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Spatial Statistics : GeoSpatial Information Modeling and Thematic Mapping - Mohammed A. Kalkhan

Spatial Statistics

GeoSpatial Information Modeling and Thematic Mapping


Published: 9th May 2011
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Geospatial information modeling and mapping has become an important tool for the investigation and management of natural resources at the landscape scale. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping reviews the types and applications of geospatial information data, such as remote sensing, geographic information systems (GIS), and GPS as well as their integration into landscape-scale geospatial statistical models and maps.

The book explores how to extract information from remotely sensed imagery, GIS, and GPS, and how to combine this with field data-vegetation, soil, and environmental-to produce a spatial model that can be reconstructed and displayed using GIS software. Readers learn the requirements and limitations of each geospatial modeling and mapping tool. Case studies with real-life examples illustrate important applications of the models.

Topics covered in this book include:

  • An overview of the geospatial information sciences and technology and spatial statistics
  • Sampling methods and applications, including probability sampling and nonrandom sampling, and issues to consider in sampling and plot design
  • Fine and coarse scale variability
  • Spatial sampling schemes and spatial pattern
  • Linear and spatial correlation statistics, including Moran's I, Geary's C, cross-correlation statistics, and inverse distance weighting
  • Geospatial statistics analysis using stepwise regression, ordinary least squares (OLS), variogram, kriging, spatial auto-regression, binary classification trees, cokriging, and geospatial models for presence and absence data
  • How to use R statistical software to work on statistical analyses and case studies, and to develop a geospatial statistical model

The book includes practical examples and laboratory exercises using ArcInfo, ArcView, ArcGIS, and other popular software for geospatial modeling. It is accessible to readers from various fields, without requiring advanced knowledge of geospatial information sciences or quantitative methods.

"[This book] covers many topics that are poorly treated by others. ... Chapter 2 on sampling is a true gem. It covers all the standard approaches, but in addition has an extensive discussion of multiphase or double sampling which Kalkhan has used extensively in his own research. There is also an extensive discussion of a case study in which a pixel nested plot (PNP) sampling design is used. This is useful material for researchers and course instructors alike. ... This reviewer enjoyed Chapter 4 immensely. It provides a stimulating discussion of geospatial analysis and modeling including the topics of variogram fitting and kriging. These are pitched at just the right level for most applied researchers who want to use these approaches as a tool to solve their spatial analysis problems. A particular treat is the explanation of spatial autoregressive approaches, binary classification trees and the GARP genetic algorithm. These are topics invariably neglected in many of the standard texts." -Nigel Waters, Geomatica, Vol. 65, No. 4, 2011

