# Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. OSM History File

## 3. Theoretical Framework

#### 3.1. Medial Axis

#### 3.2. Topological Spatial Relationships

#### 3.3. Data Quality Elements

#### 3.3.1. Positional Accuracy

**Distance:**One of relevant methods to estimate the distance in spatial sciences is the Hausdorff distance that estimates the closeness between the linear objects [49]. The Hausdorff distance has been introduced as the greatest distance between the shortest distance existing between each point from the first object and the set of the points of the second objects [50]. Nonetheless, the Hausdorff distance is sensitive to the shape of the two objects, especially to the parts far from the center. Tong et al. [51] introduced the short-line median Hausdorff distance (SMHD), indicating that, in comparison with Hausdorff and median Hausdorff, this method has a lower variance and a more efficient performance when facing complex data to measure the distance between the linear objects [52]. The SMHD between two linear objects can be calculated through Equation (3) where $L\left(P{L}_{1}\right)$ and $L\left(P{L}_{2}\right)$ are the length of the two linear objects, while $m\left(P{L}_{1},P{L}_{2}\right)$ and $m\left(P{L}_{2},P{L}_{1}\right)$ signify the median of the shortest distance between each point of the first object and the points of the corresponding object. These are calculated using Equations (4) and (5):

**Orientation:**This criterion is employed to compare the local orientation of two linear objects. The orientation difference between two linear objects can play a substantial role as a geometrical criterion in the evaluation of data quality [53]. The angle between the line formed from the beginning and ending points of the object and the horizontal axis determine the orientation of a linear object. See $P{L}_{1}$ and $P{L}_{2}$ in Figure 7, where two linear objects are considered to be parallel, while the angle difference of α and β is close to zero. In case the angle difference of the objects is close to π, it implies that these two linear objects are parallel, but have different orientations. The orientation difference between two linear objects can be estimated using Equation (6) [54]:

**Shape:**The linear objects might vary in terms of shape so that the difference in the shape of two objects is used as another well-known criterion for the evaluation of the difference between two polylines (open or close). One of the functions related to the object shape is the cumulative angle function [55,56]. To calculate the shape difference between two linear objects, Equation (7) could be adopted in which ${\theta}_{P{L}_{1}}$ and ${\theta}_{P{L}_{2}}$ are the cumulative angle functions of the linear objects $P{L}_{1}$ and $P{L}_{2}$, respectively [54]:

#### 3.3.2. Completeness

## 4. The Proposed Approach

#### 4.1. Object Matching

#### 4.2. Identification and Removal of Outliers

## 5. Implementation

#### 5.1. Case Studies

^{2}area was selected for our investigation. For implementation, the suggested comprehensive file of the OSM history was extracted from http://planet.openstreetmap.org/ in the PBF format. To separate the history file of the chosen study area from the comprehensive file, osmconvert software (http://wiki.openstreetmap.org/wiki/Osmconvert) was used. The format of the history file was converted from PBF into OSM. In this file, there are different versions of the points, ways, and relations, respectively. We concentrated on enhancing the quality of the linear data and the linear data were separated from the history file. Figure 12a presents the history file of the study area while Figure 12b shows the latest version of the information about the objects existing in OSM. The users have used 24 labels to classify the streets in the study area. The objects having the following labels which include 37,238 objects that were investigated: motorway, trunk, primary, secondary, tertiary, and residential. In order to evaluate data quality, a reference dataset generated by Tehran Municipality at a cartographic scale of 1:2000 was used, as shown in Figure 12c. The OSM datasets were provided in the Geographical Coordinate System with the WGS-1984 reference ellipsoid, which was projected onto the UTM projection, zone 39.

