Loose programming of GIS workflows with geo‐analytical concepts

Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo‐analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo‐analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo‐analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.     

Structural Alignment Methods with Applications to Geospatial Ontologies

We consider the problem of enabling interoperability and information sharing among geospatial applications that use ontologies to describe their concepts and the relationships among them. We present two fully automatic alignment methods that use the graph structures of a pair of ontologies to establish their alignment, that is, the semantic correspondences between their concepts. We have tested our methods on geospatial ontologies pertaining to wetlands and four other pairs that belong to a repository that has been used in the Ontology Alignment Evaluation Initiative (OAEI). Using these ontologies, we have compared the effectiveness (precision and recall) of our methods against the Similarity Flooding Algorithm that was proposed by others and show that for each of the tested ontologies one of our methods is at least as effective as their method. We have tuned the performance of our methods by introducing a greedy approach that reduces the number of concepts that get compared. This approach reduces runtime by approximately 30% with a minor compromise to the effectiveness of the results. To further validate our approach, we participated in the OAEI competition to align a pair of ontologies, each with a few thousand concepts.     

The framework of a geospatial semantic web-based spatial decision support system for Digital Earth

Abstract

While significant progress has been made to implement the Digital Earth vision, current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to integrate data and models for simulating social and physical processes. To achieve effectiveness of decision-making using Digital Earth for understanding the Earth and its systems, new infrastructures that provide capabilities of computational simulation are needed. This paper proposed a framework of geospatial semantic web-based interoperable spatial decision support systems (SDSSs) to expand capabilities of the currently implemented infrastructure of Digital Earth. Main technologies applied in the framework such as heterogeneous ontology integration, ontology-based catalog service, and web service composition were introduced. We proposed a partition-refinement algorithm for ontology matching and integration, and an algorithm for web service discovery and composition. The proposed interoperable SDSS enables decision-makers to reuse and integrate geospatial data and geoprocessing resources from heterogeneous sources across the Internet. Based on the proposed framework, a prototype to assist in protective boundary delimitation for Lunan Stone Forest conservation was implemented to demonstrate how ontology-based web services and the services-oriented architecture can contribute to the development of interoperable SDSSs in support of Digital Earth for decision-making.     

A minimum-volume oriented bounding box strategy for improving the performance of urban cellular automata based on vectorization and parallel computing technology

ABSTRACT

As an effective tool for simulating spatiotemporal urban processes in the real world, urban cellular automata (CA) models involve multiple data layers and complicated calibration algorithms, which make their computational capability become a bottleneck. Numerous approaches and techniques have been applied to the development of high-performance urban CA models, among which the integration of vectorization and parallel computing has broad application prospects due to its powerful computational ability and scalability. Unfortunately, this hybrid algorithm becomes inefficient when the axis-aligned bounding box (AABB) of study areas contains many unavailable cells. This paper presents a minimum-volume oriented bounding box (OBB) strategy to solve the above problem. Specifically, geometric transformation (i.e. translation and rotation) is applied to find the OBB of the study area before implementing the hybrid algorithm, and a set of functions are established to describe the spatial coordinate relationship between the AABB and OBB layers. Experiments conducted in this study demonstrate that the OBB strategy can further reduce the computational time of urban CA models after vectorization and parallelism. For example, when the cell size is 15 m and the neighborhood size is 3 × 3, an approximately 10-fold speedup in computational time can result from vectorization in the MATLAB environment, followed by an 18-fold speedup after implementing parallel computing in a quad-core processor and, finally, a speedup of 25-fold by further using an OBB strategy. We thus argue that OBB strategy can make the integration of vectorization and parallel computing more efficient and may provide scalable solutions for significantly improving the applicability of urban CA models.     

CPU+GPU异构环境下数据密集型矢量多边形地理大数据并行框架

本文提出了面向CPU+GPU异构环境的数据密集型矢量多边形地理大数据并行计算框架(PFGAP)。PFGAP将数据密集型矢量多边形地理大数据的并行计算分解为算子、数据、粒度、并行环境及任务调度5个模块,分别设计相应的负载均衡并行计算策略;通过封装并行计算实现细节及数据密集型多边形算子的快速并行化。试验采用多边形三角剖分、栅格化及投影变换作为测试算例,采用土地利用数据作为测试数据,在不同类型的并行环境中计算并行效率。结果表明,PFGAP能很好地适用于不同类型的数据集、算子及并行计算环境。利用PFGAP实现的并行算法显著地降低了串行执行时间,取得了40.03的最优并行加速比。试验还分别测试了各个模块涉及的并行策略,结果表明取得的并行效率优于现有并行策略。     

