| Geospatial Analysis - a comprehensive guide. 2nd edition © 2006-2008 de Smith, Goodchild, Longley |
| Contents, figures and tables |
|
|
Copyright and licensing information
Foreword to the Second Edition
1 Introduction and terminology
1.1.2 Spatial analysis, GIS and software tools
1.1.3 Intended audience and scope
1.2 Software tools and Companion Materials
1.2.1 GIS and related software tools
1.2.1.1 Sample software products
1.4 Terminology and Abbreviations
1.5 Common Measures and Notation
1.5.2 Statistical measures and related formulas
1.5.2.1 Counts and specific values
1.5.2.2 Measures of centrality
1.5.2.4 Measures of distribution shape
1.5.2.5 Measures of complexity and dimensionality
1.5.2.7 Data transforms and back transforms
2 Conceptual Frameworks for Spatial Analysis
2.1 The geospatial perspective
2.2.5 Multiple properties of places
2.2.9 Detail, resolution, and scale
2.3.3 Multidimensional scaling
2.3.10 Smoothing and sharpening
2.3.11 First- and second-order processes
2.5 Spatial Data Infrastructure
3 Historical and Methodological Context
3.2.1 Spatial analysis as a process
3.2.2 Analytical methodologies
3.2.3 Spatial analysis and the PPDAC model
3.2.3.1 Problem: Framing the question
3.2.3.2 Plan: Formulating the approach
3.2.3.3 Data: Data acquisition
3.2.3.4 Analysis: Analytical methods and tools
3.2.3.5 Conclusions: Delivering the results
3.2.4 The changing context of PPDAC
4 Building Blocks of Spatial Analysis
4.1 Spatial Data Models and Methods
4.2 Geometric and Related Operations
4.2.1 Length and area for vector datasets
4.2.2 Length and area for raster datasets
4.2.3.2 Terrestrial (unprojected) surface area
4.2.4 Line Smoothing and point-weeding
4.2.5.1 Polygon centroids and centres
4.2.6 Point (object) in polygon (PIP)
4.2.9 Overlay and combination operations
4.2.11 Districting and re-districting
4.2.12 Classification and clustering
4.2.12.1 Univariate classification schemes
4.2.12.2 Multivariate classification and clustering
4.2.12.3 Multi-band image classification
4.2.12.4 Uncertainty and image processing
4.2.12.5 Hyperspectral image classification
4.2.13 Boundaries and zone membership
4.2.13.3 Minimum Bounding Rectangles (MBRs)
4.2.13.5 Breaklines and natural boundaries
4.2.14 Tessellations and triangulations
4.2.14.1 Delaunay Triangulation
4.2.14.2 TINs — Triangulated irregular networks
4.2.14.3 Voronoi/Thiessen polygons
4.3.1 Spatial selection and spatial queries
4.3.3 Ratios, indices, normalisation and standardisation
4.3.4 Density, kernels and occupancy
4.3.4.2 Line and intersection densities
4.4.1.3 Extended Euclidean and Lp-metric distances
4.4.2.1 Accumulated cost surfaces and least cost paths
4.5.1 Directional analysis - overview
4.5.2 Directional analysis of linear datasets
4.5.3 Directional analysis of point datasets
4.5.4 Directional analysis of surfaces
4.6 Grid Operations and Map Algebra
4.6.1 Operations on single and multiple grids
4.6.2 Linear spatial filtering
4.6.3 Non-linear spatial filtering
5 Data Exploration and Spatial Statistics
5.1 Statistical Methods and Spatial Data
5.2 Exploratory Spatial Data Analysis
5.2.3 Cross tabulations and conditional choropleth plots
5.2.4 ESDA and mapped point data
5.2.5 Trend analysis of continuous data
5.3.1 Overview of grid-based statistics
5.3.2 Crosstabulated grid data
5.3.3 Quadrat analysis of grid datasets
5.3.4.3 Landscape metrics — table of metrics
5.4 Point Sets and Distance Statistics
5.4.1 Basic distance-derived statistics
5.4.2 Nearest neighbour methods
5.4.3 Hot spot and cluster analysis
5.4.3.1 Hierarchical nearest neighbour clustering
5.4.3.3 Kernel density clustering
5.5.1 Autocorrelation, time series and spatial analysis
5.5.2 Global spatial autocorrelation
5.5.2.3 Weighting models and lags
5.5.3 Local indicators of spatial association (LISA)
5.5.4 Significance tests for autocorrelation indices
5.6.2 Simple regression and trend surface modelling
5.6.3 Geographically Weighted Regression (GWR)
5.6.4 Spatial autoregressive and Bayesian modelling
5.6.4.1 Spatial autoregressive modelling
5.6.4.2 Conditional autoregressive and Bayesian modelling
5.6.5 Spatial filtering models
6.1.6 Statistical and fractal models
6.2.1 Gradient, slope and aspect
6.2.2.1 Profiles and cross-sections
6.2.2.2 Curvature and morphometric analysis
6.2.2.6 Longitudinal and cross-sectional curvature
6.2.2.7 Mean, maximum and minimum curvature
6.3.1 Viewsheds and RF propagation
6.4.1 Overview of watersheds and drainage
6.4.4 Drainage modelling case study
6.4.4.2 Stream network construction
6.4.4.3 Stream basin construction
6.5 Gridding, Interpolation and Contouring
6.5.1 Overview of gridding and interpolation
6.5.2 Gridding and interpolation methods
6.5.2.1 Comparison of sample gridding and interpolation methods
