空间数据立方体的多维数据组织及存储

定义空间数据立方体地理空间维、专题维和时间维分别包含的数据种类和内容;设计它们的维和维层次数据结构;表述地理空间维、专题维和时间维在概念层次和物理层次上构成空间数据立方体的方法;确定地理空间维、专题维和时间维数据的多维数组组织方法,以及多维数据的数据文件和虚拟内存存储策略;表达多维数组中记录间的关联运算和多维数组的压缩方法。     

Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

ABSTRACT

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.     

A proposed framework for feature‐level geospatial data sharing: a case study for transportation network data

Current data sharing in the Internet environment is supported using metadata at the file level. This approach has three fundamental shortcomings. First, sharing data from different sources with different semantics, data models, and acquisition methods usually requires data conversion and/or integration like data conflation. This can be tedious and error‐prone. Second, data updated from one source cannot be automatically propagated to other related data or applications. Finally, data sharing at the file level makes it difficult to provide feature‐level data for searching, accessing, and exchanging in real time over the Internet. This paper addresses these three issues by proposing a standards‐based framework for sharing geospatial data in the transportation application domain. The proposed framework uses a standard data model—geospatial data model proposed by the Geospatial One‐Stop initiative to harmonize the semantics and data models without the use of data integration methods. It uses Geography Markup Language (GML) for geospatial data coding and feature relationship, which provides a basis to propagate the data update from one source to related other sources and applications, and to search and extract data at the feature level. The framework uses the Web Feature Service (WFS) to search, access and extract data at the feature level from distributed sources. Finally, the Scalable Vector Graphics (SVG) standard was used for data display on the Web browser. Two transportation network datasets are used in the prototype case study to implement the proposed framework. The prototype allows the user to access and extract data at the feature level on the Web from distributed sources without downloading the full data file. It shows that the proposed standards‐based feature‐level data‐sharing system is capable of sharing data without data conflation, accessing, and exchanging data in real time at the feature level. The prototype also shows that changes in one database can be automatically reflected or propagated in another related database without data downloading.     

Cartographic model of river basins of European Russia

An analysis made of the worldwide existing geoinformation systems (HydroSHEDS, CCM, Ecrins, WBD, etc.) suggests that there are as yet no models of adequate quality for the basin boundaries of small rivers in the European part of Russia. For the territory of the European part of Russia with a total area of more than 4 mln. km2 the GIS technology tools were used to construct the electron vector map of river basins and their interbasin spaces. The map thus obtained displays the basins of first-order rivers for a given level of generalization (sc 1:1 000 000). The GMTED2010 model was used as the digital elevation model. A total of 63 553 basin geosystems were identified on the map, averaging 68 km2 in area. Accuracy verification of identifying the basin boundaries showed a good agreement of areal and geometric characteristics of the method used with expert approach. In test areas, the men difference of the indicators of the area of the basins identified automatically and by use of the expert approach made up 3.6%. For areas with weakly dissected lowland topography this error does not exceed 5% while it is about 2% in areas with relatively dissected elevated topography. The basin geosystems thus identified are operational-territorial units with respect to which the geospatial data base is generated to characterize the natural-resource potential of the European territory of Russia. An example is provided for the generation of the geospatial database containing hydrological information covering 1763 hydrological stations collecting streamflow data.     

Near real-time geoprocessing on the grid: A scalable approach to road traffic monitoring

The geospatial sensor web is set to revolutionise real-time geospatial applications by making up-to-date spatially and temporally referenced data relating to real-world phenomena ubiquitously available. The uptake of sensor web technologies is largely being driven by the recent introduction of the OpenGIS Sensor Web Enablement framework, a standardisation initiative that defines a set of web service interfaces and encodings to task and query geospatial sensors in near real time. However, live geospatial sensors are capable of producing vast quantities of data over a short time period, which presents a large, fluctuating and ongoing processing requirement that is difficult to adequately provide with the necessary computational resources. Grid computing appears to offer a promising solution to this problem but its usage thus far has primarily been restricted to processing static as opposed to real-time data sets. A new approach is presented in this work whereby geospatial data streams are processed on grid computing resources. This is achieved by submitting ongoing processing jobs to the grid that continually poll sensor data repositories using relevant OpenGIS standards. To evaluate this approach a road-traffic monitoring application was developed to process streams of GPS observations from a fleet of vehicles. Specifically, a Bayesian map-matching algorithm is performed that matches each GPS observation to a link on the road network. The results show that over 90% of observations were matched correctly and that the adopted approach is capable of achieving timely results for a linear time geoprocessing operation performed every 60 seconds. However, testing in a production grid environment highlighted some scalability and efficiency problems. Open Geospatial Consortium (OGC) data services were found to present an IO bottleneck and the adopted job submission method was found to be inefficient. Consequently, a number of recommendations are made regarding the grid job-scheduling mechanism, shortcomings in the OGC Web Processing Service specification and IO bottlenecks in OGC data services.     

