元胞自动机最短路径算法优化

概括了当前GIS中最短路径算法,分析了元胞自动机在最短路径分析算法中的原理及应用现状,并从两个方面对基于元胞自动机的最短路径算法进行优化即直线优化的元胞自动机最短路径算法。(1)将A*算法中的启发函数引入元胞自动机模型,提出了直线优化元胞自动机最短路径模型;(2)考虑道路网特征对最短路径算法的影响,得出具有道路网自适应性的最短路径分析模型。最后选取不同形态特征的shp道路网数据,验证了优化算法在实际应用中的适用性和高效性。     

Exploring land use policy scenarios with the use of a cellular automata-based model: urban sprawl containment and sustainable development in Thessaloniki

This study addresses the issue of urban sprawl through the application of a cellular automata (CA)-based model in the area of Thessaloniki, Greece. The model integrates a multiple regression model at the regional level with a CA model at the local level. New urban land is allocated in a disaggregated field of land units (cells) taking into account a wide range of data. Particular emphasis is placed on the way zoning regulations and land availability data are inserted into the model, so that alternative land use policy scenarios could be examined. Thessaloniki, a typical Mediterranean city, is used as a case study. The model is used to compare two scenarios of urban growth up to year 2030; the first one assuming a continuation of existing trends, whereas the second one assuming the enactment of various land use zoning regulations in order to contain urban sprawl.     

基于逻辑回归CA模型的溢油模拟与预测

结合逻辑回归方法和元胞自动机模型构建了逻辑回归CA模型,模型中的逻辑回归方法能够很方便地获取影响因子的权重,再设置起止条件等,便可以模拟出溢油的动态变化情况。把模型应用到Deep Spill项目的溢油模拟实验中,结果表明,从模型结果的形态看,模拟结果与检验结果吻合程度较高,能够很好地模拟出溢油扩散与漂移等重要的溢油特性。从统计分析中可知,模拟结果的总精度可达96.8%,Kappa系数达到0.834。     

“三规合一”服务的空间信息技术:地理模拟与优化

中国的国民经济和社会发展规划、土地利用总体规划以及城乡规划都是法定规划,但由于规划主体、技术标准和编制办法、实施手段和监督机制等的不同,导致"三规分离"、各个规划之间相互冲突的问题较为突出。虽然国家为了消除冲突,正在开展"三规合一"的有关工作,但缺乏有关技术手段的支持。本文以地理信息科学为出发点,对地理过程建模在国内外研究中的应用进行了总结,阐述了地理模拟与优化的框架体系可以成为目前中国正在进行的"三规合一"工作的重要理论和方法支撑。     

基于耗费场的最优路径算法研究

在GIS中,有一类应用是基于连续分布耗费场的最优路径计算问题,如公路选线等。该文首先采用规则格网对耗费场进行建模,进而基于规则格网表现,分别建立网络实现模型和元胞自动机实现模型,并探讨了基于元胞自动机模型的最优路径算法,通过实例研究,说明了算法的正确性。     

顾及轨道交通影响的浙中城市群土地利用多情景模拟与分析

土地利用变化受到地形地貌、自然环境、城市规划和经济发展等的影响,预测其未来情景对政策调整具有重要的参考意义。元胞自动机模型是模拟和预测不同规划政策下土地利用变化的常用方法。本文基于GlobeLand30数据集,利用浙中城市群2000-2010年土地利用变化校准FLUS模型,并模拟2010年土地利用格局,其总体精度、Kappa系数和图形优化(FOM)分别为89.74%、82.69%和29.86%。采用马尔可夫链预测2030年各类型土地总量,利用FLUS预测一般条件下(常规情景)和城市轨道交通规划站点影响下(轨交情景)浙中城市群未来土地格局。结果表明,在5 km范围内城市轨道交通站点对建设用地增长影响较大,在该区域轨交情景比常规情景面积增加45.25 km^2、且主要发生在城市边缘区。建设用地扩张主要通过侵占优质农田实现,轨交情景5 km范围内农田转化为建设用地比常规情景增加33.34 km^2,建设用地扩张强度高于常规情景,其中最低扩张强度以上占比高于常规情景3.70%。景观指数表明,2种情景中林地、草地和水域格局具有较高相似性。本研究表明,综合使用FLUS、遥感、GIS等技术方法,能够准确模拟和预测不同规划条件下未来土地利用格局,并为规划和政策调整提供高可信空间数据。     

