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在全球气候变化与城市化进程加快的背景下,极端天气的产生导致城市洪涝灾害频发,成为威胁公众生命财产安全的主要自然灾害。因此,提高城市面对暴雨洪涝的能力,是城市化进程中面临的主要任务。本文以云南省昆明市主城五区为研究区域,通过洪涝灾害韧性GIS空间量化评价,对洪灾易发点、避难场所点,以及最短逃生路径、卫生医疗设施距离等进行相关分析,研究洪灾应急规划有效方法,并通过增强城市洪灾韧性,提高昆明市对洪灾的预防能力。研究结果表明,昆明市目前仍存在一定的洪涝灾害隐患,近几年随着城市规划的不断更新,昆明市主城区排水设施对于极端暴雨已经具备一定的抵抗力,但仍有提升空间。 相似文献
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区域军事地理信息系统是GIS理论和技术在军事领域应用的一个事例,本文介绍了该系统的设计目标,设计原则,系统组成,技术途径及系统建设的体会。 相似文献
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近年来,我国各地政府相继试点了一批"智慧社区",并在试点范围内开展了一系列"智慧社区系统"建设工作,但受建设成本、消费需求、技术规范、物联网设备种类的限制,使得各区域在"智慧社区"系统应用集成方面取得的进展不一致。如何保证各应用系统具有良好的可扩展性,如何使得RFID信息集成模式更加深入应用到智慧社区管理当中去,如何使RFID、GPS、RS、GIS、WCS更好的集成,以实现巡更管理及人员定位,这都是当前基于RFID的三维GIS智慧小区应用平台建设研发与探索的核心所在。基于此,本文从系统构架、应用实践效果等方面设计了三维GIS智慧小区应用平台,并重点阐述了系统的开放性与数据更新一致性。 相似文献
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针对当前智慧社区技术依据多元化问题,本文基于GIS平台对智慧社区进行了设计与开发。在设计中,对智慧社区的数据结构和系统功能进行了设计,并采用ArcGIS的Geodatabase对数据进行存储与管理,以Multipatch数据结构对社区空间实体进行三维建模。在实现时,应用ArcGIS Engine组件,在C#环境下对由ArcGIS和SketchUp交互建模的Multipatch数据进行读取,实现了社区查询与分析功能。实验证明,基于GIS的三维智慧社区可视性、智能性较强,可以满足服务公众的需求;同时,采用此法可以充分利用已有地理信息数据,节约建设成本,加快建设周期,是一种快速构建智慧社区的可行方法。 相似文献
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在探讨智能体模型与GIS集成方法的基础上,以昆明市艾滋病传播为例,采用GIS中嵌入模型的集成方式,将利用Java开发艾滋病智能体模型与利用COM开发的GIS系统通过桥接的方式进行集成。结果表明,利用桥接的方式可以很好地实现智能体模型与GIS的集成,实现了GIS对动态地理现象的分析处理能力。 相似文献
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首先介绍了地下管线规划管理的现状与需求,提出研究的背景和目标,然后通过系统总体的分析与设计,确定系统建设的原则与架构。在此基础上,结合研究区域进行系统的功能设计与研制,建立了市政管网数据库,实现了系统管理与维护功能、各专业管线子系统管理功能等。本系统可为研究区域市政管网的规划、设计与管理提供可靠的支持。 相似文献
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城市层面的火灾风险评估主要包括火灾危险性、危害性及救援能力等方面。本文选取火灾危险性评估进行针对性研究,在大数据思维的指导下,以相关关系代替因果关系,采用多源数据对评估指标权重、分值进行率定,得出福州市城区火灾危险性时空分布图。首先利用高德地图API对消防历史出警记录进行地址解析,将近万条火灾出警地址空间落点,获得福州市历史火灾空间分布;然后综合城市用地性质现状、用地开发性质、人口分布热力图等多源异构数据,探索其与历史火灾空间分布的相关性;最后以福州城区为例,初步实现具有充分数理支撑的火灾危险性评估方法,形成火灾危险性动态评估成果,为城市消防规划等提供支撑和依据。 相似文献
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社区是城市细胞和基层单位,其防灾减灾能力建设对构建城市安全体系有着极其重要的作用。本文借助于熵权-灰靶模型和GIS叠置分析技术进行社区减灾能力综合评价方法研究,首先从灾害风险评估能力、救援与保障能力等6个方面构建起包含30个二级指标的社区减灾能力评价指标体系。经过指标序列的影响空间和标准模式的构建、灰靶变换和靶心度分级,进行社区减灾能力分级评价。以苏州新区作为案例分析研究区,借助于ArcMap10.2软件得到该区域的社区减灾能力空间分布特征图。分析结果表明,研究区社区减灾能力总体上呈现东区较好,西区较弱,区域内社区减灾能力建设不平衡特征。研究区各街道和社区的灾害风险评估能力和灾害管理能力较好,工程防御能力总体分布不均衡,经济基础支撑能力、救援与保障能力、公众认知能力较差,说明社区综合减灾能力不足,今后应从单纯依赖减灾示范社区建设转变为加强社区综合减灾能力的内涵建设。 相似文献
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为了提升南通市消防站空间布局的科学性和合理性,本文在分析现有消防站布局的基础上,利用GIS和定位-配给模型完成了南通市区消防站布局优化研究。结果表明:1)在5 min响应时间标准下,现有消防站对于重点消防单位、危险源、居民小区的覆盖率分别为87.59%、86.89%、86.48%,城市消防安全保障较好,但在该区东北和东南城乡结合部消防力量严重不足。2)在综合考虑道路阻抗、水域分布、防护子区域风险等级等因素后,本优化方案指出,该区域应在现有消防站布局的基础上增设4个消防站,即南通市野生森林动物园附近、南通市竹行小学附近、南通农场中心渔场附近和通常汽渡管理处附近。该优化研究可为南通市消防站合理规划和建设提供科学的建议。 相似文献
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K. V. S. Badarinath K. Madhavilatha T. R. Kiran Chand M. S. R. Murthy 《Journal of the Indian Society of Remote Sensing》2004,32(4):343-350
