共查询到20条相似文献，搜索用时 625 毫秒
1.
以上海市青浦区为例,采用数据包络模型、空间自相关模型、多元Logistic回归模型、地理探测器和层次聚类模型分析该区184个行政村社会?生态系统脆弱性的空间差异及地理影响机制。研究表明：① 基于熵权集结交叉的“投入?产出”效率模型对村域社会?生态系统脆弱性评价结果更具可信度和精确性,2018年行政村社会?生态系统脆弱性空间上呈由东向西逐渐降低的变化趋势,脆弱性均值为0.583;② 地理因素对经济发达村域社会?生态系统脆弱性空间分异仍旧具有重要影响。距上海市中心距离、距淀山湖距离、距青浦区中心距离和水域面积成为影响该区社会?生态系统脆弱性的4种主导地理因素,其地理影响力呈现系统结构空间差异和种类属性替代及程度转化;③ 依据地理因素影响力聚类分析将该区社会?生态系统脆弱性地理因素空间耦合模式分为10种,多地理因素耦合模式是主要决定类型,呈现中部多因素主导和两侧单因素主导并存的多元环状地域决定格局;针对不同类型提出调控区域社会?生态系统脆弱性的可行方式。 相似文献
2.
Zhe Zhang Urška Demšar Shaowen Wang Kirsi Virrantaus 《International journal of geographical information science》2018,32(2):349-366
Spatial objects can be interconnected and mutually dependent in complex ways. In Geographical Information Science, spatial objects’ topological relationships are not discussed together with their attributes’ dependencies, and the vagueness of spatial objects is often ignored during the spatial modelling process. To address this, a spatial fuzzy influence diagram (SFID) is introduced. Compared to the traditional statistical or fuzzy modelling approach, the influence diagram brings advantages in helping decision-makers structure complex interdependency problems. A questionnaire was developed to evaluate the applicability of using an influence diagram in modelling spatial objects’ dependencies. As a case study, an SFID is applied to tree-related electric outages. The result of the case study is represented as a vulnerability map of electrical networks. The map shows areas at risk due to tree-related electric outages. The results were first validated by using a visual comparison of the vulnerability map and electricity fault data. In the second validation step, the percentage of fault data, which has received values in different vulnerability categories, was calculated. The results of the case study can be used to support the decision-making process of electrical network maintenance and planning. 相似文献
3.
Taking the semi-arid area of Yulin City as an example, this study improves the vulnerability assessment methods and techniques at the county scale using the VSD (Vulnerability Scoping Diagram) assessment framework, integrates the VSD framework and the SERV (Spatially Explicit Resilience-Vulnerability) model, and decomposes the system vulnerability into three dimensions, i.e., exposure, sensitivity and adaptive capacity. Firstly, with the full understanding of the background and exposure risk source of the research area, the vulnerability indexes were screened by the SERV model, and the index system was constructed to assess the characteristics of the local eco-environment. Secondly, with the aid of RS and GIS, this study measured the spatial differentiation and evolution of the social-ecological systems in Yulin City during 2000–2015 and explored intrinsic reasons for the spatial-temporal evolution of vulnerability. The results are as follows: (1) The spatial pattern of Yulin City’s SESs vulnerability is “high in northwest and southeast and low along the Great Wall”. Although the degree of system vulnerability decreased significantly during the study period and the system development trend improved, there is a sharp spatial difference between the system vulnerability and exposure risk. (2) The evolution of system vulnerability is influenced by the risk factors of exposure, and the regional vulnerability and the spatial heterogeneity of exposure risk are affected by the social sensitivity, economic adaptive capacity and other factors. Finally, according to the uncertainty of decision makers, the future scenarios of regional vulnerability are simulated under different decision risks by taking advantage of the OWA multi-criteria algorithm, and the vulnerability of the regional system under different development directions was predicted based on the decision makers' rational risk interval. 相似文献
4.
Dr Helen Mounsey 《International journal of geographical information science》2013,27(3):259-261
Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail. 相似文献
5.
