Quality Of VGI Research Articles

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

AbstractVolunteered 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%.

ASSESSING THE RELIABILITY OF CONTRIBUTORS IN VGI USING IMPLICIT FACTORS

Abstract. VGI projects include geographic information, which are the product of many unorganized volunteers, making it a challenge to ensure the quality of their information. In this field of study, several researchers have suggested using intrinsic factors to evaluate the quality of VGI instead of using explicit methods such as comparing with real or reference datasets. In addition, the measurement of the reliability of VGI contributors as an essential intrinsic factor in determining the credibility of their contributions remains an open question. Various types of contributors’ activities and interactions are introduced and discussed in detail in this study at first. Then a comprehensive spatio-temporal contributor reliability model is proposed to assess their performance based on multiple implicit interactions between volunteers in their contribution process. Finally, several cities with different contribution rate (based on their population, number of users and area extent) are chosen and the proposed model is applied to the VGI data of selected regions, finally the results are compared and discussed.

Spatiotemporal VGI contributor reputation system based on implicit evaluation relations

The crowdsourced information in this kind of project results from numerous and not organized volunteers, making verification of the quality of their contributions a challenge. Several studies have emphasized using indirect and intrinsic measures to assess the quality of VGI in literature. Moreover, measuring the contributor’s reputation score as one of the intrinsic factors for determining whether their produced information is reliable and credible remains an open issue. This study first introduces and discusses the most effective interactions on the reputation of VGI contributors in detail and visually. Then attempts to propose a comprehensive reputation system based on various implicit evaluations among volunteers in their contribution process, depicting their performance. This system is successfully applied in linear features in study areas, and the results are shown graphically. Finally, two other existing frameworks are implemented on desired data for comparison purposes, and the results are discussed.

APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCE

Abstract. The scarcity of metrics for analysing the quality of Voluntary Geographic Information without direct comparisons with reference data makes it impossible to use this information in many areas of society. Especially in developing countries, where collaborative data can help fill the deficit of official data, studies on intrinsic parameters of quality become an alternative to conventional comparative methods for evaluating spatial data. A recurring parameter in related research is Collective Spatial Intelligence. Seeking to offer researchers on the subject a tool capable of measuring the Collective Spatial Intelligence in predefined areas, we developed a Python application that counts representative values of this intelligence in political-administrative limits. Considering that, in general, the quality of spatial data is inferred on these limits, research that seeks to explain the VGI quality without using official data as a reference can be facilitated.

ONTOLOGICAL ASSESSMENT AND SIGNIFICANCE OF SEMANTIC LEVELS OF TAGS IN OPENSTREETMAP

Abstract. User-generated contents are developing rapidly through VGI and contributors create the tags through the Web applications in a free mechanism. Semantic Knowledge in VGI like other user-generated contents needs to be combined with other authoritative data sources. One of the main challenges of integration is the semantic heterogeneity of user-generated contents which are describing the geographical objects as POIs. Geographical objects can be described in different semantic levels such as purpose or function. Significance of semantic levels defines the importance of related attributes. Analysis of significance for semantic levels of different POIs can be considered as a base to enhance the semantic quality of VGI. This paper proposes an approach based on the notions of rough set theory to measure the significance of semantic levels of tags which are applied to describe the buildings in OpenStreetMap. The proposed approach is implemented for tags which are applied to describe buildings in OpenStreetMap. Results show the high significance for tags which describing the semantic levels of geographic information constructs and purpose/ function for buildings.

Analysis of positional uncertainty of road networks in volunteered geographic information with a statistically defined buffer-zone method

ABSTRACTVolunteered geographic information (VGI) is crowdsourced information that can enrich and enhance research and applications based on geo-referenced data. However, the quality of VGI is of great concern, and positional accuracy is a fundamental basis for the VGI quality assurance. A buffer-zone method can be used for its assessment, but the buffer radius in this technique is subjectively specified; as result, different selections of the buffer radius lead to different positional accuracies. To solve this problem, a statistically defined buffer zone for the positional accuracy assessment in VGI is proposed in this study. To facilitate practical applications, we have also developed an iterative method to obtain a theoretically defined buffer zone. In addition to the positional accuracy assessment, we have derived a measure of positional quality, which comprises the assessment of positional accuracy and the level of confidence in such assessment determined with respect to a statistically defined buffer zone. To illustrate and substantiate the theoretical arguments, both numerical simulations and real-life experiments are performed using OpenStreetMap. The experimental results confirm the high significance of the proposed statistical approach to the buffer zone-based assessment of the positional uncertainty in VGI.

