Description
Clear, up-to-date coverage of methods for analyzing geographical information in a GIS context
Geographic Information Analysis, Second Edition is fully updated to keep pace with the most recent developments of spatial analysis in a geographic information systems (GIS) environment. Still focusing on the universal aspects of this science, this revised edition includes new coverage on geovisualization and mapping as well as recent developments using local statistics.
Building on the fundamentals, this book explores such key concepts as spatial processes, point patterns, and autocorrelation in area data, as well as in continuous fields. Also addressed are methods for combining maps and performing computationally intensive analysis. New chapters tackle mapping, geovisualization, and local statistics, including the Moran Scatterplot and Geographically Weighted Regression (GWR). An appendix provides a primer on linear algebra using matrices.
Complete with chapter objectives, summaries, "thought exercises," explanatory diagrams, and a chapter-by-chapter bibliography, Geographic Information Analysis is a practical book for students, as well as a valuable resource for researchers and professionals in the industry.
Chapter
Choosing the Representation to Be Used
Objects Are Not Always What They Appear to Be
Objects Are Usually Multidimensional
Objects Don’t Move or Change
Objects Don’t Have Simple Geometries
Objects Depend on the Scale of Analysis
Objects Might Have Fractal Dimension
Objects Can Be Fuzzy and/or Have Indeterminate Boundaries
1.4 Scales for Attribute Description
Interval and Ratio Measures
1.5 GIS and Spatial Data Manipulation
2 The Pitfalls and Potential of Spatial Data
2.2 The Bad News: The Pitfalls of Spatial Data
The Modifiable Areal Unit Problem
Nonuniformity of Space and Edge Effects
2.3 The Good News: The Potential of Spatial Data
Summarizing Relationships in Matrices
3 Fundamentals—Mapping It Out
3.1 Introduction: The Cartographic Tradition
3.2 Geovisualization and Analysis
3.3 The Graphic Variables of Jacques Bertin
3.4 New Graphic Variables
Animation and Graphics Scripts
3.5 Issues in Geovisualization
3.6 Mapping and Exploring Points
Located Proportional Symbol Maps
3.7 Mapping and Exploring Areas
Surface Models for Area Objects
3.8 Mapping and Exploring Fields
Point Values: Spot Heights, Benchmarks, and Bubble Plots
Other Ways of Displaying Surfaces
3.9 The Spatialization of Nonspatial Data
4 Fundamentals—Maps as Outcomes of Processes
4.1 Introduction: Maps and Processes
4.2 Processes and the Patterns They Make
A Stochastic Process and Its Realizations
4.3 Predicting the Pattern Generated by a Process
4.5 Stochastic Processes in Lines, Areas, and Fields
5.2 Describing a Point Pattern
Density-Based Point Pattern Measures
Distance-Based Point Pattern Measures
5.3 Assessing Point Patterns Statistically
Nearest-Neighbor Distances
6 Practical Point Pattern Analysis
6.1 Introduction: Problems of Spatial Statistical Analysis
6.2 Alternatives to Classical Statistical Inference
6.3 Alternatives to IRP/CSR
6.4 Point Pattern Analysis in the Real World
Background: Cancer Clusters Around Nuclear Installations
6.5 Dealing with Inhomogeneity
Approaches Based on Rates
Approaches Based on Cases/Controls
6.7 Cluster Detection: Scan Statistics
The Geographical Analysis Machine
6.8 Using Density and Distance: Proximity Polygons
6.9 A Note on Distance Matrices and Point Pattern Analysis
7 Area Objects and Spatial Autocorrelation
7.1 Introduction: Area Objects Revisited
7.2 Types of Area Objects
7.3 Geometric Properties of Areas
Spatial Pattern and Fragmentat ion
7.4 Measuring Spatial Autocorrelation
Spatial Structure and the Spatial Weights Matrix
Moran’s I , an Index of Spatial Autocorrelation
7.5 An Example: Tuberculosis in Auckland, 2001–2006
8.1 Introduction: Think Geographically, Measure Locally
8.2 Defining the Local: Spatial Structure (Again)
8.3 An Example: The Getis-Ord Gi and Gi* Statistics
8.4 Inference with Local Statistics
8.5 Other Local Statistics
Geographically Weighted Regression
A Simple Example of GWR in Action
8.6 Conclusions: Seeing the World Locally
9 Describing and Analyzing Fields
9.1 Introduction: Scalar and Vector Fields Revisited
9.2 Modeling and Storing Field Data
Step 1: Sampling the Real Surface
Step 2: Continuous Surface Description
Continuous Surface Description (1): Digitized Contours
Continuous Surface Description (2): Mathematical Functions
Continuous Surface Description (3): Point Systems
Continuous Surface Description (4): Triangulated Irregular Networks (TINs)
9.3 Spatial Interpolation
Automating Interpolation (1): Proximity Polygons
Automating Interpolation (2): The Local Spatial Average
Automating Interpolation (3): the Inverse Distance Weighted Spatial Average
Automating Interpolation (4): Even More Options!
9.4 Derived Measures on Surfaces
The Area/Height Relationship
Surface Specific Points and the Graph of a Surface
Catchments and Drainage Networks
Local Operations and Functions
Focal Operations and Functions
Zonal Operations and Functions
Global Operations and Functions
10 Knowing the Unknowable: The Statistics of Fields
10.2 Regression on Spatial Coordinates: Trend Surface Analysis
10.3 The Square Root Differences Cloud and the (Semi-) Variogram
10.4 A Statistical Approach to Interpolation: Kriging
Step 1: Describing the Spatial Variation
Step 2: Summarizing the Spatial Variation by a Regular Mathematical Function
Step 3: Using the Model to Determine Interpolation Weights by Ordinary Kriging
Other Members of the Kriging Family
11 Putting Maps Together—Map Overlay
11.2 Boolean Map Overlay and Sieve Mapping
Getting Data into the Same Coordinate System
Logical Problems in Boolean Overlay
11.3 A General Model for Alternatives to Boolean Overlay
11.4 Indexed Overlay and Weighted Linear Combination
11.6 Model-Driven Overlay Using Regression
12 New Approaches to Spatial Analysis
12.1 The Changing Technological Environment
12.2 The Changing Scientific Environment
Artificial Neural Networks (ANNs)
12.5 The Grid and the Cloud: Supercomputing for Dummies
12.6 Conclusions: Neogeographic Information Analysis?
Appendix A: Notation, Matrices, and Matrix Mathematics
A.2 Some Preliminary Notes on Notation
A.3 Matrix Basics and Notation
A.4 Simple Matrix Mathematics
A.5 Solving Simultaneous Equations Using Matrices
The Identity Matrix and the Inverse Matrix
Now, Back to the Simultaneous Equations
A.6 Matrices, Vectors, and Geometry
The Geometric Perspective on Matrix Multiplication
A.7 Eigenvectors and Eigenvalues
End User License Agreement
Geographic Information Analysis
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