地理空间元数据关联网络的构建

利用资源描述框架（RDF）设计地理空间元数据关联模型,根据地理空间元数据之间的语义关系和语义相关度的计算,以构建以元数据为节点、元数据之间的语义关系为边、语义相关度为权重的关联网络。在这一网络中,一个节点是一个地理空间元数据的资源描述图,包含属性特征（数据来源、空间特征、时间特征、内容）及其关系特征（元数据之间的语义关系、语义相关度）。实验及其分析表明,地理空间元数据关联网络可以有效地支持地理空间数据语义关联检索、推荐等应用,这与传统的基于关键词的元数据检索方式相比,具有更高的准确度。     

Framework for probabilistic geospatial ontologies

Partial knowledge about geospatial categories is important for practical use of ontologies in the geospatial domain. Degree of overlaps between geospatial categories, especially those based on geospatial actions concepts and geospatial enitity concepts, need to be specified in ontologies. Conventional geospatial ontologies do not enable specification of such information, and this presents difficulties in ontology reasoning for practical purposes. We present a framework to encode probabilistic information in geospatial ontologies based on the BayesOWL approach. The approach enables rich inferences such as most similar concepts within and across ontologies. This paper presents two case studies of using road‐network ontologies to demonstrate the framework for probabilistic geospatial ontologies. Besides inferences within the probabilistic ontologies, we discuss inferences about most similar concepts across ontologies based on the assumption that geospatial action concepts are invariable. The results of such machine‐based mappings of most similar concepts are verified with mappings of concepts extracted from human subjects testing. The practical uses of probabilistic geospatial ontologies for concept matching and measuring naming heterogeneities between two ontologies are discussed. Based on our experiments, we propose such a framework for probabilistic geospatial ontologies as an advancement of the proposal to develop semantic reference systems.     

LOST-Tree: a spatio-temporal structure for efficient sensor data loading in a sensor web browser

We present LOST-Tree, a new spatio-temporal structure to manage sensor data loading and caching in a sensor web browser. In the same way that the World Wide Web needs a web browser to load and display web pages, the World-Wide Sensor Web needs a sensor web browser to access distributed and heterogeneous sensor networks. However, most existing sensor web browsers are just mashups of sensor locations and base maps that do not consider the scalability issues regarding transmitting large amounts of sensor readings over the Internet. While caching is an effective solution for alleviating the latency and bandwidth problems, a method for efficiently loading sensor data¹ from sensor web servers is currently missing. Therefore, we present LOST-Tree as a sensor data loading component that also manages the client-side cache on a sensor web browser. By applying LOST-Tree, redundant transmissions are avoided, enabling efficient loading with cached sensor data. We demonstrate that LOST-Tree is lightweight and scalable, in terms of sensor data volume. We implemented LOST-Tree in the GeoCENS sensor web browser for evaluation with a real sensor web dataset.     

Semantic Web Services‐based process planning for earth science applications

In a Web service‐based distributed environment, individual services must be chained together dynamically to solve a complex real world problem. The Semantic Web Service has shown promise for automatic chaining of Web services. This paper addresses semi‐automatic geospatial service chaining through Semantic Web Services‐based process planning. Process planning includes three phases: process modeling, process model instantiation and workflow execution. Ontologies and Artificial Intelligence (AI) planning methods are employed in process planning to help a user dynamically create an executable workflow for earth science applications. In particular, the approach was implemented in a common data and service environment enabled by interoperable standards from OGC and W3C. A case study of the chaining process for wildfire prediction illustrates the applicability of this approach.     

Aggregation-based information retrieval system for geospatial data catalogs

Geospatial data catalogs enable users to discover and access geographical information. Prevailing solutions are document oriented and fragment the spatial continuum of the geospatial data into independent and disconnected resources described through metadata. Due to this, the complete answer for a query may be scattered across multiple resources, making its discovery and access more difficult. This paper proposes an improved information retrieval process for geospatial data catalogs that aggregates the search results by identifying the implicit spatial/thematic relations between the metadata records of the resources. These aggregations are constructed in such a way that they match better the user query than each resource individually.     

