SEE: a Spatial Exploration Environment based on adirect-manipulation paradigm

The need to provide effective tools for analyzing and querying spatial data is becoming increasingly important with the explosion of data in applications such as geographic information systems, image databases, CAD, and remote sensing. The SEE (Spatial Exploration Environment) is the first effort at applying direct-manipulation visual information seeking (VIS) techniques to spatial data analysis by visually querying as well as browsing spatial data and reviewing the visual results for trend analysis. The SEE system incorporates a visual query language (SVIQUEL) that allows users to specify the relative spatial position (both topology and direction) between objects using direct manipulation. The quantitative SVIQVEL sliders (S-sliders) are complemented by the qualitative active-picture-for-querying (APIQ) interface that allows the user to specify qualitative relative position queries. APIQ provides qualitative visual representations of the quantitative query specified by the S-sliders. This increases the utility of the system for spatial browsing and spatial trend discovery with no particular query in mind. The SVIQUEL queries are processed using a k-Bucket index structure specifically tuned for incremental processing of the multidimensional range queries that represent the class of queries that can be expressed by SVIQUEL. We have also designed a tightly integrated map visualization that helps to preserve the spatial context and a bar visualization that provides a qualitative abstraction of aggregates     

Dynamic Multi-Resolution Spatial Object Derivation for Mobile and WWW Applications

Online geographic information systems provide the means to extract a subset of desired spatial information from a larger remote repository. Data retrieved representing real-world geographic phenomena are then manipulated to suit the specific needs of an end-user. Often this extraction requires the derivation of representations of objects specific to a particular resolution or scale from a single original stored version. Currently standard spatial data handling techniques cannot support the multi-resolution representation of such features in a database. In this paper a methodology to store and retrieve versions of spatial objects at different resolutions with respect to scale using standard database primitives and SQL is presented. The technique involves heavy fragmentation of spatial features that allows dynamic simplification into scale-specific object representations customised to the display resolution of the end-user's device. Experimental results comparing the new approach to traditional R-Tree indexing and external object simplification reveal the former performs notably better for mobile and WWW applications where client-side resources are limited and retrieved data loads are kept relatively small.     

基于COM组件技术的GIS空间模型库研究

已有的研究表明 ,空间分析功能是 GIS区别于其他系统的本质特征之一 ,然而当前大多数的 GIS都没有空间模型库 ,这在一定程度上制约了 GIS空间分析能力的实现 ,而对于 GIS中相同或相似的空间分析功能 ,在许多应用系统中则又分别进行了重复或二次开发 .这样的重复工作 ,显然是对人力物力资源的浪费 .通过引入软件工程的 COM组件技术 ,在研究空间模型库构建的基础上 ,设计了空间模型组件库的总体框架 ,探讨了空间模型组件的实现技术 .研究表明 ,基于 COM组件技术的空间模型组件库 ,在增强 GIS组件复用性的同时 ,也有助于 GIS的空间分析功能的实现 .     

Contextual fuzzy cognitive map for decision support in geographicinformation systems

Fuzzy cognitive maps or FCMs have been shown to be useful when representing qualitative data. We have shown that these FCM structures can be used to represent quantitative and qualitative data. We illustrate this structure applied to geographic information system (GIS) applications. We illustrate the types of CFCMs we can generate using real census data, human expert knowledge, and quantitative data in the form of maps in a GIS. The goal of this system is to use objects (topographical and conceptual) and their relationships, either supplied by census data or generated by the GIS and to map them as layers in the GIS. Using fuzzy membership functions from experiments with GIS users, we can construct CFCMs for decision support. This will also have significant applications in intelligent servants that are able to assist and interact with the human user     

Bridging Ontologies and Conceptual Schemas in Geographic Information Integration

Integration of geographic information has increased in importance because of new possibilities arising from the interconnected world and the increasing availability of geographic information. Ontologies support the creation of conceptual models and help with information integration. In this paper, we propose a way to link the formal representation of semantics (i.e., ontologies) to conceptual schemas describing information stored in databases. The main result is a formal framework that explains a mapping between a spatial ontology and a geographic conceptual schema. The mapping of ontologies to conceptual schemas is made using three different levels of abstraction: formal, domain, and application levels. At the formal level, highly abstract concepts are used to express the schema and the ontologies. At the domain level, the schema is regarded as an instance of a generic data model. At the application level, we focus on the particular case of geographic applications. We also discuss the influence of ontologies in both the traditional and geographic systems development methodologies, with an emphasis on the conceptual design phase.     

