小城镇灾害易损性熵权与可变模糊集评估方法研究

为了客观评估小城镇的灾害易损性,提出一种熵权和可变模糊集组合评估方法.采用熵值法确定小城镇灾害易损性评估指标的权重,采用可变模糊集理论建立小城镇灾害易损性评估模型,并以湖南省小城镇为例,进行了相关研究.研究表明:熵值法通过挖掘统计数据的熵来确定评估指标的熵权,所确定的权重是客观的;可变模糊集通过相对隶属度和相对差异函数确定综合相对隶属度,并通过参数组合变换验证了评估方法的可靠性.因而熵权和可变模糊集组合评估是小城镇灾害易损性评估的一种有效方法.     

GIS中的模糊形态运算

空间数据的不确定性是当前GIS领域的研究难点之一。为了描述空间数据的模糊性,把模糊集理论引入GIS,以加强GIS对模糊现象建模的能力,因而产生了模糊数据。但现有GIS缺乏对模糊数据分析和处理的能力。该文把模糊集理论引入数学形态学,提出能处理模糊数据的模糊形态运算,并给出模糊形态运算的隶属函数,使传统的数学形态学能够处理模糊数据且容易在计算机上实现。     

制图数据的模糊分级处理模型研究

传统的制图数据分级方法存在对原始数据信息的歪曲、普适性不强及计算复杂等问题。基于此,结合现实分级问题的模糊性,提出基于模糊统计分析模型的制图数据分级处理方法。首先通过专家系统获取各模糊样本集,利用统计分析方法求得样本分布函数;然后利用分布函数获得模糊隶属函数,进而求取各模糊集的最模糊点;最后根据最模糊点获得各模糊集的区域划分,从而实现对制图数据的分级处理。该方法不需要对影响级别划分的多因子进行分析和转换,降低了计算的复杂度;另外,该方法是在获得原始数据实际分布的基础上进行的,在后续的分级过程中避免了对原始数据信息的歪曲。     

湘江新区土地利用结构多目标规划及决策偏好分析

土地利用规划需要综合考虑经济效益和生态效益。构建了经济最大化和生态效益最大化2个目标,并运用基于模糊集理论的最大(小)算子法求得兼顾经济效益和生态效益的土地利用模糊最优规划方案。随后重点探讨了不同决策偏好情景下的土地利用优化方案和综合效益的变化。研究结果表明:(1)对经济效益和生态效益的偏好程度不同,主要会导致林地、城镇建设用地和水域面积发生变化;(2)随着决策者对生态效益偏好程度的增加,城镇建设用地面积逐渐减少,林地和水域面积逐渐增加;(3)当经济效益比生态效益重要时,最优的用地规划方案主要是在城镇用地和水域面积之间进行权衡;当生态效益比经济效益重要时,最优的用地规划方案主要是在林地和城镇建设用地之间进行权衡。研究结果对湘江新区进行"资源节约型和环境友好型社会"建设中的多目标土地利用规划具有一定的决策参考价值。     

西藏阿里典型耕地土壤的主要特征及其质量评价

通过对西藏阿里地区耕地土壤的实地踏勘和采样分析，选取有机质、有效氮、有效磷、有效钾、土层厚度、pH值、物理粘粒含量、障碍层深度、地下水位、坡度等共10项土壤质量评价指标，应用基于评价指标隶属度的模糊集加权的土壤质量综合评价方法。结果表明了耕地土壤具有质地的沙质性，团聚体的低质性，富有机质的低矿化特性，以及碱性环境下的养分较丰富性等特征。灌淤土的质量较好，其次是耕作草甸土和耕作亚高山草原土，最次的是潮土。     

基于模糊评价模型的民用机场无人机监测系统评估方法

论文结合民用机场净空保护需求,针对无人机监测系统在民用机场的应用可行性和有效性进行定量评估。首先,深入分析了无人机监测系统的多维度评价指标,从系统的功能、性能、可靠性、安全性、可部署性5个评价维度设计了10个方面的系统特征,包括监测覆盖区域、有效探测距离、空间定位精度、俯仰精度、目标跟踪能力、即时性度量、报警提示置信程度、目标识别能力、可靠性度量、信息安全和可部署性度量等。然后,结合这10个系统评价特征,建立基于模糊集和模糊测度的综合评估模型,设计多类无人机监测设备在机场真实场景的实验验证。实验结果表明,该方法可有效度量无人机监测系统对民用机场的适用性和有效性等,对不同类型的技术具有较好的兼容性,评价结果对民用机场无人机监测系统选择具有较高的参考价值。     

湖南湘江新区建设用地适宜性评价研究

建设用地适宜性评价对城市规划和土地利用规划具有重要意义。将GIS和模糊综合评价方法相结合,对湖南湘江新区进行了栅格尺度的建设用地适宜性评价,并通过对现状用地和评价结果的对比,详细探讨了湘江新区今后城镇建设扩张的主要潜在区域。研究结果表明:(1)评价结果较客观地反映了研究区域建设用地适宜性的分布情况,并有效划分出了适建区、限建区和禁建区;(2)基于GIS的模糊综合评价法不但能克服多准则评价中各指标分类的不确定性,还能根据模糊集理论有效划分出不同的评价类别,是建设用地适宜性评价的有效方法。     

