Development of SLEUTH-Density for the simulation of built-up land density

Urban growth is a complex spatio-temporal phenomenon that includes built-up activities taking place both horizontally and vertically. The built-up land density in a city is a function of land desirability and suitability of a location in terms of the quality of the available public services, access to infrastructure, neighborhood type, vibrancy of socio-economic and cultural characteristics. The simulation of built-up land density may help planning and development authorities in making better land developmental decisions, making appropriate provisions for services, long term land use planning, and allocation of natural resources. In the recent past, various efforts have been made to assess built-up density in terms of the densification of built-up activities at the city scale in terms of floor space indices, building density, residential density (number of housing units per hectare) and urban volume. However, only limited research was reported on the simulation and prediction of spatially distributed built-up land density. In the present study, an algorithm is developed to simulate the spatial distribution of built-up land density as a function of a set of selected urbanization explanatory variables. Its application has been demonstrated using a newly developed version of the SLEUTH model (SLEUTH-Density) to simulating the built-up land density for Ajmer city in India. Development of SLEUTH-Density included designing a density algorithm, writing the programming code, integrating the code with the existing SLEUTH model, and testing the algorithm. The model results were validated indirectly using few built-up land density indices and directly through field verification, which were found to be in good agreement with the simulated built-up land density from SLEUTH-Density     

Calibrating SLEUTH with big data: Projecting California's land use to 2100

This study investigated the spatial consistency of the SLEUTH urban growth and land use change model using a massive data set. The research asks whether SLEUTH can yield both a reliable forecast of land use in the state of California for the year 2100 CE, and an assessment of the forecast's reliability. Data were prepared, and SLEUTH calibrated for 174 tiles made by partitioning the data within the 6 California State Plane Zones. A null hypothesis that all data divisions of California would give similar calibration outcomes so that a uniform simulated rate of growth would apply to statewide future simulations was proven false by mapping and Moran's I values. Spatial autocorrelation was found to propagate forward into the SLEUTH forecasts, resulting in major differences within the state in land use change and change rates. We also explored the spatial distribution of the rules that changed pixels between land use classes, finding that almost 99% of forecast growth in California comes from outward spread from new and existing settlements. The paper concludes with an examination of the uncertainty inherent within, and displayed by the SLEUTH forecasts.     

Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area

Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development. This study aimed to enhance the capability of the SLEUTH model, a cellular automaton model that is commonly used to measure and forecast urban growth and LULCC, by embedding an urban suitability surface from geographically weighted logistic regression (GWLR). Moreover, to examine the performance of the loosely-coupled GWLR-SLEUTH model, a layer with only water bodies excluded and a layer combining the former with an urban suitability surface from logistic regression (LR) were also used in SLEUTH in separate model calibrations. This study was applied to the largest metropolitan area in central China, the Wuhan metropolitan area (WMA). Results show that the integrated GWLR-SLEUTH model performed better than either the traditional SLEUTH model or the LR-SLEUTH model. Findings demonstrate that spatial nonstationarity existed in the drivers' impacts on the urban expansion in the study area and that terrain, transportation and socioeconomic factors were the major drivers of urban expansion in the study area. Finally, with the optimal calibrated parameter sets from the GWLR-SLEUTH model, an urban land forecast from 2017 to 2035 was conducted under three scenarios: 1) business as usual; 2) under future planning policy; and 3) ecologically sustainable growth. Findings show that future planning policy may promise a more sustainable urban development if the plan is strictly obeyed. This study recommended that spatial heterogeneity should be taken into account in the process of land change modeling and the integrated model can be applied to other areas for further validation and forecasts.     

Satellite and ground-based microclimate and hydrologic analyses coupled with a regional urban growth model

Urban development is shown to induce predictable changes in satellite-based measures of radiant surface temperature and evapotranspiration fraction—as long as certain features of the development are known. Specifically, the vegetation changes that accompany the development and the initial climatic state of the land parcel must be noted. Techniques are also developed for quantifying the effects of urbanization on the surface hydrology at a watershed scale. Streamflow and precipitation data are related graphically in order to determine a watershed's general ratio of stormwater runoff to rainfall, along with any changes in the ratio over time. Four distinct runoff responses, separated by season and antecedent moisture conditions, are distinguishable for a particular basin, with the response during the non-summer months under typical antecedent moisture conditions the most representative of and responsive to land-use patterns. This particular runoff response can be estimated from satellite-derived land cover patterns and certain physical attributes of a basin. These satellite-based microclimate and hydrologic analyses are coupled with an existing urban growth model (SLEUTH). The SLEUTH urban growth model simulates future development scenarios for a region of interest. The resulting changes in urban land use lead to the evolution of site-specific climate and hydrology based on the scheme that is presented in this paper. This study, as well as related tools and bodies of knowledge, can be used to broaden the scientific basis behind land-use management decisions.     

Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)

The concentration of people in densely populated urban areas, especially in developing countries like India and China, calls for the use of sophisticated monitoring systems, like remote sensing and Geographical Information Systems (GIS). Time series of land use/cover changes can easily be generated using sequential satellite images, which are required for the prediction of urban growth, verification of growth model outputs, estimation of impervious area, parameterization of various hydrological models, water resources planning and management and environmental studies. In the present work, urban growth of Ajmer city (India) in the last 29 years has been studied at mid‐scale level (5–25 m). Remote sensing and GIS have been used to extract the information related to urban growth, impervious area and its spatial and temporal variation. Statistical classification approaches have been used to derive the land use information from satellite images of eight years (1977–2005). The Shannon's entropy and landscape metrics (patchiness and map density) are computed in order to quantify the urban form (impervious area) in terms of spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative factors. Results reveal that land development (200%) in Ajmer is more than three times the population growth (59%). Shannon's entropy and landscape metrics has revealed the spatial distribution of the sprawl.     

基于SLEUTH模型的城市增长模拟研究——以长沙市为例

元胞自动机具有模拟二维空间演化过程的能力,并且在时间上也是离散的,因此具有很强的时空动态性,它的这种特性使其模拟城市增长具有明显的优势。SLEUTH模型是元胞自动机模型的一种具体实现形式,通过介绍该模型的增长参数、增长规则以及校正过程,提出了可提高模拟精度的增长参数与增长规则改进方法。最后基于GIS方法和遥感数据源,将该模型应用于长沙市,重建了1996~2005年长沙市历史城市增长过程,预测了2010年长沙市城市增长情景,证明了SLEUTH模型模拟城市增长的可行性。     

Assessments of urban growth in the Tampa Bay watershed using remote sensing data

Urban development has expanded rapidly in the Tampa Bay area of west-central Florida over the past century. A major effect associated with this population trend is transformation of the landscape from natural cover types to increasingly impervious urban land. This research utilizes an innovative approach for mapping urban extent and its changes through determining impervious surfaces from Landsat satellite remote sensing data. By 2002, areas with subpixel impervious surface greater than 10% accounted for approximately 1800 km², or 27 percent of the total watershed area. The impervious surface area increases approximately three-fold from 1991 to 2002. The resulting imperviousness data are used with a defined suite of geospatial data sets to simulate historical urban development and predict future urban and suburban extent, density, and growth patterns using SLEUTH model. Also examined is the increasingly important influence that urbanization and its associated imperviousness extent have on the individual drainage basins of the Tampa Bay watershed.     

Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal

The SLEUTH model (slope, landuse, exclusion, urban extent, transportation and hillshade), formerly called the Clarke Cellular Automaton Urban Growth Model, was developed for and tested on various cities in North America, including Washington, DC, and San Francisco. In contrast, this research calibrated the SLEUTH model for two European cities, the Portuguese metropolitan areas of Lisbon and Porto. The SLEUTH model is a cellular automaton model, developed with predefined growth rules applied spatially to gridded maps of the cities in a set of nested loops, and was designed to be both scaleable and universally applicable. Urban expansion is modeled in a modified two-dimensional regular grid. Maps of topographic slope, land use, exclusions, urban extents, road transportation, and a graphic hillshade layer form the model input. This paper examines differences in the model's behavior when the obviously different environment of a European city is captured in the data and modeled. Calibration results are included and interpreted in the context of the two cities, and an evaluation of the model's portability and universality of application is made. Questions such as scalability, sequential multistage optimization by automated exploration of model parameter space, the problem of equifinality, and parameter sensitivity to local conditions are explored. The metropolitan areas present very different spatial and developmental characteristics. The Lisbon Metropolitan Area (the capital of Portugal) has a mix of north Atlantic and south Mediterranean influences. Property is organized in large patches of extensive farmland comprised of olive and cork orchards. The urban pattern of Lisbon and its environs is characterized by rapid urban sprawl, focused in the urban centers of Lisbon, Oeiras, Cascais Setúbal, and Almada, and by intense urbanization along the main road and train lines radiating from the major urban centers. The Porto Metropolitan Area is characterized by a coastal Atlantic landscape. The urban pattern is concentrated among the main nuclei (Porto and Vila Nova de Gaia) and scattered among many small rural towns and villages. There are very small isolated patches of intensive agriculture and pine forests in a topography of steep slopes. These endogenous territorial characteristics go back in time to the formation of Portugal — with a “Roman-Visigod North” and an “Arabic South” [Firmino, 1999 (Firmino, A., 1999. Agriculture and landscape in portugal. Landscape and Urban planning, 46, 83–91); Ribeiro, Lautensach, & Daveau, 1991 (Ribeiro, O., Lautensach, H., & Daveau, S., 1991. Geografia de portugal (4 Vols., published between 1986 and 1991). Lisbon, Portugal: João Sá de Costa)]. The SLEUTH model calibration captured these city characteristics, and using the standard documented calibration procedures, seems to have adapted itself well to the European context. Useful predictions of growth to 2025, and investigation of the impact of planning and transportation construction can be investigated as a consequence of the successful calibration. Further application and testing of the SLEUTH model in non-Western environments may prove it to be the elusive universal model of urban growth, the antithesis of the special case urban models of the 1960s and 1970s.     