#### 5.2. Identification of the Corresponding Objects in the History File

#### 5.3. Preprocessing

#### 5.4. Extracting the Medial Axis

#### 5.5. Topological Check of the Objects

#### 5.6. Evaluation of the Approach

## 6. Discussion and Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Ballatore, A.; Jokar Arsanjani, J. Placing Wikimapia: An exploratory analysis. Int. J. Geogr. Inf. Sci.
**2018**, 32, 1–18. [Google Scholar] [CrossRef] - Hashemi, P.; Abbaspour, R.A. Assessment of logical consistency in OpenStreetMap based on the spatial similarity concept. In Openstreetmap in Giscience; Springer: Berlin, Germany, 2015; pp. 19–36. [Google Scholar]
- Goetz, M. Towards generating highly detailed 3D CityGML models from OpenStreetMap. Int. J. Geogr. Inf. Sci.
**2013**, 27, 845–865. [Google Scholar] [CrossRef] - OSM Statistics. Available online: http://wiki.Openstreetmap.Org/wiki/stats (accessed on 24 June 2017).
- Full History Dump. Available online: http://wiki.Openstreetmap.Org/wiki/planet.Osm/full (accessed on 23 June 2017).
- Barron, C.; Neis, P.; Zipf, A. A comprehensive framework for intrinsic OpenStreetMap quality analysis. Trans. GIS
**2014**, 18, 877–895. [Google Scholar] [CrossRef] - Grira, J.; Bédard, Y.; Roche, S. Spatial data uncertainty in the VGI world: Going from consumer to producer. Geomatica
**2010**, 64, 61–72. [Google Scholar] - Feick, R.; Roche, S. Understanding the value of VGI. In Crowdsourcing Geographic Knowledge; Springer: Berlin, Germany, 2013; pp. 15–29. [Google Scholar]
- De Longueville, B.; Ostländer, N.; Keskitalo, C. Addressing vagueness in volunteered geographic information (VGI)—A case study. Int. J. Spat. Data Infrastruct. Res.
**2010**, 5, 1725–0463. [Google Scholar] - Kounadi, O. Assessing the Quality of OpenStreetMap Data. Master’s Thesis, Geographical Information Science, Department of Civil, Environmental and Geomatic Engineering, University College of London, London, UK, 2009. [Google Scholar]
- Girres, J.F.; Touya, G. Quality assessment of the french OpenStreetMap dataset. Trans. GIS
**2010**, 14, 435–459. [Google Scholar] [CrossRef] - Haklay, M. How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environ. Plan. B Plan. Des.
**2010**, 37, 682–703. [Google Scholar] [CrossRef] - Zielstra, D.; Zipf, A. A comparative study of proprietary geodata and volunteered geographic information for germany. In Proceedings of the 13th AGILE International Conference on Geographic Information Science, Guimaras, Portugal, 11–14 May 2010. [Google Scholar]
- Neis, P.; Zielstra, D.; Zipf, A. The street network evolution of crowdsourced maps: Openstreetmap in germany 2007–2011. Future Internet
**2011**, 4, 1–21. [Google Scholar] [CrossRef] - Forghani, M.; Delavar, M.R. A quality study of the OpenStreetMap dataset for tehran. ISPRS Int. J. Geo-Inf.
**2014**, 3, 750–763. [Google Scholar] [CrossRef] - Arsanjani, J.J.; Mooney, P.; Zipf, A.; Schauss, A. Quality assessment of the contributed land use information from OpenStreetMap versus authoritative datasets. In Openstreetmap in Giscience; Springer: Berlin, Germany, 2015; pp. 37–58. [Google Scholar]
- Mohammadi, N.; Malek, M. VGI and reference data correspondence based on location-orientation rotary descriptor and segment matching. Trans. GIS
**2015**, 19, 619–639. [Google Scholar] [CrossRef] - Lyu, H.; Sheng, Y.; Guo, N.; Huang, B.; Zhang, S. Geometric quality assessment of trajectory-generated VGI road networks based on the symmetric arc similarity. Trans. GIS
**2017**, 21, 984–1009. [Google Scholar] [CrossRef] - Brovelli, M.A.; Minghini, M.; Molinari, M.; Mooney, P. Towards an automated comparison of OpenStreetMap with authoritative road datasets. Trans. GIS
**2017**, 21, 191–206. [Google Scholar] [CrossRef] - Dorn, H.; Törnros, T.; Zipf, A. Quality evaluation of VGI using authoritative data—A comparison with land use data in Southern Germany. ISPRS Int. J. Geo-Inf.
**2015**, 4, 1657–1671. [Google Scholar] [CrossRef] - Arsanjani, J.J.; Barron, C.; Bakillah, M.; Helbich, M. Assessing the quality of OpenStreetMap contributors together with their contributions. In Proceedings of the AGILE, Vienna, Austria, 3–7 June 2013; pp. 14–17. [Google Scholar]
- Haklay, M.; Basiouka, S.; Antoniou, V.; Ather, A. How many volunteers does it take to map an area well? The validity of linus’ law to volunteered geographic information. Cartogr. J.
