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

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## 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

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**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.

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**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