Simulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin,China

This study presents an optimized algorithm into the cellular automata (CA) models for urban growth simulation in Binhai New Area of Tianjin, China. The optimized CA model by particle swarm optimization (PSO) was compared with the logistic-based cellular automata (LOGIT-CA) model to see the effects of the simulation. The study evaluated the stochastic disturbance in the development of urban growth using the Monte Carlo method; the coefficient d determined the state of urban growth. The validation was conducted by both cross-tabulation test and structural measurements. The results showed that the simulations of PSO-CA were better than LOGIT-CA model, indicating an improvement in the spatio-temporal simulation of urban growth and land use changes in study area. Since the simulations reached their best values when the coefficient was between 1 and 2, the urban growth in the study area was in the period of conversion from spontaneous growth to edge-expansion and infilling growth.     

多粒度时空对象支持下湿地时空行为认知与表达初探

湿地作为一种活的空间实体,其时空行为错综复杂,认知和表达一直是该领域研究的重点和难点。全空间信息系统以多粒度时空对象直接对现实世界进行抽象和描述,比传统GIS更符合人的认知特点。因此,本文基于多粒度时空对象建模思想,分别对湿地数据组织、时空行为认知和表达进行了初探。结果表明：湿地时空行为可按对象粒度、数量和特征属性进行分类,且行为表达可通过动态展示,分析建模和模型预测层层递进,较以往表征方式具有动态关联近实时等优势,可为湿地资源的动态监测提供一种新的研究思路。     

Temporal logic and operation relations based knowledge representation for land cover change web services

Providing land cover spatio-temporal information and geo-computing through web service is a new challenge for supporting global change research, earth system simulation and many other societal benefit areas. This requires an integrated knowledge representation and web implementation of static land cover and change information, as well as the related operations for geo-computing. The temporal logic relations among land cover snapshots and increments were examined with a matrix-based three-step analysis. Twelve temporal logic relations were identified and five basic spatial operations were formalized with set operators, which were all used to develop algorithms for deriving implicit change information. A knowledge representation for land cover change information was then developed based on these temporal logic and operation relations. A prototype web-service system was further implemented based on OWL-DL. Both online access and conversion of land cover spatio-temporal information can be facilitated with such a web service system.     

一种时空过程的梯形分级描述框架及其建模实例

以对象建模视图和事件模型为基础的自底向上的时空过程表达方法,缺少对地理时空现象的显式定义,在时空因果链的细化表达方面存在不足。本文设计一种时空过程的梯形分级描述框架STP-TRAP,对时空过程进行自顶向下的显式化建模和集成时空因果链的分级描述,并在事件模型基础上,通过引入约束关系描述扩展时空因果链的表达模式。土地利用变迁过程建模实例与土地利用管理信息系统的设计和实现证明,该框架对土地利用变迁时空过程表达效果较好,为土地利用变更数据管理提供了新的思路。     

利用生物地理学优化算法获取城市扩展元胞自动机模型参数

提出了一种基于生物地理学优化算法寻找城市扩展元胞自动机（cellular automata,CA）模型最佳参数的方法。转换规则制定及相应权重参数获取是构建城市扩展CA的核心和难点。生物地理学优化算法（biogeography-based optimization,BBO）通过模拟生物物种在栖息地的分布、迁移和灭绝来求解优化问题。利用BBO算法自动获取城市扩展CA模型参数值,构建BBO-CA模型进行城市扩展模拟实验,并与粒子群算法（particle swarm optimization,PSO）、蚁群算法（ant colony optimization,ACO）、遗传算法（genetic algorithm,GA）及逻辑回归（logistic regression,LR）等方法相比较。结果表明,BBO算法具有较好的收敛性,可有效地快速自动寻找城市扩展CA模型最佳参数组合,获取的空间变量权重参数较为合理;BBO-CA模型明显提升了城市用地模拟精度,城市用地模拟精度为72.5%,相对PSO、ACO、GA、LR各算法分别提升了1.1%、1.2%、2.7%和4.0%,Kappa系数达到0.700,分别提升了0.015、0.016、0.034和0.046,且整体空间布局与实际情况更为接近,验证了应用BBO算法的可行性与优势。     

The cognition of change: scaling deformations in mind and spatial theories

The dynamics of the earth and its inhabitants have become a core topic and focus in research in the spatial sciences. The spatio-temporal data avalanche challenges researchers to provide efficient and effective means to process spatio-temporal data. It is of vital importance to develop mechanisms that allow for the transition of data not only into information but also into knowledge. Knowledge representation techniques from artificial intelligence play an important role in laying the foundations for theories dealing with spatio-temporal data. Specifically, the advances in the area of qualitative spatial representation and reasoning (QSTR) have led to promising results. Categorical distinctions of spatio-temporal information identified by QSTR calculi potentially correspond to those relevant to humans. This article presents the first behavioral evaluation of qualitative calculi modeling geographic events associated with scaling deformations of entities, that is, changes in size by either expansion or contraction. Examples of such dynamics include a lake flooding its surroundings or an expanding oil spill in the ocean. We compare four experiments using four different semantic domains. Each domain consists of two spatially extended entities: one entity is undergoing scaling deformations while the other is static. We kept the formal QSTR characterization, which are paths through a topologically defined conceptual neighborhood graph, identical across all semantic domains. Our results show that for geographic events associated with scaling deformations (a) topological relations are not equally salient cognitively; (b) domain semantics has an influence on the conceptual salience of topological relations.     