Developing a cellular automata model of urban growth to inform spatial policy for flood mitigation: A case study in Kampala,Uganda

Urban growth may intensify local flooding problems. Understanding the spatially explicit flood consequences of possible future land cover patterns contributes to inform policy for mitigating these impacts. A cellular automata model has been coupled with the openLISEM integrated flood modeling tool to simulate scenarios of urban growth and their consequent flood; the urban growth model makes use of a continuous response variable (the percentage of built-up area) and a spatially explicit simulation of supply for urban development. The models were calibrated for Upper Lubigi (Kampala, Uganda), a sub-catchment that experienced rapid urban growth during 2004–2010; this data scarce environment was chosen in part to test the model's performance with data inputs that introduced important uncertainty. The cellular automata model was validated in Nalukolongo (Kampala, Uganda). The calibrated modeling ensemble was then used to simulate urban growth scenarios of Upper Lubigi for 2020. Two scenarios, trend conditions and a policy of strict protection of existing wetlands, were simulated. The results of simulated scenarios for Upper Lubigi show how a policy of only protecting wetlands is ineffective; further, a substantial increase of flood impacts, attributable to urban growth, should be expected by 2020. The coupled models are operational with regard to the simulation of dynamic feedbacks between flood and suitability for urban growth. The tool proved useful in generating meaningful scenarios of land cover change and comparing their policy drivers as flood mitigation measures in a data scarce environment.     

沈阳市未来城市扩展模拟预案研究

利用基于遥感手段获取的沈阳市城市发展历史数据对SLEUTH模型进行校正,对未来（2005-2030年）的城市扩展过程进行不同生态环境保护政策下的情景模拟,并对其扩展格局和生态环境影响进行分析与比较。结果显示,未来沈阳市城市化进程将进入一个加速发展阶段;2016年以后各政策预案条件下的城市扩展格局和可持续性呈现出明显的差异;目前趋势发展预案（Ⅰ）下,城市扩展导致大量的土地资源被侵占,城市景观格局日益复杂化;环境保护发展预案（Ⅱ）和生态可持续发展预案（Ⅲ）下城市扩展空间受到较大约束,部分自然资源得以保护,城市扩展格局在模拟期内相对比较紧凑;研究表明必须采取严格的城市规划与增长管理措施,引导和控制沈阳市未来城市扩展,保护基本农田和城市生态支撑系统;SLEUTH模型为城市土地资源可持续利用提供了一种有用的规划工具。     

Urban expansion simulation from the perspective of land acquisition-based on bargaining model and ant colony optimization

As a major method to serve the demand for land requirement in Chinese urban construction, land acquisition has been intensified since the speeding up of urbanization. However, clashes arise during the process of land acquisition, out of conflicts of interests, which to some extent have affected the development and direction of urban expansion, as well as social harmony and stability. Therefore, the urban expansion simulation should be based on smooth land acquisition technique. In this study, urban expansion was simulated from the perspective of land acquisition, based on coupled bargaining model and modified ant colony optimization (ACO) algorithm. First, the bargaining model with fairness preferences should be set up for the government and farmers, to search for the candidate areas for urban expansion, both sides of which could reach a consensus without conflicts of interest. Then, urban expansion simulation is applied to all the candidate areas, taking advantage of the modified ACO algorithm. The model considered the built-up area in Wuhan city as the demonstration area and simulated conditions of land use in 2016 and 2026. The result showed that the coupled model could simulate decision-making behavior of the government and farmers in land acquisition veritably, so as to protect farmers' economic interests, with an increase of over 50% on average, and ensure government's appropriate profit. Moreover, the simulation accuracies of the coupled model was found to be better than that of the traditional cellular automata model, and the Kappa coefficient was 0.65, which supports the effectiveness of the model in simulating urban expansion. Further, it was estimated that the urban land use of Wuhan will cover 516.22 km² in 2026, and the southeastern part of the city will be the hot spot area of urban expansion.     

