“Contextualized VGI” Creation and Management to Cope with Uncertainty and Imprecision

Abstract

This paper investigates the causes of imprecision of the observations and uncertainty of the authors who create Volunteer Geographic Information (VGI), i.e., georeferenced contents generated by volunteers when participating in some citizen science project. Specifically, various aspects of imprecision and uncertainty of VGI are outlined and, to cope with them, a knowledge-based approach is suggested based on the creation and management of "contextualized VGI". A case study example in agriculture is reported where contextualized VGI can be created about in situ crops observations by the use of a smart app that supports volunteers by means of both an ontology and the representation of the context of the geo-localization. Furthermore, an approach to cope with both ill-defined knowledge and volunteer's uncertainty or imprecise observations is defined based on a fuzzy ontology with uncertainty level-based approximate reasoning. By representing uncertainty and imprecision of VGI, users, i.e., consumers, can exploit quality checking mechanisms to filter VGI based on their needs.

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

“…Integrity constraints can be defined and implemented in a collaborative system in order to guide the contributor to produce consistent data, to check that a change in a feature does not introduce inconsistencies or deteriorate the quality of existing data. Although research in this direction already exists, in most cases the methods for post-creation error fixing are proposed or they guide the contributor into choosing the type of a new feature [37,38]. Regarding real-time quality assessment, there have been a few research studies conducted: one focuses on attribute assessment [15], another one proposes near-real-time comparison of addresses with references [39], and a last one identifies errors that do not respect integrity constraints [40].…”

Highlighting Current Trends in Volunteered Geographic Information

“…Finally, along the same lines, Bordogna et al [10] outline the development of a fuzzy ontology for improving the attribution process of a VGI project. Using the notion of "contextualized VGI", the paper presents a case study in agriculture where contextualized VGI of crop observations is realized through an application that uses an ontology and geographic context.…”

CS Projects Involving Geoinformatics: A Survey of Implementation Approaches

“…Another main critical and still disregarded topic is how to interpret "no data values" (i.e., lack of data in some geographic areas) and bias due to both volunteer attitudes and field logistics (e.g., shots of slow-moving animals are more likely to be contributed, because they are easy to catch, while fast-running animal shots are harder; more observations are provided in easily-accessible locations than in remote ones). Nevertheless, strategies and technological means have been proposed to manage such biases, incompleteness, and uncertainty [37][38][39][40].…”

“…Furthermore, the adoption of shared vocabularies (and/or domain ontologies) and common metadata schema could help normalize data creation and retrieval and enable semantic interoperability between projects. This would allow a semantic-aware fruition and reuse of CS data [16,37,41]. Extending data quality and interoperability of practices means shifting CS initiatives from the "ghetto" of amateurs to the level of authoritative science.…”