Cumulative spatial impact layers: A novel multivariate spatio‐temporal analytical summarization tool

Scientific inquiry often requires analysis of multiple spatio‐temporal datasets, ranging in type and size, using complex multi‐step processes demanding an understanding of GIS theory and software. Cumulative spatial impact layers (CSIL) is a GIS‐based tool that summarizes spatio‐temporal datasets based on overlapping features and attributes. Leveraging a recursive quadtree method, and applying multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets by calculating data, record, or attribute density. Providing an efficient and robust method for summarizing disparate, multi‐format, multi‐source geospatial data, CSIL addresses the need for a new integration approach and resulting geospatial product. The built‐in flexibility of the CSIL tool allows users to answer a range of spatially driven questions. Example applications are provided in this article to illustrate the versatility and variety of uses for this CSIL tool and method. Use cases include addressing regulatory decision‐making needs, economic modeling, and resource management. Performance reviews for each use case are also presented, demonstrating how CSIL provides a more efficient and robust approach to assess a range of multivariate spatial data for a variety of uses.     

A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling

Volunteered geographic information (VGI) is an emerging phenomenon where anyone can create geographic information and share it with others. Compared with traditional authoritative geospatial data, it has several advantages, such as enriched data, instant updates, and low cost. The object matching method is widely used in VGI quality assessment and data updates. However, VGI matching faces certain challenges, such as the levels of detail that vary from object to object, the uneven distribution of data quality, and the automated matching requirement. To resolve these problems, this article proposes a new matching method that effectively combines the advantages of minimum bounding rectangle combinatorial optimization (MBRCO) and relaxation labeling. The proposed method (1) avoids setting the similarity threshold and weights and does not require training samples. This process is realized based on contextual information and optimization. (2) It overcomes the disadvantage that the MBRCO algorithm cannot distinguish adjacent buildings with similar shapes. Our approach is experimentally validated using two publicly available spatial datasets: OpenStreetMap and AutoNavi map. The experimental studies show that the proposed automatic matching method outperforms all the threshold-based MBRCO methods and achieves high accuracy with a precision of 97.8% and a recall of 99.2%.     

A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

Spatial data infrastructures, which are characterized by multi‐represented datasets, are prevalent throughout the world. The multi‐represented datasets contain different representations for identical real‐world entities. Therefore, update propagation is useful and required for maintaining multi‐represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real‐world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi‐represented feature matching based on spatial similarity and a back‐propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one‐to‐one matching but also to one‐to‐many and many‐to‐many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN‐based feature matching approach.     

VGI数据与地形图数据的自动融合研究

随着科学技术的不断发展,志愿者地理信息（volunteered geographic information,VGI）已经成为地理空间数据中最为重要的来源之一。为了充分利用志愿者地理信息,需要进行VGI与传统地形图数据的匹配与融合。开发了一种全新的数据自动匹配与融合算法,其目的是将ATKIS道路网数据（由德国联邦测绘局所采集的官方数据）与AOSD数据（由大量志愿者携带定位仪器进行户外徒步或骑行所获取的轨迹数据）匹配并融合起来,从而丰富传统地理信息数据的内容,并实现数据的增值。考虑到ATKIS数据与AOSD数据在空间表达上的差异很大,所开发的算法包括了道路要素的智能化分割、道路要素匹配、道路网数据融合以及融合后道路网内部要素间的匹配运算与数据集成等4个过程。大量实地数据的测试结果表明,该算法具有匹配成功率高、准确率高、运算速度快等优点。     