6.5.2.2 Contour plots of sample gridding and interpolation methods
6.6 Deterministic Interpolation Methods
6.6.1 Inverse distance weighting (IDW)
6.6.4 Radial basis and spline functions
6.6.6 Triangulation with linear interpolation
6.6.7 Triangulation with spline-like interpolation
6.6.8 Rectangular or bi-linear interpolation
6.6.14 Topogrid/Topo to raster
6.7 Geostatistical Interpolation Methods
6.7.1.2 Geostatistical references
6.7.1.9 Sill, range and nugget
6.7.1.12 Indicator semivariance
6.7.1.14 Comments on geostatistical software packages
6.7.1.15 Semivariance modelling
6.7.1.17 Madograms and Rodograms
6.7.1.18 Periodograms and Fourier analysis
6.7.2.6 Median-Polishing and Kriging
6.7.2.13 Conditional simulation
7 Network and Location Analysis
7.1 Introduction to Network and Location Analysis
7.1.1 Overview of network and location analysis
7.1.4 Algorithms and computational complexity theory
7.2 Key Problems in Network and Location Analysis
7.2.1 Overview — network analysis
7.2.1.1 Key problems in network analysis
7.2.1.2 Network analysis software
7.2.1.3 Key problems in location analysis
7.2.2 Heuristic and meta-heuristic algorithms
7.2.2.1 Greedy heuristics and local search
7.2.2.2 Interchange heuristics
7.2.2.5 Cross-entropy (CE) methods
7.2.2.7 Lagrangian multipliers and Lagrangian relaxation
7.2.2.8 Ant systems and ant colony optimisation (ACO)
7.3 Network Construction, Optimal Routes and Optimal Tours
7.3.4 Shortest (network) path problems
7.3.4.1 Overview of shortest path problems
7.3.4.5 GIS implementations of SPAs
7.3.4.6 Further SPAs applications
7.3.5 Tours, travelling salesman problems and vehicle routing
7.3.5.1 Capacitated vehicle routing
7.4 Location and Service Area Problems
7.4.2 Larger p-median and p-centre problems
7.4.2.3 Comparison of alternative p-median heuristics
7.5.1 Network traversal problems
8 Geocomputational methods and modelling
8.1 Introduction to Geocomputation
8.1.1 Geocomputational methods
8.1.2 Modelling dynamic processes within GIS
8.1.2.1 Representing time and change within GIS
8.1.2.2 Linkage/coupling versus integration/embedding
8.2.1 Introduction to geosimulation
8.2.3 Agents and agent-based models
8.2.4 Applications of agent-based models
8.2.5 Advantages of agent-based models
8.2.6 Limitations of agent-based models
8.2.7 Explanation or prediction?
8.2.8 Developing an agent-based model
8.2.9 Types of simulation/modelling (s/m) systems for agent-based modelling
8.2.10 Guidelines for choosing a simulation/modelling (s/m) system
8.2.11 Simulation/modelling (s/m) systems for agent-based modelling
8.2.12 Verification and calibration of agent-based models
8.2.13 Validation and analysis of agent-based model outputs
8.3 Artificial Neural Networks (ANN)
8.3.1 Introduction to artificial neural networks
8.3.1.1 Multi-level perceptrons (MLP)
8.3.1.2 Learning and back-propagation for MLPs
8.3.1.3 MLP Example 1: Function approximation
8.3.1.4 MLP Example 2: Landcover change modelling (LCM)
8.3.1.5 MLP Example 3: Spatial interaction modelling
8.3.2 Radial basis function networks
8.3.3 Self organising networks
8.3.3.1 Self Organising Maps (SOMs)
8.3.3.2 SOM unsupervised classification of hyper-spectral image data
8.3.3.3 Optimisation using SOM concepts
8.4 Genetic Algorithms and Evolutionary Computing
8.4.1 Genetic algorithms — introduction
8.4.2 Genetic algorithm components
8.4.2.1 Encoding or representation
8.4.2.3 Population initialisation
8.4.3.2 GA Example 2: Clustering
8.4.3.3 GA Example 3: Map labelling
8.4.3.4 GA Example 4: Optimum location
8.4.4 Evolutionary computing and genetic programming
Principal software products cited
Associations and academic bodies
Online technical dictionaries/definitions
Spatial data, test data and spatial information sources
Selected data and information sources
Statistics and Spatial Statistics links
Figure 2‑1 An example map showing points, lines, and areas appropriately symbolised
Figure 2‑2 Topological relationships
Figure 2‑3 Three alternative ways of defining neighbourhood, using simple GIS functions
Figure 2‑4 Four distinct patterns of twelve points in a study area
Figure 2‑5 The process of statistical inference
Figure 3‑1 The components of a GIS
Figure 3‑2 Analytical process — Mitchell
Figure 3‑3 Analytical process — Draper
Figure 3‑4 PPDAC as an iterative process
Figure 4‑1 Area calculation using Simpson’s rule
Figure 4‑2 3x3 grid neighbourhood
Figure 4‑3 5x5 grid neighbourhood
Figure 4‑4 Triangular approximation of surface area
Figure 4‑5 Surface model of DEM for OS TQ81NE tile
Figure 4‑6 Smoothing techniques
Figure 4‑8 Polygon centroid (M2) and alternative polygon centres
Figure 4‑9 Centre and centroid positioning
Figure 4‑10 Polygon centre selection