《自然地理学》2013,34(2):130-153
Contamination of ground water has been a major environmental concern in recent years. The potential for ground-water contamination by pesticides depends on porous media, solute, and hydrologic parameters. Although sophisticated deterministic computer models are available for assessing aquifer-contamination potential on a site-by-site basis, most deterministic models are too complex for vulnerability assessment on a regional scale because they require input data that are spatially and temporally variable, and which may not be available at this scale. Therefore, development of an affordable model that is robust under conditions of uncertainty at the watershed scale with minimum input of field data becomes a useful ground-water management tool. The purpose of this study was to examine the usefulness of fuzzy rule-based techniques in predicting aquifer vulnerability to pesticides at the regional scale. The objectives were to (1) develop fuzzy rule-based models using the same input parameters contained in an index-based model (i.e., the modified DRASTIC model), (2) determine the sensitivity of fuzzy rule model predictions, (3) compare the outputs of the fuzzy rule-based models with those of the modified DRASTIC model and with the results of aquifer water-quality analyses, and (4) examine the spatial variability of field parameters around contaminated wells of the Alluvial aquifer in Woodruff County Arkansas. The fuzzy rule-based model for objective (1) was developed using similar parameter weights and ratings as the modified DRASTIC model. For objective (2), fuzzy rule-based models were created using fewer parameters than the modified DRASTIC model. Sensitivity of the fuzzy rule-based models was determined using different combinations of weights of the four input parameters in DRASTIC. It was found that variations in the weights of the input parameters and number of fuzzy sets influenced the location of the aquifer-vulnerability categories as well as the area within each fuzzy category. The fuzzy rule models tended to predict somewhat higher vulnerabilities of the Alluvial aquifer than the modified DRASTIC model. The fuzzy rule base that had the soil-leaching index (S) as the highest weight was chosen as the best fuzzy rule model in predicting potential contamination by pesticides of the aquifer. In general, the fuzzy rule models tended to overestimate the vulnerability of the aquifer in the study area. 相似文献
6.
The damage of dwelling houses constitutes the primary cause of casualties and asset loss in seismic disasters that occurred in Chinese rural areas. The structure of houses is crucial for assessing the vulnerability of rural houses. However, at present, available data on rural housing structure are incomplete and their spatial scales are inconsistent. This paper estimated the amount and ratio of rural houses in five structures, namely ’wood’, ’brick’, ’mixed’, ’reinforced concrete’, and ’other’, for 2380 counties across China. With the percent-age sampling census data in 2005, four accuracy levels were specified. Then, a set of down-scaling models were established, where the impact of climate, economic development level and ethnic minority cultural factors on rural housing structure, as well as the spatial autocorrelation of neighboring spatial units were considered. Based on the estimation results, a database of county-level rural housing structure was established, based on which the vul-nerability of rural houses in different areas was clarified. 相似文献
7.
基于VSD框架的半干旱地区社会—生态系统脆弱性演化与模拟 总被引：9，自引：0，他引：9
利用VSD（Vulnerability scoping diagram）评估框架,改进了针对县域尺度的脆弱性评估方法和技术;以黄土高原半干旱地区的榆林市为例,对VSD框架和SERV（Spatially Explicit Resilience-Vulnerability）模型进行整合,将系统脆弱性分解为暴露、敏感性、适应能力3个维度。应用SERV模型筛选脆弱性指标,在充分理解区域地理背景和暴露风险源的基础上,构建了符合当地生态环境特征的指标体系,运用RS与GIS空间技术,定量测度了榆林市2000-2011年社会—生态系统脆弱性空间分异特征及演化趋势,探讨了脆弱性时空演化内在原因。结果显示：榆林市社会—生态系统脆弱性呈现“西北东南高,长城沿线低”的空间格局,2000-2011年间系统脆弱性程度明显降低,系统发展趋势好转,但系统脆弱性与暴露风险空间差异显著;其中,暴露风险因子是系统脆弱性演化的关键因素,区域脆弱性与暴露风险空间异质受社会敏感性和经济适应能力等因子影响。最后,采用OWA多准则算法,基于决策者不确定性偏好,模拟了不同决策风险设置下区域脆弱性未来情景,并基于决策者理性风险区间,预测了不同发展导向下区域系统脆弱性差异,为研究区可持续性评估和降低脆弱性的风险预警机制建设提供决策参考。 相似文献
8.
