一种基于双重距离约束的多层次空间聚类方法

以往的双重空间聚类方法通常实现的是单一层次聚类,虽然顾及了地理实体的位置属性和专题属性,但是在实施过程中,实体的空间邻近和属性相似的表示和衡量,使用了不同的变量和标准,降低了算法的效率.文章采用双重距离作为实体间的相似性度量,通过对点实体构建的Delaunay三角网中的边施加同时顾及整体与局部特性的双重距离约束,实现了点实体的多层次空间聚类.通过实际算例分析与比较,验证了方法的有效性.     

热点分析在国情监测数据分析中的应用初探

进行地理国情监测是为了使管理部门及时掌握不断变化的自然以及人文环境的变迁信息。地理国情监测包括数据的采集、存储、统计和分析等多项内容。其中数据分析包括对数据基本统计分析以及数据挖掘。作为深层次数据分析和挖掘的有效工具,空间聚类分析可以很好地分析数据之间的内在联系。本文选用热点分析,对规划容积率与土地利用数据进行分析,得到规划数据的空间聚集性结果与土地利用数据之间的关系,为地理国情监测数据分析提供参考。     

结合凝聚层次聚类的极化SAR海冰分割

针对全极化高分辨率合成孔径雷达(SAR)多噪声的特点,选择合适的海冰SAR影像纹理特征量,并基于凝聚层次聚类的思想,提出一种全极化SAR海冰分割方法。将该方法的结果与K-Means、迭代自组织聚类(ISODATA)和模糊C均值算法等经典分割方法相比较,发现该方法的碎斑明显减少,且分割结果较为准确,证明了该方法的有效性。     

DEM and SAR image based flood feature extraction techniques to map the deep and shallow flood inundated regions of known as well as remote disaster regions

It is viable to differentiate the deep and shallow flood inundated regions through a new flood feature extraction techniques named as ‘Digital Elevation Model (DEM) and Synthetic Aperture Radar (SAR) image based flood feature extraction model’. The proposed model has been built mainly on the top of DEM of the disaster region without adopting standard multi-layer GIS techniques. To meet the time related factors of flood early warning system the image clustering operations has been automated at three different levels which bifurcates the input datasets and extracts the much required end results such as deep flooded regions, shallow flood inundated regions and non-flooded regions. The model has been tested with SAR flood images of known geographical region as well as remote geographical region. The proposed model can be automated against the input SAR sensor image and corresponding DEM of the respective SAR scene of any part of the world.     

基于RTK技术的地面摄影测量方法在牲畜采食践踏路迹研究上的应用

以新疆精河县小海子山地草原牲畜采食践踏路迹为研究对象,在野外利用Trimble GPS—RTK测量仪和普通数码相机,获取了4个典型样区的像控点3维空间坐标和立体像对。在室内运用ERDAS数字摄影系统进行立体量测,获取样区DEM及正射影像数据,制作采食践踏路迹分布图,为进一步研究牲畜采食践踏路迹的形态特征及生态影响提供基础测量数据。     

大连九龙地磁台场地梯度的跟踪测量分析

介绍了大连九龙地磁监测中心建设过程中场地磁场梯度的跟踪测量情况。根据中国“地震及前兆数字观测技术规范”电磁部分的要求,在地磁台站建筑施工过程中,需要用质子旋进式磁力仪在施工现场对场地磁场背景进行监测。     

Landscapes of Risk: The Geography of Fatal Traffic Collisions in Indiana, 2003 to 2011

Potential death from traffic collisions is a common hazard faced by nearly everyone. This analysis uses geographical approaches to examine fatal collision risk in the state of Indiana from 2003 to 2011. The state averaged 757 fatal collisions annually. About 60 percent occurred in urban or suburban areas. Risk was highest in low-density exurban and rural areas and on highways, county roads, and interstates. County sheriffs and state police were more likely to face fatal collisions than municipal police. Significant black spots existed around the Illinois and Kentucky state lines. Fatal collisions were less clustered than nonfatal collisions. Fatal collisions exhibited positive spatial autocorrelation but the global Moran's I for counties was close to random. Approximately 10 percent of ninety-two counties had significant local spatial autocorrelation in fatal collision rates. Police and emergency response resource distributions should be considered in light of fatal collision black spots in the state.     

Automated geographic context analysis for volunteered information

Several studies show the impacts of (geo)social media and Volunteered Geographic Information (VGI) during crisis events, and have found intrinsic value for rescue teams, relief workers and humanitarian assistance coordinators, as well as the affected population. The main challenge is how emergency management and the public can capitalize on the abundance of this new source of information by reducing the volume to credible and relevant content.In this paper, we present the GeoCONAVI (Geographic CONtext Analysis for Volunteered Information) approach and a prototype system, designed to retrieve, process, analyze and evaluate social media content on forest fires, producing relevant, credible and actionable VGI usable for crisis events. The novelty of the approach lies in the enrichment of the content with additional geographic context information, and use of spatio-temporal clustering to support scoring and validation. Thus, the system is focusing on integrating authoritative data sources with VGI. Evaluation in case studies shows that the prototype system can handle large amounts of data with common-off-the-shelf hardware, produces valuable results, and is adaptable to other types of crisis events.     

瀑布沟水库区域小震重新定位与地震性质研究

利用双差定位法对瀑布沟水库及邻区1 834次小震进行了重新定位,并对距水库水域最近的2个小震集中区的地震性质进行了分析。重新定位后得到1 708次小震结果,定位残差由原来的0.93s降为0.21s,水平向估算误差平均为0.6km,垂直向估算误差平均为2.9km; 平面空间分布显示,重新定位地震主要分布在研究区的西南(A区)、库中区(C区)和水库大坝附近(D区); A区小震密集与其处于鲜水河断裂中南段、安宁河北段和大凉山断裂北段交会区的特殊地理位置有关。C,D区高度集中分布的小震与水库蓄水无关,属于各种建设施工造成的爆破地震。     

FunnelCloud: a cloud-based system for exploring tornado events

ABSTRACT

Recent research has shown an increase in the number of extreme tornado outbreaks per year. The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorological data. Currently, there are a large number of available meteorological datasets that can be used for such analysis. However, much of these data are distributed across multiple websites and are not accessible in a central location. This poses a significant challenge for a scientist who is interested in exploring meteorological patterns associated with tornado events. This paper presents a novel system which uses cloud-based technology for integrating, storing, exploring, analyzing, and visualizing meteorological data associated with tornado outbreaks. The system employs a novel NoSQL database schema and web services architecture for data integration and provides a user friendly interface that allows scientists to explore the spatio-temporal pattern of tornado events. Furthermore, scientists can use this interface to analyze the relationship between different meteorological variables and properties of tornado outbreaks using a number of spatio-temporal statistical and data mining methods. The efficacy of the system is demonstrated on a use case centered on the analysis of climatic indicators of large spatio-temporally clustered tornado outbreaks.