Statistical Geoinformatics for Human Environment Interface

ISBN-13: 9781138372726 / Angielski / Miękka / 2018 / 223 str.

Wayne L. Myers;Ganapati P. Patil

Opis

Statistical Geoinformatics for Human Environment Interface presents two paradigms for studying both space and interface with regard to human/environment: localization and multiple indicators. The first approach localizes thematic targets by treating space as a pattern of vicinities, with the pattern being a square grid and the placement of vicinities centrically referenced. The second approach explores human/environment interface as an abstraction through indicators, neutralizing the common conundrum of how to reconcile disparate spatial structures such as points, lines, and polygons. These paired paradigms enable:The capacity to cope with complexity Systematic surveillanceVisualization and communication Preliminary prioritizationCoupling of GIS and statistical softwareAvenues for automationIllustrating the interdisciplinary nature of geoinformatics, this book offers a novel approach to the spatial analysis of human influences and environmental resources. It includes practical strategies for statistical and spatial analysis.

Recenzje

"… 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

Spis treści

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.