黄河三角洲水资源可再生性综合评价研究

对黄河三角洲水资源自然、社会可再生过程进行可再生性评价,综合构建了多层次评价指标体系;基于集对分析理论,建立指标集对评价模型,利用CRITIC法确定指标权重,引进可变模糊集模型确定样本的模糊隶属度,通过样本的级别特征值来反映水资源可再生程度,并在此基础上运用因子分析法识别、归纳影响因素,从而实现水资源可再生性的评价与分析。结果表明:黄河三角洲2007—2014年水资源可再生性评价综合得分波动式增加,表明水资源可再生能力趋于增强;但分值总体较低,水资源可再生能力形势不容乐观;评价结果与实际情况相符,说明评价方法是合理可行的,具有一定的现实指导价值。     

地理环境下的克鲁格曼式城市体系模拟分析

采用层次分析法和模糊集方法,以浙江省为研究区,构造一种能刻画地理影响程度的空间异质性的地理空间,修正克鲁格曼城市体系模型的均质空间假设,以期观察城市体系模型在经济和地理环境下的不同表现。模拟结果显示:1地理环境对模拟城市数目的波动具有持续、稳定的推动效应,但波动幅度不大,说明即使存在外生差异,城市体系演化仍旧主要靠经济系统的内生力量。2在经济或地理环境下,模拟城市的空间分布具有均衡结构,均呈现近乎对称的六边形格局。但纳入地理环境,能明显提高模拟结果与实际结果的吻合程度。3在地理环境下,4种权重模式的模拟结果各不相同。其中模拟城市的空间分布对经济权重占优模式的反应结果与真实环境比较一致,说明经济地理因素对浙江城市体系演化有较大的影响。     

乡村旅游地居民幸福感影响因素及其作用路径

采用结构方程模型与模糊集定性比较分析结合的方法,在检验影响因素净效应的基础上分析乡村旅游地居民高幸福感的条件组合,归纳引致居民幸福感的路径类型。研究发现：(1)情感团结、环境获益感知、社会文化获益感知与自我发展对居民幸福感产生显著正向影响,但简单的前因变量均不构成居民高幸福感的必要条件,各变量均需依赖与其他要素的条件组合共同作用;(2) 7种条件组合对居民高幸福感的形成具有较高解释力。自我发展与经济获益感知依赖最为明显、经济获益感知在组合中呈现双向表现、社区参与的表现并不突出;(3)居民高幸福感存在4种作用路径,情感团结、社会获益感知在4种路径中均起到了核心作用。     

Fuzzy representation of geographical boundaries in GIS

Abstract

Polygon boundaries on thematic maps are conventionally considered to be sharp lines representing abrupt changes of phenomena. However, in reality changes of environmental phenomena may also be partial or gradual. Indiscriminate use of sharp lines to represent different types of change creates a problem of boundary inaccuracy. Specifically, in the context of vector-based GIS, use of sharp lines to represent gradual or partial changes may cause misunderstanding of geographical information and reduce analysis accuracy.

In this paper, the expressive inadequacy of the conventional vector boundary representation is examined. A more informative technique—the fuzzy representation of geographical boundaries—is proposed, in which boundaries describe not only the location but also the rate of change of environmental phenomena. Four methods of determining fuzzy boundary membership grades from different kinds of geographical data are described. An example of applying the fuzzy boundary technique to data analysis is presented and the advantages of the technique are discussed.     

Propagating error in land-cover-change analyses: impact of temporal dependence under increased thematic complexity

We examined the impact of temporal dependence between patterns of error in classified time-series imagery through a simulation modeling approach. This research extended the land-cover-change simulation model we previously developed to investigate: (1) the assumption of temporal independence between patterns of error in classified time-series imagery; and (2) the interaction of patterns of change and patterns of error in a post-classification change analysis. In this research, the thematic complexity of the classified land-cover maps was increased by increasing the number of simulated land-cover classes. Simulating maps with increased categorical resolution permitted the incorporation of: (1) higher-order, more complex spatial and temporal interactions between land-cover classes; and (2) patterns of error that better reproduce the complex error interactions that often occur in time-series classified imagery. The overall modeling framework was divided into two primary components: (1) generation of a map representing true change; and (2) generation of a suite of change maps that had been perturbed by specific patterns of error. All component maps in the model were produced using simulated annealing, which enabled us to create a series of map realizations with user-defined spatial and temporal patterns. Comparing the true map of change to the error-perturbed maps of change using accuracy assessment statistics showed that increasing the temporal dependence between classification errors did not improve the accuracy of resulting maps of change when the categorical scale of the land-cover classified maps was increased. The increased structural complexity within the time series of maps effectively inhibited the impact of temporal dependence. However, results demonstrated that there are interactions between patterns of error and patterns of change in a post-classification change analysis. These interactions played a major role in determining the accuracy associated with the maps of change.     