**2010**, 47, 315–322. [Google Scholar] [CrossRef] - Leeuw, J.D.; Said, M.; Ortegah, L.; Nagda, S.; Georgiadou, Y.; DeBlois, M. An assessment of the accuracy of volunteered road map production in Western Kenya. Remote Sens.
**2011**, 3, 247–256. [Google Scholar] [CrossRef] - Neis, P.; Zipf, A. Analyzing the contributor activity of a volunteered geographic information project—The case of OpenStreetMap. ISPRS Int. J. Geo-Inf.
**2012**, 1, 146–165. [Google Scholar] [CrossRef] - Antoniou, V.; Touya, G.; Raimond, A.-M. Quality analysis of the parisian osm toponyms evolution. In European Handbook of Crowdsourced Geographic Information; Capineri, C., Haklay, M., Huang, H., Antoniou, V., Kettunen, J., Ostermann, F., Purves, R., Eds.; University of Zurich: Zurich, Switzerland, 2016; pp. 97–112. [Google Scholar]
- Jonietz, D.; Zipf, A. Defining fitness-for-use for crowdsourced points of interest (POI). ISPRS Int. J. Geo-Inf.
**2016**, 5, 149. [Google Scholar] [CrossRef] - Keßler, C.; De Groot, R.T.A. Trust as a proxy measure for the quality of volunteered geographic information in the case of OpenStreetMap. In Geographic Information Science at the Heart of Europe; Springer: Berlin, Germany, 2013; pp. 21–37. [Google Scholar]
- Keßler, C.; Trame, J.; Kauppinen, T. Tracking editing processes in volunteered geographic information: The case of OpenStreetMap. In Proceedings of the Workshop on Identifying Objects, Processes and Events in Spatio-Temporally Distributed Data (IOPE 2011), Belfast, ME, USA, 12–16 September 2011. [Google Scholar]
- Rehrl, K.; Gröchenig, S. A framework for data-centric analysis of mapping activity in the context of volunteered geographic information. ISPRS Int. J. Geo-Inf.
**2016**, 5, 37. [Google Scholar] [CrossRef] - Sehra, S.S.; Singh, J.; Rai, H.S. Assessing OpenStreetMap data using intrinsic quality indicators: An extension to the qgis processing toolbox. Future Internet
**2017**, 9, 15. [Google Scholar] [CrossRef] - D’Antonio, F.; Fogliaroni, P.; Kauppinen, T. VGI edit history reveals data trustworthiness and user reputation. In Proceedings of the AGILE 2014, Castellón, Spain, 3–6 June 2014. [Google Scholar]
- Touya, G.; Antoniou, V.; Olteanu-Raimond, A.-M.; Van Damme, M.-D. Assessing crowdsourced poi quality: Combining methods based on reference data, history, and spatial relations. ISPRS Int. J. Geo-Inf.
**2017**, 6, 80. [Google Scholar] [CrossRef] - Van Exel, M.; Dias, E.; Fruijtier, S. In The impact of crowdsourcing on spatial data quality indicators. In Proceedings of the 6th GIScience International Conference on Geographic Information Science, Zurich, Switzerland, 14–17 September 2010; p. 213. [Google Scholar]
- Mooney, P.; Corcoran, P. Characteristics of heavily edited objects in OpenStreetMap. Future Internet
**2012**, 4, 285–305. [Google Scholar] [CrossRef] - Wiki. Available online: http://wiki.Openstreetmap.Org/wiki/planet.Osm (accessed on 25 July 2017).
- Blum, H. A transformation for extracting descriptors of shape. In Models for the Perception of Speech and Visual Form; MIT Press: Cambridge, MA, USA, 1967. [Google Scholar]
- Blum, H.; Nagel, R.N. Shape description using weighted symmetric axis features. Pattern Recognit.
**1978**, 10, 167–180. [Google Scholar] [CrossRef] - Attali, D.; Boissonnat, J.-D.; Edelsbrunner, H. Stability and computation of medial axes-a state-of-the-art report. In Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration; Springer: Berlin, Germany, 2009; pp. 109–125. [Google Scholar]
- Armstrong, C.G. Modelling requirements for finite-element analysis. Comput. Aided Des.
**1994**, 26, 573–578. [Google Scholar] [CrossRef] - Hisada, M.; Belyaev, A.G.; Kunii, T.L. A skeleton-based approach for detection of perceptually salient features on polygonal surfaces. In Computer Graphics Forum; Wiley Online Library: Hoboken, NJ, USA, 2002; pp. 689–700. [Google Scholar]
- Brandt, J.W.; Jain, A.K.; Algazi, V.R. Medial axis representation and encoding of scanned documents. J. Vis. Comm. Image Represent.
**1991**, 2, 151–165. [Google Scholar] [CrossRef] - Gold, C.; Dakowicz, M. The crust and skeleton—Applications in GIS. In Proceedings of the 2nd International Symposium on Voronoi Diagrams in Science and Engineering, Seoul, Korea, 10–13 October 2005; pp. 33–42. [Google Scholar]
- Xia, H.; Tucker, P.G. Fast equal and biased distance fields for medial axis transform with meshing in mind. Appl. Math. Model.
