Statistical geoinformatics for human environment interface

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, 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.

Author Notes

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.

Preface	p. ix
1 Statistical Geoinformatics of Human Linkage with Environment	p. 1
1.1 Introduction	p. 1
1.2 Human Environment Informational Interface and Its Indicators	p. 1
1.3 The "-matics" of Geoinformatics	p. 3
1.4 Spatial Synthesis of Disparate Data by Localization as Vicinity Variates	p. 4
1.5 Spatial Posting of Tabulations (SPOTing)	p. 5
1.6 Exemplifying County Context	p. 6
1.7 Posting Points and Provisional Proximity Perimeters for Lackawanna County	p. 7
1.8 Surveillance with Software Sentinels	p. 10
1.9 Backdrop: Distributed Data Depots and Digital Delivery	p. 12
References	p. 13
2 Localizing Fixed-Form Features	p. 15
2.1 Introduction	p. 15
2.2 Locality Layer as Poly-Place Purview	p. 15
2.3 Localizing Layer of Proximity Perimeters	p. 19
2.4 Localizing Linears by Determining Densities	p. 22
2.5 Transfer from Perimeters to Points	p. 24
2.6 Apportioning Attributes of Partial Polygons	p. 28
2.7 Backdrop: GIS Generics	p. 28
References	p. 30
3 Precedence and Patterns of Propensity	p. 31
3.1 Introduction	p. 31
3.2 Prescribing Precedence	p. 34
3.3 Product-Order Precedence Protocol	p. 35
3.4 Precedence Plot	p. 36
3.5 Propensities as Progression of Precedence	p. 38
3.6 Progression Plot	p. 40
3.7 Reversing Ranks	p. 41
3.8 Inconsistency Indicator	p. 42
3.9 Backdrop: Statistical Software	p. 43
References	p. 45
4 Raster-Referenced Cellular Codings and Map Modeling	p. 47
4.1 Introduction	p. 47
4.2 Fixed-Frame Micromapping with Conceptual Cells	p. 47
4.3 Cover Classes and Localizing Logic	p. 48
4.4 Raster Regions and Associated Attributes	p. 51
4.5 Map Modeling	p. 52
4.6 Layer Logic	p. 55
References	p. 56
5 Similar Settings as Clustered Components	p. 57
5.1 Introduction	p. 57
5.2 CLAN Clusters	p. 59
5.3 CLUMP Clusters	p. 64
5.4 CLAN Cluster Centroids	p. 68
5.5 Salient Centroids	p. 69
5.6 Graded Groups by Representative Ranks	p. 71
5.7 Rank Rods	p. 72
5.8 Salient Sequences by Representative Ranks	p. 74
References	p. 78
6 Intensity Images and Map Multimodels	p. 79
6.1 Introduction	p. 79
6.2 Intensity as Frequency of Occurrence	p. 79
6.3 Hillshades and Slopes	p. 86
6.4 Interposed Distance Indicators	p. 88
6.5 Backdrop: Pictures as Pixels and Remote Sensing	p. 90
References	p. 93
7 High Spots, Hot Spots, and Scan Statistics	p. 95
7.1 Introduction	p. 95
7.2 SaTScan™	p. 96
7.3 Concentration of CIT Core Development	p. 97
7.4 Complexion of CIT Developments	p. 100
7.5 Particular Proximity	p. 104
7.6 Upper Level Set (ULS) Scanning	p. 109
7.7 Backdrop: Python Programming	p. 110
References	p. 110
8 Shape, Support, and Partial Polygons	p. 113
8.1 Introduction	p. 113
8.2 Inscribed Octagons	p. 113
8.3 Matching Margins and Adjusting Areas	p. 117
8.4 Shape and Support for Local Roads	p. 119
8.5 Precedence Plot for Shapes and Supports	p. 121
8.6 Supports Spanning Several Partial Polygons	p. 123
References	p. 124
9 Semisynchronous Signals and Variant Vicinities	p. 125
9.1 Introduction	p. 125
9.2 Distal Data	p. 129
9.3 Median Models	p. 132
9.4 Pairing/Placement Patterns of Signal Strengths	p. 136
Reference	p. 141
10 Auto-Association: Local Likeness and Distance Decline	p. 143
10.1 Introduction	p. 143
10.2 Cluster Companions	p. 143
10.3 Kindred Clusters	p. 147
10.4 Local Averages	p. 147
10.5 LISA: Local Indicator of Spatial Association	p. 151
10.6 Picking Pairs at Lagged Locations	p. 152
10.7 Empirical (Semi-)Variogram	p. 155
10.8 Moran's I and Similar Spatial Statistics	p. 158
References	p. 160
11 Regression Relations for Spatial Stations	p. 161
11.1 Introduction	p. 161
11.2 Trend Surfaces	p. 161
11.3 Regression Relations among Vicinity Variates	p. 164
11.4 Restricted Regression	p. 171
References	p. 171
12 Spatial Stations as Surface Samples	p. 173
12.1 Introduction	p. 173
12.2 Interpolating Intensity Indicators as Smooth Surfaces	p. 177
12.3 Spline Smoothing	p. 180
12.4 Kriging	p. 181
References	p. 182
13 Shifting Spatial Structure	p. 183
13.1 Introduction	p. 183
13.2 Space-Time Hotspots	p. 183
13.3 Salient Shifts	p. 183
13.4 Paired Plots	p. 186
13.5 Primary Partition Plots	p. 186
13.6 Backdrop: Spectral Detection of Change with Remote Sensing	p. 189
References	p. 189
14 Synthesis and Synopsis with Allegheny Application	p. 191
14.1 Introduction	p. 191
14.2 Localization Logic	p. 191
14.3 Locality Layer	p. 193
14.4 Localizing Layer	p. 194
14.5 Poly-Place Purviews	p. 196
14.6 Significant Spatial Sectors with Scan Statistics	p. 197
14.7 Scale Sensitivity and Partial Precedence	p. 198
14.8 Cluster Components and Cluster Companions	p. 200
14.9 Trend Surfaces	p. 202
14.10 Surveillance Systems: Sentinel Stations and Signaling	p. 205
14.11 Scripted Sentinels	p. 206
14.12 Smart-Sentinel Socialization	p. 206
References	p. 207
Index	p. 209

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Table of Contents