Title
A general framework for spatial data inspection and assessment
Abstract
Abstract The quality aspects of spatial data are very important in the decision-making process. However, the quality inspection of spatial data is still dependent on manual checking, and there is an urgent need to develop an automatic or semi-automatic generic system for spatial data quality inspection. In this paper, we present a general framework that automatically copes with spatial data quality inspection based on various spatial data quality standards and specifications. The framework involves all descriptions of given spatial data, a data quality model characterized by quality elements, scheme batch checking and spatial data quality assessment based on quality control and assessment procedures. It is implemented in Unified Modeling Language with four main sets of classes: data dictionary, quality model, scheme checking and quality assessment. Accordingly, we have designed four structured Extensible Markup Language files for the framework to organize and describe the data dictionary, quality model, scheme check and quality assessment. It is very easy for users to describe the data requirements using the data dictionary file, and to extend the quality elements or check rules using the quality model file. Users can design the specified checks and quality assessment schemes without coding by configuring the scheme check files and quality assessment scheme files. The framework also incorporates a checking tool capable of solving the difficulties inherent in the diversity of spatial data quality standards and specifications. The proposed framework and its implementation, as a quality inspection system, will facilitate automatic multiple spatial data quality inspection and acceptance. As a result, the quality of diversified spatial data can be ensured and improved, which is extremely important in the era of spatial big data.
Year
DOI
Venue
2015
10.1007/s12145-014-0196-9
Earth Science Informatics
Keywords
Field
DocType
Spatial data quality,Inspection,Data assessment,Quality standards
Spatial analysis,Data mining,Data quality,XML,Unified Modeling Language,Computer science,Data dictionary,Big data,Database,Information quality,Spatial data infrastructure
Journal
Volume
Issue
ISSN
8
4
1865-0481
Citations 
PageRank 
References 
0
0.34
8
Authors
5
Name
Order
Citations
PageRank
Yiliang Wan100.34
Wenzhong Shi277886.23
Lipeng Gao330.79
Pengfei Chen46213.05
Yong Hua500.34