The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007–2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions.

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Geocoding is a method used to convert address information into geographical coordinates. It plays a vital role in displaying the relationship between geographic features and semantic information expressed in texts. The objective of this study is to reveal the quality of online geocoding from postal addresses in Turkey provided by Google Maps and Bing Maps services. The quality of geocoding services in urban areas is evaluated using two particular metrics; positional accuracy and address similarity. Positional accuracy measures the distances between point features obtained through the online geocoding and reference data. Address similarity indicates the relationship between two postal addresses based on a similarity index known as the Levenshtein distance. The same performance assessment was also made with the United States' address data to make comparisons and discussions. The results show that services have different geocoding capabilities in both countries because of the differences in the addressing formats.

OpenStreetMap (OSM), a widely‐used open‐source geographic information system platform, provides a vast geographic dataset in which users contribute both geometric information (nodes, ways, and relations) and semantic information (tags). This method of voluntary contributions is governed by the collective effort of the users. It is widely acknowledged that the quantity of tag information is substantial, but its quality is often poor. Researchers are therefore trying to assess the quality of the tags and enhance the data through various integration experiments. This article investigates the validity of the tags for geographical objects in metropolitan areas using municipal data and a reverse geocoding technique. The proposed method evaluates the data quality and the matching process carried out by reverse geocoding, using municipal points of interest as a reference. The accuracy of the tag and address information and road network centrality metrics were assessed for the OSM objects that were matched to the locations of interest. The tags were found to match the points of interest with an accuracy of 88%. Furthermore, the tag values were categorized and analyzed based on their similarity. It is concluded that in metropolitan settings where centers of interest are closely located, the accuracy of tags and addresses tends to decrease.

Summary The Global Positioning System (GPS), although it has existed for only 30 years, is an important source for active tectonics, resulting in estimates of plate motions very close to geologic estimates over millions of years (Reilinger et al. 2010). GPS is also used for elastic block models to calculate slip rates for a better understanding of Earth’s active crustal deformation. GPS-derived velocity fields may be used as the basis for clustering analysis to create a preliminary definition of block geometry. In this study, we used published horizontal velocity fields to evaluate the effects of data dependencies on determining the optimum number of clusters with algorithms. For this purpose, we used different variations of velocity fields in Turkey and tested four different algorithms that are Davies Bouldin Index, Elbow Method, GAP Statistics Algorithm and Silhouette Method. We also clustered velocity components with the k-means technique and compared the results with previous studies.