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.

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.

Multi-representation databases (MRDBs) are used in several geographical information system applications for different purposes. MRDBs are mainly obtained through model and cartographic generalizations. Simplification is the essential operator of cartographic generalization, and streams and lakes are essential features in hydrography. In this study, a new algorithm was developed for the simplification of streams and lakes. In this algorithm, deviation angles and error bands are used to determine the characteristic vertices and the planimetric accuracy of the features, respectively. The algorithm was tested using a high-resolution national hydrography dataset of Pomme de Terre, a sub-basin in the USA. To assess the performance of the new algorithm, the Bend Simplify and Douglas-Peucker algorithms, the medium-resolution hydrography dataset of the sub-basin, and Töpfer’s radical law were used. For quantitative analysis, the vertex numbers, the lengths, and the sinuosity values were computed. Consequently, it was shown that the new algorithm was able to meet the main requirements (i.e., accuracy, legibility and aesthetics, and storage).

Road-matching processes establish links between multi-sourced road lines representing the same entities in the real world. Several road-matching methods have been developed in the last three decades. The main issue related to this process is selecting the most appropriate method. This selection depends on the data and requires a pre-process (i.e., accuracy assessment). This paper presents a new matching method for roads composed of different patterns. The proposed method matches road lines incrementally (i.e., from the most similar matching to the least similar). In the experimental testing, three road networks in Istanbul, Turkey, which are composed of tree, cellular, and hybrid patterns, provided by the municipality (authority), OpenStreetMap (volunteered), TomTom (private), and Basarsoft (private) were used. The similarity scores were determined using Hausdorff distance, orientation, sinuosity, mean perpendicular distance, mean length of triangle edges, and modified degree of connectivity. While the first four stages determined certain matches with regards to the scores, the last stage determined them with a criterion for overlapping areas among the buffers of the candidates. The results were evaluated with manual matching. According to the precision, recall, and F-value, the proposed method gives satisfactory results on different types of road patterns.

Mapping as an action in volunteered geographic information is complex in light of the human diversity within the volunteer community. There is no integrated solution that models and fixes all data heterogeneity. Instead, researchers are attempting to assess and understand crowdsourced data. Approaches based on statistics are helpful to comprehend trends in crowd-drawing behaviors. This study examines trends in contributors’ first decisions when drawing OpenStreetMap (OSM) buildings. The proposed approach evaluates how important the properties of a point are in determining the first point of building drawings. It classifies the adjacency types of the buildings using a random forest classifier for the properties and aids in inferring drawing trends from the relative impact of each property. To test the approach, detached and attached building groups in Istanbul and Izmir, Turkey, were used. The result had an 83% F-score. In summary, the volunteers tended to choose as first points those further away from the street and building centroid and provided lower point density in the detached buildings than the attached ones. This means that OSM volunteers paid more attention to open spaces when drawing the first points of the detached buildings in the study areas. The study reveals common drawing trends in building-mapping actions.

Bu çalışmada 2005-2014 yılları arasında kentsel büyümeye bağlı olarak artan konut sayısının ana ulaşım ağında yarattığı trafik yoğunluğu ilişkisi İstanbul özelinde incelenmiştir. Bu amaçla İstanbul’da kentsel büyümenin yoğun olarak görüldüğü ilçeler (Konut bölgeleri) ile bu ilçeleri etkileyen ana ulaşım hatları belirlenmiştir. Geleceğe yönelik tahmin modellerinde kullanılabilen konut sayısı ile trafik yoğunluğu arasındaki ilişkiyi belirlemek amacıyla regresyon analizi yapılmış ve korelasyon katsayıları hesaplanmıştır. Analizler neticesinde, Avrupa yakasında özellikle Büyükçekmece, Beylikdüzü, Esenyurt ve Avcılar ilçelerindeki kentsel büyümeye bağlı olarak artan konutlaşmanın beraberinde trafik yoğunluğunu da getirdiği gözlemlenmektedir. Anadolu yakasında ise Sultanbeyli, Pendik ve Tuzla ilçelerinde son yıllardaki artan konutlaşmaya bağlı olarak bu ilçeleri etkileyen ana ulaşım hatlarında trafik yoğunluğunda artış olduğu gözlemlenmektedir. Konut bölgelerinde gelecekteki trafik yoğunluğun tahmin edilebilmesi için gereken denklemler regresyon analizi sonucu elde edilmiştir.