地理信息系统集成平台框架结构研究

提出了基于客户／服务器结构的地理信息系统集成平台总体结构,探讨了基于元数据的地理信息系统数据集成平台以建立物理上分布而逻辑上集中的分布式地理信息系统数据库,提出了应用符合３ＮＦ范式的关系数据库进行模型管理的模式,在此基础上探讨了地理信息系统可视化建模工具。     

M-估计法广义变分同化FY-3B/IRAS通道亮温

采用FY-3B/IRAS亮温资料进行广义变分同化研究。广义变分同化结合了经典变分同化和稳健M-估计两者的优点。区别于经典变分同化依赖于先前的质量控制并要求误差服从高斯分布,把M-估计法耦合到经典变分同化框架中,得到广义变分同化,其弱化了同化前的质量控制和误差服从高斯分布这两个条件。目标能量泛函包含M-估计以保证对离群值具有稳健性,从而能够得到较好的同化结果。对比变分同化前后的FNL资料湿度与GDAS湿度相关系数作为同化结果检验评价。具体操作过程在FNL作为背景场的基础上分别采用经典和M-估计不同的权重因子变分同化FY3B/IRAS资料,把得到的分析场与GDAS进行相关性比较,由于湿度具有较强的非高斯性,文中首先评估了安徽省13个站GPS/PWV和积分相关湿度廓线得到大气可降水量(即GDAS/PWV和FNL/PWV资料)的相关性,进一步基于信息熵自由度思想进行了近一个月IRAS 20个通道对分析场的影响贡献率诊断研究。     

运用社交网络分析淹水空间信息之关联性

随着社交网络的普遍发展,大量的讯息透过智能手机发布在个人的微博或其他社交网站。台湾地区的社交网站以脸书(Facebook)的使用量最大,平均每天有近千万笔的讯息量,大多数的讯息多以食衣住行或个人讯息为主,但从本研究所撷取自2010年至2015年的数据中显示,公众在社交网站所分享的信息中具有降雨、淹水或相关灾情的讯息,而这些讯息具有极高比例的正确性。由于社交网站无法提供私人讯息,故本研究将从社交信息中,以地点为单位撷取大量的数据信息再辅以语意关键词萃取出有关可作为淹水预判的讯息数据。为检核资料的可性度,本研究透过历史台风数据FLO-2D仿真重建淹水之空间信息进行检核。从研究比对分析中发现,经萃取后的公众信息其与灾害的关联性及正确性相当显着,故透过社交网站中大量的非结构讯息,透过语意及空间的转换,可萃取转化为防灾信息,对广域的都市治理而言,此一讯息将可作为预判区域淹水或防救灾情报之有效参考。     

珠江三角洲区域一体化研究综合信息系统的设计与应用

在分析地理信息系统技术发展与应用现状的基础上,设计了珠江三角洲区域一体化研究综合信息系统的结构框架。根据珠江三角洲的实际情况,并在经济地理学,城市地理学,区域科学等理论的指导下,开发了针对该区域一体化研究的模型分析系统。基于元数据库的数据集成与模型管理技术,能对不同数据源,不同数据类型与时空特性的多元数据处理与分析。本综合信息系统在区域产业布局,资源窨组合,城市体系空间结构演化等方面得到应用,将为     

浅谈高校测绘实验室信息化管理平台的构建

就高校测绘实验室信息化管理平台的构建进行浅析与探讨,主要针对测绘实验室所承担的工作内容,并结合实际问题进行阐述。从实验室管理员的角度,结合教师、学生、企业三方面考虑,构建高校测绘实验室信息化管理平台不仅可以提高实验教学质量,而且还能实现实验室资源的优化配置。     

全景图增强地理现实的空间认知问题

基于全景图的增强地理现实平台是一种新的空间认知工具,研究其空间认知问题是进行技术研究的基础。文中介绍全景图增强地理现实的主要技术流程和优势;阐述基于全景图增强地理现实进行空间认知的基础模型;分析全景图和地理信息数据的信息匹配问题,包括比例尺和位置匹配两个方面;介绍地理信息在全景图像上的表达方式,重点解决增强目标和属性信息的选择问题;最后通过与街景地图、虚拟现实平台进行空间认知比较,得出全景图增强地理现实平台的有效性和主要适用范围,表明该平台具有独特的空间认知优势。     

Remote sensing and information value (IV) model for regional mapping of fluvial channels and topographic wetness in the Saudi Arabia

This study aims to map regions of near surface fluvial channels, mega-basins and topographic wetness in Saudi Arabia using remote sensing data and an information value (IV) model, which is a modified approach of weight of evidence. We used the new version of the Shuttle Radar Topographic Mission (SRTM) to delineate the fluvial channels, mega-basin, and slope. These hydrological parameters were used to index the topographic wetness of each mega-basin in the region based on IV in a Geographic Information System. We validated our method using the Space Imaging Radar-C and Landsat 8 images and compared the textural features (fluvial channels) evident from SRTM digital elevation model and to determine whether these patterns were different. Our results revealed that the region is drained by nine tributaries and that the Err Rub Al Khali and Sahba mega-basins have the highest value of the IV and topographic wetness values; the Arran and coastal mega-basins have the lowest value of the IV and topographic wetness values. An integrated approach is timely and economically effective and can be applied throughout the arid and semi-arid regions to help hydrologists and urban developers.     

Temporal replication of the national land cover database using active machine learning

Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time.     

On predicting and improving the quality of Volunteer Geographic Information projects

Initiatives that rely upon the contributions of volunteers to reach a specific goal are growing more and more with the success of Web 2.0–interactive applications. Also scientific projects are testing and exploiting volunteers' collaboration, but the quality of information obtained with this approach is often puzzling. This paper offers a rich overview of many scientific projects where geographic contributions are committed to volunteers, to the aim of defining strategies to improve information quality. By describing real examples of Volunteer Geographic Information (VGI), the contribution establishes a categorization based on the characteristics of the information, tasks, and scopes of the projects. After a discussion on the relationships of categories and VGI quality, the paper analyses techniques to improve the quality of volunteered information according to the moment of its assessment (i.e., ex ante, ex post, or both with respect to information creation). The paper outlines the main limitations of the different approaches and indicates some guidelines for future developments.     

A hybrid approach to modeling territorial control in violent armed conflicts

Territorial control is central to the understanding of violent armed conflicts, yet reliable and valid measures of this concept do not exist. We argue that geospatial analysis provides an important perspective to measure the concept. In particular, measuring territorial control can be seen as an application of calculating service areas around points of control. The modeling challenge is acute for areas with limited road infrastructure, where no complete network is available to perform the analysis, and movements largely occur off road. We present a new geospatial approach that applies network analysis on a hybrid transportation network with both actual road data and hexagon‐fishnet‐based artificial road data representing on‐road and off‐road movements, respectively. Movement speed or restriction can be readily adjusted using various input data. Simulating off‐road movement with hexagon‐fishnet‐based artificial road data has a number of advantages including scalability to small or large study areas and flexibility to allow all‐directional travel. We apply this method to measuring territorial control of armed groups in Sub‐Saharan Africa where inferior transport infrastructure is the norm. Based on the Uppsala Conflict Data Program's (UCDP) Georeferenced Event Data (GED) as well as spatial data on terrain, population locations, and limited transportation networks, we enhance the delineation of the specific areas directly controlled by each warring party during civil wars within a given travel time.