Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping, 1st Edition (Hardback) book cover

Spatial Statistics

GeoSpatial Information Modeling and Thematic Mapping, 1st Edition

By Mohammed A. Kalkhan

CRC Press

184 pages | 51 B/W Illus.

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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

Table of Contents

Geospatial Information Technology

Remotely Sensed Data

Instantaneous Field of View (IFOV) at Nadir (Resolution on the Ground)




The SPOT (System Probatori D’Observation de la Terre)

MODIS (Moderate Resolution Imaging Spectroradiometer)

ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer)

Active Remotely Sensed Data



Derived Remotely Sensed Data

Vegetation Indices

The Tasseled Cap Transformation

Geographic Information Systems (GIS)

Thematic Data Layers

Geospatial Data Conversion

Using ERDAS-IMAGINE Software

Using ARCINFO Software

Select Area of Interest (Study Site)

Topographic Data

Global Positioning System (GPS)

GPS Services

The GPS Satellite System and Fact

GPS Applications


Data Sampling Methods and Applications

Data Representation

Data Collection and Source of Errors

Data Types

Sampling Methods and Applications

Sampling Designs

Simple Random Sampling

Stratified Random Sampling

Systematic Sampling

Nonaligned Systematic Sample

Cluster Sampling

Multiphase (Double) Sampling

Double Sampling and Mapping Accuracy

Pixel Nested Plot (PNP): Case Study

Plot Design


Characteristics of Different Plot Shapes

Plot Size


Spatial Pattern and Correlation Statistics


Spatial Sampling

Errors in Spatial Analysis

Spatial Variability and Method of Prediction

Spatial Pattern

Spatial Point Pattern

Linear Correlation Statistic

Case Study

Statistical Example

Spatial Correlation Statistics

Moran’s I and Geary’s C

Cross-Correlation Statistic

Inverse Distance Weighting (IDW)

Statistical Example


Geospatial Analysis and Modeling–Mapping

Stepwise Regression

Statistical Example

Ordinary Least Squares (OLS)

Variogram and Kriging

Ordinary Kriging

Simple Kriging

Universal Kriging

Developing Variogram Model and Kriging to Predict Plant Diversity at GSENM, Utah

Spatial Autoregressive (SAR)

Statistical Example

Binary Classification Tree (BCTs)


Geospatial Models for Presence and Absence Data

GARP Model

Maxent Model

Logistic Regression

Classification and Regression Tree (CART)

Envelope Model


R Statistical Package

Overview of R Statistics (R)

What Is R?

Strengths of R/S

The R Environment


Working with R on Your COMPUTER

Begin to Use R

Statistical Analysis Examples Using R

Common Statistics

Common Graphics

Common Programming

Create and Examine a Logical Vector

Working on Graphical Display of Data (Data distributions)

Develop a Histogram

Data Comparison between the Data and an Expected Normal Distribution

More Statistical Analysis

Reading New Variable (Enter new data set, WEIGHT)

Plotting Weight and Height

Test of Association

Some Basic Regression Analysis

Case Study

Test for Spatial Autocorrelation Using Moran’s I

Test for Spatial Autocorrelation Using Geary’s C

Test for Spatial Cross-Correlation Using Bi-Moran’s I

Trend Surface Analysis

Test for Spatial Autocorrelation of the Residuals

Test for Moran’s I for Residuals

Using Spatial AR Model without Regression

Using Spatial AR with Regression (Using All Independent Variables as with OLS Model)

Analysis of Residuals

Develop Variogram Model (Modeling Fine Scale Variability)

Plotting Variogram Model


Working with Geospatial Information Data

Exercise 1: Working with Remotely Sensed Data

Exercise 2: Derived Remote Sensing Data and

Digital Elevation Model (DEM)

Deriving Slope and Aspect from DEM Data

Resample GRID

Exercise 3: Geospatial Information Data Extraction

Deriving SLOPE and ASPECT from DEM Data (ELEVATION)

Resample GRID

Select Area of Interest (Study Site)

Data Extraction

Steps for Converting the Geospatial Model to a Thematic Map Product

Working with Vegetation Indices and Tasseled Cap Transformation

Develop Thematic Layer in ARCVIEW or ARCMAP

Map Layout



About the Author

Dr. Mohammed A. Kalkhan has over 20 years experience in research and teaching at Colorado State University in Fort Collins, Colorado. As a member of the Natural Resource Ecology Laboratory (NREL) there, he has also served as an affiliate faculty in the Department of Forest, Rangeland, and Watershed Stewardship, and as an advisor for the Interdisciplinary Graduate Certificate in Geospatial Science, Graduate Degree Program in Ecology (GDPE), The School of Global Environmental Sustainability (SOGES), and Department of Earth Resources (currently the Department of Geosciences) at Colorado State University (CSU).

Dr. Kalkhan received his BSc in Forestry (1973) and MSc in Forest Mensuration (1980) from the College of Agriculture and Forestry, the University of Mosul, Iraq. He received his PhD in forest biometrics- remote sensing applications from the Department of Forest Sciences at Colorado State University, USA, in 1994. From 1975 to 1982, he was a lecturer in the Department of Forestry, College of Agriculture and Forestry, University of Mosul. In 1994, he joined the Natural Resource Ecology Laboratory.

Dr. Kalkhan’s main interests are in the integration of field data, remote sensing, and GIS with geospatial statistics to understand landscape parameters through the use of a complex model with thematic mapping approaches, including sampling methods and designs, biometrics, determination of uncertainty and mapping accuracy assessment.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
TECHNOLOGY & ENGINEERING / Environmental / General
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems