Study on seismic numerical model on the Chinese mainland and its vicinity

IntroductionSeismicprocessisextremelycomplicatedjustashasbeenprovedbytheeallhquakepredictionpractice.Thecomplexityofseismicprocessmainlydisplaystheinhomogeneityofseismicspace-timedistribution.Therearealotoffactorscausingthecomplexity,includingthemediumofseismicsource,themechanicalprocessofseismicsource,mutualeffectofgroupseismicactivity,andsoon.Studyontheseismicactivitylawstillremainsinalowlevel,especiallyintheaspectofseismicoccurrenceprocess.Manyscholarstrytostudytheseismicactivebehavioringr…     

Developing land use scenario dynamics model by the integration of system dynamics model and cellular automata model

Models of land use change are useful tools for un-derstanding the analysis of the cause and conse-quences of land use changes, assessing the impacts of land use system on ecological system and supporting land use planning and policy[1,2]. Modeling land use scenario changes and its potential impacts on the structure and function of the ecosystem in the typical regions are regarded as one of the good ways to un-derstand the interactive mechanism between land use system and ecological system[3―10…     

细胞自动机用于地震偏移:数值模拟试验

利用细胞自动机模型进行了地震偏移处理的尝试．采用格子Boltzmann模型和三角形网格,以向下两个方向的粒子代表下行波,根据归一化的地震记录计算粒子数密度,在相应的时刻在代表地表的网格点上设定粒子数密度,以下行粒子在网格中的演化模拟波场延拓过程．粒子系统演化结束时即得到偏移后的波场图象．本文对一个常速介质理论模型进行了偏移处理,得到了满意的结果。     

基于局部化转换规则的元胞自动机土地利用模型

传统土地利用元胞自动机(Cellular automata,CA)模型基于空间同质性假设,使用全局性模型建立元胞转换规则,忽略了土地利用变化驱动因素的驱动作用在空间上的变化。以美国佛罗里达州的橙县(Orange County)2003-2009年土地利用变化为例,提出了基于局部化转化规则的CA土地利用模型,其中元胞的土地利用类型适宜性由地理加权多项logit模型(Geographically weighted multinomial logit,GWML)获得。结果表明:GWML模型较传统全局性多项logit(Multinomial logit,MNL)模型有更高的数据解释能力。基于GWML模型的土地利用CA模型能反映局部土地利用变化模式,因而较基于MNL模型的CA模型具有更高的模拟精度。所得结论对未来国内地区的研究有借鉴意义。     

Modelling urban expansion using a multi agentbased model in the city of Changsha

Although traditional urban expansion simulation models can simulate dynamic features, these models fail to address complex changes produced by different agents' behaviors. The paper has built up a set of spatial-temporal land resource allocation rules and developed a dynamic urban expansion model based on a multi-agent system, which can simulate the interaction among different agents, such as residents, peasants, and governments. This model is applied to simulate urban expansion process taking Changsha City, in China as a study area. The results show that this model can not only reflect basic characteristics of urban expansion, but also help explain the reasons for urban expansion process and understand the effect of agents' behavior on the expansion process, and provide insights into the causing factors behind the expansion. In addition, in contrast to simulation results with land use classification map from remote sensing images, the precision of the simulation reached over 68% with higher precision than cellular automata model according to the cell-by-cell comparison. The results suggest that the model can help to provide land use decision making support to government and urban planners.     

基于多智能体的城市人口分布动态模拟与预测

城市人口分布变化过程是复杂的动态系统,掌握其规律在城市规划和社会可持续发展中有重要意义。用相互作用的多智能体系统(MAS)、元胞自动机(CA)环境及城市人口密度模型构建精确到街道的城市人口分布模型,并以长沙为例,分析城市人口分布的演变过程,为相关的调控提供决策依据。研究结果表明,其模拟的城市人口分布格局与实际情况吻合较好,在多种因素的影响下,长沙市将形成“市中心人口快速增长,近郊区人口缓慢增长,沿湘江畔、沿五一大道及岳麓区高新技术开发区人口密集”的发展格局。与以往的模型进行相比,所获得的模拟结果精度更高,更接近于实际的空间分布格局。     

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data–task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.     

Modelling sustainable urban growth in a rapidly urbanising region using a fuzzy-constrained cellular automata approach

This article presents an application of a fuzzy-constrained cellular automata model to simulate the spatio-temporal processes of urban growth in the rapidly growing Gold Coast City in Southeast Queensland, Australia. Urban growth has been captured in the model as a continuous process in space and over time, which has been affected by a set of primary and secondary transition rules. The primary transition rules deal with the propensity of a local area for development and the impact of its neighbouring cells on such development, while the secondary transition rules reflect the influences of environmental and institutional factors on urban growth. Application of the model demonstrates its re-applicability to different regions and the effectiveness of the cellular automata technique in studying urban dynamics. It also provides tools to explore sustainable urban growth options under different socio-environmental and planning control factors. A sustainable urban future of the region is achievable if development is managed to maintain a balance amongst ecological conservation, economic growth and the contemporary Australian lifestyle.     

The inclusion of differentially assessed lands in urban growth model calibration: a comparison of two approaches using SLEUTH

This research sought to understand the role that differentially assessed lands (lands in the United States given tax breaks in return for their guarantee to remain in agriculture) play in influencing urban growth. Our method was to calibrate the SLEUTH urban growth model under two different conditions. The first used an excluded layer that ignored such lands, effectively rendering them available for development. The second treated those lands as totally excluded from development. Our hypothesis was that excluding those lands would yield better metrics of fit with past data. Our results validate our hypothesis since two different metrics that evaluate goodness of fit both yielded higher values when differentially assessed lands are treated as excluded. This suggests that, at least in our study area, differential assessment, which protects farm and ranch lands for tenuous periods of time, has indeed allowed farmland to resist urban development. Including differentially assessed lands also yielded very different calibrated coefficients of growth as the model tried to account for the same growth patterns over two very different excluded areas. Excluded layer design can greatly affect model behavior. Since differentially assessed lands are quite common through the United States and are often ignored in urban growth modeling, the findings of this research can assist other urban growth modelers in designing excluded layers that result in more accurate model calibration and thus forecasting.     

A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing

This article presents a novel cellular automata (CA) approach to simulate the spatio-temporal process of urban land-use change based on the simulated annealing (SA) algorithm. The SA algorithm enables dynamic optimisation of the CA's transition rules that would otherwise be difficult to configure using conventional mathematical methods. In this heuristic approach, an objective function is constructed based on a theoretical accumulative disagreement between the simulated land-use pattern and the actual land-use pattern derived from remotely sensed imagery. The function value that measures the mismatch between the actual and the simulated land-use patterns would be minimised randomly through the SA process. Hence, a set of attribution parameters that can be used in the CA model is achieved. An SA optimisation tool was developed using Matlab and incorporated into the cellular simulation in GIS to form an integrated SACA model. An application of the SACA model to simulate the spatio-temporal process of land-use change in Jinshan District of Shanghai Municipality, PR China, from 1992 to 2008 shows that this modelling approach is efficient and robust and can be used to reconstruct historical urban land-use patterns to assist with urban planning policy-making and actions. Comparison of the SACA model with a typical CA model based on a logistic regression method without the SA optimisation (also known as LogCA) shows that the SACA model generates better simulation results than the LogCA model, and the improvement of the SACA over the LogCA model is largely attributed to higher locational accuracy, a feature desirable in most spatially explicit simulations of geographical processes.