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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 surveillance
- Visualization and communication
- Preliminary prioritization
- Coupling of GIS and statistical software
- Avenues for automation
Illustrating the interdisciplinary nature of geoinformatics, Statistical Geoinformatics for Human Environment Interface offers a novel approach to the spatial analysis of human influences and environmental resources. It includes practical strategies for statistical and spatial analysis.
Statistical Geoinformatics of Human Linkage with Environment
Introduction
Human Environment Informational Interface and Its Indicators
The "-matics" of Geoinformatics
Spatial Synthesis of Disparate Data by Localization as Vicinity Variates
Spatial Posting of Tabulations (SPOTing)
Exemplifying County Context
Posting Points and Provisional Proximity Perimeters for Lackawanna County
Surveillance with Software Sentinels
Backdrop: Distributed Data Depots and Digital Delivery
Localizing Fixed-Form Features
Introduction
Locality Layer as Poly-Place Purview
Localizing Layer of Proximity Perimeters
Localizing Linears by Determining Densities
Transfer from Perimeters to Points
Apportioning Attributes of Partial Polygons
Backdrop: GIS Generics
Precedence and Patterns of Propensity
Introduction
Prescribing Precedence
Product–Order Precedence Protocol
Precedence Plot
Propensities as Progression of Precedence
Progression Plot
Reversing Ranks
Inconsistency Indicator
Backdrop: Statistical Software
Raster-Referenced Cellular Codings and Map Modeling
Introduction
Fixed-Frame Micromapping with Conceptual Cells
Cover Classes and Localizing Logic
Raster Regions and Associated Attributes
Map Modeling
Layer Logic
Similar Settings as Clustered Components
Introduction
CLAN Clusters
CLUMP Clusters
CLAN Cluster Centroids
Salient Centroids
Graded Groups by Representative Ranks
Rank Rods
Salient Sequences by Representative Ranks
Intensity Images and Map Multimodels
Introduction
Intensity as Frequency of Occurrence
Hillshades and Slopes
Interposed Distance Indicators
Backdrop: Pictures as Pixels and Remote Sensing
High Spots, Hot Spots, and Scan Statistics
Introduction
SaTScan™
Concentration of CIT Core Development
Complexion of CIT Developments
Particular Proximity
Upper Level Set (ULS) Scanning
Backdrop: Python Programming
Shape, Support, and Partial Polygons
Introduction
Inscribed Octagons
Matching Margins and Adjusting Areas
Shape and Support for Local Roads
Precedence Plot for Shapes and Supports
Supports Spanning Several Partial Polygons
Semisynchronous Signals and Variant Vicinities
Introduction
Distal Data
Median Models
Pairing/Placement Patterns of Signal Strengths
Auto-Association: Local Likeness and Distance Decline
Introduction
Cluster Companions
Kindred Clusters
Local Averages
LISA: Local Indicator of Spatial Association
Picking Pairs at Lagged Locations
Empirical (Semi-)Variogram
Moran’s I and Similar Spatial Statistics
Regression Relations for Spatial Stations
Introduction
Trend Surfaces
Regression Relations among Vicinity Variates
Restricted Regression
Spatial Stations as Surface Samples
Introduction
Interpolating Intensity Indicators as Smooth Surfaces
Spline Smoothing
Kriging
Shifting Spatial Structure
Introduction
Space–Time Hotspots
Salient Shifts
Paired Plots
Primary Partition Plots
Backdrop: Spectral Detection of Change with Remote Sensing
Synthesis and Synopsis with Allegheny Application
Introduction
Localization Logic
Locality Layer
Localizing Layer
Poly-Place Purviews
Significant Spatial Sectors with Scan Statistics
Scale Sensitivity and Partial Precedence
Cluster Components and Cluster Companions
Trend Surfaces
Surveillance Systems: Sentinel Stations and Signaling
Scripted Sentinels
Smart-Sentinel Socialization
Index
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.