事件驱动的城市公共交通时空数据模型研究

以模型驱动架构为理论基础,以事件驱动的方法进行面向对象的时空数据建模研究,建立城市公共交通领域的概念模型、静态结构模型、动态行为模型和时态行为模型,并结合软件模型实现时空数据面向对象的组织与操作。事件驱动的面向对象时空数据模型实现了关系数据库和对象关系型GIS平台下面向对象的数据建模,扩展空间对象的动态行为与时态行为特征,实现对复杂空间对象的操作并保证了其空间关系及时态一致性。本研究为行业空间数据建模提供理论和方法上的参考,通过在广州市公共交通规划管理地理信息系统中的应用对模型的实用性进行验证。     

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。     

集成GIS的元胞自动机在城市扩展模拟中的应用

元胞自动机CA(Cellular Automata)是一种"自下而上"的动态模拟模型,具有模拟城市复杂系统时空演化过程的能力。CA和GIS的集成使二者在时空建模方面相互补充,能使CA模拟结果可视化显示。这里以郑州市为例,设计了城市CA模型,借助VB 6.0和MapX 5.0建立了一个与GIS无缝集成的2维CA模拟系统,并结合郑州市2005~2007年遥感影像图和土地利用图,对郑东新区进行了模拟,取得较好的效果。     

基于遥感及GIS的精确时空虚拟现实研究

本文探讨了GIS支持下遥感影像精确时空建模及其虚拟现实的理论和方法。描述了自然景观对象的时空表达、精确时空模型单元的基本关系 ,阐述了基于遥感和GIS的时空模型可视化一般理论 ,及时空数据库支持下真实景观的影像模拟方法 ,最后给出了目前研究存在的若干技术问题     

基于时空对象的高铁网络建模方法

全空间信息系统将现实世界中的实体抽象为多粒度时空对象,并对其进行表达与分析,较传统地理信息系统来说更符合人类的认知。对高铁网络对象的基本特点及现有交通网络建模方法进行了分析,可知目前现有的建模方法难以支持具有动态性、关联性及多粒度特性的高铁网络对象的表达。依据多粒度时空对象建模思想,提出了一种适合高铁交通数据的全面表达与操作的建模方法;并对高铁网络对象化建模方法的优势及建模过程进行了探讨,设计了概念模型和逻辑模型;通过实例对提出的建模方法进行了验证,并采用可视化表达的方法检验高铁网络时空对象数据的有效性。实验结果表明,基于时空对象的建模方法能够较好描述高铁时空实体多维动态特征,能为后续高铁网络时空查询、分析提供支持。     

Assessing the Effectiveness of Dynamic Symbols in Cartographic Communication

Abstract

The age of the Internet poses new challenges to cartography. While cartographic animation has become a practical alternative, it is mostly used to depict both spatio-temporal and non-temporal changes. The paper concentrates on the use of dynamic symbols on a static base map. A visual test was carried out to obtain some perspectives about the cartographic communication of blinking point and line symbols. The results confirmed that dynamic symbols tended to attract the attention of the users in general and even in situations when the foreground–background contrasts were poor. Their effectiveness was more pronounced with symbols of a larger size but tended to diminish with an increasing number of blinking symbols. It was also observed that users were able to perceive differences in the frequency of flickering, particularly when the variation was large. The line animation method (marquee and grow-out) did not yield a substantial difference in visual responses in the case of blinking line symbols.     

LiDAR虚拟仿真实践教学平台设计与建设

为适应测绘工作从三维空间测量向多维空间、从静态测量向动态实时测量、从地面向全域空间拓展的高新技术发展趋势,更好地应对信息化教育背景下的知识获取和传授方式、教与学关系等教改创新要求。本实验教学项目在现有实训课程基础上,以建构筑物三维空间信息快速采集与建模的真实测绘项目为基础,将三维激光测量技术、虚拟仿真技术与LiDAR技术进行深度融合;设计并建设了LiDAR虚拟仿真实践教学平台,运用C#开发程序界面和Pr视频编辑软件制作仿真动画,结合VLCPlayer视频播放插件,实现了对LiDAR仪器认知,测量模拟,数据处理和实践展示等功能,帮助学生理解并掌握LiDAR的相关知识和操作技能,取得了良好的教学效果。     

Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images

Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model’s parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China’s Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model’s parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.     

Modeling sprawl of unauthorized development using geospatial technology: case study in Kuantan district,Malaysia

Abstract

The paper explores a framework combining remote sensing and GIS-cellular automata (CA) concepts aimed at improving the modeling of unauthorized land use sprawl. Remote sensing data have been used in urban modeling and analysis, the use of high-resolution remote sensing data in assessing unauthorized development is quite unexplored. This work has demonstrated systematic combination utilization of geospatial analyses tools to acquire a new level of information to enable urban modeling and sprawl analysis in assisting urban sustainable management. In this study, Kuantan city, Malaysia was selected in simulation of the unauthorized land use with CA concept for a period of 15 years (2000–2015), with main input time-series land use observation from 1995 to 2005. The 2000 and 2005 land use input was also used as calibrated and test assessment of the simulation. The results show excellent agreement between in-situ changes of the unauthorized land use classes and the corresponding simulated classes within the same periods. In conclusion, CA model can lead to new levels of understanding of how urban areas grow and change as in view of digital earth aspiration.     

人口密度模型与CA集成的城市化时空模拟实验

有机地集成城市经典模型与元胞自动机 (CA)是一种有意义的理论实验。本文分无约束和有约束两种条件构建了城市化CA模型 ,推导出了基于均质地理背景和孤立城市的假设的城市人口密度时空模型 ,并进行了二者集成的实验研究 ,得出了如下结论 :(1)CA是城市化时空模拟的有效方法 ;(2 )经典的地理、城市模型可以有效地集成到城市化CA模型中 ,起到控制城市化轨迹的基本作用 ,在某种程度上能够弥补CA建模过于简单的不足。     

大数据环境下道路场景高时空分辨率众包感知方法EI北大核心CSCD

道路场景作为人类发展演变中最重要、最复杂、最典型的载体之一,是道路基础设施与活动行为共同构成的综合体,链接和构建“人地关系”。道路场景感知从二维抽象简略到三维精细丰富、从静态过去式向动态现在时发展,成为智慧城市、智能交通、无人驾驶的关键技术支撑,是我国新型城镇化战略、交通强国战略的核心技术保障。本文立足于时空交通大数据,提出基于道路场景静态基础设施“形”和动态活动行为“流”的高时空分辨率道路场景感知方法。该方法从静态路网“形”角度,以“点-线-面-体”等要素为研究脉络,构建高精度道路地图众包感知的理论体系;在活动行为“流”感知上,突破传统的点模式分析局限,发展了道路网络空间活动流的时空建模与多尺度分析方法。本文揭示了静态基础设施“形”结构与动态活动行为“流”模式交互作用下的道路场景演化规律,形成以“形”控“流”、借“流”定“形”、“形”“流”叠置的高时空精度道路场景众包感知理论体系,为智慧城市、智能交通的发展提供核心技术与数据支撑。     

Simulating Complex Adaptive Geographic Systems: A Geographically Aware Intelligent Agent Approach

The objective of this paper is to present a spatially explicit agent-based simulation framework with a supporting software package to explore complex adaptive geographic systems. This framework is particularly suitable for modeling entities that are contextually aware, knowledge driven, and adaptive because it represents them as geographically aware intelligent agents. Fundamental advances in the explicit representation of contextual information, knowledge structures, and learning processes are needed for modeling intelligent agents situated within geographic systems. The representation of these agents requires the integration of agent-based models, machine learning, and GIS. Existing software packages for agent-based modeling, however, often provide insufficient support for this integration. The agent-based simulation package presented here is specifically designed to achieve such integration by assisting the development of agent-based models from the simulation framework. Object-oriented modeling techniques were used to implement this simulation package, which includes four modules: simulation, visualization, learning, and geoprocessing. In particular, the learning and geoprocessing modules facilitate the representation of adaptive behavior in agents within spatially explicit environments. The utility of the agent-based simulation package is illustrated using two simulation models: one of adaptive elk behavior and another of pedestrian movement. The successful design of the simulation models suggests that the modeling framework with the supporting software package is well suited to the resolution of complex adaptive geographic problems.     

A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree,Markov chain and cellular automata models in GIS

Urban development is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. To understand urban expansion, it is important to establish models that can simulate urbanization process and its deriving factors behaviours, monitor deriving forces interactions and predict spatio-temporally probable future urban growth patterns explicitly. In this research, therefore, we presented a hybrid model that integrates the chi-squared automatic integration detection decision tree (CHAID-DT), Markov chain (MC) and cellular automata (CA) models to analyse, simulate and predict future urban expansions in Tripoli, Libya in 2020 and 2025. First, CHAID-DT model was applied to investigate the contributions of urban factors to the expansion process, to explore their interactions and to provide future urban probability map; second, MC model was employed to estimate the future demand of urban land; third, CA model was used to allocate estimated urban land quantity on the probability map to present future projected land use map. Three satellite images of the study area were obtained from the periods of 1984, 2002 and 2010 to extract land use maps and urban expansion data. We validated the model with two methods, namely, receiver operating characteristic and the kappa statistic index of agreement. Results confirmed that the proposed hybrid model could be employed in urban expansion modelling. The applied hybrid model overcame the individual shortcomings of each model and explicitly described urban expansion dynamics, as well as the spatio-temporal patterns involved.     

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.