VGTrust: measuring trust for volunteered geographic information

ABSTRACTVolunteered Geographic Information (VGI) has emerged as a large, up-to-date, and easily accessible data source. VGI can allow authoritative mapping agencies to undertake continuous improvement of their own data, adding a currency dimension previously unattainable due to high associated costs. VGI also benefits scientific and social research by facilitating quick and low-cost research data capture by the public. VGI, however, through its diversity of authorship, presents a quality assurance risk to the use of this data. This research presents a formulaic model that addresses VGI quality issues, by quantifying trust in VGI. Our ‘VGTrust’ model assesses information about a data author, and the spatial and temporal trust associated with the data they create, to produce an overall VGTrust rating metric. This metric is both easy to understand and interpret. A facilitated case study, ‘Building Our Footprints’ is presented which tests the feasibility of VGTrust model in a real-world data capture exercise run by Land Information New Zealand, New Zealand’s mapping organisation. By overcoming the trust issues in VGI, this research will allow the integration of VGI and authoritative data and potentially expand the application of VGI, thereby leveraging the power of the crowd for productive and innovative re-use.

Data trustworthiness and user reputation as indicators of VGI quality

ABSTRACTVolunteered geographic information (VGI) has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations. But the issue of deciding the quality of VGI without resorting to a comparison with authoritative data remains an open challenge. This article first formulates the problem of quality assessment of VGI data. Then presents a model to measure trustworthiness of information and reputation of contributors by analyzing geometric, qualitative, and semantic aspects of edits over time. An implementation of the model is running on a small data-set for a preliminary empirical validation. The results indicate that the computed trustworthiness provides a valid approximation of VGI quality.

Using meta-quality to assess the utility of volunteered geographic information for science

BackgroundVolunteered geographic information (VGI) has strong potential to be increasingly valuable to scientists in collaboration with non-scientists. The abundance of mobile phones and other wireless forms of communication open up significant opportunities for the public to get involved in scientific research. As these devices and activities become more abundant, questions of uncertainty and error in volunteer data are emerging as critical components for using volunteer-sourced spatial data.MethodsHere we present a methodology for using VGI and assessing its sensitivity to three types of error. More specifically, this study evaluates the reliability of data from volunteers based on their historical patterns. The specific context is a case study in surveillance of tsetse flies, a health concern for being the primary vector of African Trypanosomiasis.ResultsReliability, as measured by a reputation score, determines the threshold for accepting the volunteered data for inclusion in a tsetse presence/absence model. Higher reputation scores are successful in identifying areas of higher modeled tsetse prevalence. A dynamic threshold is needed but the quality of VGI will improve as more data are collected and the errors in identifying reliable participants will decrease.ConclusionsThis system allows for two-way communication between researchers and the public, and a way to evaluate the reliability of VGI. Boosting the public’s ability to participate in such work can improve disease surveillance and promote citizen science. In the absence of active surveillance, VGI can provide valuable spatial information given that the data are reliable.

Quality assessment of building footprint data using a deep autoencoder network

ABSTRACTVolunteered geographic information (VGI), OpenStreetMap (OSM), has been used in many applications, especially when official spatial data are unavailable or outdated. However, the quality of VGI remains a valid concern. In this paper, we use the matched results between OSM building footprints and official data as the samples for training an autoencoder network, which encodes and reconstructs the sample populations according to unknown complex multivariate probability distributions. Then, the OSM data are assessed based on the theory that small probability samples contribute little to the autoencoder network and that they can be recognized by the higher reconstructed errors during training. In the method described here, the selected measures, including data completeness, positional accuracy, shape accuracy, semantic accuracy and orientation consistency between OSM and official data, are used as the inputs for a deep autoencoder network. Finally, building footprint data from Toronto, Canada, are evaluated, and experiments show that the proposed method can assess the OSM data comprehensively, objectively and accurately.

A VERSION-SIMILARITY BASED TRUST DEGREE COMPUTATION MODEL FOR CROWDSOURCING GEOGRAPHIC DATA

Quality evaluation and control has become the main concern of VGI. In this paper, trust is used as a proxy of VGI quality, a version-similarity based trust degree computation model for crowdsourcing geographic data is presented. This model is based on the assumption that the quality of VGI objects mainly determined by the professional skill and integrity (called reputation in this paper), and the reputation of the contributor is movable. The contributor’s reputation is calculated using the similarity degree among the multi-versions for the same entity state. The trust degree of VGI object is determined by the trust degree of its previous version, the reputation of the last contributor and the modification proportion. In order to verify this presented model, a prototype system for computing the trust degree of VGI objects is developed by programming with Visual C# 2010. The historical data of Berlin of OpenStreetMap (OSM) are employed for experiments. The experimental results demonstrate that the quality of crowdsourcing geographic data is highly positive correlation with its trustworthiness. As the evaluation is based on version-similarity, not based on the direct subjective evaluation among users, the evaluation result is objective. Furthermore, as the movability property of the contributors’ reputation is used in this presented method, our method has a higher assessment coverage than the existing methods.

