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The assessment of OpenStreetMap (OSM) data quality has become an interdisciplinary research area over the recent years. The question of whether the OSM road network should be updated through periodic data imports from public domain data, or whether the currency of OSM data should rather rely on more traditional data collection efforts by active contributors, has led to perpetual debates within the OSM community. A US Census TIGER/Line 2005 import into OSM was accomplished in early 2008, which generated a road network foundation for the active community members in the US. In this study we perform a longitudinal analysis of road data for the US by comparing the development of OSM and TIGER/Line data since the initial TIGER/Line import. The analysis is performed for the 50 US states and the District of Columbia, and 70 Urbanized Areas. In almost all tested states and Urbanized Areas, OSM misses roads for motorized traffic when compared with TIGER/Line street data, while significant contributions could be observed in pedestrian related network data in OSM compared with corresponding TIGER/Line data. We conclude that the quality of OSM road data could be improved through new OSM editor tools allowing contributors to trace current TIGER/Line data. 相似文献
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This article investigates how workout trajectories from a mobile sports tracking application can be used to provide automatic route suggestions for bicyclists. We apply a Hidden Markov Model (HMM)‐based method for matching cycling tracks to a “bicycle network” extracted from crowdsourced OpenStreetMap (OSM) data, and evaluate its effective differences in terms of optimal routing compared with a simple geometric point‐to‐curve method. OSM has quickly established itself as a popular resource for bicycle routing; however, its high‐level of detail presents challenges for its applicability to popularity‐based routing. We propose a solution where bikeways are prioritized in map‐matching, achieving good performance; the HMM‐based method matched correctly on average 94% of the route length. In addition, we show that the extremely biased nature of the trajectory dataset, which is typical of volunteered user‐generated data, can be of high importance in terms of popularity‐based routing. Most computed routes diverged depending on whether the number of users or number of tracks was used as an indicator of popularity, which may imply varying preferences among different types of cyclists. Revising the number of tracks by diversity of users to surmount local biases in the data had a more limited effect on routing. 相似文献
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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. 相似文献
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Quality assessment of OpenStreetMap data using trajectory mining 总被引：1，自引：0，他引：1
Anahid Basiri Mike Jackson Pouria Amirian Amir Pourabdollah Monika Sester Adam Winstanley 《地球空间信息科学学报》2016,19(1):56-68
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. 相似文献
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The growing penetration of GPS equipped smartphones allowing users to constantly share geographic information on their current whereabouts has led the way to an augmentation of existing Social Network Sites with location‐based features or the creation of new ones exclusively around geographic information. Within these Location Based Social Networks vast amounts of geographic information are allocated, which attracted the attention of researchers with various scientific backgrounds. This article presents a comprehensive definition of this special type of Social Network Site and an overview of research activities, which are currently conducted using the data. Finally, a research agenda for GIScience is drawn from existing research directions. 相似文献
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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. 相似文献
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The Annotation Process in OpenStreetMap 总被引：2，自引：1，他引：1
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. 相似文献
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Modeling aggregated expertise of user contributions to assess the credibility of OpenStreetMap features 总被引：1，自引：0，他引：1 
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下载免费PDF全文 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. 相似文献
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《International Journal of Digital Earth》2013,6(11):861-876
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. 相似文献
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Assessing the Completeness of Bicycle Trail and Lane Features in OpenStreetMap for the United States 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 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. 相似文献
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With the rapid growth and popularity of mobile devices and location‐aware technologies, online social networks such as Twitter have become an important data source for scientists to conduct geo‐social network research. Non‐personal accounts, spam users and junk tweets, however, pose severe problems to the extraction of meaningful information and the validation of any research findings on tweets or twitter users. Therefore, the detection of such users is a critical and fundamental step for twitter‐related geographic research. In this study, we develop a methodological framework to: (1) extract user characteristics based on geographic, graph‐based and content‐based features of tweets; (2) construct a training dataset by manually inspecting and labeling a large sample of twitter users; and (3) derive reliable rules and knowledge for detecting non‐personal users with supervised classification methods. The extracted geographic characteristics of a user include maximum speed, mean speed, the number of different counties that the user has been to, and others. Content‐based characteristics for a user include the number of tweets per month, the percentage of tweets with URLs or Hashtags, and the percentage of tweets with emotions, detected with sentiment analysis. The extracted rules are theoretically interesting and practically useful. Specifically, the results show that geographic features, such as the average speed and frequency of county changes, can serve as important indicators of non‐personal users. For non‐spatial characteristics, the percentage of tweets with a high human factor index, the percentage of tweets with URLs, and the percentage of tweets with mentioned/replied users are the top three features in detecting non‐personal users. 相似文献
