Abstract
The increased development of Volunteered Geographic Information (VGI) and its potential role in GIScience studies raises questions about the resulting data quality. Several studies address VGI quality from various perspectives like completeness, positional accuracy, consistency, etc. They mostly have consensus on the heterogeneity of data quality. The problem may be due to the lack of standard procedures for data collection and absence of quality control feedback for voluntary participants. In our research, we are concerned with data quality from the classification perspective. Particularly in VGI-mapping projects, the limited expertise of participants and the non-strict definition of geographic features lead to conceptual overlapping classes, where an entity could plausibly belong to multiple classes, e.g., lake or pond, park or garden, marsh or swamp, etc. Usually, quantitative and/or qualitative characteristics exist that distinguish between classes. Nevertheless, these characteristics might not be recognizable for non-expert participants. In previous work, we developed the rule-guided classification approach that guides participants to the most appropriate classes. As exemplification, we tackle the conceptual overlapping of some grass-related classes. For a given data set, our approach presents the most highly recommended classes for each entity. In this paper, we present the validation of our approach. We implement a web-based application called Grass&Green that presents recommendations for crowdsourcing validation. The findings show the applicability of the proposed approach. In four months, the application attracted 212 participants from more than 35 countries who checked 2,865 entities. The results indicate that 89% of the contributions fully/partially agree with our recommendations. We then carried out a detailed analysis that demonstrates the potential of this enhanced data classification. This research encourages the development of customized applications that target a particular geographic feature.
Highlights
Web and information revolutions, the increased availability of location sensing devices, and the advanced communication technologies facilitate the evolution of free geographic content, which is known as Volunteered Geographic Information (VGI) [1]
We discuss the results that have been obtained by the application from various perspectives: participant and contribution patterns (Section 6.1), the participant responses to recommendations (Section 6.2), and the potential enhanced data classification (Section 6.3)
The participants examined the classification of 2,865 entities; 1,060 out of these entities have been checked by participants related to Germany, as shown in Figure 8b, which is relevant to the data set used here
Summary
The increased availability of location sensing devices, and the advanced communication technologies facilitate the evolution of free geographic content, which is known as Volunteered Geographic Information (VGI) [1]. We are concerned with the VGI format, in which the public participates in mapping processes regardless of their prior geographic experience. In the past, these processes were performed exclusively by cartographers at mapping agencies and in specialized organizations. Several studies have concluded that the quality of VGI is heterogeneous [10]. This finding impacts the utility of VGI as a complementary source or as an alternative to authoritative data sources [11,12,13,14]
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