# Exploring Data Model Relations in OpenStreetMap
## 2 Related Work and Applications  
As mentioned in the previous section, there are few published articles directly dealing with research on relations in OSM from the academic community. However, there is some attention drawn to relations in a few papers published in the literature. **Related Academic Work: [PERSON] et al. [8]** consider a methodology for intrinsic quality analysis of OSM data. Much of the focus of their proposed methodology is on nodes and ways with the authors concluding that future work must involve relations (e.g., turn restrictions, bus routes, etc.). Current software, such as software to import the OSM history into a relational database does not support the import of Relations to the database. Subsequently relations are not considered as a part of this proposed methodology. The work of [PERSON] et al. [9] on vandalism detection in OSM certainly mentions the usage of relations. However, the work considers relations only from the viewpoint of how many relations have been created or edited by a particular OSM contributor. [PERSON] et al. [10] consider the problem of description logics for knowledge representation and reasoning. They mention, without specific implementation or proof, that relations in OSM are spatial objects which have an inherent structure of containment, bordering, and overlapping, which can be exploited to generate topological relations (e.g., contains). The addition of tagging on relations supplies spatial information allow the relations to be naturally represented as spatial knowledge concepts. [PERSON] et al. [6] use OSM as an input data source for their open source microscopic traffic simulation package SUMO (Simulation of Urban MObility) [11]. SUMO uses the relations in OSM to understand traffic flow using turn restrictions, connected routes, street segments, etc. [PERSON] et al. [5] uses the relations in OSM to extract explicit turn restriction information for their work in development of an Open Source analysis toolbox for street network comparison between geographic datasets. The work by [PERSON] et al. [12] provides an up-to-date review of various quality measures and indicators for selected types of VGI and existing quality assessment methods. In addition to this review the authors also propose a classification of VGI with current methods utilized to assess the quality of selected types of VGI. In a recent edited volume by [PERSON] et al. [13], there are several chapters outlining recent work on what motivates citizens to provide VGI and the subsequent factors which govern or predict its validity and quality. To effectively study how crowdsourced geographic data evolves over time authors such as [PERSON] [14] suggest that distinguishing contributed crowdsourced data from volunteered crowdsourced data is important to start to understand the nature of sources of crowdsourced data of any provenance and to help begin to identify possible biases. [PERSON] argues that there is a significant difference in contributions provided in an automated versus manual way. [PERSON] et al. [15] present a model that attempts to leverage the characteristics of urban-based crowdsourcing in order to describe its digital evolution over time. The authors conclude that \"digital mapping of spatial urban information is governed by complex dynamics\". Geo-demographic factors are of major importance and have great influence over how the early stages of mapping are shaped. Further consideration of how crowdsourced datasets, such as OSM, evolve over time reveal biases with the mapping patterns. Studies, such as those outlined in [PERSON] et al. [16], develop methods to quantitatively measure the bias introduced by more established users in comparison to occasional contributors in crowdsourced mapping. The authors found strong bias in terms of the geographical areas being mapped. [PERSON] and [PERSON] [17] also consider urban biases in Volunteered Geographic Information.