A Snake-based Approach for TIGER Road Data Conflation

The TIGER (Topologically Integrated Geographic Encoding and Referencing) system has served the U.S. Census Bureau and other agencies' geographic needs successfully for two decades. Poor positional accuracy has however made it extremely difficult to integrate TIGER with advanced technologies and data sources such as GPS, high resolution imagery, and state/local GIS data. In this paper, a potential solution for conflation of TIGER road centerline data with other geospatial data is presented. The first two steps of the approach (feature matching and map alignment) remain the same as in traditional conflation. Following these steps, a third is added in which active contour models (snakes) are used to automatically move the vertices of TIGER roads to high-accuracy roads, rather than transferring attributes between the two datasets. This approach has benefits over traditional conflation methodology. It overcomes the problem of splitting vector road line segments, and it can be extended for vector imagery conflation as well. Thus, a variety of data sources (GIS, GPS, and Remote Sensing) could be used to improve TIGER data. Preliminary test results indicate that the three-step approach proposed in this paper performs very well. The positional accuracy of TIGER road centerline can be improved from an original 100 plus meters' RMS error to only 3 meters. Such an improvement can make TIGER data more useful for much broader application.     

A points of interest matching method using a multivariate weighting function with gradient descent optimization

Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning.     

面向更新信息提取与集成的空间实体匹配方法

本文为了自动发现提取新版基础地理数据层中的更新变化信息,并将其集成到用户数据层中,提出了一种依据属性、几何、拓扑关系等多源信息的空间实体复合式匹配方法.首先,阐述了该方法的基本过程以及确定实体是否匹配的判别规则;然后,在定义空间实体主要变化类型的基础上,讨论了根据匹配结果提取新增、消失等变化实体及其更新信息的基本策略;...     

Geospatial Information Integration for Authoritative and Crowd Sourced Road Vector Data

This article describes results from a research project undertaken to explore the technical issues associated with integrating unstructured crowd sourced data with authoritative national mapping data. The ultimate objective is to develop methodologies to ensure the feature enrichment of authoritative data, using crowd sourced data. Users increasingly find that they wish to use data from both kinds of geographic data sources. Different techniques and methodologies can be developed to solve this problem. In our previous research, a position map matching algorithm was developed for integrating authoritative and crowd sourced road vector data, and showed promising results ( Anand et al. 2010 ). However, especially when integrating different forms of data at the feature level, these techniques are often time consuming and are more computationally intensive than other techniques available. To tackle these problems, this project aims at developing a methodology for automated conflict resolution, linking and merging of geographical information from disparate authoritative and crowd‐sourced data sources. This article describes research undertaken by the authors on the design, implementation, and evaluation of algorithms and procedures for producing a coherent ontology from disparate geospatial data sources. To integrate road vector data from disparate sources, the method presented in this article first converts input data sets to ontologies, and then merges these ontologies into a new ontology. This new ontology is then checked and modified to ensure that it is consistent. The developed methodology can deal with topological and geometry inconsistency and provide more flexibility for geospatial information merging.     

利用松弛标记法进行空间场景匹配

相似性度量是地理学中的关键组成部分,被广泛应用于空间检索、空间信息整合及空间数据挖掘中。因为空间场景中实体个数的差异及空间对象间的关系难以精确相等,若执行空间场景的完全精确匹配,可能会使得检索结果为空。顾及尺度差异,从空间场景中进行空间语义理解,建立了多尺度空间场景的形式化描述模型,并提取场景中稳定的特征构建空间场景特征矩阵。建立场景间的初始匹配概率矩阵后,基于松弛标记法迭代更新概率矩阵,直到矩阵收敛于一全局最小值并确定匹配的实体对,从而进行空间场景相似性评估。采用武汉居民地域数据进行场景匹配实验,并对不同邻域搜索半径下的匹配时间及精确度进行对比与分析,实验结果表明,基于松弛标记法的空间场景匹配方法具有较高的精确度。     

Optimal spatial data matching for conflation: A network flow‐based approach

Spatial data conflation involves the matching and merging of counterpart features in multiple datasets. It has applications in practical spatial analysis in a variety of fields. Conceptually, the feature‐matching problem can be viewed as an optimization problem of seeking a match plan that minimizes the total discrepancy between datasets. In this article, we propose a powerful yet efficient optimization model for feature matching based on the classic network flow problem in operations research. We begin with a review of the existing optimization‐based methods and point out limitations of current models. We then demonstrate how to utilize the structure of the network‐flow model to approach the feature‐matching problem, as well as the important factors for designing optimization‐based conflation models. The proposed model can be solved by general linear programming solvers or network flow solvers. Due to the network flow formulation we adopt, the proposed model can be solved in polynomial time. Computational experiments show that the proposed model significantly outperforms existing optimization‐based conflation models. We conclude with a summary of findings and point out directions of future research.     