**2011**, 35, 5804–5819. [Google Scholar] [CrossRef] - Cheng, S.-W.; Funke, S.; Golin, M.; Kumar, P.; Poon, S.-H.; Ramos, E. Curve reconstruction from noisy samples. Comput. Geom.
**2005**, 31, 63–100. [Google Scholar] [CrossRef] - Egenhofer, M.J.; Herring, J. Categorizing Binary Topological Relations between Regions, Lines, and Points in Geographic Databases; Technical Report; Department of Surveying Engineering, University of Maine: Orono, ME, USA, 1991. [Google Scholar]
- Egenhofer, M.J.; Franzosa, R.D. Point-set topological spatial relations. Int. J. Geogr. Inf. Syst.
**1991**, 5, 161–174. [Google Scholar] [CrossRef] [Green Version] - ISO 19157:2013: Geographic information—Data Quality; International Organization for Standardization (ISO): Geneva, Switzerland, 2013.
- Chehreghan, A.; Ali Abbaspour, R. An assessment of spatial similarity degree between polylines on multi-scale, multi-source maps. Geocarto Int.
**2017**, 32, 471–487. [Google Scholar] [CrossRef] - Abbas, I. Base de Données Vectorielles et Erreur Cartographique: Problèmes Posés par le Contrôle Ponctuel, une Méthode Alternative Fondée sur la Distance de Hausdorff: Le Contrôle Linéaire; ABES: Montpellier, France, 1994. [Google Scholar]
- Li, L.; Goodchild, M.F. An optimisation model for linear feature matching in geographical data conflation. Int. J. Image Data Fusion
**2011**, 2, 309–328. [Google Scholar] [CrossRef] - Tong, X.; Liang, D.; Jin, Y. A linear road object matching method for conflation based on optimization and logistic regression. Int. J. Geogr. Inf. Sci.
**2014**, 28, 824–846. [Google Scholar] [CrossRef] - Chehreghan, A.; Ali Abbaspour, R. An assessment of the efficiency of spatial distances in linear object matching on multi-scale, multi-source maps. Int. J. Geogr. Inf. Sci.
**2017**, 1–20. [Google Scholar] [CrossRef] - Olteanu Raimond, A.-M.; Mustière, S. Data matching—A matter of belief. In Headway in Spatial Data Handling; Springer: Berlin, Germany, 2008; pp. 501–519. [Google Scholar]
- Zhang, M. Methods and Implementations of Road-Network Matching. Ph.D. Dissertation, Technical University of Munich, Munich, Germany, 2009. [Google Scholar]
- Veltkamp, R.C. Shape matching: Similarity measures and algorithms. In Proceedings of the International Conference onShape Modeling and Applications, Herzliya, Israel, 22–24 June 2001; IEEE: Piscataway, NJ, USA, 2001; pp. 188–197. [Google Scholar]
- Ali Abbaspour, R.; Chehreghan, A.; Karimi, A. Assessing the efficiency of shape-based functions and descriptors in multi-scale matching of linear objects. Geocarto Int.
**2017**, 33, 1–14. [Google Scholar] [CrossRef] - Wang, M.; Li, Q.; Hu, Q.; Zhou, M. Quality analysis of OpenStreetMap data. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2013**, 2, W1. [Google Scholar] - Samal, A.; Seth, S.; Cueto, K. A feature-based approach to conflation of geospatial sources. Int. J. Geogr. Inf. Sci.
**2004**, 18, 459–489. [Google Scholar] [CrossRef] - Chehreghan, A.; Ali Abbaspour, R. A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm. Cartogr. Geogr. Inf. Sci.
**2017**, 1–15. [Google Scholar] [CrossRef] - Chehreghan, A.; Ali Abbaspour, R. A new descriptor for improving geometric-based matching of linear objects on multi-scale datasets. GISci. Remote Sens.
**2017**, 54, 836–861. [Google Scholar] [CrossRef] - Dehghani, A.; Chehreghan, A.; Ali Abbaspour, R. Matching of urban pathways in a multi-scale database using fuzzy reasoning. Geod. Cartogr.
**2017**, 43, 92–104. [Google Scholar] [CrossRef] - Mustière, S.; Devogele, T. Matching networks with different levels of detail. GeoInformatica
**2008**, 12, 435–453. [Google Scholar] [CrossRef] - Fan, H.; Yang, B.; Zipf, A.; Rousell, A. A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data. Int. J. Geogr. Inf. Sci.
**2016**, 30, 748–764. [Google Scholar] [CrossRef] - Ester, M.; Kriegel, H.-P.; Sander, J.; Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Knowledge Discovery and Data Mining, Portland, OR, USA, 2–4 August 1996; pp. 226–231. [Google Scholar]
- Han, J.; Kamber, M.; Pei, J. Data Mining: Concepts and Techniques: Concepts and Techniques; Elsevier: New York, NY, USA, 2011. [Google Scholar]