空间离群点的检测算法

空间离群点是指与其邻居具有明显区别的属性值的空间对象。已有的空间离散点检测算法一个主要的缺陷就是这些方法导致一些真正的离群点被忽略而把一些非离群点当成了空间离群点。提出了一种迭代算法,该算法通过多次迭代检测离群点,取得较好效果。实验表明该算法具有较好的实用性。     

基于GeoSOT剖分编码的多尺度空间信息区域包含关系计算方法

针对现阶段不同层次空间信息缺乏联系的问题,提出了一种基于剖分编码的多尺度空间信息间区域包含关系计算方法。首先,以GeoSOT剖分框架为组织基础,在不同尺度层上,以剖分面片作为基本单元组合表示地理对象,依据对象表达多尺度剖分编码模型将地理对象赋予剖分编码。然后,提出了基于剖分编码的区域包含关系计算方法,并给出了算法实现的具体流程。进一步采用剖分信息树状结构对空间信息进行统一管理,为剖分体系下组织与管理不同层次的空间信息提供了一种有效的解决思路。     

Approximate analysis of binary topological relations between geographic regions with indeterminate boundaries

 The development of formal models of spatial relations is a topic of great importance in spatial reasoning, geographic information systems (GIS) and computer vision, and has gained much attention from researchers across these research areas during the past two decades. In recent years significant achievements have been made on the development of models of spatial relations between spatial objects with precisely defined boundaries. However, these models cannot be directly applied to spatial objects with indeterminate boundaries which are found in many applications in geographic analysis and image understanding. This article develops a method for approximately analyzing binary topological relations between geographic regions with indeterminate boundaries based upon previous work on topological spatial relations and fuzzy sets. In addition, examples are given to demonstrate the method and related concepts. It is shown that the eight binary topological relations between regions in a two-dimensional space can be easily determined by the method.     

Composing Cardinal Direction Relations Based on Interval Algebra

Direction relations between extended spatial objects are important commonsense knowledge. Skiadopoulos proposed a formal model for representing direction relations between compound regions (the finite union of simple regions), known as SK-model. It perhaps is currently one of most cognitive plausible models for qualitative direction information, and has attracted interests from artificial intelligence and geographic information system. Originating from Allen first using composition table to process time interval constraints; composing has become the key technique in qualitative spatial reasoning to check the consistency. Due to the massive number of basic directions in SK-model, its composition becomes extraordinary complex. This paper proposed a novel algorithm for the composition. Basing the concepts of smallest rectangular directions and its original directions, it transforms the composition of basic cardinal direction relations into the composition of interval relations corresponding to Allen''s interval algebra. Comparing with existing methods, this algorithm has quite good dimensional extendibility, that is, it can be easily transferred to the tridimensional space with a few modifications.     

基于移动Agent的地理信息公共服务平台研究与设计

阐述了基于移动Agent的空间信息服务以及移动Agent与空间信息服务结合的优势,在此基础上,对基于移动Agent的地理信息公共服务平台框架和空间信息服务集成流程的实现方式进行了研究与设计。该套框架和流程能够使各级地理信息公共服务平台在不同层面上实现共享与互操作,并为空间信息服务和其他应用/服务之间提供有效的集成机制。     

Linking GIS and water resources management models: an object-oriented method

Many challenges are associated with the integration of geographic information systems (GISs) with models in specific applications. One of them is adapting models to the environment of GISs. Unique aspects of water resource management problems require a special approach to development of GIS data structures. Expanded development of GIS applications for handling water resources management analysis can be assisted by use of an object oriented approach. In this paper, we model a river basin water allocation problem as a collection of spatial and thematic objects. A conceptual GIS data model is formulated to integrate the physical and logical components of the modeling problem into an operational framework, based on which, extended GIS functions are developed to implement a tight linkage between the GIS and the water resources management model. Through the object-oriented approach, data, models and users interfaces are integrated in the GIS environment, creating great flexibility for modeling and analysis. The concept and methodology described in this paper is also applicable to connecting GIS with models in other fields that have a spatial dimension and hence to which GIS can provide a powerful additional component of the modeler’s tool kit.     

基于SIG框架的上海城市空间信息应用服务系统的体系与实现

基于空间信息网格SIG框架的上海城市空间信息应用服务系统中城市空间特征、绿化、环保、水系、土地、道路网络等各类城市空间信息资源分布在网络的不同节点上；建立了集中的元数据库提供相关数据的描述信息；以分布在不同节点上的GIS Web Service提供空间数据处理服务。平台以门户网站的方式提供元数据检索，利用检索结果获取分布式的各类空间信息数据资源；提供空间数据处理网络应用程序，调用GIS Web Service，提供格式转换、坐标转换、地图综合、空间分析等空间数据处理功能；提供GIS Web Service的搜索和注册。此平台已经在上海公务网中实际运行。     

Using a ‘value-added’ approach for contextual design of geographic information

The aim of this article is to demonstrate how a ‘value-added’ approach can be used for user-centred design of geographic information. An information science perspective was used, with value being the difference in outcomes arising from alternative information sets. Sixteen drivers navigated a complex, unfamiliar urban route, using visual and verbal instructions representing the distance-to-turn and junction layout information presented by typical satellite navigation systems. Data measuring driving errors, navigation errors and driver confidence were collected throughout the trial. The results show how driver performance varied considerably according to the geographic context at specific locations, and that there are specific opportunities to add value with enhanced geographical information. The conclusions are that a value-added approach facilitates a more explicit focus on ‘desired’ (and feasible) levels of end user performance with different information sets, and is a potentially effective approach to user-centred design of geographic information.     

