A comprehensive quality assessment framework for linear features from Volunteered Geographic Information

ABSTRACT

The majority of spatial data provided as Volunteered Geographic Information (VGI) are roads and other linear map features. Such data have been widely used in routing and navigation, road network update, emergency response, urban planning and more. Due to the lack of cartographic standards and issues with volunteer credibility, the quality of VGI linear features remains a concern and could seriously hinder the broad application of VGI data. This research proposes a comprehensive quality assessment framework for VGI linear features which adopts factor analysis to integrate two novel quality metrics with six other commonly used metrics, and further examines the spatial autocorrelation and semantic correlation of VGI linear feature quality. The OpenStreetMap road network of Allegheny County, Pennsylvania (USA) was selected as an example to test the proposed framework. Our results suggest that the proposed metrics, Box-counting dimension difference and Link accuracy are feasible for detecting quality issues and are important supplements to the common quality metrics. The findings also show that significant spatial autocorrelation exists in spatial completeness, positional accuracy, and logical consistency. Road type such as Tertiary, Residential, Service and Link has been proven to be a typical indicator of the different quality elements for VGI linear features.

KEYWORDS:

• Volunteered geographic information
• linear features
• quality assessment
• factor analysis
• OpenStreetMap

Acknowledgments

The authors are grateful to the Western Pennsylvania Regional Data Center (data.wprdc.org) for providing reference road data of Allegheny County. The authors would like to express special thanks to all the anonymous reviewers and the editor for their comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data and codes availability statement

The data and codes that support the findings of this study are available in ‘figshare.com’ with the identifier at the permanent link: https://doi.org/10.6084/m9.figshare.11815533.v1.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China[42071358, 41671406];Scientific Research Project of Department of Natural Resources of Hubei Province[ZRZY2020KJ01];Fundamental Research Funds for the Central Universities[CCNU20TS035].

Notes on contributors

Hao Wu

Hao Wu is currently a professor at Central China Normal University. His research interests include GIScience, remote sensing and GNSS, focusing on land use and cover change, spatio-temporal data analysis and mining, and volunteered geographic information.

Anqi Lin

Anqi Lin is a Ph. D. candidate at Central China Normal University and her research interests focus on spatio-temporal data analysis and mining, and volunteered geographic information.

Keith C. Clarke

Keith C. Clarke is currently a professor at University of California Santa Barbara. His research interests are environmental simulation modeling, urban growth using cellular automata, terrain mapping and analysis, and real-time visualization.

Wenzhong Shi

Wenzhong Shi is the Head and Chair Professor in the Department of Land Surveying and Geoinformatics at the Hong Kong Polytechnic University. His research interests include GIScience and remote sensing, focusing on uncertainties and quality control of spatial data, satellite images and LiDAR data, 3D modeling, and human dynamics.

Abraham Cardenas-Tristan

Abraham Cardenas-Tristan is currently a professor at Autonomus University of San Luis Potosí, San Luis, Mexico. His current research interests include point cloud processing and deformation monitoring of mine slope.

Zhenfa Tu

Zhenfa Tu is currently an associate professor at Central China Normal University. His research interests include GIScience and software development, focusing on spatial data organization and management.