A citizen data-based approach to predictive mapping of spatial variation of natural phenomena

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (B_cont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. B_cont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (?0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.     

1:40万《腾格里沙漠地貌图》的编制

腾格里沙漠虽经过多次小规模专题考察,始终未见有沙漠专题地图编制出版,多数只作为专著中的插图加以表现。为此,编制《腾格里沙漠地貌图》显得十分重要,不仅能反映出腾格里沙漠的全貌,还能体现沙漠发展规律、演变特点、分布特征等。依据腾格里沙漠的图幅范围、地貌类型以及印刷纸张规格的要求,将《腾格里沙漠地貌图》的比例尺定为1∶40万。选用1∶25万地形图作为地理基础底图,采用1∶10万地形图作为沙丘符号编绘过渡性的工作底图,专题内容编绘以Google Earth影像资料为主。《腾格里沙漠地貌图》的地貌类型包括风积地貌、风蚀地貌以及其他地貌,其中风积地貌是重点展示的内容。根据腾格里沙漠地貌类型,在编图过程中共设计了16种形态结构类型符号,采用分层设色表示沙丘相对高度,用箭头符号表示沙丘移动方向。     

GIS‐based route planning in landslide‐prone areas

Many parts of the world with young mountain chains, such as the Himalayas, are highly susceptible to landslides. Due to general ruggedness and steep slopes, roads provide the only way of transportation and connectivity in such terrains. Generally, landslide hazards are overlooked during route planning. In this study, in a test area in the Himalayas, various thematic layers, viz. landslide distribution, landslide hazard zonation, landuse/landcover, drainage order and lithology are generated and integrated using Remote Sensing–GIS techniques. The integrated data layer in raster form has been called a ‘thematic cost map’ and provides an estimate of the cost of route development and maintenance. The relative cost assignment is based on experts' knowledge. Route planning is based on neighbourhood analysis to find various movement possibilities from a pixel to its immediate neighbours. A number of patterns such as those analogous to movements in chess games have been considered. Two new neighbourhood patterns, named here Knight31 and Knight32, have been conceived in addition to commonly used Rook, Bishop and Knight patterns. The neighbourhood movement cost for moving from one pixel to a connected neighbour has been calculated for a 7×7 pixel window considering distance, gradient cost and thematic cost. Dijkstra's algorithm has been applied to compute the least‐cost route between source and destination points. A few examples are presented to show the utility of this approach for a landslide‐safe automatic route planning for a highly rugged hilly terrain.     

卫星影象上专题类型转绘方法的分析

对资源环境影象信息进行解译，最终要转换为不同类型的线划界线或图纹符号。计算机兼容磁带的图象处理，现在还难以对任何专题类型都能分类和转换成界线。因此，广泛应用的粗制卫星象片作信息解释后，还必须解决类型界线向地理底图正确转绘。本文着重分析按不同精度的影象图上类型界线转换问题。并提出,由于地理底图是经过制图综合的，它的点位精度比纠正后的景象图形精度低，转绘最后步骤应是手工的地理相关转绘。     

A formal framework for the representation of stack-based terrains

This paper presents a formal framework for the representation of three-dimensional geospatial data and the definition of common geographic information system (GIS) spatial operations. We use the compact stack-based representation of terrains (SBRT) in order to model geological volumetric data, both at the surface and subsurface levels, thus preventing the large storage requirements of regular voxel models. The main contribution of this paper is fitting the SBRT into the geo-atom theory in a seamless way, providing it with a sound formal geographic foundation. In addition we have defined a set of common spatial operations on this representation using the tools provided by map algebra. More complex geoprocessing operations or geophysical simulations using the SBRT as representation can be implemented as a composition of these fundamental operations. Finally a data model and an implementation extending the coverage concept provided by the Geography Markup Language standard are suggested. Geoscientists and GIS professionals can take advantage of this model to exchange and reuse geoinformation within a well-specified framework.     