Approaches to Semantic Similarity Measurement for Geo-Spatial Data: A Survey

Semantic similarity is central for the functioning of semantically enabled processing of geospatial data. It is used to measure the degree of potential semantic interoperability between data or different geographic information systems (GIS). Similarity is essential for dealing with vague data queries, vague concepts or natural language and is the basis for semantic information retrieval and integration. The choice of similarity measurement influences strongly the conceptual design and the functionality of a GIS. The goal of this article is to provide a survey presentation on theories of semantic similarity measurement and review how these approaches – originally developed as psychological models to explain human similarity judgment – can be used in geographic information science. According to their knowledge representation and notion of similarity we classify existing similarity measures in geometric, feature, network, alignment and transformational models. The article reviews each of these models and outlines its notion of similarity and metric properties. Afterwards, we evaluate the semantic similarity models with respect to the requirements for semantic similarity measurement between geospatial data. The article concludes by comparing the similarity measures and giving general advice how to choose an appropriate semantic similarity measure. Advantages and disadvantages point to their suitability for different tasks.     

A holistic approach to aligning geospatial data with multidimensional similarity measuring

Semantically aligning the heterogeneous geospatial datasets (GDs) produced by different organizations demands efficient similarity matching methods. However, the strategies employed to align the schema (concept and property) and instances are usually not reusable, and the effects of unbalanced information tend to be neglected in GD alignment. To solve this problem, a holistic approach is presented in this paper to integrally align the geospatial entities (concepts, properties and instances) simultaneously. Spatial, lexical, structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting. The presented approach is validated with real geographical semantic webs, Geonames and OpenStreetMap. Compared with the well-known extensional-based aligning system, the presented approach not only considers more information involved in GD alignment, but also avoids the artificial parameter setting in metric aggregation. It reduces the dependency on specific information, and makes the alignment more robust under the unbalanced distribution of various information.     

基于语义Web的地理服务发现框架

在传统Web服务发现架构中,仅凭接口参数的结构化相似度计算匹配方法远远不能满足复杂地理服务的发现需求。本文结合语义Web知识,设计了地理服务发现框架,框架支持地理服务语义化描述、带有语义信息的服务注册、基于语义推理的服务发现。其中,服务匹配算法是服务发现的关键,本文基于传统的四级匹配算法,提出采用分级匹配思想,并在I/O匹配中利用本体分类树,将本体相似度求解转换为分类树中节点距离求解的方法的地理服务发现匹配算法。实例证明,改进的算法不但能区分匹配等级,而且能区分同一匹配等级之间的相似度大小,能较好地满足地理服务的发现的需求。     

面状地图空间信息度量方法研究

如何计算地图的空间信息含量是评价制图质量、地图综合算法以及实现空间信息有效传输的重要基础。本文在详细分析现有的空间信息度量方法的基础上,提出了一种度量面状地图空间信息的新方法。该方法分别从几何、专题、拓扑以及专题拓扑四个方面来度量地图空间信息。通过算例分析发现,本文方法便于定量计算和分析地图空间的信息量。     

A spatial parallel heuristic approach for solving very large‐scale vehicle routing problems

The vehicle routing problem (VRP) is one of the most prominent problems in spatial optimization because of its broad applications in both the public and private sectors. This article presents a novel spatial parallel heuristic approach for solving large‐scale VRPs with capacity constraints. A spatial partitioning strategy is devised to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. An additional local search and perturbation mechanism around the border area of spatial cells is used to improve route segments across spatial cells to overcome the border effect. The results of one man‐made VRP benchmark and three real‐world super‐large‐scale VRP instances with tens of thousands of nodes verify that the presented spatial parallel heuristic approach achieves a comparable solution with much less computing time.     

Geo-Pragmatics for the Geospatial Semantic Web

Geo‐Pragmatics is introduced here as an enhanced representation for ontologies in which geospatial, geographical and geoscientific concepts are not only defined, but their pragmatic context is also captured and potentially reasoned with. A framework for representing such context is developed using three core aspects: dimensions, agents and roles. Dimensions consist of a concept's origins, uses and effects; these are generated by the interaction of human, machine and natural agents, and involve entities with roles developed from method‐driven perspectives and epistemic‐driven versions. The relationship between these core aspects is explored conceptually and implications for geoscientific ontologies are discussed, including identification of a basic ontological type, the situated concept, whose meaning is defined by its geographical‐historical context. Geo‐pragmatics should help geoscientists evaluate the scientific merit, and fitness for scientific use, of geoscientific ontologies in emerging e‐science initiatives.     

ParSymG: a parallel clustering approach for unsupervised classification of remotely sensed imagery

ABSTRACT

Symmetry is a common feature in the real world. It may be used to improve a classification by using the point symmetry-based distance as a measure of clustering. However, it is time consuming to calculate the point symmetry-based distance. Although an efficient parallel point symmetry-based K-means algorithm (ParSym) has been propsed to overcome this limitation, ParSym may get stuck in sub-optimal solutions due to the K-means technique it used. In this study, we proposed a novel parallel point symmetry-based genetic clustering (ParSymG) algorithm for unsupervised classification. The genetic algorithm was introduced to overcome the sub-optimization problem caused by inappropriate selection of initial centroids in ParSym. A message passing interface (MPI) was used to implement the distributed master–slave paradigm. To make the algorithm more time-efficient, a three-phase speedup strategy was adopted for population initialization, image partition, and kd-tree structure-based nearest neighbor searching. The advantages of ParSymG over existing ParSym and parallel K-means (PKM) alogithms were demonstrated through case studies using three different types of remotely sensed images. Results in speedup and time gain proved the excellent scalability of the ParSymG algorithm.     