A VERSION-SIMILARITY BASED TRUST DEGREE COMPUTATION MODEL FOR CROWDSOURCING GEOGRAPHIC DATA

Abstract. Quality evaluation and control has become the main concern of VGI. In this paper, trust is used as a proxy of VGI quality, a version-similarity based trust degree computation model for crowdsourcing geographic data is presented. This model is based on the assumption that the quality of VGI objects mainly determined by the professional skill and integrity (called reputation in this paper), and the reputation of the contributor is movable. The contributor’s reputation is calculated using the similarity degree among the multi-versions for the same entity state. The trust degree of VGI object is determined by the trust degree of its previous version, the reputation of the last contributor and the modification proportion. In order to verify this presented model, a prototype system for computing the trust degree of VGI objects is developed by programming with Visual C# 2010. The historical data of Berlin of OpenStreetMap (OSM) are employed for experiments. The experimental results demonstrate that the quality of crowdsourcing geographic data is highly positive correlation with its trustworthiness. As the evaluation is based on version-similarity, not based on the direct subjective evaluation among users, the evaluation result is objective. Furthermore, as the movability property of the contributors’ reputation is used in this presented method, our method has a higher assessment coverage than the existing methods.

MEASURES AND INDICATORS OF VGI QUALITY: AN OVERVIEW

Abstract. The evaluation of VGI quality has been a very interesting and popular issue amongst academics and researchers. Various metrics and indicators have been proposed for evaluating VGI quality elements. Various efforts have focused on the use of well-established methodologies for the evaluation of VGI quality elements against authoritative data. In this paper, a number of research papers have been reviewed and summarized in a detailed report on measures for each spatial data quality element. Emphasis is given on the methodology followed and the data used in order to assess and evaluate the quality of the VGI datasets. However, as the use of authoritative data is not always possible many researchers have turned their focus on the analysis of new quality indicators that can function as proxies for the understanding of VGI quality. In this paper, the difficulties in using authoritative datasets are briefly presented and new proposed quality indicators are discussed, as recorded through the literature review. We classify theses new indicators in four main categories that relate with: i) data, ii) demographics, iii) socio-economic situation and iv) contributors. This paper presents a dense, yet comprehensive overview of the research on this field and provides the basis for the ongoing academic effort to create a practical quality evaluation method through the use of appropriate quality indicators.

YOU DESCRIBE IT, I WILL NAME IT: AN APPROACH TO ALLEVIATE THE EFFECT OF USERS’ SEMANTICS IN ASSIGNING TAGS TO FEATURES IN VGI

Abstract. As an important factor of VGI quality, this paper focuses on uncertainty arisen in assigning tags to features by VGI users. The VGI portals ask their users to assign (or tag) one or more data types to features, from a set of pre-defined types, whose meanings may be vague for the user, or distinctions between some of them are not clear, i.e. depend on the users’ semantics. This research believes such uncertainties are the results of perceptual issues arising in serial communication between the system and the user. We categorize the problem, and then utilize semantic modelling to reduce such uncertainties. A hierarchy of feature types is produced. At each step, users are asked a simple question with clear distinct answers, which gradually directs the user to the right type. We will describe the approach and present the initial results for the hierarchy produced for major linear features of OpenStreetMap.

How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets

Within the framework of Web 2.0 mapping applications, the most striking example of a geographical application is the OpenStreetMap (OSM) project. OSM aims to create a free digital map of the world and is implemented through the engagement of participants in a mode similar to software development in Open Source projects. The information is collected by many participants, collated on a central database, and distributed in multiple digital formats through the World Wide Web. This type of information was termed ‘Volunteered Geographical Information’ (VGI) by Goodchild, 2007. However, to date there has been no systematic analysis of the quality of VGI. This study aims to fill this gap by analysing OSM information. The examination focuses on analysis of its quality through a comparison with Ordnance Survey (OS) datasets. The analysis focuses on London and England, since OSM started in London in August 2004 and therefore the study of these geographies provides the best understanding of the achievements and difficulties of VGI. The analysis shows that OSM information can be fairly accurate: on average within about 6 m of the position recorded by the OS, and with approximately 80% overlap of motorway objects between the two datasets. In the space of four years, OSM has captured about 29% of the area of England, of which approximately 24% are digitised lines without a complete set of attributes. The paper concludes with a discussion of the implications of the findings to the study of VGI as well as suggesting future research directions.