Generation of fire danger maps play a vital role in forest fire management like forest fire research, locating lookout towers,
risk assessment and for various other simulation studies. The present study addresses remote sensing and GIS applications
in generating fire danger maps for tropical deciduous forests. Fire danger variables such as fuel type, topography, temperature,
and relative humidity have been used in modeling fire danger. Information on local climate patterns and past fire records
has been used to derive fire frequency map of the study area. Intermediate indices were derived using multiple regressions,
where fire frequency data is taken as dependent variable. Results indicate that forests near human settlements are more vulnerable
to forest fires. 相似文献
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R. K. Somashekar P. Ravikumar C. N. Mohan Kumar K. L. Prakash B. C. Nagaraja 《Journal of the Indian Society of Remote Sensing》2009,37(1):37-50
The Bandipur National Park situated in the Western Ghats of Karnataka State, is one of the biodiversity hotspots of the world.
During recent years, this park has witnessed repeated fires, affecting considerable areas under vegetation. The temporal satellite
data from 1997 to 2006 have been analyzed to map the burnt areas using Remote Sensing (RS) and Geographic Information System
(GIS) techniques. The vegetation cover is moist deciduous, dry deciduous, scrub forests and teak plantation. Information on
extent of the burnt areas and the type of vegetation affected were derived forest range-wise. The fire prone regions have
been identified by integrating vegetation type/density, road and settlement network and past history of forest fire occurrence,
by assigning subjective weightage according to their fire-inducing capability or their sensitivity to fire. Comparison between
each temporal dataset in terms of the extent of burnt area was also carried out to interpret fire incidence pattern. Three
categories of fire risk regions such as Low, Moderate and High fire intensity zones were identified and it was found that
almost 40% of the study area falls under low risk zone. An evaluation of the existing fire management systems and the implication
of fire prevention programmes has been discussed, besides an assessment of causal factors for fire incidence in the park. 相似文献
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近年来,风暴潮发生的频率逐年增加,给沿海地区造成巨大的经济财产损失,已成为影响沿海地区社会可持续发展的主要灾害。因此,实现快速的风暴潮风险区划等级评价,对于风暴潮灾害预测预警具有重要意义。本文以青岛地区为例,研究利用GIS分析方法进行风暴潮风险区划的等级评价,实现了风暴潮基础数据读取转换、风暴潮风险区划等级评价和风暴潮灾害预报产品制作一体化处理。 相似文献
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Eduardo Eiji Maeda Antonio Roberto Formaggio Yosio Edemir Shimabukuro Gustavo Felipe Balué Arcoverde Matthew C. Hansen 《International Journal of Applied Earth Observation and Geoinformation》2009
The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods. 相似文献