宁夏永宁县试验区内各合并村庄受到外部扰动时会表现出一定程度的脆弱性,并通过敏感性与适应力状况表达出来。基于结构方程模型原理构建合并村庄测量模型、一阶脆弱性SEM,运算处理试验区内8个合并村庄1600份有效问卷的调查数据,优化出合并村庄脆弱性评估指标,估算评估指标权重;依据优化出的脆弱性评估指标及评估指标权重构建灰色定权聚类模型,从扰动、敏感性、适应力3个维度评估合并村庄的脆弱性。结果表明:试验区内合并村庄整体上扰动效果差、敏感性较强、适应力一般。脆弱性规律为扰动效果好则敏感性弱、适应力强、脆弱性程度低,扰动效果差则敏感性较强或强、适应力一般、脆弱性程度高或较高。 相似文献
9.
甘南高原乡村社会固有脆弱性及其影响因素 总被引：2，自引：1，他引：1
开展社会固有脆弱性研究不仅有助于了解区域社会系统的可持续发展状态,更有助于寻求提高社会系统适应能力的恰当策略。通过构建乡村社会固有脆弱性分析框架和评价指标体系,并以甘南高原为研究区,在分析乡村社会固有脆弱性特征的基础上识别了其关键影响因子。结果表明：① 甘南高原乡村社会固有脆弱性呈“梭型”分异,其中,高、中、低脆弱性乡镇分别占18.95%、47.37%、33.68%。② 社会固有脆弱性存在明显的区域差异性,从农区-半农半牧区-牧区、高山峡谷区-山地丘陵区-山原区,社会固有脆弱性趋于增加;同时,随着少数民族聚居度降低、经济发展水平升高,社会固有脆弱性趋于降低。③ 社会固有脆弱性在空间上呈“北高南低”的不均衡分布,敏感性呈“中间高四周低”的集聚分布,适应能力呈相对均衡的“碎片化”分布。④ 气温、生育结构、民族结构、住房安全性、社会弱势群体比重、经济条件、信息可得性和参保比例是影响社会固有脆弱性的关键因子。最后,提出相应的对策建议。 相似文献
10.
高温给城市人口健康和社会发展带来的脆弱性后果愈发严重,如何科学量化与评估城市高温人口脆弱性,为制定更具针对性的高温适应对策提供科学依据成为当前国际研究热点。在城市高温人口脆弱性分析框架基础上,以西安市为例,整合遥感影像、手机信令、POI、社会经济等多源数据,从高温暴露、敏感性、适应能力3个维度构建高温影响下人口脆弱性评估指标体系和脆弱性测度模型,揭示高温人口脆弱性等级分布特征和空间异质性,识别人口脆弱性空间地域及其致脆因子类型。结果表明：① 西安市高温暴露、敏感性和适应能力都表现出显著的空间集聚特征,且总体上均呈现出“中心-边缘”结构,即城市中心地区形成“高暴露、高敏感、高适应”,城市边缘表现为“低暴露、低敏感、低适应”。② 西安市人口脆弱性以低值和较低水平为主,脆弱性也具有显著的空间集聚性,脆弱性较高地区主要集中在城市三环以内,形成断续的“岛状”分布形态,脆弱性低值和较低值地区分布在城市边缘。③ 不同致脆类型的面积占比依次为综合主导型（37.3%）>高温暴露主导型（33.3%）>适应能力不足主导型（23.6%）>人口敏感主导型（5.8%）;高温暴露主导型广泛分布在城市中心、北部和西部等地,人口敏感主导型相对集中在城市中心偏南地区,适应能力不足主导型主要分布在城市边缘,综合主导型主要集中在城市南部,北部也有大量分散式分布。本研究可在城市高温人口脆弱性评估方法,城市高温人口脆弱性的减缓与治理等方面提供借鉴和启示。 相似文献
11.