Quality assessment of linear data

This paper presents methods to evaluate the geometric quality of spatial data. Firstly, a point‐based method is presented, adapting conventional assessment methods whereby common points between datasets are compared. In our approach, initial matches are established automatically and refined further through interactive editing. Second, a line‐based method which uses correspondences between line segments is proposed. Here, the geometry of line segments in vector is transformed into a set of rasterized values so that their combination at each pixel can restore their original vector geometry. Matching is performed on rasterized line segments and their matching lengths and displacements are measured. Experimental results show that the line‐based approach proposed is efficient to evaluate the geometric quality of spatial data without requirements of topological relationships among line features.     

Fuzzy set approach to assessing similarity of categorical maps

For the evaluation of results from remote sensing and high-resolution spatial models it is often necessary to assess the similarity of sets of maps. This paper describes a method to compare raster maps of categorical data. The method applies fuzzy set theory and involves both fuzziness of location and fuzziness of category. The fuzzy comparison yields a map, which specifies for each cell the degree of similarity on a scale of 0 to 1. Besides this spatial assessment of similarity also an overall value for similarity is derived. This statistic corrects the cell-average similarity value for the expected similarity. It can be considered the fuzzy equivalent of the Kappa statistic and is therefore called K_Fuzzy. A hypothetical case demonstrates how the comparison method distinguishes minor changes and fluctuations within patterns from major changes. Finally, a practical case illustrates how the method can be useful in a validation process.     

基于GIS栅格数据的空间模糊综合评判方法与实践

针对模糊综合评判多集中于对区域整体的综合评判的局限,提出了基于GIS的空间模糊综合评判方法。介绍了该方法的关键内容——隶属层的形成过程以及基于栅格数据的综合运算方法,并将该方法应用于门头沟煤矿区生态环境综合评价。     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

区域气候变化适应度评价——以咸阳市为例

选取经济、农业、社会和资源环境系统作为区域气候变化适应度的评价子系统,运用AHP分析法,确定各评价因子的权重,运用模糊综合评判分析法,确定对气候变化适应的隶属度并判断适应度。结果表明,咸阳市1998-2007年对气候变化适应的隶属度呈逐年增加趋势,适应度处于由"较弱"向"一般"过渡状态,区域整体及各子系统对气候变化的适应处于低水平状态。其中经济系统适应度由"弱"到"强",受不利因素影响程度小;农业系统适应度由"弱"到"一般",受不利因素影响程度较大;社会系统适应度由"较弱"到"较强";资源环境系统适应度由"弱"到"一般",受不利因素的影响程度较大,稳定性差。     

Mining significant crisp-fuzzy spatial association rules

Spatial association rule mining (SARM) is an important data mining task for understanding implicit and sophisticated interactions in spatial data. The usefulness of SARM results, represented as sets of rules, depends on their reliability: the abundance of rules, control over the risk of spurious rules, and accuracy of rule interestingness measure (RIM) values. This study presents crisp-fuzzy SARM, a novel SARM method that can enhance the reliability of resultant rules. The method firstly prunes dubious rules using statistically sound tests and crisp supports for the patterns involved, and then evaluates RIMs of accepted rules using fuzzy supports. For the RIM evaluation stage, the study also proposes a Gaussian-curve-based fuzzy data discretization model for SARM with improved design for spatial semantics. The proposed techniques were evaluated by both synthetic and real-world data. The synthetic data was generated with predesigned rules and RIM values, thus the reliability of SARM results could be confidently and quantitatively evaluated. The proposed techniques showed high efficacy in enhancing the reliability of SARM results in all three aspects. The abundance of resultant rules was improved by 50% or more compared with using conventional fuzzy SARM. Minimal risk of spurious rules was guaranteed by statistically sound tests. The probability that the entire result contained any spurious rules was below 1%. The RIM values also avoided large positive errors committed by crisp SARM, which typically exceeded 50% for representative RIMs. The real-world case study on New York City points of interest reconfirms the improved reliability of crisp-fuzzy SARM results, and demonstrates that such improvement is critical for practical spatial data analytics and decision support.     

形态学图像处理方法在扎龙湿地景观格局分析中的应用

以数学形态学图像处理技术为主要方法,对扎龙湿地景观格局进行分类,形成核心、斑块、边缘和孔隙4种格局类型,并在此基础上与土地覆盖类型图叠加处理,分析湿地不同景观格局与土地覆盖类型的关系。结果表明,基于形态学图像处理技术的景观格局分类保持了像元水平的精度,优于传统的分类方式。1979-2008年,扎龙湿地的核心湿地减少,斑块、边缘和孔隙都相应增加。作为湿地中的优势类型,含地表水的湿地逐渐转移到非核心的斑块、边缘和孔隙湿地中,原有的核心湿地逐渐变得破碎。