Assessing the Completeness of Bicycle Trail and Lane Features in OpenStreetMap for the United States

This article assesses the completeness of bicycle trail and on‐street lane features in OpenStreetMap (OSM). Comparing OSM cycling features with reference data from local planning agencies for selected US Urbanized Areas shows that OSM bicycle trails tend to be more completely mapped than bicycle lanes. Manual evaluation of mapped cycling features in OSM and Google Maps for selected test areas within the Central Business Districts of Portland (OR) and Miami (FL) through comparison with governmental datasets, satellite imagery, and Google Street View, shows that the Bicycle layer in Google Maps can help to identify some missing or erroneously mapped OSM cycling links. However, Google Maps was also found to have some gaps in its data layers, suggesting that consultation of current trail and lane data from local planning authorities, if available, should be considered as an additional data source for bicycle related planning projects.     

Positional Accuracy of TIGER 2000 and 2009 Road Networks

The Topologically Integrated Geographic Encoding and Referencing (TIGER) data are an essential part of the US Census and represent a critical element in the nation's spatial data infrastructure. TIGER data for the year 2000, however, are of limited positional accuracy and were deemed of insufficient quality to support the 2010 Census. In response the US Census Bureau embarked on the MAF/TIGER Accuracy Improvement Project (MTAIP) in an effort to improve the positional accuracy of the database, modernize the data processing environment and improve cooperation with partner agencies. Improved TIGER data were released for the entire US just before the 2010 Census. The current study characterizes the positional accuracy of the TIGER 2009 data compared with the TIGER 2000 data based on selected road intersections. Three US counties were identified as study areas and in each county 100 urban and 100 rural sample locations were selected. Features in the TIGER 2000 and 2009 data were compared with reference locations derived from high resolution natural color orthoimagery. Results indicate that TIGER 2009 data are much improved in terms of positional accuracy compared with the TIGER 2000 data, by at least one order of magnitude across urban and rural areas in all three counties for most accuracy metrics. TIGER 2009 is consistently more accurate in urban areas compared with rural areas, by a factor of at least two for most accuracy metrics. Despite the substantial improvement in positional accuracy, large positional errors of greater than 10 m are relatively common in the TIGER 2009 data, in most cases representing remnant segments of minor roads from older versions of the TIGER data. As a result, based on the US Census Bureau's suggested accuracy metric, the TIGER 2009 data meet the accuracy expectation of 7.6 m for two of the three urban areas but for none of the three rural areas. The suggested metric is based on the National Standard for Spatial Data Accuracy (NSSDA) protocol and was found to be very sensitive to the presence of a small number of very large errors. This presents challenges during attempts to characterize the accuracy of TIGER data or other spatial data using this protocol.     

Towards an Automated Comparison of OpenStreetMap with Authoritative Road Datasets

OpenStreetMap (OSM) is an extraordinarily large and diverse spatial database of the world. Road networks are amongst the most frequently occurring spatial content within the OSM database. These road network representations are usable in many applications. However the quality of these representations can vary between locations. Comparing OSM road networks with authoritative road datasets for a given area or region is an important task in assessing OSM's fitness for use for applications like routing and navigation. Such comparisons can be technically challenging and no software implementation exists which facilitates them easily and automatically. In this article we develop and propose a flexible methodology for comparing the geometry of OSM road network data with other road datasets. Quantitative measures for the completeness and spatial accuracy of OSM are computed, including the compatibility of OSM road data with other map databases. Our methodology provides users with significant flexibility in how they can adjust the parameterization to suit their needs. This software implementation is exclusively built on open source software and a significant degree of automation is provided for these comparisons. This software can subsequently be extended and adapted for comparison between OSM and other external road datasets.     

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.     