Lian-Fa Li Jin-Feng Wang Hareton Leung 《International journal of geographical information science》2013,27(12):1759-1784
Vulnerability refers to the degree of an individual subject to the damage arising from a catastrophic disaster. It is affected by multiple indicators that include hazard intensity, environment, and individual characteristics. The traditional area aggregate approach does not differentiate the individuals exposed to the disaster. In this article, we propose a new solution of modeling vulnerability. Our strategy is to use spatial analysis and Bayesian network (BN) to model vulnerability and make insurance pricing in a spatially explicit manner. Spatial analysis is employed to preprocess the data, for example kernel density analysis (KDA) is employed to quantify the influence of geo-features on catastrophic risk and relate such influence to spatial distance. BN provides a consistent platform to integrate a variety of indicators including those extracted by spatial analysis techniques to model uncertainty of vulnerability. Our approach can differentiate attributes of different individuals at a finer scale, integrate quantitative indicators from multiple-sources, and evaluate the vulnerability even with missing data. In the pilot study case of seismic risk, our approach obtains a spatially located result of vulnerability and makes an insurance price at a finer scale for the insured buildings. The result obtained with our method is informative for decision-makers to make a spatially located planning of buildings and allocation of resources before, during, and after the disasters. 相似文献
12.
Tarek Rashed John Weeks 《International journal of geographical information science》2013,27(6):547-576
Assessing urban vulnerability to natural hazards such as earthquakes can be regarded as an ill-structured problem (i.e. a problem for which there is no unique, identifiable, objectively optimal solution). A review of the literature indicates a number of contrasting definitions of what vulnerability means, as well as numerous conflicting perspectives on what should or should not be included within the broad assessment of vulnerability in cities. This paper reports on the findings from a project in which a GIS methodology has been developed to assess urban vulnerability through a spatial analytical procedure. First, we highlight the deficiencies of current GIS approaches to urban vulnerability analysis and discuss the ill-structured nature of the vulnerability problem. We then propose a working definition for vulnerability assessment in which vulnerability is thought of as a spatial decision problem under the conditions of uncertainty. Next, we present a methodology to incorporate this definition into a GIS framework that combines elements from the techniques of spatial multicriteria analysis and fuzzy logic. The application of this methodology is then illustrated with a case study from Los Angeles County. The results suggest that the proposed methodology may provide a new approach for analyzing vulnerability that can add to our understanding of human/hazards interaction. 相似文献
13.
基于局部聚类的网络Voronoi图生成方法研究 总被引：1，自引：1，他引：0
提出一种将网络约束下的Voronoi和空间聚类相结合的方法,通过构造局部的聚类分析方法对网络边进行加权,根据实际的点过程性质可以把权重定义为加权或者乘权,进行标准化后与道路段本身长度融合进行计算,依此生成网络Voronoi图,以期理解城市街道的空间特性。以武汉市江汉区为例,对城市网格管理系统产生的城市事件进行算法验证,结果表明,该方法提供了一种灵活的网络约束下的服务区域划分工具,可用于基于网络空间点过程影响下的服务区划分,也可用于系统性地定量刻画城市管理的动态特性。 相似文献
14.
