基于 CA 模型的土地利用变化研究

土地利用变化趋势及驱动力分析研究是目前全球变化研究的热点问题之一,如何合理并准确地模拟预测出土地利用变化的趋势是研究的核心。土地利用动态变化十分复杂,传统的GIS模型能很好地解决部分空间相关问题,但对复杂的时空动态变化地理现象却难以模拟。元胞自动机（ CA ）是“自下而上”的动态模拟建模框架,其时间、空间、状态都离散,是一种空间相互作用和时间因果关系都为局部的网格动力学模型,具有模拟复杂系统时空演化过程的能力。 CA模型的这些特点在土地利用演化的模拟方面较为合适,集成于GIS下的CA模型将会改善CA的模拟环境,使模拟分析的结果更加准确。     

实时GIS时空数据模型

为满足动态目标与传感器等实时观测数据获取、存储、管理、分析与可视化的要求,需要发展一种新型地理信息系统—实时GIS。本文根据实时GIS中各种地理要素的特点以及存贮管理要求,提出了一种面向动态地理对象与动态过程模拟的实时GIS时空数据模型,它将时空过程、地理对象、事件、事件类型、状态、观测等相关要素整合在一个时空数据模型中。基于该模型研发了新一代实时GIS,并以四种动态地理对象（包括移动对象、原位传感器对象、视频对象和过程模拟对象）的时空数据的实时接入、存储与可视化为例,验证的模型的可行性。     

GeoSOS在城市扩展中的应用

城市扩展是一个复杂的时空转换过程,元胞自动机(CA)是一种时空离散、状态简洁,利用简单的局部规则来模拟复杂系统时空演化过程的格网动力模型,CA在城市增长、扩展和土地利用演化的模拟等方面有着巨大的优势。本文基于Geo SOS for Arc GIS平台,分别利用Logistic-CA、ANN-CA和DT-CA这3种模型对长春市主城区1995—2005年、2005—2015年的城市扩展情况进行了模拟,结果表明Logistic-CA、DT-CA两种模型更适用于研究单一土地利用类型的模拟,ANN-CA更适用于涉及多种土地利用类型转换的模拟。而后,利用综合表现最佳的DT-CA模型对长春市主城区2015—2025年的城市扩展进行预测,模拟结果可为相关部门对土地规划的宏观决策提供一定的参考和数据支持。     

基于ECA-GIS模型的土地利用时空变化模拟方法

在城市土地利用认知基础上,本文将ECA-GIS模型的时空数据模型应用于城市土地利用演变模拟中,论述了ECA-GIS模型在模拟土地利用时空演变上的规则,并设计了基于ECA-GIS模型的城市土地利用演化模型,研究了ECA-GIS模型在土地利用演变上的模拟方法和过程。     

时态GIS数据模型及基态修正时空数据模型的扩展

针对GIS数据库更新和动态管理技术的需求，研讨了几种有代表性的时空数据模型，并利用历史库、过程库、现实库对时空过程和时空关系的描述、模拟，来扩展基态修正模型。     

基于遥感与GIS技术的土地利用时空特征研究

为了研究土地利用、土地覆盖的时空变化，本文在遥感技术与GIS技术的支持下，对土地利用的的时间动态特征和空间动态特征进行了定量分析。具体表现为通过数学建模，以湖北省为例，对湖北省近五年来的土地利用类型、土地利用程度、耕地状况、森林植被覆盖、城市扩展、水域湖泊状况等时空特征进行了动态分析，同时对湖北省土地利用变化的驱动机制进行了分析，为定性定量研究我国的土地利用、土地覆盖的时空演变提供了一种思路与方法。     

吉林市城市扩展监测及模拟研究

随着我国城市化进程的加快,其引起的城市数量的增加和城市规模的扩大已经引起学术界广泛的重视,定期或不定期地获得城市扩展信息、了解城市动态变化趋势,可为城市土地资源的规划和管理提供有力的依据。本文基于元胞自动机(CA)原理,充分利用CA在土地利用空间格局演化模拟和空间局部优化方面的优势特点,结合遥感和GIS 技术建立城市空间扩展 CA 模型,对吉林市建成区的演化过程进行模拟。结果表明,开发的CA模型具有较好的模拟效果。     

地理模型的服务化封装方法研究

<正>经过半个世纪的发展,GIS已经形成了包括GIS技术、地理信息科学与地理信息服务的综合体系。同时,随着应用领域的扩大,GIS在时空分析、过程模拟、预测预报和决策支持等方面正面临着严峻的挑战。通过地理模型的共享与集成来拓展GIS的地理分析能力是GIS发展的增长点。国内外地理学家面向不同的研究目标已经建立了大量的地理模型。由于模型设计和实现上的差异,造成了     