Multicriteria generalization (MCG): a decision-making framework for formalizing multiscale environmental data reduction

Environmental simulation models need automated geographic data reduction methods to optimize the use of high-resolution data in complex environmental models. Advanced map generalization methods have been developed for multiscale geographic data representation. In the case of map generalization, positional, geometric and topological constraints are focused on to improve map legibility and communication of geographic semantics. In the context of environmental modelling, in addition to the spatial criteria, domain criteria and constraints also need to be considered. Currently, due to the absence of domain-specific generalization methods, modellers resort to ad hoc methods of manual digitization or use cartographic methods available in off-the-shelf software. Such manual methods are not feasible solutions when large data sets are to be processed, thus limiting modellers to the single-scale representations. Automated map generalization methods can rarely be used with confidence because simplified data sets may violate domain semantics and may also result in suboptimal model performance. For best modelling results, it is necessary to prioritize domain criteria and constraints during data generalization. Modellers should also be able to automate the generalization techniques and explore the trade-off between model efficiency and model simulation quality for alternative versions of input geographic data at different geographic scales. Based on our long-term research with experts in the analytic element method of groundwater modelling, we developed the multicriteria generalization (MCG) framework as a constraint-based approach to automated geographic data reduction. The MCG framework is based on the spatial multicriteria decision-making paradigm since multiscale data modelling is too complex to be fully automated and should be driven by modellers at each stage. Apart from a detailed discussion of the theoretical aspects of the MCG framework, we discuss two groundwater data modelling experiments that demonstrate how MCG is not just a framework for automated data reduction, but an approach for systematically exploring model performance at multiple geographic scales. Experimental results clearly indicate the benefits of MCG-based data reduction and encourage us to continue expanding the scope of and implement MCG for multiple application domains.     

1∶35万《库姆塔格沙漠地貌图》的编制

沙漠地貌图是认识和研究沙漠的基础图件,编制《库姆塔格沙漠地貌图》是国家科技基础性工作专项“库姆塔格沙漠综合科学考察”内容之一。本文全面总结了《库姆塔格沙漠地貌图》编制的思路和技术。考虑到库姆塔格沙漠地貌类型、分布规律及其形成和演变的需要,以及印刷纸张规格的限定,《库姆塔格沙漠地貌图》比例尺确定为1∶35万。选用的地理基础底图为20世纪70年代的1∶25万地形图,并以1∶10万地形图上作等高线和其他重要地理内容的补充。专题内容以2000—2007年的TM卫星数据为主,部分细节内容以Google Earth影像资料作补充。地貌图专题内容包含地貌类型、高度以及活动性等3层信息。地貌类型包括风成地貌、流水地貌、干燥剥蚀地貌、冰川冰缘地貌以及其他地貌等5大类,其中风成地貌是《库姆塔格沙漠地貌图》重点展示的内容。风沙地貌共分为13种类型,以符号表示,沙丘高度通过分层设色表示,用箭头符号表示沙丘移动方向。为了真实反映沙丘类型及其排列规律,本图对主要沙丘（高度一般大于10 m）都是准确定位描绘,而由于制图比例尺的限制,对一些次要沙丘（高度一般小于10 m）未作定位描绘,仅作示意。由于库姆塔格沙漠发育于阿尔金山北麓的倾斜洪积平原上,所以,洪积扇和干河道也是本图重点反映的内容。《库姆塔格沙漠地貌图》尽量应用现代计算机制图和数字化技术,对可以数字化的信息全部数字化。本图为研究库姆塔格沙漠提供了丰富信息。     

Comparison of thematic maps using symbolic entropy

Comparison of thematic maps is an important task in a number of disciplines. Map comparison has traditionally been conducted using cell-by-cell agreement indicators. More recently, other methods have been proposed that take into account not only spatially coincident cells in two maps, but also their surroundings or the spatial structure of their differences. The objective of this article is to propose a framework for map comparison that considers (1) the patterns of spatial association in two maps, in other words, the map elements in their surroundings; (2) the equivalence of those patterns; and (3) the independence of patterns between maps. Two new statistics for the spatial analysis of qualitative data are introduced that are based on the symbolic entropy of the maps. As well, all inferential elements to conduct hypothesis testing are developed. The framework is illustrated using real and simulated maps.     

多尺度地图空间相似关系基本问题研究

基于多尺度地图数据库建设中地图自动综合的终止判断条件,引出多尺度地图空间相似关系问题。基于集合论给出空间相似关系的定义,论述空间相似关系的5个性质,即反身性、对称性、非传递性、多尺度自相似性与尺度依赖性;并从图形相似和属性相似的角度论述多尺度地图空间相似关系的分类体系。该成果是研究地图自动综合和地图空间相似查询等的基础。     

国土资源知识库的建设与应用

为满足决策分析的需要和指导国土资源数据利用,从数据—知识—应用3个层次对国土资源数据进行了深层次的挖掘和利用,按照知识获取→知识表示→知识库设计、存储→知识利用技术流程建设国土资源知识库,包括国土资源空间知识库、国土资源影像知识库、国土资源专题知识库、国土资源元知识库,并给出知识库应用于国土资源综合分析系统的实例。