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Statistical Geoinformatics for Human Environment Interface
     

Statistical Geoinformatics for Human Environment Interface

by Wayne L. Myers, Ganapati P. Patil
 

ISBN-10: 1420082876

ISBN-13: 9781420082876

Pub. Date: 08/01/2012

Publisher: Taylor & Francis

A clear, concise discussion of spatial patterns of interaction between complexes of environmental process factors and human process factors, this book facilitates the convergence of the two sides for focus on the interactions between human factors and environmental factors at the interface. It recognizes the information resources in the repertoire of each side and

Overview

A clear, concise discussion of spatial patterns of interaction between complexes of environmental process factors and human process factors, this book facilitates the convergence of the two sides for focus on the interactions between human factors and environmental factors at the interface. It recognizes the information resources in the repertoire of each side and elucidates the informational elements with special relevance to the interface and ways of handling these elements effectively. The author takes an intellectual middle ground between spatial statistics, multivariate analysis, and geographic information systems.

Product Details

ISBN-13:
9781420082876
Publisher:
Taylor & Francis
Publication date:
08/01/2012
Series:
Chapman & Hall/CRC Applied Environmental Statistics Series , #7
Pages:
223
Product dimensions:
6.30(w) x 9.40(h) x 0.80(d)

Table of Contents

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

References appear at the end of each chapter.

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