基于遥感技术的长春市城区城镇化动态监测研究与实践

主要以吉林省长春市城区为探讨的研究对象,根据国家测绘地理信息局提出的开展地理国情监测工作的要求,阐述了在吉林省典型区域开展地理省情监测的意义及遥感技术在吉林省省情监测中的应用的优势和主要方面,并着重探讨了其中的城镇化监测研究思路,对利用RS、GIS技术对长春市城区进行土地利用遥感信息提取、分析长春市城区土地利用时空差异的各项指标、驱动因子分析、城镇化监测数据管理系统研建等方面做了详细说明,并对土地利用变化幅度、动态度、城市扩展强度、城市空间紧凑度等相关指数进行解释说明。     

土地利用动态监测中的时空数据模型研究

针对传统GIS数据模型存在的问题,开展了基于特征的时空数据模型研究.结合我国土地利用动态监测,提出了一个新的时空数据模型-基于变化特征状态的时空数据模型(SCFSTDM),该模型保持了地理现象的完整性,地理信息的完备性以及时空专题信息的有机集成,模型有利于面向目标定向分析方法的应用和时空分析与推理的实现,有利于地理数据的共享,设计和开发了基于SCFSTDM的时态土地利用信息系统,实现了基于特征实例的时空复合查询,时空推理以及动态播放等功能.     

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.     

基于支持向量机的元胞自动机及土地利用变化模拟

提出了利用遥感数据，并采用支持向量机来确定元胞自动机非线性转换规则的新方法。元胞自动机在模拟复杂地理现象时，需要采用非线性转换规则。目前元胞自动机主要采用线性方法来获取转换规则，在反映复杂的非线性地理现象时有一定的局限性。以城市扩张的模拟为例，将模拟城市系统的主要特征变量映射到Hilbert空间后，通过SVM建立最优分割超平面，分割超平面的分类决策函数由径向基核（Radial Basis Kernel）构造。利用历史遥感数据校正超平面的决策函数，确定城市元胞自动机的非线性转换规则，计算出城市发展概率。利用所提出的方法，对深圳市1988-2010年的城市发展进行了模拟，取得了较理想的模拟效果。研究结果表明，基于SVM-CA模型的模拟精度比传统MCE方法模拟精度高，MoranⅠ指数与实际更为接近。     

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.     

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。     

基于CA模型的城市空间扩展研究——义乌市为例

本文引入地理信息系统(GIS)和元胞自动机的有机集成而构筑的GeoCA-Urban模型模拟义乌市城市空间扩展的动态演化过程。结果表明:从发展速度分析,义乌市城市发展经历了一个起步—缓慢发展—爆炸发展的过程,而且爆炸式发展还在继续,从发展的空间布局上分析,义乌市在交通(主要是浙赣铁路和主要公路)、水系(义乌江)和城市中心辐射作用下,经历了带状(东北西南向)—椭圆—圆形的发展过程。通过设置转换规则、参数,真实直观地再现展示了城市空间系统的演化过程,为城市规划等提供了辅助决策。     

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.     

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.     

Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change

This research demonstrates the application of association rule mining to spatio‐temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A→B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio‐temporal association rule occurs when there is a spatio‐temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)‐based data pre‐processing is used to integrate diverse data sets, extract spatio‐temporal relationships, classify numeric data into ordinal categories, and encode spatio‐temporal relationship data in tabular format for use by conventional (non‐spatio‐temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio‐temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.     

Extraction of spatio‐temporal data about historical events from text documents

Often, we are faced with questions regarding past events and the answers are hidden in the historical text archives. The growing developments in geographic information retrieval and temporal information retrieval techniques have given new ways to explore digital text archives for spatio‐temporal data. The question is how to retrieve the answers from the text documents. This work contributes to a better understanding of spatio‐temporal information extraction from text documents. Natural language processing techniques were used to develop an information extraction approach using the GATE language processing software. The developed framework uses gazetteer matching, spatio‐temporal relationship extraction and pattern‐based rules to recognize and annotate elements in historical text documents. The extracted spatio‐temporal data is used as input for GIS studies on the time–geography context of the German–Herero resistance war of 1904 in Namibia. Related issues when analyzing the historical data in current GIS are discussed. Particularly problematic are movement data in small scale with poor temporal density and trajectories that are short or connect very distant locations.