Urban growth modeling of Kathmandu metropolitan region, Nepal

The complexity of urban system requires integrated tools and techniques to understand the spatial process of urban development and project the future scenarios. This research aims to simulate urban growth patterns in Kathmandu metropolitan region in Nepal. The region, surrounded by complex mountainous terrain, has very limited land resources for new developments. As similar to many cities of the developing world, it has been facing rapid population growth and daunting environmental problems. Three time series land use maps in a fine-scale (30 m resolution), derived from satellite remote sensing, for the last three decades of the 20th century were used to clarify the spatial process of urbanization. Based on the historical experiences of the land use transitions, we adopted weight of evidence method integrated in cellular automata framework for predicting the future spatial patterns of urban growth. We extrapolated urban development patterns to 2010 and 2020 under the current scenario across the metropolitan region. Depending on local characteristics and land cover transition rates, this model produced noticeable spatial pattern of changes in the region. Based on the extrapolated spatial patterns, the urban development in the Kathmandu valley will continue through both in-filling in existing urban areas and outward rapid expansion toward the east and south directions. Overall development will be greatly affected by the existing urban space, transportation network, and topographic complexity.     

Patch-based cellular automata model of urban growth simulation: Integrating feedback between quantitative composition and spatial configuration

Urban land use change modeling can enhance our understanding of processes and patterns of urban growth that emerge from human-environment interactions. Cellular automata (CA) is a common approach for urban land use change modeling that allows for discovering and analyzing potential urban growth pathways through scenario building. Fundamental components of CA such as neighborhood configuration, transition rules, and representation of geographic entities have been examined in depth in the literature. However, trade-offs in the quantitative composition that urban gains from different non-urban land types and their dynamic feedback with the spatial configuration of urban growth are often ignored. The urban CA model proposed in this study links the quantitative composition with the spatial configuration of urban growth by incorporating a trade-off mechanism that adaptively adjusts the combined suitability of occurrence for non-urban land types based on analysis of transition intensity. Besides, a patch growing module based on seeding and scanning mechanisms is used to simulate the occurrence and spreading of spontaneous urban growth, and a time Monte Carlo (TMC) simulation method is employed to represent uncertainties in the decision-making process of urban development. Application of the model in an ecologically representative city, Ezhou, China, reveals improvement on model performance when feedback between the quantitative composition and spatial configuration of urban growth is incorporated. The averaged figure of merit and K-fuzzy indices are 0.5354 and 0.1954 with respect to cell-level agreement and pattern similarity, indicating the utility and reliability of the proposed model for the simulation of realistic urban growth.     

利用SLEUTH模型进行北京城市扩展模拟研究

元胞自动机模型具有较强的模拟空间复杂系统时空演变的能力,可以进行有效的城市增长和土地利用演化方面的模拟,现已成为城市地理学研究的重要工具。SLEUTH模型是元胞自动机模型的一种具体实现,它包含了四种城市增长方式和五个增长系数,再加上两个控制因素层一起作用于输入数据,能够较好地模拟城市的扩展。本文详细介绍了SLEUTH模型的校正和运行过程,并修改控制参数将其应用于北京城市扩展的模拟和预测中,通过分析模型的运行结果,深入地研究了北京城市发展的模式和成因,同时也指出了模型存在的问题。     

Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China

This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai’s urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study’s approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends.     

Simulating slum growth in Lagos: An integration of rule based and empirical based model

Demographic forecasts put Lagos as one of the cities with the highest population growth. Past trends show correlations between urban growth and slum growth, thereby creating a major challenge for sustainable city planning. This study explores the drivers of slum development in Lagos, and simulates scenarios for slum growth through coupling logistic regression with the cellular automata-based SLEUTH model. RapidEye (2009 and 2015) and Sentinel-2 (2015) imagery were used to create slum extents maps for each time point, and then used for the calibration and prediction, respectively, of the model. The driving forces of slum development in Lagos were analyzed, and the correlated spatial drivers compiled to create a probability map of slum development using the logistic regression model. The probability map was incorporated with the exclusion layer of the modified SLEUTH to simulate scenarios of slum growth in Lagos by 2035. Three scenarios were designed based on the modification of the exclusion layer and the transition rules. The Scenario 1 ‘business as usual’, depicts slum development following the present trend; the scenario 2 ‘excessive growth’, considers the demographic projection for the city; while the scenario 3 ‘limited government influence’, asserts limited interference by the government in slum management/control. Factors including distance to markets, distance to shoreline, distance to local government administrative buildings, land prices, etc. were predictors of slum development in Lagos. The prediction model, based on the logistic regression, reached an overall accuracy of 79.17% and a relative operation characteristics value of 0.85. The three scenarios show further densification of the existing slums, and increase in their area by 1.18 km² (scenario 1), 4.02 km² (scenario 2), and 1.28 km² (scenario 3). New slums are predicted at the fringe of the south-eastern part of the city. The foreseen limited spatial growth of the slums is due to the high density of the city, thus new slums may likely develop in the neighboring zones to Lagos when land in the city is no longer available.     

