Statistical Geoinformatics for Human Environment Interface: 1st Edition (Paperback) book cover

Statistical Geoinformatics for Human Environment Interface

1st Edition

By Wayne L. Myers, Ganapati P. Patil

Chapman and Hall/CRC

223 pages | 148 B/W Illus.

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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, 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.


"… 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

Table of Contents

Statistical Geoinformatics of Human Linkage with Environment


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


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


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


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


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


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



Concentration of CIT Core Development

Complexion of CIT Developments

Particular Proximity

Upper Level Set (ULS) Scanning

Backdrop: Python Programming

Shape, Support, and Partial Polygons


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


Distal Data

Median Models

Pairing/Placement Patterns of Signal Strengths

Auto-Association: Local Likeness and Distance Decline


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


Trend Surfaces

Regression Relations among Vicinity Variates

Restricted Regression

Spatial Stations as Surface Samples


Interpolating Intensity Indicators as Smooth Surfaces

Spline Smoothing


Shifting Spatial Structure


Space–Time Hotspots

Salient Shifts

Paired Plots

Primary Partition Plots

Backdrop: Spectral Detection of Change with Remote Sensing

Synthesis and Synopsis with Allegheny Application


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


References appear at the end of each chapter.

About the Authors

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.

About the Series

Chapman & Hall/CRC Applied Environmental Statistics

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
SCIENCE / Earth Sciences / Geology
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems