Statistical Geoinformatics for Human Environment Interface

Wayne L. Myers is Professor Emeritus of Forest Biometrics at the Pennsylvania State University. He is a Certified Forester of the Society of American Foresters, an Emeritus Member of the American Society of Photogrammetry and Remote Sensing, and a 40-year member of the American Statistical Association. Dr. Myers specializes in landscape analysis using GIS and remote sensing in conjunction with multivariate approaches to analysis and prioritization. Ganapati P. Patil is Director of the Center for Statistical Ecology and Environmental Statistics and Distinguished Professor Emeritus of Mathematical and Environmental Statistics at the Pennsylvania State University. He is a fellow of the American Statistical Association, American Association of Advancement of Science, Institute of Mathematical Statistics, International Statistical Institute, Royal Statistical Society, International Association for Ecology, International Indian Statistical Association, Indian National Institute of Ecology, and Indian Society for Medical Statistics. Dr. Patil has served on panels for numerous international organizations, including the United Nations Environment Programme, U.S. National Science Foundation, U.S. Environmental Protection Agency, U.S. Forest Service, and U.S. National Marine Fisheries Service. He has authored/coauthored more than 300 research papers and more than 30 cross-disciplinary volumes.

"… a refreshingly different approach to geospatial analysis, which has the potential to unify the disparate worlds of raster and vector GIS and to provide an integrated treatment of space and time. Readers accustomed to more traditional approaches to geoinformatics may find the book particularly thought provoking."—Sally E. Goldin, Photogrammetric Engineering and Remote Sensing, December 2013

• Statistical Geoinformatics of Human Linkage with Environment. Localizing Fixed-Form Features. Precedence and Patterns of Propensity. Raster-Referenced Cellular Codings and Map Modeling. Similar Settings as Clustered Components. Intensity Images and Map Multimodels. High Spots, Hot Spots, and Scan Statistics. Shape, Support, and Partial Polygons. Semisynchronous Signals and Variant Vicinities. Auto-Association: Local Likeness and Distance Decline. Regression Relations for Spatial Stations. Spatial Stations as Surface Samples. Shifting Spatial Structure. Synthesis and Synopsis with Allegheny Application. Index.