一种不透水面精细制图新方法及其在城市SDGs指标评估上的应用

城市不透水面是城市化程度的重要指示器,也是城市环境的重要敏感因子。联合国提出的城市可持续发展SDG11.3.1指标——城市土地使用率与人口增长率之比（LCRPGR）需要有效监测土地城镇化与人口城镇化关系。针对其监测与评估中高分辨率和高精度城市用地产品缺失,以及低纬度地区城市可持续发展研究较少的问题。基于Google Earth Engine平台,提出一种多时相升降轨SAR与光学影像等多源数据融合的不透水面提取方法,提取了2015年和2018年10 m分辨率印度不透水面。根据人口格网界定城市范围,将范围内不透水面面积与城市人口进行耦合,用于指标计算。研究结果表明：①精度验证结果显示,两期产品总体精度（OA）高于91％,Kappa系数高于0.82,R²值分别为0.85和0.86,并与其他产品细节对比,证明了方法的有效性;②印度总体不透水面面积由2015年的47 499.35 km²增加到2018年的49 944.69 km²,城市平均LCRPGR为0.76,表明其城市人口城镇化大于土地城镇化,城市可持续发展面临挑战。结合空间分析,印度城市可持续发展水平存在南北差异、东西差异以及沿海与内陆的差异。     

Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation

Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibrated based on the urban morphological patterns that emerge from a simulation starting from urban genesis, i.e., a land cover map completely void of urban land. The application of the model uses the calibrated parameters to simulate urban growth forward in time from a known urban configuration.This approach to calibration is embedded in a new framework for the calibration and validation of a Constrained Cellular Automata (CCA) model of urban growth. The investigated model uses just four parameters to reflect processes of spatial agglomeration and preservation of scarce non-urban land at multiple spatial scales and makes no use of ancillary layers such as zoning, accessibility, and physical suitability. As there are no anchor points that guide urban growth to specific locations, the parameter estimation uses a goodness-of-fit (GOF) measure that compares the built density distribution inspired by the literature on fractal urban form. The model calibration is a novel application of Markov Chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC). This method provides an empirical distribution of parameter values that reflects model uncertainty. The validation uses multiple samples from the estimated parameters to quantify the propagation of model uncertainty to the validation measures.The framework is applied to two UK towns (Oxford and Swindon). The results, including cross-application of parameters, show that the models effectively capture the different urban growth patterns of both towns. For Oxford, the CCA correctly produces the pattern of scattered growth in the periphery, and for Swindon, the pattern of compact, concentric growth. The ability to identify different modes of growth has both a theoretical and practical significance. Existing land use patterns can be an important indicator of future trajectories. Planners can be provided with insight in alternative future trajectories, available decision space, and the cumulative effect of parcel-by-parcel planning decisions.     

Development and application of GIS-based analysis/synthesis modeling techniques for urban planning of Istanbul Metropolitan Area

Istanbul is the largest city in Turkey with an area of around 5750 km² and population of around 10.58 M (2000). The population is increasing because of mass immigration. Planned and unplanned housing are increasing while green areas are decreasing in area. Rapid, uncontrolled and illegal urbanization accompanied by insufficient infrastructure has caused degradation of forest, water basin and barren lands in the metropolitan area, especially within the past two decades. The study includes taking administrative planning decisions about the development of urban sustainability in the metropolitan area of Istanbul, producing rationalist solutions to the problems in environmental and natural resources, and conservation of small-scale (historical, tourist, residential, industrial, etc.) specific urban areas. By the help of this study, settlement suitability analyses have been achieved according to the natural thresholds of the area. Additionally, some kinds of formations, which may limit the development of the metropolitan area of Istanbul, such as natural structures, natural structure, ecological corridors and natural hazard areas, have been defined and developed model on the ArcGIS 9.1 platform. In the light of the policies of the European Union about sustainable cities, environmental impact assessments and sustainability policies have been determined for both the existing settlements and sustainable development areas.