模糊栅格区域的层次拓扑关系模型

空间区域拓扑关系建模是空间推理和地理信息系统(GIS)等领域的一个主要研究内容,近年来模糊空间区域建模及其拓扑关系分析显现出越来越重要的作用．根据栅格数据模型下模糊区域的特征和实际应用领域中拓扑关系分析的特点,提出了一种模糊栅格区域的层次拓扑关系模型,该模型利用3个谓词的真值来分析栅格区域间的拓扑关系,将分明栅格区域作为特例统一处理,能够根据谓词的多种真值实现模糊栅格区域在多个层次上的拓扑关系分析．该模型表达能力强、易于实现,并且在实际应用中得到了令人满意的结果．     

Extending the geographic extent of existing land cover data using active machine learning and covariate shift corrective sampling

Consistent land cover data provided at national and regional scales are increasingly relevant for a wide range of research topics from landscape ecology to population dynamics. As one example, the National Land Cover Database (NLCD) provides a valuable resource for research conducted at broad geographic scales across the US where survey-based land cover data are not available. However, the national extent of the NLCD (and similar databases produced in other countries) prevents studies from reaching across borders and thus limits potential applications at broader (e.g. multinational) scales. This article presents a framework for automated spatial extrapolation of a national land cover database, such as the NLCD using Landsat imagery alone. The extrapolation of high quality land cover data represents a unique opportunity to efficiently generate similar quality data for regions not originally covered. Extending the NLCD in the spatial domain based on remote-sensing imagery alone manifests itself as a domain adaptation challenge know as covariate shift, where the distribution of spectral information for the target data does not follow that of the source data. To overcome this problem, the algorithm implements a novel corrective sampling technique that facilitates the spatial extrapolation of land cover data. Using the corrected sample, an active machine learning routine was implemented with a maximum entropy classifier to replicate the NLCD for a different geographic extent. This framework was tested in three study sites to assess stability under different landscape conditions and the overall generalizability of the approach. Results produced similar levels of overall agreement as the NLCD when compared against reference datasets, showing that the NLCD can effectively be extended to new geographic extents using the proposed framework.     

Spatial co-location pattern mining for location-based services in road networks

With the evolution of geographic information capture and the emergency of volunteered geographic information, it is getting more important to extract spatial knowledge automatically from large spatial datasets. Spatial co-location patterns represent the subsets of spatial features whose objects are often located in close geographic proximity. Such pattern is one of the most important concepts for geographic context awareness of location-based services (LBS). In the literature, most existing methods of co-location mining are used for events taking place in a homogeneous and isotropic space with distance expressed as Euclidean, while the physical movement in LBS is usually constrained by a road network. As a result, the interestingness value of co-location patterns involving network-constrained events cannot be accurately computed. In this paper, we propose a different method for co-location mining with network configurations of the geographical space considered. First, we define the network model with linear referencing and refine the neighborhood of traditional methods using network distances rather than Euclidean ones. Then, considering that the co-location mining in networks suffers from expensive spatial-join operation, we propose an efficient way to find all neighboring object pairs for generating clique instances. By comparison with the previous approaches based on Euclidean distance, this approach can be applied to accurately calculate the probability of occurrence of a spatial co-location on a network. Our experimental results from real and synthetic data sets show that the proposed approach is efficient and effective in identifying co-location patterns which actually rely on a network.     

Activity knowledge discovery: Detecting collective and individual activities with digital footprints and open source geographic data

Digital footprints collected from social media platforms are often clustered using methods such as the density-based spatial clustering of applications with noise (DBSCAN) and its variants to identify daily travel activities (e.g., dwelling, working, entertainment, and eating). However, these clustering methods mostly only consider the spatial distribution of travel activity points while ignoring their geographic context, resulting in the aggregation of digital footprints representing different activity types into one cluster. In addition, existing works only focus on examining people's travel activities at either the collective (i.e., macro) or individual (i.e., micro) level. To this end, this study utilizes geographic context information and develops a novel activity knowledge discovery framework to better detect frequent travel activities at both levels. First, we develop a multi-level spatial clustering method to aggregate digital footprints of a group of users into collective clusters (i.e., activity zones) by inferring and integrating the underlying activities performed at each zone with OpenStreetMap (OSM) datasets that can inform geographic context of the activity zones. Next, we introduce a location-aware clustering method to detect activity zones and associate activity types at the individual level by aggregating individual footprints based on the collective results. As case studies, digital footprints from 49 selected Twitter users are analyzed to evaluate the proposed framework. The results reveal that: (1) The multi-level spatial clustering method can often detect significant collective activity zones; and (2) The location-aware clustering method can aggregate individual digital footprints into activity zones more effectively compared with existing density-based spatial clustering methods (e.g., DBSCAN).