In the past half century, we have experienced two major waves of methodological development in the study of human behavior in space and time. The fIrst wave was the well known "quantitative revolution" which propelled geography from a mainly descriptive discipline to a scientifIc discipline using formalism such as probability, statistics, and a large-number of mathematical methods for analyzing spatial structures and processes under certainty and uncertainty. The second wave is the recent advancement of geographical information systems which equips geographers with automation in the storage, retrieval, analysis, and display of data. Both developments have significant impacts on geographical studies in general and solutions to real life spatio-temporal problems in particular. They have found applications in urban and regional planning, automated mapping and facilities management, transportation planning and management, as well as environmental planning and management, to name but a few examples. Both developments have one thing in common. They one way or the other use computer to process and analyze data. However, not until recently, there has been very little interaction between the two. Quantitative models have largely been developed independent of the underlying data models and structures representing the spatial phenomena or processes under study. Display of analysis results has been primitive in terms of the utilization of computer graphic technologies. Formal models, in addition to their technical difficulties, have poor capability in communication with users. Geographical information systems, on the other hand, have originally been developed with a slight intention to entertain powerful analytical models.

Inhalt

1 Introduction.- 1.1 On Complexity of Spatial Decision Making.- 1.2 Basic Notions of Spatial Decision Support Systems.- 1.3 A General Architecture for Intelligent Spatial Decision Support Systems.- 1.4 Purpose and Structure of the Monograph.- 2 Symbolic Approaches to Spatial Knowledge Representation and Inference.- 2.1 A Note on Knowledge Representation and Inference.- 2.2 Propositional and Predicate Logic.- 2.2.1 Propositional Logic.- 2.2.2 Predicate Logic.- 2.3 Production Systems.- 2.3.1 Rules and Decision Trees.- 2.3.2 Inference in Production Systems.- 2.4 Semantic Networks.- 2.4.1 Basic Features of a Semantic Network.- 2.4.2 Semantic-Network Representations of Spatial Relations.- 2.4.3 Multiple-Predicate Representations by Partitioned Semantic Networks.- 2.4.4 Inference in Semantic Networks.- 2.5 Frames.- 2.5.1 Frame-Based Representation of Knowledge.- 2.5.2 Frame-Based Spatial Inference.- 2.6 Object-Oriented Approach.- 2.6.1 Hierarchical Representation of Spatial Knowledge.- 2.6.2 Inheritance in an Object Hierarchy.- 2.7 A Note on Hybrid Representations.- 3 Fuzzy Logic Approaches to Spatial Knowledge Representation and Inference.- 3.1 A Note on Fuzzy Logic.- 3.2 Fuzzy Propositions.- 3.2.1 Fuzzy Propositions and their Translations.- 3.2.2 Composition of Fuzzy Propositions.- 3.3 Fuzzy IF-THEN Propositions and their Translations.- 3.4 Fuzzy Inference.- 3.4.1 On Fuzzy Spatial Inference.- 3.4.2 Rules of Inference Based on the Entailment Principle.- 3.4.3 Rules of Inference Based on the Extension Principle.- 3.4.4 Fuzzy Inference Based on Truth-value.- 3.4.5 Inferences with Quantified Propositions.- 3.5 Linguistic Approximation in Fuzzy Inference.- 3.6 Fuzzy Rule-Based Inference Requiring Precise Output.- 3.6.1 Inference Involving Precise Output and Fuzzy Input.- 3.6.2 Inference Involving Precise Output and Precise Input.- 4 Management of Uncertainty in Knowledge Representation and Inference.- 4.1 On Measures of Confidence.- 4.2 Probabilistic Approaches.- 4.2.1 Measure of Confidence.- 4.2.2 Probabilistic Inference.- 4.2.3 Inference by Subjective Bayesian Methods.- 4.3 Certainty Factor.- 4.3.1 Measure of Confidence.- 4.3.2 Combination of Evidence and Propagation of Uncertainty.- 4.4 Shafer-Dempster's Belief Functions.