Temporal Analysis of Activity Patterns of Editors in Collaborative Mapping Project of OpenStreetMap

In the recent years Wikis have become an attractive platform for social studies of the human behaviour. Containing millions records of edits across the globe, collaborative systems such as Wikipedia have allowed researchers to gain a better understanding of editors participation and their activity patterns. However, contributions made to Geo-wikis_wiki-based collaborative mapping projects_ differ from systems such as Wikipedia in a fundamental way due to spatial dimension of the content that limits the contributors to a set of those who posses local knowledge about a specific area and therefore cross-platform studies and comparisons are required to build a comprehensive image of online open collaboration phenomena. In this work, we study the temporal behavioural pattern of OpenStreetMap editors, a successful example of geo-wiki, for two European capital cities. We categorise different type of temporal patterns and report on the historical trend within a period of 7 years of the project age. We also draw a comparison with the previously observed editing activity patterns of Wikipedia.Comment: Submitte

Exploring maintenance practices in crowd-mapping

Crowd-mapping is a form of collaborative work that empowers users to gather and share geographic knowledge. OpenStreetMap is one of the most successful examples of such paradigm, where the goal of building a global map of the world is collectively performed by over 2M contributors. Despite geographic information being intrinsically evolving, little research has so far gone into analysing maintenance practices in these domains. In this paper, we perform a preliminary exploration to quantitatively capture maintenance dynamics in geographic crowd-sourced datasets, in terms of: the extent to which different maintenance actions are taking place, the type of spatial information that is being maintained, and who engages in these practices. We apply this method to 117 countries in OSM, over one year of mapping activity. Our findings reveal that, although maintenance practices vary substantially from country to country in terms of how widespread they are, strong commonalities exist in terms of what metadata is being maintained and by whom

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

Designing a bidirectional workflow for OpenStreetMap data integration in an INSPIRE based SDI

More educated people, with more sophisticated devices, are creating new trends: crowded source data and citizen science. Citizens are able to capture data and to reason about it. They are producers and consumers of their own data. Meanwhile, local administration is struggling with less resources and more pressure to become more efficient. In this paper we try to bridge the gap between these two different worlds, apart from each other, proposing a workflow. We discuss the advantages and the technical challenges we might face if crowded source data can be used by the administration. To make this possible, we take advantage of the existing European regulation, the INSPIRE directive, and designed a workflow to implement the rules regarding data specifications to the OpenStreetMap (OSM) data to combine it seamless with official existing datasets.(undefined