TIGER空间数据模型及空间数据标准问题探讨

以TIGER这种既成事实的空间数据格式为例,详细介绍了TIGER的数据模型及文件组织形式,对TI-GER/Line如何描述空间对象信息进行分析,把这种格式作为空间对象拓扑模型实际实施的一种范例作以详细介绍,以起到抛砖引玉的作用,使读者能够更深入地了解空间对象表示的基本要求,对未来空间数据标准化起到一定的参考作用。     

Modeling aggregated expertise of user contributions to assess the credibility of OpenStreetMap features

The emergence of volunteered geographic information (VGI) during the past decade has fueled a wide range of research and applications. The assessment of VGI quality and fitness‐of‐use is still a challenge because of the non‐standardized and crowdsourced data collection process, as well as the unknown skill and motivation of the contributors. However, the frequent approach of assessing VGI quality against external data sources using ISO quality standard measures is problematic because of a frequent lack of available external (reference) data, and because for certain types of features, VGI might be more up‐to‐date than the reference data. Therefore, a VGI‐intrinsic measure of quality is highly desirable. This study proposes such an intrinsic measure of quality by developing the concept of aggregated expertise based on the characteristics of a feature's contributors. The article further operationalizes this concept and examines its feasibility through a case study using OpenStreetMap (OSM). The comparison of model OSM feature quality with information from a field survey demonstrates the successful implementation of this novel approach.     

Matching authority and VGI road networks using an extended node-based matching algorithm

The amount of volunteered geographic information (VGI) has increased over the past decade, and several studies have been conducted to evaluate the quality of VGI data. In this study, we evaluate the completeness of the road network in the VGI data set OpenStreetMap (OSM). The evaluation is based on an accurate and efficient network-matching algorithm. The study begins with a comparison of the two main strategies for network matching: segment-based and node-based matching. The comparison shows that the result quality is comparable for the two strategies, but the node-based result is considerably more computationally efficient. Therefore, we improve the accuracy of node-based algorithm by handling topological relationships and detecting patterns of complicated network components. Finally, we conduct a case study on the extended node-based algorithm in which we match OSM to the Swedish National Road Database (NVDB) in Scania, Sweden. The case study reveals that OSM has a completeness of 87% in the urban areas and 69% in the rural areas of Scania. The accuracy of the matching process is approximately 95%. The conclusion is that the extended node-based algorithm is sufficiently accurate and efficient for conducting surveys of the quality of OSM and other VGI road data sets in large geographic regions.     

The Annotation Process in OpenStreetMap

In this article we describe the analysis of 25,000 objects from the OpenStreetMap (OSM) databases of Ireland, United Kingdom, Germany, and Austria. The objects are selected as exhibiting the characteristics of “heavily edited” objects. We consider “heavily edited” objects as having 15 or more versions over the object's lifetime. Our results indicate that there are some serious issues arising from the way contributors tag or annotate objects in OSM. Values assigned to the “name” and “highway” attributes are often subject to frequent and unexpected change. However, this “tag flip‐flopping” is not found to be strongly correlated with increasing numbers of contributors. We also show problems with usage of the OSM ontology/controlled vocabularly. The majority of errors occurring were caused by contributors choosing values from the ontology “by hand” and spelling these values incorrectly. These issues could have a potentially detrimental effect on the quality of OSM data while at the same time damaging the perception of OSM in the GIS community. The current state of tagging and annotation in OSM is not perfect. We feel that the problems identified are a combination of the flexibility of the tagging process in OSM and the lack of a strict mechanism for checking adherence to the OSM ontology for specific core attributes. More studies related to comparing the names of features in OSM to recognized ground‐truth datasets are required.     

Analysis of Interaction and Co‐editing Patterns amongst OpenStreetMap Contributors

OpenStreetMap (OSM) is a very well known and popular Volunteered Geographic Information (VGI) project on the Internet. In January 2013 OSM gained its one millionth registered member. Several studies have shown that only a small percentage of these registered members carry out the large majority of the mapping and map editing work. In this article we discuss results from a social‐network based analysis of seven major cities in OSM in an effort to understand if there is quantitative evidence of interaction and collaboration between OSM members in these areas. Are OSM contributors working on their own to build OSM databases in these cities or is there evidence of collaboration between OSM contributors? We find that in many cases high frequent contributors (“senior mappers”) perform very large amounts of mapping work on their own but do interact (edit/update) contributions from lower frequency contributors.     

