Modelling Inhomogeneous Geodata Quality in a Dataset’s Metadata

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

Abstract. Extensive data quality descriptions as a vital part of a dataset’s metadata are widely accepted, albeit their provision in a formalized manner is often lacking. This is due to a number of problems that are frequently encountered by geodata producing scientists. As one of these problems, we identified missing, unknown or unused options to model inhomogeneity of data quality across space, time, and theme in a dataset’s metadata. Detailed information of inhomogeneous geodata quality beyond dataset-wide statistical measures (variance, min, max, etc.) is often only described in dataset accompanying papers or quality reports. These text-based approaches prevent precise querying and hinder the development of advanced data discovery tools that could make valuable use of inhomogeneous data quality information. We propose a profile for the data quality vocabulary (DQV) that allows to model inhomogeneous geodata quality. Considering established vocabularies typically used to describe geographic metadata, as well as ensuring compatibility with the default version of DQV, enhances the usability and thus, minimizes the effort for data producers to provide formalized descriptions of inhomogeneous data quality.

Details

Original languageEnglish
Article number59
Number of pages6
JournalAGILE: GIScience Series
Volume3
Publication statusPublished - 2022
Peer-reviewedYes

Conference

TitleAGILE 2022
SubtitleArtificial Intelligence in the service of Geospatial Technologies
Conference number25
Duration14 - 17 June 2022
LocationVilnius Gediminas Technical University
CityVilnius
CountryLithuania

External IDs

ORCID /0000-0002-5181-4368/work/144671073
Mendeley e41feb86-70a3-30fa-8e92-3157317bc04c

Keywords

Library keywords