Assessing Data Completeness of VGI through an Automated Matching Procedure for Linear Data

New, free and fast growing spatial data sources have appeared online, based on Volunteered Geographic Information (VGI). OpenStreetMap (OSM) is one of the most representative projects of this trend. Its increasing popularity and density makes the study of its data quality an imperative. A common approach is to compare OSM with a reference dataset. In such cases, data matching is necessary for the comparison to be meaningful, and is usually performed manually at the data preparation stage. This article proposes an automated feature‐based matching method specifically designed for VGI, based on a multi‐stage approach that combines geometric and attribute constraints. It is applied to the OSM dataset using the official data from Ordnance Survey as the reference dataset. The results are then used to evaluate data completeness of OSM in several case studies in the UK.     

Enrichment of topographic road database for the purpose of routing and navigation

With growing demand on multi-purpose or multi-modal navigation, the route calculation needs to traverse semantically enriched road networks for different transportation modes. Currently, operational route planning algorithms reveal rather limited performances or their potential for comprehensive applications are constrained by the unavailable or insufficient interoperation among the underlying geo-data that are separately maintained in different spatial databases. To overcome this limitation, a novel approach has been proposed to integrate the routing-relevant information from different data sources, which involves three processes: (1) automatic matching to identify the corresponding road objects between different datasets; (2) interaction to refine the automatic matching result; and (3) transferring the routing-relevant information from one data-set to another. In process (1), the Delimited Stroke Oriented algorithm is employed to achieve the automatic data matching between different datasets, which has revealed a high matching rate and certainty. However uncertain matching problems occur in areas where topological conditions are too complicated or inconsistent. The remaining unmatched or wrongly matched objects are treated in process (2), with the help of a series of interaction tools. On the basis of refined matching results after the interaction, process (3) is dedicated to automatic integration of the routing-relevant information from different data sources.     

An iterative approach based on contextual information for matching multi‐scale polygonal object datasets

Object matching facilitates spatial data integration, updating, evaluation, and management. However, data to be matched often originate from different sources and present problems with regard to positional discrepancies and different levels of detail. To resolve these problems, this article designs an iterative matching framework that effectively combines the advantages of the contextual information and an artificial neural network. The proposed method can correctly aggregate one‐to‐many (1:N) and many‐to‐many (M:N) potential matching pairs using contextual information in the presence of positional discrepancies and a high spatial distribution density. This method iteratively detects new landmark pairs (matched pairs), based on the prior landmark pairs as references, until all landmark pairs are obtained. Our approach has been experimentally validated using two topographic datasets at 1:50 and 1:10k. It outperformed a method based on a back‐propagation neural network. The precision increased by 4.5% and the recall increased by 21.6%, respectively.     

多源点要素的全局一致性自动匹配

多源点要素的自动匹配是空间数据集成与融合的重要基础性工作.本文通过计算点要素的匹配可信度指标,建立点要素的一致性优化匹配模型,并转化为二分图最大带权匹配问题,从而实现了多源点要素的全局一致性匹配,其匹配结果更好地顾及了所有匹配要素相互之间的一致性和相似性.实验表明:相比传统方法,本文方法具有较高的匹配准确率,能够适应更...     