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Until recently, land surveys and digital interpretation of remotely sensed imagery have been used to generate land use inventories. These techniques however, are often cumbersome and costly, allocating large amounts of technical and temporal costs. The technological advances of web 2.0 have brought a wide array of technological achievements, stimulating the participatory role in collaborative and crowd sourced mapping products. This has been fostered by GPS-enabled devices, and accessible tools that enable visual interpretation of high resolution satellite images/air photos provided in collaborative mapping projects. Such technologies offer an integrative approach to geography by means of promoting public participation and allowing accurate assessment and classification of land use as well as geographical features. OpenStreetMap (OSM) has supported the evolution of such techniques, contributing to the existence of a large inventory of spatial land use information. This paper explores the introduction of this novel participatory phenomenon for land use classification in Europe's metropolitan regions. We adopt a positivistic approach to assess comparatively the accuracy of these contributions of OSM for land use classifications in seven large European metropolitan regions. Thematic accuracy and degree of completeness of OSM data was compared to available Global Monitoring for Environment and Security Urban Atlas (GMESUA) datasets for the chosen metropolises. We further extend our findings of land use within a novel framework for geography, justifying that volunteered geographic information (VGI) sources are of great benefit for land use mapping depending on location and degree of VGI dynamism and offer a great alternative to traditional mapping techniques for metropolitan regions throughout Europe. Evaluation of several land use types at the local level suggests that a number of OSM classes (such as anthropogenic land use, agricultural and some natural environment classes) are viable alternatives for land use classification. These classes are highly accurate and can be integrated into planning decisions for stakeholders and policymakers. 相似文献
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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. 相似文献
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ABSTRACTNatural disasters, such as wildfires, earthquakes, landslides, or floods, lead to an increase in topical information shared on social media and in increased mapping activities in volunteered geographic information (VGI) platforms. Using earthquakes in Nepal and Central Italy as case studies, this research analyzes the effects of natural disasters on short-term (weeks) and longer-term (half year) changes in OpenStreetMap (OSM) mapping behavior and tweet activities in the affected regions. An increase of activities in OSM during the events can be partially attributed to those focused OSM mapping campaigns, for example, through the Humanitarian OSM Team (HOT). Using source tags in OSM change-sets, it was found that only a small portion of external mappers actually travels to the affected regions, whereas the majority of external mappers relies on desktop mapping instead. Furthermore, the study analyzes the spatio-temporal sequence of posted tweets together with keyword filters to identify a subset of users who most likely traveled to the affected regions for support and rescue operations. It also explores where, geographically, earthquake information spreads within social networks. 相似文献
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Geographic masks are techniques used to protect privacy when publishing sensitive data in maps, but are not well adopted among researchers and may be difficult to execute for some GIS users. We developed a client‐side web application called MaskMy.XYZ that makes geographic masking easy to perform. It executes donut geomasking, a well‐known geographic mask, on thousands of points in seconds, and visualizes the original and masked point patterns in an integrated web map for visual comparison. MaskMy.XYZ also features metrics for both privacy protection and information loss, and allows users to rapidly and iteratively adjust masking parameters based on these metrics. The user interface was designed to prioritize usability, and clear documentation has been included to educate users about geographic masks, which is otherwise only found in niche literatures. By developing this application, we hope that geographic masks will be more widely adopted such that privacy is better protected in research. 相似文献
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Enhancing data privacy with semantic trajectories: A raster‐based framework for GPS stop/move management 下载免费PDF全文
下载免费PDF全文 Tracking facilities on smartphones generate enormous amounts of GPS trajectories, which provide new opportunities to study movement patterns and improve transportation planning. Converting GPS trajectories into semantically meaningful trips is attracting increasing research effort with respect to the development of algorithms, frameworks, and software tools. There are, however, few works focused on designing new semantic enrichment functionalities taking privacy into account. This article presents a raster‐based framework which not only detects significant stop locations, segments GPS records into stop/move structures, and brings semantic insights to trips, but also provides possibilities to anonymize users’ movements and sensitive stay/move locations into raster cells/regions so that a multi‐level data sharing structure is achieved for a variety of data sharing purposes. 相似文献
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Fourier transforms have been used in the analysis of landscapes that exhibit the influence of cyclic structures or other morphogenetic controls. Two‐dimensional Fourier transforms have been most successful when modeling features with a high frequency over the sample space. This research focuses on applications of 2D discrete Fourier transforms for karst and spur and groove coral reefs, using ArcGIS geoprocessing tools extended with Python NumPy numerical methods. Ten‐meter digital elevation data from Puerto Rico and Kentucky holokarst landscapes and five‐meter bathymetry from more unidirectional spur and groove coral reefs at Midway Atoll were analyzed. Our method identifies the dominant contributing waves in frequency space, and analyzed power contributions by 5° and 15° azimuth bins. A limiting factor in this analysis is the spatial extent of consistent morphology in the landscape. In contrast to time‐domain Fourier analysis, dominant landform frequencies can thus be of low magnitude, creating an imprecise estimate of wave morphometry and direction since this is derived from the combination of inverted x and y frequency values, and the limited frequency grain inherent in the discrete model degrades precision in the solution. Simulated karst and spur & groove landscapes were used to evaluate the grain of waveform orientation solutions. 相似文献