‐model, land use drivers are encoded with conditional probabilities extracted from historical data to represent inter‐dependencies between the drivers. Using the ID sub‐model, the decision of changing from one land use state to another is made based on utility theory. The model was applied to simulate future land use changes in the Greater Vancouver Regional District (GVRD), Canada from 2001 to 2031. The results indicate that the model is able to detect spatio‐temporal drivers and generate various scenarios of land use change making it a useful tool for exploring complex planning scenarios.     

Cellular Automata Based Model of Urban Spatial Growth

In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) model for simulating future urban growth of an Indian city. In the model remote sensing data and GIS were used to provide the empirical data about urban growth while Markov chain process was used to predict the amount of land required for future urban use based on the empirical data. Multi-criteria evaluation (MCE) technique was used to reveal the relationships between future urban growth potential and site attributes of a site. Finally using the CA model, land for future urban development was spatially allocated based on the urban suitability image provided by MCE, neighbourhood information of a site and the amount of land predicted by Markov chain process. The model results were evaluated using Kappa Coefficient and future urban growth was simulated using the calibrated model     

Assessing the effect of temporal dynamics on urban growth simulation: Towards an asynchronous cellular automata

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models.     

Modelling future impacts of urban development in Kuwait with the use of ABM and GIS

During the last six decades, Kuwait has experienced rapid and unprecedented population growth with only a small increase in the urban areas. The alarming rise in urban density in Kuwait has caused issues for the residents' lifestyles, the economy and the environment. These issues have been aggravated by urban planning which perpetuated a city‐centric urban form without modelling the impacts of current patterns of urban growth. A spatial model using Agent Based Modelling (ABM) and Geographical Information Systems (GIS) is proposed to model disaggregate future changes in land‐use patterns given forecast population estimates and planning policies. The two main impacts considered are housing shortage and traffic congestion, as these are the two most significant social impacts for Kuwaitis. This article discusses the design methodology and parameterization of the ABM and the agent groups. It characterizes urban growth by rules for different citizen groups, historical growth patterns and the influence of decision‐makers. The model is validated against data for the period 1995‐2015 and simulations run to 2050; the results predict that continued city‐centric growth will aggravate the problems, with more than 50% increase in housing shortage and congestion unless the government intervenes to rectify the situation.     

Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan,China

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.     

Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years. The most evident implication of urbanization is the physical growth of cities. We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China. We present new spatial measures that describe ‘urban land growth types’ and ‘fluctuations in urban land growth’ within the monitoring time span with a temporal interval of five-year steps. We evaluate the correlations between urban land growth rates and socioeconomic data. Results show that (1) distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter, e.g. coastal areas exhibit the most dramatic growth rates; (2) the spatial distribution characteristics of ‘urban land growth types’ and ‘fluctuations in urban land growth’ follow similar spatial patterns across China, e.g. significant differences exist between the eastern region and other regions; and (3) a moderate correlation exists between urban area growth rate and urban population growth rate at an R² of 0.37. By contrast, we determine no significant correlation between urban area growth rate and tertiary industry value growth rate.     

土地利用变化模拟模型及应用研究进展

元胞自动机CA(Cellular Automata)和多智能体ABM(Agent-Based Model)模型是土地利用格局和演化模拟的主流方法,两者在模拟自然因素影响和人文驱动机制方面具有突出优势,为LUCC研究提供了重要的工具。当前,ABM无论在模型构建还是应用研究方面,CA和ABM均取得了显著进展。论文从数据基础、模拟尺度、CA转换规则挖掘、ABM行为规则定义、CA和ABM的耦合4个方面梳理土地利用模拟模型和方法的研究进展。并总结这些模型在虚拟城市模拟与理论验证、真实城市模拟与规划预测以及多类用地模拟与辅助决策等方面的应用。最后,总结土地利用模拟模型在精细模拟和全球变化研究方面存在的局限性,认为未来发展将主要集中于解决从2维模型向3维模型发展、大数据与规则精细挖掘以及大尺度模拟与知识迁移等问题。     

Evaluation and development of sustainable urban land use plans through spatial optimization

Along with rapid global urbanization, cities are challenged by environmental risks and resource scarcity. Sustainable urban planning is central to address the dilemma of economic growth and ecosystem protection, where the use of land is critical. Sustainable land use patterns are spatially explicit in nature, and can be structured and addressed using spatial optimization integrating GIS and mathematical models. This research discusses prominent sustainability concerns in land use planning and suggests a generalized multi‐objective spatial optimization model to facilitate conventional planning. The model is structured to meet land use demand while satisfying the requirements of the physical environment, society and economy. Unlike existing work relying on raster data, due to its simple data structure and ease of spatial relationship evaluation, this research develops an approach for identifying land use solutions based on vector data that better reflects the actual shape and spatial layout of land parcels as well as the ways land use information is managed in practice. An evolutionary algorithm is developed to find the set of efficient (Pareto) solutions given the complexity of vector‐based representations of space. The proposed approach is applied in an empirical study of Dafeng, China in order to support local urban growth and development. The results demonstrate that spatial optimization can be a powerful tool for deriving effective and efficient land use planning strategies. A comparison to results using a raster data approach supports the superiority of land use optimization using vector data as part of planning practice.     

一种基于机器学习的城市发展模拟元胞模型

利用元胞自动机(Cellular Automata,CA)模拟土地利用变化,已经成为认识和理解其复杂动态演化过程的有效手段.传统的元胞自动机基于线性转换规则,较难表达土地利用变化的非线性边界问题.本文研究利用最小二乘支持向量机方法(LS-SVM),将原空间下的非线性可分问题,通过高斯径向基核函数映射到高维特征空间,简化...     

基于FLUS模型的土地利用变化模拟与预测方法研究

模拟和预测土地利用演变过程是规划者把握城市扩张趋势,从而确定更合理的城市用地布局的重要途径之一,对指导国土空间规划具有重要意义.研究基于CA原理改进的FLUS模型,通过耦合GeoSOS-FLUS及ArcGIS软件,从2011年土地利用数据中获取元胞转换概率,模拟了2018年土地利用变化情况.模拟精度较高,证明选取的模拟...     

Integrating the system dynamic and cellular automata models to predict land use and land cover change

Land use and land cover change (LULCC) is a widely researched topic in related studies. A number of models have been established to simulate LULCC patterns. However, the integration of the system dynamic (SD) and the cellular automata (CA) model have been rarely employed in LULCC simulations, although it allows for combining the advantages of each approach and therefore improving the simulation accuracy. In this study, we integrated an SD model and a CA model to predict LULCC under three future development scenarios in Northern Shanxi province of China, a typical agro-pastoral transitional zone. The results indicated that our integrated approach represented the impacts of natural and socioeconomic factors on LULCC well, and could accurately simulate the magnitude and spatial pattern of LULCC. The modeling scenarios illustrated that different development pathways would lead to various LULCC patterns. This study demonstrated the advantages of the integration approach for simulating LULCC and suggests that LULCC is affected to a large degree by natural and socioeconomic factors.