**Figure 3.**(

**a**) A part of the street network from OSM of Tehran; (

**b**) a sample of the OSM history file for Sepand Street (yellow line) in version 1.

**Figure 4.**Medial axis for a 2D curve (adapted from [43]).

**Figure 6.**Eight kinds of topological relations for lines in a two-dimensional space [46].

**Figure 12.**Study area: (

**a**) the OSM data history file; (

**b**) the latest version of the OSM; and (

**c**) the reference dataset.

**Figure 13.**An example of identifying the corresponding objects: (

**a**) using the ID in the history file; and (

**b**) calculating the common buffer area.

**Figure 15.**The medial axis approximation using the Voronoi diagram method: (

**a**) a sample point of the shape boundary; (

**b**) Delaunay triangulation of the boundary points; (

**c**) discarding triangles that are outside the shape; (

**d**) applying the Voronoi diagram; (

**e**) extracting the Voronoi diagram vertices; and (

**f**) connecting the Voronoi diagram’s vertices based on Algorithm 2.

**Figure 19.**(

**a**) The computed positional accuracy of the latest version of OSM dataset; and (

**b**) the computed positional accuracy of the enhanced dataset.

**Figure 20.**(

**a**) Length percentage of the latest version of the OSM dataset; and (

**b**) the length percentage of the enhanced dataset.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Nasiri, A.; Ali Abbaspour, R.; Chehreghan, A.; Jokar Arsanjani, J.
Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap. *ISPRS Int. J. Geo-Inf.* **2018**, *7*, 253.
https://doi.org/10.3390/ijgi7070253

**AMA Style**

Nasiri A, Ali Abbaspour R, Chehreghan A, Jokar Arsanjani J.
Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap. *ISPRS International Journal of Geo-Information*. 2018; 7(7):253.
https://doi.org/10.3390/ijgi7070253

**Chicago/Turabian Style**

Nasiri, Afsaneh, Rahim Ali Abbaspour, Alireza Chehreghan, and Jamal Jokar Arsanjani.
2018. "Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap" *ISPRS International Journal of Geo-Information* 7, no. 7: 253.
https://doi.org/10.3390/ijgi7070253