Describes the State-of-the-Art in Spatial Data Mining, Focuses on Data Quality
Substantial progress has been made toward developing effective techniques for spatial information processing in recent years. This science deals with models of reality in a GIS, however, and not with reality itself. Therefore, spatial information processes are often imprecise, allowing for much interpretation of abstract figures and data. Quality Aspects in Spatial Data Mining introduces practical and theoretical solutions for making sense of the often chaotic and overwhelming amount of concrete data available to researchers.
In this cohesive collection of peer-reviewed chapters, field authorities present the latest field advancements and cover such essential areas as data acquisition, geoinformation theory, spatial statistics, and dissemination. Each chapter debuts with an editorial preview of each topic from a conceptual, applied, and methodological point of view, making it easier for researchers to judge which information is most beneficial to their work.
Chapters Evolve From Error Propagation and Spatial Statistics to Address Relevant Applications
The book advises the use of granular computing as a means of circumventing spatial complexities. This counter-application to traditional computing allows for the calculation of imprecise probabilities – the kind of information that the spatial information systems community wrestles with much of the time.
Under the editorial guidance of internationally respected geoinformatics experts, this indispensable volume addresses quality aspects in the entire spatial data mining process, from data acquisition to end user. It also alleviates what is often field researchers’ most daunting task by organizing the wealth of concrete spatial data available into one convenient source, thereby advancing the frontiers of spatial information systems.
"It is a gem, and for the price and its wealth of information, it is a steal . . . If you are concerned about spatial data quality, and anyone using spatial data should be, then you really cannot do without this remarkable book."
– Nigel Waters, in Geomatica, 2009, Vol. 63, No. 2
Foreword: Granular Computing – Computing with Uncertain, Imprecise, and Partially True Data, L.A. Zadeh
Systems Approaches to Spatial Data Quality
Querying Vague Spatial Objects in Databases with VASA, A. Pauly and M. Schneider
Assessing the Quality of Data with a Decision Model, A. Frank
Semantic Reference Systems Accounting for Uncertainty: A Requirements Analysis, S. Schade
Elements of Semantic Mapping Quality: A Theoretical Framework, M. Bakillah, M.A. Mostafavi, Y. Bédard, and J. Brodeur
A Multicriteria Fusion Approach for Geographical Data Matching,A.-M. Olteanu
Geostatistics and Spatial Data Quality for DEMs
A Preliminary Study on Spatial Sampling for Topographic Data, H.X. Mao, W.Z. Shi, and Y. Tian
Predictive Risk Mapping of Water Table Depths in a Brazilian Cerrado Area, R.L. Manzione, M. Knotters, G.B.M. Heuvelink, J.R. Von Asmuth, and G. Câmara
Modeling Data Quality with Possibility Distributions, G. Navratil
A Comparison of Geostatistics and Fuzzy Applications for Digital Elevation Models, R. Sunila and K. Kollo
Propagation of Positional Measurement Errors to Field Operations, S. de Bruin, G.B.M. Heuvelink, and J.D. Brown
Error Propagation Analysis Techniques Applied to Precision Agriculture and Environmental Models, M. Marinelli, R. Corner, and G. Wright
Aspects of Error Propagation in Modern Geodetic Networks, M. Vermeer and K. Kollo
Analysis of the Quality of Collection 4 and 5 Vegetation Index Time Series from MODIS, R.R. Colditz, C. Conrad, T. Wehrmann, M. Schmidt, and S. Dech
Modeling DEM Data Uncertainties for Monte Carlo Simulations of Ice Sheet Models, F. Hebeler and R.S. Purves
Geostatistical Texture Classification of Tropical Rainforest in Indonesia, A. Wijaya, P.R. Marpu, and R. Gloaguen
Quality Assessment for Polygon Generalization, E.S. Podolskaya, K.-H. Anders, J.-H. Haunert, and M. Sester
Effectiveness of High-Resolution LIDAR DSM for Two-Dimensional Hydrodynamic Flood Modeling in an Urban Area, T.H.M. Rientjes and T.H. Alemseged
Uncertainty, Vagueness, and Indiscernibility: The Impact of Spatial Scale in Relation to the Landscape Elements, A.J. Comber, P.F. Fisher, and A. Brown
A Quality-Aware Approach for the Early Steps of the Integration of Environmental Systems, A. Guemeida, R. Jeansoulin, and G. Salzano
Analyzing and Aggregating Visitor Tracks in a Protected Area, E.S. Dias, A.J. Edwardes, and R. S. Purves
What Communicates Quality to the Spatial Data Consumer?, A.T. Boin and G.J. Hunter
Judging and Visualizing the Quality of Spatio-Temporal Data on the Kakamega-Nandi Forest Area in West Kenya, K. Huth, N. Mitchell, and G. Schaab
A Study on the Impact of Scale-Dependent Factors on the Classification of Landcover Maps, A.M. Lechner, S.D. Jones, and S.A. Bekessy
Formal Languages for Expressing Spatial Data Constraints and Implications for Reporting of Quality Metadata, P. Watson
Epilogue: Putting Research into Practice, M.F. Goodchild