Abstract:In the area of Volunteered Geographical Information (VGI), the issue of spatial data quality is a clear challenge. The data that is contributed to VGI projects does not comply with standard spatial data quality assurance procedures, and the contributors operate without central coordination and strict data collection frameworks. However, similar to the area of open source software development, it is suggested that the data holds an intrinsic quality assurance measure through the analysis of the number of contributors who have worked on a given spatial unit. The assumption that as the number of contributors increases so does the quality is known as 'Linus' Law' within the Open Source community. This paper describes three studies that were carried out to evaluate this hypothesis for VGI using the OpenStreetMap dataset, showing that this rule indeed applies in the case of positional accuracy.

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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.

Geo-tagged photographs are used increasingly as a source of Volunteered Geographic Information (VGI), which could potentially be used for land use and land cover applications. The purpose of this paper is to analyze the feasibility of using this source of spatial information for three use cases related to land cover: Calibration, validation and verification. We first provide an inventory of the metadata that are collected with geo-tagged photographs and then consider what elements would be essential, desirable, or unnecessary for the aforementioned use cases. Geo-tagged photographs were then extracted from Flickr, Panoramio and Geograph for an area of London, UK, and classified based on their usefulness for land cover mapping including an analysis of the accompanying metadata. Finally, we discuss protocols for geo-tagged photographs for use of VGI in relation to land cover applications.

Abstract:With the development of location-aware devices and the success and high use of Web 2.0 techniques, citizens are able to act as sensors by contributing geographic information. In this context, data quality is an important aspect that should be taken into account when using this source of data for different purposes. The goal of the paper is to analyze the quality of crowdsourced data and to study its evolution over time. We propose two types of approaches: (1) use the intrinsic characteristics of the crowdsourced datasets; or (2) evaluate crowdsourced Points of Interest (POIs) using external datasets (i.e., authoritative reference or other crowdsourced datasets), and two different methods for each approach. The potential of the combination of these approaches is then demonstrated, to overcome the limitations associated with each individual method. In this paper, we focus on POIs and places coming from the very successful crowdsourcing project: OpenStreetMap. The results show that the proposed approaches are complementary in assessing data quality. The positive results obtained for data matching show that the analysis of data quality through automatic data matching is possible but considerable effort and attention are needed for schema matching given the heterogeneity of OSM and the representation of authoritative datasets. For the features studied, it can be noted that change over time is sometimes due to disagreements between contributors, but in most cases the change improves the quality of the data.