![]() This analysis is based on each trajectory’s average speed and some recognizable patterns such as sequential stops. This can be done by analyzing trajectories, categorizing them into car, pedestrian, bicycle, wheelchair, bus, and tram. For example, from the analysis of tracking data, some indoor corridors which have been wrongly tagged as “tunnel” can be found. Such patterns and rules may highlight anomalies and unusual data patterns as potential errors which can then be manually examined or in some cases automatically corrected. Thus, it is possible to learn rules and recognize patterns over trajectories and use these to check OSM data validity. Rules for error detection and data correction, based on detected categories or specific types of error, can be also based on users’ travel behaviors and patterns. This paper focuses on this approach, which is finding bugs and errors based on spatial knowledge extracted from anonymous tracking data of users. The idea presented in this paper is to analyze users’ travel behaviors to derive rules, which can be used for checking and validating the quality of user-generated data. Such rules are mainly based on logical assumptions and mapping agencies specification standards, for example, that two crossing roads at the same level must have an intersection. Many of current quality-assurance-related applications have focused on comparing OSM data with other sources of data, such as from Google Maps and Ordnance Survey (UK) to evaluate OSM positional, temporal, and thematic accuracy and completeness of coverage.ĭata-validation methods, such as node spacing on specified feature polygons in an OSM snapshot, can either be based on user-checking or on predefined automated rules which can detect and correct bugs and errors. ![]() A recent study has shown the essentiality of an expert-validation phase for OSM data quality assurance (Salk et al. Citation2012 Koukoletsos, Haklay, and Ellul Citation2012). The quality aspects of OSM have been investigated by different researchers (Amirian et al. Although OpenStreetMap (OSM) data have been widely used by a range of different applications, its reliability and accuracy have been questioned since most contributors are not doing so as geospatial data experts (Hashemi and Abbaspour Citation2015 Salk et al.
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