Characteristics of Heavily Edited Objects in OpenStreetMap

Abstract

This paper describes the results of an analysis of the OpenStreetMap (OSM) database for the United Kingdom (UK) and Ireland (correct to April 2011). 15; 640 OSM ways (polygons and polylines), resulting in 316; 949 unique versions of these objects, were extracted and analysed from the OSM database for the UK and Ireland. In our analysis we only considered “heavily edited” objects in OSM: objects which have been edited 15 or more times. Our results show that there is no strong relationship between increasing numbers of contributors to a given object and the number of tags (metadata) assigned to it. 87% of contributions/edits to these objects are performed by 11% of the total 4128 contributors. In 79% of edits additional spatial data (nodes) are added to objects. The results in this paper do not attempt to evaluate the OSM data as good/poor quality but rather informs potential consumers of OSM data that the data itself is changing over time. In developing a better understanding of the characteristics of “heavily edited” objects there may be opportunities to use historical analysis in working towards quality indicators for OSM in the future

Assessing OpenStreetMap Data Using Intrinsic Quality Indicators: An Extension to the QGIS Processing Toolbox

“…Hence, assessment through intrinsic quality indicators would certainly encourage researchers to have a deeper understanding of datasets. The recent developments of OSM have witnessed intrinsic quality indicators to assess the data using history files [29,69,74,94]. Three frameworks have been developed on these lines by Barron et al [74], Ballatore and Zipf [69] and Rehrl and Gröchenig [94].…”

“…Metadata in OSM are the count of tags, the current maximum version for each tag, etc. [29]. Further, information about contributors who upload and edit the map is also metadata.…”

“…Flanagin and Metzger [81] and Mooney and Corcoran [29] have identified and analyzed the motivations of contributors and their feature editing. Rehrl et al [82] have elaborated the methods used by researchers to evaluate VGI data quality based on contributors' motivations, experience, etc.…”

Conceptualising the geographic world: the dimensions of negotiation in crowdsourced cartography

“…Many definitional conflicts spring from cultural and linguistic di culties encountered when defining intrinsically vague geographic concepts, and from the polysemy of terms. These mostly intensional conflicts can result in extensional conflicts in the map, in which contributors disagree on whether individual objects fit a definition or not, called 'tag wars' (Mooney and Corcoran 2012). The discussion on the conceptualisation of buildings in Key:building includes definitional conflicts.…”

Rating crowdsourced annotations: evaluating contributions of variable quality and completeness

“…This was, however, an exploratory analysis based upon a small data-set that may be unrepresentative of typical crowdsourced datasets: the analysis was focused on data from a low number of volunteers, 7, and only a small amount, <2%, of cases were missing. Volunteers vary greatly in features such as motivation, skill level and time to contribute, and hence, it is common to find that while many people may contribute to a project, most of the data comes from a relatively small number of people (Mooney and Corcoran 2012). Additionally, although there are sometimes barriers to involvement (Haklay 2013), it is generally relatively easy to contribute data as barriers are often low and so, it is perhaps inevitable that at least some of the people providing data will have undesirable characteristics.…”