Tessellations in GIS: Part II–making changes

Abstract

We attempt to describe the role of tessellated models of space within the discipline of Geographic Information Systems (GIS) – a speciality coming largely out of Geography and Land Surveying, where there was a strong need to represent information about the land’s surface within a computer system rather than on the original paper maps. We look at some of the basic operations in GIS, including dynamic and kinetic applications. We examine issues of topology and data structures, and produced a tessellation model that may be widely applied both to traditional “object” and “field” data types. The Part I of this study examined object and field spatial models, the Voronoi extension of objects, and the graphs that express the resulting adjacencies. The required data structures were also briefly described, along with 2D and 3D structures and hierarchical indexing. The importance of graph duality was emphasized. Here, this second paper builds on the structures described in the first, and examines how these may be modified: change may often be associated with either viewpoint or time. Incremental algorithms permit additional point insertion, and applications involving the addition of skeleton points, for map scanning, contour enrichment or watershed delineation and simulation. Dynamic algorithms permit skeleton smoothing, and higher order Voronoi diagram applications, including Sibson interpolation. Kinetic algorithms allow collision detection applications, free-Lagrange flow modeling, and pen movement simulation for map drawing. If desired these methods may be extended to 3D. Based on this framework, it can be argued that tessellation models are fundamental to our understanding and processing of geographical space, and provide a coherent framework for understanding the “space” in which we exist.     

鄱阳湖生态环境动态监测服务系统

针对目前生态环境监测中多源多尺度数据获取、定量遥感模型半自动化条件制约海量数据的快速处理与分析以及静态的时空快照服务难以表达动态变化过程等问题,该文从基于传感网的数据多元化实时感知获取、基于数据仓库的生态信息主题化动态汇聚、基于多尺度WFS的智能化服务、基于动态数据驱动的仿真动态模拟知识化应用方面考虑,设计湖泊流域生态环境动态监测服务系统。以鄱阳湖为例进行验证,为鄱阳湖流域的生态监测以及江西省生态文明试验区建设提供服务。     

Evaluating urban public facilities of Shenzhen by application of open source data

Abstract

This article applies open source data of public facilities through data mining, not only to evaluate the public facilities from an objective dimension, but also to reflect the sensory opinions of the group factually, eventually realizing the evaluation measurement of urban public facilities. The research takes Shenzhen city as an empirical case and chooses typical public facilities to mine data, resolve address and weight to explore the application of public facilities evaluation under dimension reduction of open source data. The empirical study consists of three parts. First, as the objective evaluation, we estimate the density distribution and per capita of public facility through data mining and address resolution. Second, as the subjective evaluation, we carry on the location analysis to high-score public facility through attention and satisfaction data of Internet evaluation. Finally, as mentioned above, we calculate the weight of objective and subjective evaluation of public facility, eventually formatting the comprehensive evaluation of public facilities.     

水质关键因素光谱遥感监测技术分析

针对传统水体水质分析方法过度依赖于实地测量和实验室化学分析,需要耗费大量人力物力且效率不高的问题,从水质遥感监测数据获取手段、典型反演监测模型、不同水体光谱遥感监测应用状况、水质遥感监测技术应用及前景等方面分析归纳了使用遥感技术监测水质的新方法。从可用数据类型、数据特征等方面对水质遥感监测数据获取的3种主要手段进行了比较;从复杂程度、使用场景、优缺点等方面对4种典型的反演模型进行了归纳;对遥感数据和模型在Ⅰ类和Ⅱ类水体应用中的常用组合方式进行了分析总结。     

顾及拓扑与尖角的分类矢量数据分组压缩算法

针对现有拆分重组法对存在复杂内环以及复杂相交岛屿多边形的分类矢量数据的压缩结果中包含裂缝、重叠及大量尖锐夹角的问题,提出了一种顾及拓扑一致性与尖角的分组压缩方法。依据分类矢量数据中多边形与多边形及其内环的相交关系对矢量矢量数据分组,以修正后拆分重组法结合直接分割法和间接分割法对分类矢量数据分组压缩,压缩结果中不存在裂缝与重叠现象,维护了分类矢量数据的拓扑一致性;通过取回尖角点前后被删除的点,解决了压缩结果中尖角过多的问题。以Landsat8遥感影像分类矢量数据实验验证了文章算法,并与直接分割法、间接分割法、拆分重组法进行了对比分析,验证了本文方法的有效性。     

Use of an inside buffer method to extract the extent of urban areas from DMSP/OLS nighttime light data in North China

Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime imagery provides a valuable data source for mapping urban areas. However, the spatial extents of large cities are often overestimated because of the effect of over-glow from nighttime light if a fixed thresholding technique is used. In the work reported here, an inside buffer method was developed to solve this issue. The method is based on the fact that the area overestimated is proportional to the extent of the lit area if a fixed threshold is used to extract urban areas in a region/county. Using this method, the extents of urban areas in North China were extracted and validated by interpretations from Landsat Thematic Mapper images. The results showed that the lit areas had a significant linear relationship with the urban areas for 120 representative cities in North China in 2000, with an R² value of over 0.95. This demonstrates that the inside buffer method can be used to extract urban areas. The validation results showed that the inside buffer model developed in 2000 can be directly used to extract the extent of urban areas using more recent nighttime light imagery. This is of great value for the timely updating of urban area databases in large regions or countries.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

青藏高原地区云水时空变化特征及其与降水的联系

利用1984-2009年ISCCP的云量、云光学厚度(COT)、云水路径(CWP)资料,分析了青藏高原云水的分布特征、变化趋势,及其与夏秋季降水、冬春季降雪的联系.结果表明:青藏高原地区大气中的云水有着显著的季节变化与水平分布差异;青藏高原春夏季总云量、高云云量高于秋冬季,CWP、COT与总云量的分布特征具有较好的一致性.高原云量高值区位于喀喇昆仑山与高原东南部;可可西里地区由于羌塘高压的下沉作用为云量低值区.青藏高原总云量在1984-2009年间呈现减少趋势;而CWP在高原总体以增加为主,但在各区域上的变化不一致,高原东部CWP增加而西部出现较弱的减小,这与来自孟加拉湾的水汽输送增加有关.青藏高原中东部地区夏秋季降水受云量减少影响较小而与CWP的增加相一致呈增长趋势;该地区冬春季降雪略有减少,与总云量的年际变化具有正相关.     

Fifteen Years of Ocean Observations with China Argo

For 15 years since the beginning of China Argo project, China has deployed over 350 profiling floats in Pacific and Indian ocean, and constructed China Argo ocean observing network. Moreover, we have setup the Argo data receiving, processing and distributing system, and developed various Argo data products using Argo observations, which has promoted the progress of ocean data sharing in China. The abundant Argo data have become a main data source in oceanic and atmospheric basic researches and operational applications. A batch of important achievements in basic research and operational application have been brought, e.g. in aspects of tropical cyclone (typhoon), ocean circulation, meso-scale eddy, turbulence, heat/salt storage and transport and water mass, as well as in ocean, atmosphere/climate operational forecasting and predicting. With the extension of the international Argo program from “Core Argo” to “Global Argo”, we are faced with great challenges in the long-term maintaining and sustained developing of our Argo ocean observing network. It is suggested that we should take the opportunity to construct China regional Argo ocean observing network as soon as possible in adjacent northwestern Pacific and Indian ocean using Chinese BeiDou profiling floats, which will make us to take responsibility and obligation of a big country for addressing global climate changes and preventing natural disasters.     

A Survey of the Spatio-Temporal Data Model

Traditional GIS(Geographic Information System)mostly can only describe the transient state data and does not have the ability to deal with the temporal dynamic data. When the data changes, new data will take the place of the old data, namely the transient state change into another, and the old data will disappear. Therefore, it is unable to make an analysis of the updating changes of the data and predict the development trend of the future. In this case, TGIS (Temporal Geographic Information System) emerges and expands the time dimension on the basis of traditional GIS. Spatio-temporal data model is the key to TGIS. Spatio-temporal data modeling is not only related to dynamic expression of spatio-temporal objects, but also gives an important support for spatio-temporal analysis and reasoning. This paper summarized the theories and applications status at home and abroad of spatio-temporal data model in detail, illustrated family tree of spatio-temporal data model for the nearly fifty years, discussed improvement and application status of Base State with Amendments Model, Event-based Spatio-temporal Data Model, Object-oriented Data Model and other spatio-temporal data models, and raised the existing problems of spatio-temporal data model. The current existing problems mainly includes: ①There are a lot of spatio-temporal data models put forward, but some of them only focus on semantic design and neglect the verified; ②Most of existing spatio-temporal data model are for vector data, only the Event-based Spatio-temporal Data Model is raster data structure; ③At present, the expression the time-space information of geographic entity is relative separated with spatio-temporal data model; ④Spatio-temporal data model is mainly used in cadastral management, land use and forestry data updating, less application in other fields. In the end, the future development direction was put forward of spatio-temporal data model. In the time of big data and “Internet plus”, it is necessary to explore the big data spatio-temporal data model that supports multiple data formats.