基于2000—2018年中国省际面板数据,利用“自下而上”法和Super-SBM模型测度30个省（区、市）的旅游业碳减排潜力,借助修正的引力模型和社会网络分析方法探究其空间关联网络特征及影响因素。结果表明：①中国旅游业碳减排潜力的空间关联日趋紧密,网络密度和网络关联数呈增长态势,网络效率和网络等级度呈下降态势;②东部区域在空间关联网络中居于核心位置,对降低旅游业碳减排潜力所需要素的掌控与支配能力较强;西部区域在网络中居于边缘位置,难以影响和控制其他省（区、市）;③北京、天津、江苏和上海属于“净受益”板块,广东、浙江和福建属于“经纪人”板块,吉林、内蒙古等23省（区、市）属于“净溢出”板块;④空间邻接关系、经济发展水平差异、产业结构差异、技术创新水平差异和旅游业人数规模差异共同驱动着中国旅游业碳减排潜力空间关联网络结构的形成与演化。 相似文献
15.
采用熵值法确定权重,运用探索性空间数据分析方法对东北地区34个地级及以上城市2008—2018年城镇化质量进行测度并分析其时空格局特征。结果发现：从整体变化趋势来看,东北地区整体的城镇化质量呈现显著提升趋势,但2015—2018年增长曲线呈现出“U”字型波动特征,各子系统中“城市活力”得分呈现显著下降趋势。从时空格局特征来看,东北地区城镇化质量分布的时空分异明显,整体显著提升趋势下部分城市出现“阶段性”下降,通过LISA集聚图分析发现,东北地区城镇化质量的“高值”集聚区域主要分布在“哈长”城市群和辽中南城市群,“低值”集聚区域主要分布在黑龙江省北部地区。 相似文献
16.
Calvin A. Whealton Jery R. Stedinger Jared D. Smith Teresa E. Jordan Franklin G. Horowitz Maria C. Richards 《International journal of geographical information science》2020,34(10):2053-2076
ABSTRACT The focus of this work is general methods for prioritization or screening of project sites based on the favorability of multiple spatial criteria. We present a threshold-based transformation of each underlying spatial favorability factor into a continuous scale with a common favorability interpretation across all criteria. We compare several methods of computing site favorability and propagating uncertainty from the data to the favorability metrics. Including uncertainty allows decision makers to determine if seeming differences among sites are significant. We address uncertainty using Taylor series approximations and analytical distributions, which are compared to computationally intensive Monte Carlo simulations. Our methods are applied to siting direct-use geothermal energy projects in the Appalachian Basin, where our knowledge about any particular site is limited, yet sufficient data exist to estimate favorability. We consider four factors that contribute to site favorability: the thermal resource described by the depth to 80°C rock, natural reservoir productivity described by rock permeability and thickness, potential for induced seismicity, and the estimated cost of surface infrastructure for heat distribution. Those factors are combined in three ways. We develop favorability uncertainty propagation and sensitivity analysis methods. All methods are general and can be applied to other multi-criteria spatial screening problems. 相似文献
17.
基于集对分析的大庆市经济系统脆弱性评价 总被引：24，自引：2，他引：22
集对分析是研究客观事物之间确定性与不确定性联系的一种有效的系统理论与方法。将多个评价指标合成一个与最优评价集的相对贴近度,用来评价经济系统的脆弱性程度。基于经济系统脆弱性的内涵,从经济系统对区域可采石油资源逐渐枯竭的敏感性及应对能力两个方面建立了脆弱性评价指标体系,利用熵值法确定各评价指标的权重,运用集对分析法构建经济系统脆弱性评估模型。以典型石油城市大庆为例,分析1991年以来大庆经济系统脆弱性的演变特征及主要影响因素。结果表明:①大庆经济系统对不利扰动的敏感性呈现波动上升趋势,由1991年的0.504增至2007年的0.573;区域应对不利扰动的能力不断增强,由1991年的0.268增至2007年的0.771;经济系统脆弱性整体上呈现不断下降趋势,由初期的0.619降至2007年的0.402。②应对能力的强弱对大庆经济系统脆弱性的影响具有主导作用。③原油产量增长率、人均GDP和工业全员劳动生产率等是影响经济系统脆弱性程度的关键因子。④区域应对能力的"障碍度"分析表明,固定资产投资密度一直是第一障碍因素,而产业结构的限制集中出现在2000年以前。研究认为,大庆经济系统脆弱性呈下降趋势,但仍处于中等脆弱状态,需要重点关注主要敏感因子与障碍因子的发展变化。 相似文献
18.