Understanding the potential relationship between the socio-economic variables and contributions to OpenStreetMap

OpenStreetMap (OSM) has seen an exponential increase in the last few years and large volumes of geodata have been received from volunteered individuals. The collected geodata are heterogeneous in terms of different dimensions such as spatial patterns of contributions, quality, patterns of contributing individuals, and type of contributions. Because contributors’ personal information is anonymously stored by the OSM administrators, alternative methods are needed to investigate the role of contributors’ characteristics on their mapping behavior. This study is intended to explore the potential socio-economic characteristics of contributors in highly contributed areas to have better insights about the latent patterns of involved individuals in a highly dynamic state of the most active country in OSM, Germany. A logistic regression model (LRM) is applied to discover the potential correlations between dependent and independent variables. The findings explain that the areas with high population density, middle level of education, high income, high rate of overnight stays, high number of foreigners, and residents aged from 18 to 69 are more likely to be involved in OSM. Furthermore, the degree of dynamism in OSM is a function of proximity to built-up areas. Finally, concluding remarks concerning the independent variables and model sensitivity are presented.     

A geovisual analytics exploration of the OpenStreetMap crowd

It is sometimes easy to forget that massive crowdsourced data products such as Wikipedia and OpenStreetMap (OSM) are the sum of individual human efforts stemming from a variety of personal and institutional interests. We present a geovisual analytics tool called Crowd Lens for OpenStreetMap designed to help professional users of OSM make sense of the characteristics of the “crowd” that constructed OSM in specific places. The tool uses small multiple maps to visualize each contributor’s piece of the crowdsourced whole, and links OSM features with the free-form commit messages supplied by their contributors. Crowd Lens allows sorting and filtering contributors by characteristics such as number of contributions, most common language used, and OSM attribute tags applied. We describe the development and evaluation of Crowd Lens, showing how a multiple-stage user-centered design process (including testing by geospatial technology professionals) helped shape the tool’s interface and capabilities. We also present a case study using Crowd Lens to examine cities in six continents. Our findings should assist institutions deliberating OSM’s fitness for use for different applications. Crowd Lens is also potentially informative for researchers studying Internet participation divides and ways that crowdsourced products can be better comprehended with visual analytics methods.     

运用Delaunay三角网提取OpenStreetMap主干道多边形

众源开放街道地图(OpenStreetMap,OSM)作为志愿者地理信息的典型代表,其数据质量制约了城市主干道提取。为此,引入约束Delaunay三角网,运用类似Voronoi图的空间等剖分几何构造表达道路线密度,通过线密度变化与三角形边长表征主干道形态特征,并集成两个指标构建主干道识别模型;类比林火蔓延扩展过程,提出了基于Delaunay三角网的OSM主干道多边形提取方法。运用5个城市的OSM数据进行实验,结果表明,该方法能有效提取主干道多边形,并且与OSM路网中的道路等级属性趋于一致。     

An Exploration of Future Patterns of the Contributions to OpenStreetMap and Development of a Contribution Index

OpenStreetMap (OSM) represents one of the most well‐known examples of a collaborative mapping project. Major research efforts have so far dealt with data quality analysis but the modality of OSM's evolution across space and time has barely been noted. This study aims to analyze spatio‐temporal patterns of contributions in OSM by proposing a contribution index (CI) in order to investigate the dynamism of OSM. The CI is based on a per cell analysis of the node quantity, interactivity, semantics, and attractivity (the ability to attract contributors). Additionally this research explores whether OSM has been constantly attracting new users and contributions or if OSM has experienced a decline in its ability to attract continued contributions. Using the Stuttgart region of Germany as a case study the empirical findings of the CI over time confirm that since 2007, OSM has been constantly attracting new users, who create new features, edit the existing spatial objects, and enrich them with attributes. This rate has been dramatically growing since 2011. The utilization of a Cellular Automata‐Markov (CA‐Markov) model provides evidence that by the end of 2016 and 2020, the rise of CI will spread out over the study area and only a few cells without OSM features will remain.     