A high-performance cellular automata model for urban simulation based on vectorization and parallel computing technology

Cellular automata (CA) models can simulate complex urban systems through simple rules and have become important tools for studying the spatio-temporal evolution of urban land use. However, the multiple and large-volume data layers, massive geospatial processing and complicated algorithms for automatic calibration in the urban CA models require a high level of computational capability. Unfortunately, the limited performance of sequential computation on a single computing unit (i.e. a central processing unit (CPU) or a graphics processing unit (GPU)) and the high cost of parallel design and programming make it difficult to establish a high-performance urban CA model. As a result of its powerful computational ability and scalability, the vectorization paradigm is becoming increasingly important and has received wide attention with regard to this kind of computational problem. This paper presents a high-performance CA model using vectorization and parallel computing technology for the computation-intensive and data-intensive geospatial processing in urban simulation. To transfer the original algorithm to a vectorized algorithm, we define the neighborhood set of the cell space and improve the operation paradigm of neighborhood computation, transition probability calculation, and cell state transition. The experiments undertaken in this study demonstrate that the vectorized algorithm can greatly reduce the computation time, especially in the environment of a vector programming language, and it is possible to parallelize the algorithm as the data volume increases. The execution time for the simulation of 5-m resolution and 3 × 3 neighborhood decreased from 38,220.43 s to 803.36 s with the vectorized algorithm and was further shortened to 476.54 s by dividing the domain into four computing units. The experiments also indicated that the computational efficiency of the vectorized algorithm is closely related to the neighborhood size and configuration, as well as the shape of the research domain. We can conclude that the combination of vectorization and parallel computing technology can provide scalable solutions to significantly improve the applicability of urban CA.     

A Time Monte Carlo method for addressing uncertainty in land-use change models

One of the main objectives of land-use change models is to explore future land-use patterns. Therefore, the issue of addressing uncertainty in land-use forecasting has received an increasing attention in recent years. Many current models consider uncertainty by including a randomness component in their structure. In this paper, we present a novel approach for tuning uncertainty over time, which we refer to as the Time Monte Carlo (TMC) method. The TMC uses a specific range of randomness to allocate new land uses. This range is associated with the transition probabilities from one land use to another. The range of randomness is increased over time so that the degree of uncertainty increases over time. We compare the TMC to the randomness components used in previous models, through a coupled logistic regression-cellular automata model applied for Wallonia (Belgium) as a case study. Our analysis reveals that the TMC produces results comparable with existing methods over the short-term validation period (2000–2010). Furthermore, the TMC can tune uncertainty on longer-term time horizons, which is an essential feature of our method to account for greater uncertainty in the distant future.     

基于影响因子动态更新的都市圈国土-人口协同模拟

都市圈作为典型的复杂巨系统,其国土、人口、交通等要素呈现出共同演化、协同发展的特征。现有基于元胞自动机的空间模拟方法通常分别进行国土或人口模拟,忽略了国土、人口之间的协同演化,从而限制了模拟性能。本文提出一种影响因子动态更新策略用以表征国土、人口两种要素间的相互作用,拓展经典元胞自动机模型构建协同模拟算法,实现了都市圈国土与人口空间分布态势的精准模拟。本文以深莞惠都市圈为例,对提出算法进行了实验验证。实验结果表明,本文提出协同模拟算法在深莞惠都市圈的国土模拟品质因素为0.274、人口模拟平均绝对百分比误差23.55%,分别优于传统基于随机森林的元胞自动机算法0.24和29.33%;相较于传统模型,在国土模拟中本模型在对于建设用地的模拟准确度提升了约3%,在人口模拟中本模型在对人口高密度的区域模拟误差降低了约6%。本文进一步预测了深莞惠都市圈2030年国土和人口发展空间态势。研究结果可为都市圈重大基础设施选址和发展情景推演提供技术支撑。     

土地利用变化研究中的细胞自动机与灰色局势决策

把细胞自动机和灰色局势决策结合起来对土地利用变换机制进行模拟。实验证明,基于灰色局势决策规则的细胞自动机是对土地利用变换机制从宏观和微观角度进行模拟的有效方法。