多级索引驱动的地名信息检索方法

针对地理空间数据交换和共享平台的地名数据日益增多及不同部门的数据差异所造成的地名检索效率低下问题,该文分析了平台中地名的表达特征,设计了面向地名信息的多级索引库组织方式,提出了地名特征词典的构建方法,设计并开发原型系统,实现了基于Lucene和地名特征词的检索框架。实验表明:多级索引通过基础索引、特征索引、分类索引三者联动的方式降低了地名检索的复杂度,具有较高的检索效率和准确度,应用于浙江省地理空间数据交换和共享平台取得了良好的效果。     

多基线机载合成孔径雷达影像匹配的SANCC法

为了有效利用合成孔径雷达(synthetic aperture radar,SAR)多基线影像的几何信息和辐射信息,提高匹配精度,提出了一种适用于多基线SAR幅度影像的自适应归一化互相关系数和(sum of adaptive normalized cross-correlation,SANCC)影像匹配方法。该方法首先利用SAR成像参数、平台参数和物方高程范围构建匹配方向线;然后引入Gestalt原理的接近性和相似性原则对窗口内的像素加权,计算获得沿匹配方向线的SANCC值;最后采用winner-take-all(WTA)优化策略获取多基线影像的匹配结果和物方三维信息。利用国产机载SAR系统获取的多基线影像进行匹配试验,结果表明与常规的相关匹配方法相比,该方法可以获得更为密集、精确的匹配点,有效减少了由重复纹理造成的误匹配,并能较好地解决由纹理匮乏导致的匹配难题。     

A Rule-Based Strategy for the Semantic Annotation of Geodata

The ability to represent geospatial semantics is of great importance when building geospatial applications for the Web. This ability will enhance discovery, retrieval and translation of geographic information as well as the reuse of geographic information in different contexts. The problem of generating semantic annotations has been recognized as one of the most serious obstacles for realizing the Geospatial Semantic Web vision. We present a rule‐based strategy for the semantic annotation of geodata that combines Semantic Web and Geospatial Web Services technology. In our approach, rules are employed to partially automate the annotation process. Rules define conditions for identifying geospatial concepts. Based on these rules, spatial analysis procedures are implemented that allow for inferring whether or not a feature in a dataset represents an instance of a geospatial concept. This automated evaluation of features in the dataset generates valuable information for the creation and refinement of semantic annotations on the concept level. The approach is illustrated by a case study on annotating data sources containing representations of lowlands. The presented strategy lays the foundations for the specification of a semantic annotation tool for geospatial web services that supports data providers in annotating their sources according to multiple domain views.     

A framework for connecting two interoperability universes: OGC Web Feature Services and Linked Data

Diverse studies have shown that about 80% of all available data are related to a spatial location. Most of these geospatial data are available as structured and semi‐structured datasets, and often use distinct data models, are encoded using ad‐hoc vocabularies, and sometimes are being published in non‐standard formats. Hence, these data are isolated within silos and cannot be shared and integrated across organizations and communities. Spatial Data Infrastructures (SDIs) have emerged and contributed to significantly enhance data discovery and accessibility based on OGC (Open Geospatial Consortium) Web services. However, finding, accessing, and using data disseminated through SDIs are still difficult for non‐expert users. Overcoming the current geospatial data challenges involves adopting the best practices to expose, share, and integrate data on the Web, that is, Linked Data. In this article, we have developed a framework for generating, enriching, and exploiting geospatial Linked Data from multiple and heterogeneous geospatial data sources. This proposal allows connecting two interoperability universes (SDIs, more specifically Web Feature Services, WFS, and Semantic Web technologies), which is evaluated through a study case in the (geo)biodiversity domain.     

地理信息公共服务平台数据更新技术——以数字沈阳地理空间框架为例

结合沈阳市地理信息公共服务平台数据更新项目,研究地理共享平台数据的更新方式、更新技术以及更新发布一体化流程等关键技术。采用增量更新技术矢量数据,基于历史要素与现势要素节点、分线段的位置关系,检测并提取数据库中更新的要素;利用分词检测方法检测地名信息的匹配度,进行地名数据的更新;基于地名与路网数据,对公交数据进行检测、采集、优化等流程,完成公交数据的更新;并建立平台数据更新与发布一体化流程。经过作业流程控制与编写辅助程序,极大提高数据更新的速度与质量控制水平。