Seda Şalap-Ayça Piotr Jankowski Keith C Clarke Phaedon C Kyriakidis Atsushi Nara 《International journal of geographical information science》2018,32(4):637-662
The paper presents a computationally efficient meta-modeling approach to spatially explicit uncertainty and sensitivity analysis in a cellular automata (CA) urban growth and land-use simulation model. The uncertainty and sensitivity of the model parameters are approximated using a meta-modeling method called polynomial chaos expansion (PCE). The parameter uncertainty and sensitivity measures obtained with PCE are compared with traditional Monte Carlo simulation results. The meta-modeling approach was found to reduce the number of model simulations necessary to arrive at stable sensitivity estimates. The quality of the results is comparable to the full-order modeling approach, which is computationally costly. The study shows that the meta-modeling approach can significantly reduce the computational effort of carrying out spatially explicit uncertainty and sensitivity analysis in the application of spatio-temporal models. 相似文献
19.
L. R. Lilburne T. H. Webb G. L. Benwell 《International journal of geographical information science》2013,27(3):257-279
It is becoming easier to combine environmental data and models to provide information for problem-solving by environmental policy analysts, decision-makers, and land managers. However, the scale dependencies of each of these (data, model, and problem) can mean that the resulting information is misleading or even invalid. This paper describes the development of a systematic framework (dubbed the ‘Scale Matcher’) for identifying and matching the scale requirements of a problem with the scale limitations of spatial data and models. The Scale Matcher framework partitions the complex array of scale issues into more manageable components that can be individually quantified. First, the scale characteristics of data, model, and problem are separated into their scale components of extent, accuracy, and precision, and each is associated with suitable metrics. Second, a comprehensive set of pairwise matches between these components is defined. Third, a procedure is devised to lead the user through a process of systematically comparing or matching each scale component. In some cases, the matches are simple comparisons of the relevant metrics. Others require the combination of data variability and model sensitivity to be investigated by randomly simulating data and model imprecision and inaccuracy. Finally, a conclusion is drawn as to the scale compatibility of the Data–Model–Problem trio based on the overall procedure result. Listing the individual match results as a set of scale assumptions helps to draw attention to them, making users more aware of the limitations of spatial modelling. Application of the Scale Matcher is briefly illustrated with a case study, in which the scale suitability of two sources of soil map data for identifying areas of vulnerability to groundwater pollution was tested. The Scale Matcher showed that one source of soil map data had unacceptable scale characteristics, and the other was marginal for addressing the problem of nitrate leaching vulnerability. The scale-matching framework successfully partitioned the scale issue into a series of more manageable comparisons and gave the user more confidence in the scale validity of the model output. 相似文献
20.
随着各种“城市病”问题层出不断,以及重大公共事件突发的不确定性,弹性城市正成为城市发展的新方向。在长江三角洲区域一体化上升为国家战略的背景下,利用熵值?综合指数法测算该地区41个城市2003—2018年弹性建设水平,并利用空间分析法考察其空间分布特征。研究表明：① 研究区内各城市综合弹性指数总体呈上升趋势,且各城市间差距不断缩小,基本形成沿长江、环杭州湾和浙南地区高水平弹性城市格局,但皖北、苏北等长江三角洲外围大部分地区仍处于较低弹性水平;② 各城市弹性指数在空间上呈现明显的集聚特征,“高?高”集聚于上海及其周边城市,“低?低”集聚于皖北、苏北地区,而湖州、宣城等市表现出“低?高”集聚特征;③ 工程、社会和经济弹性的分布与变化特征相对一致,直辖市、省会城市和经济强市水平较高,而研究区内的城市生态弹性水平普遍较低。未来长江三角洲地区城市工程、社会和经济弹性建设应着重缩小地区差距,自然本底较好地区应加强对生态环境的维护,经济发达地区则应提升生态环境治理水平。 相似文献