利用Web挖掘技术改善公众网络地图查询服务

针对影响公众网络地图查询服务质量的一些因素,提出利用Web挖掘技术来加以改善,这主要体现于三个环节:从万维网中发现并提取地址信息以扩充空间数据库;通过对扩充后的数据库进行空间分析与推理来增强查询功能;根据分析用户查询日志来指导数据采编工作以及提供针对性的查询服务。在文章的最后给出了原型系统的设计框架与试验实例。     

A geospatial search engine for discovering multi-format geospatial data across the web

The volume of publically available geospatial data on the web is rapidly increasing due to advances in server-based technologies and the ease at which data can now be created. However, challenges remain with connecting individuals searching for geospatial data with servers and websites where such data exist. The objective of this paper is to present a publically available Geospatial Search Engine (GSE) that utilizes a web crawler built on top of the Google search engine in order to search the web for geospatial data. The crawler seeding mechanism combines search terms entered by users with predefined keywords that identify geospatial data services. A procedure runs daily to update map server layers and metadata, and to eliminate servers that go offline. The GSE supports Web Map Services, ArcGIS services, and websites that have geospatial data for download. We applied the GSE to search for all available geospatial services under these formats and provide search results including the spatial distribution of all obtained services. While enhancements to our GSE and to web crawler technology in general lie ahead, our work represents an important step toward realizing the potential of a publically accessible tool for discovering the global availability of geospatial data.     

一种基于描述逻辑的空间语义相似性计算方法

语义相似性计算广泛应用在认知心理学和计算机领域。针对地理信息智能发现服务中语义标注的应用需求,面向本体概念描述,提出了一种描述逻辑的空间语义相似性计算方法。该方法首先获取搜索和目标概念及上下文定义;然后进行标准正规化形式转换,创建映射矩阵并建立局部相似性方程;最后构建全局归一化相似性方程。通过领域专家参与实验,验证了该算法符合人类认知并且是可信的。     

A method for analyzing the expansion and shrinkage of point pattern

This article proposes a new method for analyzing the spatial expansion and shrinkage of point patterns. Spatial expansions of epidemic diseases and market areas are represented as the expansions of point patterns when disease cases and store customers are represented as points. The spatial expansion and shrinkage have been studied in many scientific fields. Existing analytical methods, however, are not sufficient for treating complicated spatiotemporal patterns. To answer this demand, this article develops a new method for analyzing the expansion and shrinkage of points. Three vector measures evaluate the degree and direction of expansion and shrinkage as functions of location and time. They are visualized as vector maps, which are valid for capturing the global spatiotemporal pattern as well as for discussing the local variation. Summary measures of these vectors allow us to grasp the overall spatiotemporal pattern efficiently. To test the validity of the proposed method, this article applies it to the analysis of visitors to Shinjuku and Ginza in Tokyo. The proposed measures permitted us to evaluate the spatiotemporal pattern of the visitors in detail and to consider its underlying structure from various perspectives, which indicated the soundness of the technique.     

A binocular parallel rendering method for VR globes

ABSTRACT

The scene-rendering mechanism based on binocular vision is one of the key techniques for the VR globe to achieve immersion-type visualization of global 3D scenes. However, this special rendering mechanism also requires that the 3D scene is continuously drawn twice within one frame, which significantly affects the rendering efficiency of VR globes. Therefore, we propose a binocular parallel rendering method. This method first improves the current rendering process of VR globes by assigning the rendering tasks for the left and right camera of VR to be processed on different CPU cores, thereby achieving parallel rendering of binocular scenes. Second, due to the problem of inconsistent resolution of binocular scenes caused by different viewpoints for the left and right cameras, we propose a resolution synchronize algorithm. this algorithm conducts real-time synchronization on the resolution of scene in the rendering process and thus avoids the problem of erroneous binocular stereo matching. Finally, we validate the effectiveness of the method in this paper through experiments. The results of experiments indicate that while the method in this paper can ensure the consistency of binocular scene resolution, it can decrease the frame time of VR globes by approximately 27% on average.     

基于语义映射的空间数据转换及其应用研究

数据转换是GIS工程建设中的一个十分重要的内容,传统的方法往往只是进行数据格式的转换,忽略了空间数据之间的内在联系,本文将语义映射和空间操作相结合,实现空间数据和属性数据的重构,从而实现异构数据的快速转换。