Spatial Data Quality in GIS Data: A Review

Understanding spatial data quality is important in Geographic Information Science (GIS) applications. Spatial data are used in a variety of critical areas, including urban planning, environmental management, emergency response, and natural resource management where the accuracy and precision of spat...

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Veröffentlicht in:International Conference on Parallel, Distributed and Grid Computing (PDGC ...) S. 737 - 742
Hauptverfasser: Gupta, Punit, McArdle, Gavin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 18.12.2024
Schlagworte:
Accuracy
Benchmark testing
Classification
cloud classification
Completeness
Data integrity
Data Quality
Geographic information systems
Precision
Reviews
Spatial databases
Time series analysis
Urban planning
Vectors
ISSN:2573-3079
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Zusammenfassung:Understanding spatial data quality is important in Geographic Information Science (GIS) applications. Spatial data are used in a variety of critical areas, including urban planning, environmental management, emergency response, and natural resource management where the accuracy and precision of spatial data can have a significant impact on decision-making, especially when used with predictive analysis. A review of the importance of spatial data quality in GIS data is necessary to understand the factors that affect the quality of spatial data and strategies used to interrogate and maintain spatial data quality. While there is no standard definition for spatial data quality, typically the term refers to the accuracy, completeness, consistency, and currency of the data. In this work, a review of various applications of spatial data quality in GIS is carried out. The goal is to provide a generalized SDQ (Spatial Data Quality) benchmark to reduce errors in spatial data across various domains.
ISSN:2573-3079
DOI:10.1109/PDGC64653.2024.10984306