In recent years, the VGI phenomenon offered an alternative mechanism for the production, acquisition and compilation of geographic information regarding measurable elements on the Earth surface and environmental and socio-cultural phenomena happening on it. As such VGI offers notable advantages in terms of affordability and updating, but suffers from a general lack of quality assurance as highlighted by several authors in literature. Both VGI and its SMGI subset present an intrinsic heterogeneity in data (Girres and Touya, 2010; Haklay et al., 2010; van Exel et al., 2010), due to different participants’ knowledge basis, to collaborative production processes and to specific systems that may be used for acquisition and production by the different participants. Moreover, unlike the traditional authoritative geographic information that these sources potentially complement, VGI and SMGI carry no assurance of quality, raising questions about the reliability, accuracy and credibility for practices. Nonetheless, the technological interoperability offered by SDIs might be proficiently used to integrate these sources with the official information (Craglia, 2007) and, at the same time, several authors are proposing different methodologies to assess of VGI and SMGI quality, fostering the integration with authoritative information.
The label ‘authoritative’ refers to geographic Information and spatial data produced by experts, professionals and mapping agencies, which operate under institutional or legal frameworks with a specific aim (Goodchild and Glennon, 2010; Ball, 2010). Therefore, the production of this type of information by highly trained experts complies with specific requirements in terms of quality and assurance procedures, guarantying the accuracy and the quality standards of the final product (Goodchild and Glennon, 2010; Elwood et al. 2012). Moreover, the authoritativeness of A-GI is assured by metadata that describe contents, quality, authorship, accuracy and use of this information conditions (Nogueras-Iso et al., 2004). On the contrary, VGI and SMGI are acquired, produced and shared by individuals’ groups, which voluntary operate for a specific purpose or interest but without common practices in terms of quality and assurance procedures or in absence of adequate training.
In order to deal with the issues arising from the uncertainty underlying VGI and SMGI, several studies used different elements of spatial data quality such as: positional, thematic and temporal accuracies, logical consistency, and completeness (ISO/TC 211, 2002). The first developed studies concerned the investigation and comparison of OSM data, one of the most popular VGI project (Haklay and Weber, 2008), with A-GI from different countries worldwide. A number of studies assessed the positional accuracy and
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 45 completeness of OSM’ roads network (Haklay, 2010; Zielstra and Zipf, 2010), natural features (Mooney et al., 2010), as well as both the features (Girres and Touya, 2010). How the accuracy of the VGI position may be very high for manmade features rather than for natural features have been demonstrated by these studies (Bègin et al., 2013). Furthermore, the accuracy and the quality are proved to be high, even though the existence of semantic differences among features of voluntary and official datasets (Al-Bakri and Fairbairn, 2012). Nonetheless, a quality issue emerged in the datasets completeness, which concern places with low density population and consequent scarce number of contributions. Due to the particular nature of VGI and SMGI, new assessment methods should be used to integrate the traditional approaches and to take into account the heterogeneity nature and the collaborative production processes underlying this information (Van Exel et al., ibidem; Sieber, 2007). In this respect, Maue (2007) proposes the inclusion of information regarding authorship, reputation and knowledge basis of the author into metadata in order to validate the geographic information. Flanagin and Metzeger (2008) suggest the implementation of a validation system directly in the production interface, meanwhile Bishr and Mantelas (2008) proposed the implementation of automatic filters for assessing quality and reliability of information. More recent approaches suggested the integration and comparison of VGI and SMGI with official information from SDIs to automatically validate or reject the contributed information (Spinsanti and Osterman, 2013).
The aforementioned approaches for assessing VGI and SMGI, in spite of difference in methods, may be classified according to three main categories proposed by Goodchild and Li (2012):
1) the crowd-sourcing approach, 2) the social approach,
3) the geographic approach.
First of all, the crowd-sourcing approach benefits from the crowd, namely the people, in order to validate and correct the errors that an individual might make, thanks to the ability of a group. This approach relies on the Linus’s Law (Raymond, 1999), formerly the capacity of the crowd to converge on the truth, which is used in the context of software engineering to identify and correct software’ bugs. This principle might be proficiently used also to validate and assess VGI and SMGI, and several studies verified this principle (Latonero and Shklovski, 2010) focusing on the relationships between contributors’ density and data quality (Napolitano and Mooney, 2012; Neis et al., 2011) or between the number of edits and features quality (Keßler et al., 2011; Mashhadi et al., 2012). However, when VGI and SMGI refer to a barely populated location or to an area affected by lack of interest, this approach may require a change of technologies. Secondly, the social approach is based on the acknowledged reputation of the authors to assess the quality of the data they produce, leading toward the creation of a hierarchy of trusted individuals, who may act as
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 46 moderators (Goodchild and Li, 2012). As a matter of fact, the tracking of individuals’ contributions may allow the calculation of reliability metrics, providing trustworthy basis about the geographic information. Several studies rely on this approach using a data-centric evaluation based on the editing history of VGI contributions, exclusively (Keßler et al., 2011; Mooney and Corcoran, 2012), while other studies assess the contributions according to specific data and users characteristics (van Exel and Dias, 2011).
Finally, the geographic approach relies on a comparison of VGI or SMGI with the geographic knowledge, benefits from specific rules, which govern what may occur or not at certain locations (Goodchild and Li, 2012). As an example, the First Law of Geography by Tobler (1970) that says “all things are related, but nearby things are more related than distant things” is an efficient rule to assess the reliability of voluntary information. Indeed, a fact about a location should be consistent with both what is already known about that area and other similar acknowledged facts regarding the same location. However, the rules governing the geographic domain range from the very simple to the highly abstract, causing major challenges for the development of an adequate system for VGI and SMGI assessment.
At the current stage, VGI and SMGI may present notable advantages in terms of affordability and timely data, as well as in the capability to provide information that has never figured in mapping practices before. On the other hand, this type of information suffers from heterogeneity and incomplete coverage, failing to be considered as a proper and valid alternative source to A-GI, though it may play a major role in exploratory analysis and in integrating official datasets.
In literature several applications were found for different application domains (i.e. disaster events, political events, media events, social studies and urban planning), promoting methodologies and innovative tools to provide solutions for dealing with aforementioned issues.