Spatial data quality and beyond

Abstract

Issues of accuracy, uncertainty, and spatial data quality have been on the top of most GIScience research agendas around the world from the late 1980s. Ever since then, growing research efforts have been directed toward uncertainty characterization in spatial information, analysis, and applications, aiming for better understanding of spatial uncertainty and thus improved methods and techniques for assessing and managing data quality. Impressive progress has been made in various issues concerning data quality. In addition, growing research on extensions to the conventional norms of data quality, such as the quality aspects of geospatial information services, has been observed. Chinese researchers have contributed to this great cause by keeping abreast with the developments abroad and striving for their own innovative work. This paper reviews the past research on data quality-related issues and provides a perspective on future developments. These will be seen not only in continued research on theoretical and technical issues concerning data quality, but also in developments of tools for quality assessment and decision-making under uncertainty through geospatial information processing and applications.

Keywords:

• spatial data quality
• accuracy
• error modeling
• uncertainty
• geostatistics
• validation
• geographic information services

Acknowledgments

The authors would like to extend their heart-felt gratitude to Prof. Michael Goodchild for his pioneering work on GIScience and dedication to advising, helping, and supporting Chinese researchers in their exploration of the wonderful world of GIScience. Comments from anonymous reviewers were received with thanks. This special issue's Guest Editors have given the authors helpful advice about orientation, structuring, and revision of the paper. The work is supported by the National Natural Science Foundation of China (Grant Nos. 41071286 and 41171346).