Assessing OpenStreetMap Data Using Intrinsic Quality Indicators: An Extension to the QGIS Processing Toolbox

Abstract

OpenStreetMap (OSM) is a recent emerging area in computational science. There are several unexplored issues in the quality assessment of OSM. Firstly, researchers are using various established assessment methods by comparing OSM with authoritative dataset. However, these methods are unsuitable to assess OSM data quality in the case of the non-availability of authoritative data. In such a scenario, the intrinsic quality indicators can be used to assess the quality. Secondly, a framework for data assessment specific to different geographic information system (GIS) domains is not available. In this light, the current study presents an extension of the Quantum GIS (QGIS) processing toolbox by using existing functionalities and writing new scripts to handle spatial data. This would enable researchers to assess the completeness of spatial data using intrinsic indicators. The study also proposed a heuristic approach to test the road navigability of OSM data. The developed models are applied on Punjab (India) OSM data. The results suggest that the OSM project in Punjab (India) is progressing at a slow peace, and contributors' motivation is required to enhance the fitness of data. It is concluded that the scripts developed to provide an intuitive method to assess the OSM data based on quality indicators can be easily utilized for evaluating the fitness-of-use of the data of any region.

OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data

“…In recent years, several studies utilized the history of the OSM dataset, e.g., for conducting intrinsic quality assessments in cases where comparison datasets do not exist [26,27]. Intrinsic quality indicators have been developed to evaluate the fitness for purpose of OSM data [13,17,18,24]. Further studies assess the behavior of OSM contributors and contribution patterns [5,[28][29][30][31].…”

“…However, the analyses are limited to smaller regions due to performance limitations. Another set of intrinsic quality indicators is made available by Sehra et al [18] who implemented an extension of a QGIS processing toolbox to assess the completeness and road navigability of OSM data. Nevertheless, this study is limited to analysing line type features and compares only two points in time.…”

“…Nevertheless, this study is limited to analysing line type features and compares only two points in time. [18] Moving on to flexible analysis-tools, Osmium [19] is a framework that enables the user to work with OSM data, e.g., by providing conversion functions between several file formats. It also allows the extraction of parts of the data through specified attribute filters or spatial extents.…”

“…Consequently, it is not utilizable for many potential users. Although many software tools [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] address this problem partially, none of them is able to provide the following range of functionality in one single integrated tool: fast and easy reconstruction of historic geometric and semantic states of the data as well as fast spatial, temporal and tag queries. Therefore, we developed a framework called OpenStreetMap History Database (OSHDB).…”

Geospatial preparedness: Empirical study of the joint effort to provide geospatial support to disaster response

“…AsNeis and Zielstra's (2014) review shows, research related to OpenStreetMap has been focused on data quality and contributor patterns. Numerous papers have studied the level of accuracy through different methods(Basiri et al, 2016;Eckle & de Albuquerque, 2015;Sehra, Singh, & Rai, 2017), with reliability being one of the main concerns regarding this platform.…”