Prefacep. xi
About the Authorp. xiii
Geospatial Information Technologyp. 1
Remotely Sensed Datap. 1
Instantaneous Field of View (IFOV) at Nadir (Resolution on the Ground)p. 3
IkONOSp. 4
Sensor Characteristicsp. 5
IKONOS Specificationsp. 5
ORBIMAGE (GeoEye)p. 5
OrbView-2 Specificationsp. 6
OrbView-3 Specificationsp. 6
QuickBirdp. 6
QuickBird Satellite Sensor Characteristicsp. 7
The SPOT (System Probatori d'Observation de la Terre)p. 7
SPOT-5 Satellite Sensor Characteristicsp. 8
MODIS (Moderate Resolution Imaging Spectroradiometer)p. 9
MODIS Overviewp. 10
Technical Specifications of MODISp. 10
MODIS Land Productsp. 10
ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer)p. 13
ASTER Uniquenessp. 13
History of ASTERp. 14
Organizational Framework of ASTERp. 14
Active Remotely Sensed Datap. 15
Radarp. 15
Lidarp. 17
Lidar System Differencesp. 18
How Does Lidar Work?p. 18
Derived Remotely Sensed Datap. 19
Vegetation Indicesp. 19
The Tasseled Cap Transformationp. 22
Geographic Information Systems (GIS)p. 25
Thematic Data Layersp. 26
Geospatial Data Conversionp. 27
sUsing ERDAS-IMAGINE Softwarep. 27
sUsing ARCINFO Softwarep. 29
Select Area of Interest (Study Site)p. 31
Topographic Datap. 31
Global Positioning System (GPS)p. 32
GPS Servicesp. 33
The GPS Satellite System and Factsp. 33
GPS Applicationsp. 34
Referencesp. 35
Data Sampling Methods and Applicationsp. 39
Data Representationp. 39
Data Collection and Source of Errorsp. 39
Data Typesp. 39
Sampling Methods and Applicationsp. 40
Sampling Designsp. 41
Simple Random Samplingp. 41
Stratified Random Samplingp. 42
Systematic Samplingp. 42
Nonaligned Systematic Samplep. 44
Cluster Samplingp. 44
Multiphase (Double) Samplingp. 44
Double Sampling and Mapping Accuracyp. 45
Pixel Nested Plot (PNP): Case Studyp. 46
Plot Designp. 49
Issuesp. 49
Characteristics of Different Plot Shapesp. 49
Plot Sizep. 51
What to Recordp. 51
Issuesp. 51
Referencesp. 52
Spatial Pattern and Correlation Statisticsp. 57
Scalep. 57
Spatial Samplingp. 58
Errors in Spatial Analysisp. 58
Spatial Variability and Method of Predictionp. 58
Spatial Patternp. 59
Spatial Point Patternp. 59
Quadrant Count Methodp. 63
Linear Correlation Statisticp. 63
Case Studyp. 64
Statistical Examplep. 65
Spatial Correlation Statisticsp. 65
Moran's I and Geary's Cp. 66
Cross-Correlation Statisticp. 67
Inverse Distance Weighting (IDW)p. 67
Statistical Examplep. 69
Develop Inverse Distance Weightingp. 69
Develop Moran's Ip. 69
Develop Geary's Cp. 71
Develop Bi-Moran's Ip. 73
Referencesp. 75
Geospatial Analysis and Modeling-Mappingp. 79
Stepwise Regressionp. 79
Statistical Examplep. 80
Ordinary Least Squares (OLS)p. 81
Variogram and Krigingp. 83
Ordinary Krigingp. 85
Simple Krigingp. 86
Universal Krigingp. 87
Developing Variogram Model and Kriging to Predict Plant Diversity at GSENM, Utahp. 87
Model Cross-Validationp. 91
Spatial Autoregressive (SAR)p. 91
Statistical Examplep. 92
Using Spatial AR Model (without Regression)p. 94
Using Spatial AR Model (with Regression, OLS Model) Using R or S-Plusp. 94
Example on How to Develop Plot of Standard Normal Distributionp. 95
Analysis of Residuals for Plant Species Richness (gsenmplant) Datap. 95
Weighted SAR Modelp. 96
Binary Classification Tree (BCTs)p. 97
Cokrigingp. 100
Geospatial Models for Presence and Absence Datap. 104
GARP Modelp. 105
Maxent Modelp. 106
Logistic Regressionp. 106
Classification and Regression Tree (CART)p. 107
Envelope Modelp. 108
Referencesp. 108
R Statistical Packagep. 115
Overview of R Statistics (R)p. 116
What Is R?p. 116
Strengths of R/Sp. 116
The R Environmentp. 117
Scriptsp. 118
Working with R on Your COMPUTERp. 118
Begin to Use Rp. 118
Statistical Analysis Examples Using Rp. 119
Common Statisticsp. 119
Common Graphicsp. 119
Common Programmingp. 120
Create and Examine a Logical Vectorp. 121
Working on Graphical Display of Data (Data Distributions)p. 121
Develop a Histogramp. 122
Data Comparison between the Data and an Expected Normal Distributionp. 122
More Statistical Analysisp. 124
Reading New Variable (Enter new data set, WEIGHT)p. 124
Plotting Weight and Heightp. 126
Test of Associationp. 126
Some Basic Regression Analysisp. 127
Case Studyp. 128
Test for Spatial Autocorrelation Using Moran's Ip. 131
Test for Spatial Autocorrelation Using Geary's Cp. 132
Test for Spatial Cross-Correlation Using Bi-Moran's Ip. 133
Trend Surface Analysisp. 134
Test for Spatial Autocorrelation of the Residualsp. 136
Test for Moran's I for Residualsp. 137
Using Spatial AR Model without Regressionp. 138
Using Spatial AR with Regression (Using All Independent Variables as with OLS Model)p. 138
Analysis of Residualsp. 140
Develop Variogram Model (Modeling Fine Scale Variability)p. 140
Plotting Variogram Modelp. 143
Referencesp. 143
Working with Geospatial Information Datap. 145
Working with Remotely Sensed Datap. 145
Derived Remote Sensing Data and Digital Elevation Model (DEM)p. 145
Deriving Slope and Aspect from DEM Datap. 147
ResampleGRIDp. 147
Geospatial Information Data Extractionp. 148
Deriving SLOPE and ASPECT from DEM Data (ELEVATION)p. 149
Resample GRIDp. 149
Select Area of Interest (Study Site)p. 150
Data Extractionp. 150
Steps for Converting the Geospatial Model to a Thematic Map Productp. 152
Working with Vegetation Indices and Tasseled Cap Transformationp. 154
Vegetation Indicesp. 154
Tasseled Capp. 155
Develop Thematic Layer in ARCVIEW or ARCMAPp. 157
VIEWS (Working Only with ARCVIEW)p. 157
Map Layoutp. 159
Referencesp. 160
Indexp. 163
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781420069761
ISBN-10: 1420069764
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 184
Published: 9th May 2011
Publisher: Taylor & Francis Inc
Country of Publication: US
Dimensions (cm): 24.13 x 16.51  x 1.91
Weight (kg): 0.41
Edition Number: 1