OSM辅助车载LiDAR点云三维道路边界精细提取

道路边界精确提取建模是城市道路管理、智能交通规划和高精度地图制作等领域的重要课题之一。本文提出了一种基于车载激光雷达点云数据和开源街道地图(OSM)的三维道路边界精确提取方法。首先,针对原始车载LiDAR点云数据应用布料模拟滤波分离地面点,再结合相对高程分析获取道路边界点候选数据集。然后,应用OSM矢量道路网数据的节点辅助道路边界点候选点集进行分段。最后,在各分段点云数据集中基于随机抽样一致性算法获得三维道路边界点集。通过直道、弯道及高密度复杂场景3种不同类型的城区道路边界路段分类提取试验。结果表明,利用该方法进行道路边界提取的准确率和召回率分别达96.12%和95.17%,F1值达92.11%,本文方法可用于高精度道路边界的三维精细提取与矢量化,进而为智能交通与无人驾驶导航提供支撑。     

Effects of reverse geocoding on OpenStreetMap tag quality assessment

OpenStreetMap (OSM), a widely-used open-source geographic information system platform, provides a vast geographic dataset in which users contribute both geometric information (nodes, ways, and relations) and semantic information (tags). This method of voluntary contributions is governed by the collective effort of the users. It is widely acknowledged that the quantity of tag information is substantial, but its quality is often poor. Researchers are therefore trying to assess the quality of the tags and enhance the data through various integration experiments. This article investigates the validity of the tags for geographical objects in metropolitan areas using municipal data and a reverse geocoding technique. The proposed method evaluates the data quality and the matching process carried out by reverse geocoding, using municipal points of interest as a reference. The accuracy of the tag and address information and road network centrality metrics were assessed for the OSM objects that were matched to the locations of interest. The tags were found to match the points of interest with an accuracy of 88%. Furthermore, the tag values were categorized and analyzed based on their similarity. It is concluded that in metropolitan settings where centers of interest are closely located, the accuracy of tags and addresses tends to decrease.     

Quality assessment of OpenStreetMap data using trajectory mining

OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations.     

基于众源轨迹数据的行人路网提取

目前,导航位置服务应用提供的路线大多基于机动车道路网数据,难以满足行人导航的需求,有无完备的行人道路网络成为制约行人导航应用的重要因素,因此,本文提出了一种基于莫尔斯理论的行人路网提取方法.首先对轨迹进行预处理,清除轨迹数据中的冗余和噪声,并对原始轨迹进行合理分割,形成清晰的轨迹集合;然后利用莫尔斯理论,对步行轨迹密度...     

路网更新的轨迹-地图匹配方法

全面准确的路网信息作为智慧城市的重要基础之一,在城市规划、交通管理以及大众出行等方面具有重要意义和价值。然而,传统的基于测量的路网数据获取方式往往周期较长,不能及时反映最新的道路信息。近几年,随着定位技术在移动设备的广泛运用,国内外学者在研究路网信息获取时逐渐将视野转向移动对象的轨迹数据中所蕴含的道路信息。当前,基于移动位置信息的路网生成和更新方法多是直接面向全部轨迹数据施加道路提取算法,在处理大规模轨迹或者大范围道路时,计算量极大。为此,本文基于轨迹地图匹配技术,提出一种采用"检查→分析→提取→更新"过程的螺旋式路网数据更新策略。其主要思想是逐条输入轨迹,借助HMM地图匹配发现已有路网中的问题路段,进而从问题路段周边局部范围内的轨迹数据中提取并更新相关道路信息。该方法仅在局部范围内利用少量轨迹数据来修复路网,避免了对整个轨迹数据集进行计算,从而有效减少了计算量。基于OpenStreetMap的武汉市区路网数据以及武汉市出租车轨迹数据的试验表明,本文提出的路网更新方法不仅可行,而且灵活高效。