Uncertainty Modelling and Quality Control for Spatial Data

The rapid developments in GIS such as data integration and representation, spatial data products, and new spatial data mining methods, are forcing users to deal with data of unknown or ambiguous quality; thus, unpredictable risk in decision making is inevitable. This book covers aspects of spatial data quality that have been scarcely addressed. In the era of big data, the mass volume of heterogeneous data and the proper use of the acquired geospatial data remain a significant challenge for GIS users and need to be investigated.

Offers New Insight on Uncertainty Modelling

Focused on major research relative to spatial information, Uncertainty Modelling and Quality Control for Spatial Data introduces methods for managing uncertainties—such as data of questionable quality—in geographic information science (GIS) applications. By using original research, current advancement, and emerging developments in the field, the authors compile various aspects of spatial data quality control. From multidimensional and multi-scale data integration to uncertainties in spatial data mining, this book launches into areas that are rarely addressed.

Topics covered include:

• New developments of uncertainty modelling, quality control of spatial data, and related research issues in spatial analysis



• Spatial statistical solutions in spatial data quality



• Eliminating systematic error in the analytical results of GIS applications



• A data quality perspective for GIS function workflow design



• Data quality in multi-dimensional integration



• Research challenges on data quality in the integration and analysis of data from multiple sources



• A new approach for imprecision management in the qualitative data warehouse



• A multi-dimensional quality assessment of photogrammetric and LiDAR datasets based on a vector approach



• An analysis on the uncertainty of multi-scale representation for street-block settlement

Uncertainty Modelling and Quality Control for Spatial Data serves university students, researchers and professionals in GIS, and investigates the uncertainty modelling and quality control in multi-dimensional data integration, multi-scale data representation, national or regional spatial data products, and new spatial data mining methods.