- 4.4.1 Measure of Confidence.- 4.4.2 Combination of Evidence.- 4.5 Possibility-Necessity Approach.- 4.5.1 Measure of Confidence.- 4.5.2 Macro-level Fuzzy Inference.- 4.5.3 A Synthesis of Truth Values and the Possibility-Necessity Measure.- 4.6 A Note on the Theory of Inclusion.- 5 Neural Network Approaches to Spatial Knowledge Representation and Inference.- 5.1 A Remark on Symbolic and Neural Network Approaches to Knowledge Representation and Inference.- 5.2 A Brief Review of Neural Networks Research.- 5.3 Spatial Knowledge Representation and Inference by Feedforward Neural Networks.- 5.3.1 logical Processing with Simple Feedforward Neural Networks.- 5.3.2 Fuzzy Logical Processing with Simple Feedforward Neural Networks.- 5.3.3 Knowledge-based Feedforward Neural Networks.- 5.3.4 Applications of Feedforward Neural Networks in Spatial Information Processing.- 5.4 Spatial Knowledge Representation and Inference by Recurrent Neural Networks.- 5.4.1 Autoassociative Memories - Hopfield Networks as an Example.- 5.4.2 Heteroassociative Memories - Bidirectional Associative Memories as an Example.- 5.5 A Note on Hybrid Spatial Decision Support Systems.- 6 Knowledge Acquisition for Spatial Inference - The Case of Genetic Algorithms.- 6.1 The Necessity of Automatic Knowledge Acquisition.- 6.2 A Brief Note on Genetic Algorithms.- 6.3 A Formalism of Canonical Genetic Algorithms.- 6.4 Rule Learning Using Genetic Algorithms.- 6.4.1 Learning Precise Rules in Expert Systems Using Genetic Algorithms.- 6.4.2 Some Empirical Studies.- 6.4.3 Learning Fuzzy Rules in Expert Systems Using Genetic Algorithms.- 6.5 Evolving Neural Networks Using Genetic Algorithms.- 6.5.1 Learning of Connection Weights with Fixed Topology Using Genetic Algorithms.- 6.5.2 Evolving Neural Network Topologies by Genetic Algorithms.- 6.6 A Remark on Genetic Algorithms.- 7 Spatial Data Models and Data Structures.- 7.1 A Note on Data Models and Data Structures.- 7.2 Spatial Data Models and Data Structures within the Layer-Viewed Framework.- 7.2.1 Vector Data Models and Data Structures.- 7.2.2 Raster Data Models and Data Structures.- 7.3 Relational Database for Precise Data.- 7.3.1 Basic Concepts of Relational Model and Relational Database.- 7.3.2 Relational Languages.- 7.3.3 Relation Query Language.- 7.4 Fuzzy Relational Model and Database.- 7.4.1 Fuzzy Relational Database.- 7.4.2 Fuzzy Queries and Fuzzy Relational Algebra.- 7.5 Issues of Spatial Database Accuracy.- 7.5.1 Error Models for Spatial Features.- 7.5.2 Spatial Queries under Certainty and Uncertainty - Point-in-Polygon Query as an Example.- 7.6 Spatial Data Models and Data Structures within the Object-Viewed Framework.- 8 Management of Models in Spatial Decision Support Systems.- 8.1 The Necessity of a Systematic Management of Models in Spatial Decision Support Systems.- 8.2 Model Classification and Organization.- 8.2.1 Model Classification Based on Decision Problems.- 8.2.2 Model Classification Based on Technical Conditions.- 8.2.3 Formulation of Specific Spatial Optimization Models.- 8.3 Model-Data Linkage - Spatial Network Analysis as a Case Study.- 8.3.1 On Model-data Linkage.- 8.3.2 Spatial Network Analysis in the Context of Geographical Information Systems.- 9 An Expert System Shell for Building Spatial-Decision- Support-System Development Tool.- 9.1 On the Architecture of a Spatial-Decision-Support-System Development Tool.- 9.2 The Fuzzy-Logic-Based Expert System Shell (FLESS) - The Nerve Center of the Spatial-Decision-Support-System Development Tool.- 9.2.1 Knowledge Base Development.- 9.2.2 Fuzzy Knowledge Base.- 9.2.3 Tracing.- 9.2.4 Data Exchange.- 9.2.5 Operations.- 9.3 Equipping FLESS with Automatic Knowledge-Acquisition Capabilities.- 9.4 Application of FLESS in Building Expert Systems for Spatial Classification Problems.- 9.4.1 A Note on Spatial Classification.- 9.4.2 An Expert System for Land-type Classification.- 10 A Spatial Decision Support System for Flood Simulation and Damage A…