Now ubiquitous in modern life, spatial data present great opportunities to transform many of the processes on which we base our everyday lives. However, not only do these data depend on the scale of measurement, but also handling these data (e.g., to make suitable maps) requires that we account for the scale of measurement explicitly. Scale in Spatial Information and Analysis describes the scales of measurement and scales of spatial variation that exist in the measured data. It provides you with a series of tools for handling spatial data while accounting for scale.
The authors detail a systematic strategy for handling scale issues from geographic reality, through measurements, to resultant spatial data and their analyses. They also explore a process-pattern paradigm in approaching scale issues. This is well reflected, for example, in chapters dealing with terrain analysis, in which scale in terrain derivatives is described in relation to the processing involved in the derivation of specific terrain variables from elevation data, and area classes, which are viewed as driven by class-forming covariates. Lastly, this book provides coverage of some of the issues related to scale that are relatively under-represented in the literature, such as the effects of scale on information content in remotely sensed images, and the interaction between scale and uncertainty that is increasingly important for spatial information and analysis.
By taking a rigorous, scientific approach to scale and its various meanings in relation to the geographic world, the book alleviates some of the frustration caused by dealing with issues of scale. While past research has led to an increasing number of journal articles and a few books dedicated to scale modeling and change of scale, this book helps you to develop coherent strategies for scale modeling, highlighting applicability for a variety of fields, from geomatic engineering and geoinformatics to environmental modeling.
Introduction Issue of Scale Models of Scale Scaling Up and Down Book Chapters Geographic Representations Geo-Atoms Geo-Fields Geo-Objects Hierarchical Data Structures Discussion Geospatial Measurements Framework for Spatial Sampling Optical Remote Sensing and Resolution Microwave Remote Sensing and Resolution Discussion Geostatistical Models of Scale Geostatistical Fundamentals and Variograms Variogram Regularization and Deregularization Statistics for Determining Measurement Scales Discussion Lattice Data and Scale Models Lattice Data Spatial Autocorrelation and Its Measures Local Models Discussion Geostatistical Methods for Scaling Kriging Indicator Approaches Upscaling by Block Kriging Downscaling by Area-to-Point Kriging Geostatistical Inverse Modeling Discussion Methods for Scaling Gridded Data Upscaling Downscaling Discussion Multiscale Data Conflation Multivariate Geostatistics Image Fusion Other Multiscale Methods Discussion Scale in Terrain Analysis Digital Elevation Data and Their Scales Terrain Derivatives Models of Scale in Topography Methods for Scaling Terrain Variables Discussion Scale in Area-Class Mapping Area-Class Mapping Spatial Scales and Patterns in Area Classes Scaling Area-Class Information Discussion Information Content Information Theory Information Content in Remotely Sensed Images Image Resolution and Information Content Information Content in Map Data Discussion Uncertainty Characterization Accuracy Metrics and Assessment Geostatistical Approaches to Validation Analytical Approaches to Error Propagation Geostatistical Simulation Discussion Epilogue References Index