Location Theory and Decision Analysis
Analytics of Spatial Information Technology
2nd Edition
Published on 26. August 2011
XXXIII, 701 pages
978-3-642-15663-2 (ISBN)
Description
Employing state-of-the art quantitative models and case studies, Location Theory and Decision Analysis provides the methodologies behind the siting of such facilities as transportation terminals, warehouses, housing, landfills, state parks and industrial plants. Through its extensive methodological review, the book serves as a primer for more advanced texts on spatial analysis, including the monograph on Location, Transport and Land-Use by the same author. Given the rapid changes over the last decade, the Second Edition includes new analytic contributions as well as software survey of analytics and spatial information technology.
While the First Edition served the professional community well, the Second Edition has substantially expanded its emphasis for classroom use of the volume. Extensive pedagogic materials have been added, going from the fundamental principles to open-ended exercises, including solutions to selected problems. The text is of value to engineering and business programs that offer courses in Decision and Risk Analysis, Muticriteria Decision-Making, and Facility Location and Layout. It should also be of interest to public policy programs that use geographic Information Systems and satellite imagery to support their analyses.
More details
Other editions
Additional editions
Book
08/2011
2nd Edition
Springer
€213.99
Shipment within 5-7 days
Content
- Intro
- Contents
- Dedication
- Preface to the Second Edition
- MOTIVATION FOR A SECOND EDITION
- PEDAGOGY
- STATE-OF-THE-ART
- SCIENTIFIC COMPUTING
- INFORMATION TECHNOLOGY
- USE OF THIS BOOK
- ACKNOWLEDGEMENTS
- Preface
- ORGANIZATION OF THE BOOK
- SOFTWARE
- ACKNOWLEDGEMENTS
- ABOUT THE COVER
- REFERENCES
- 1 Introduction
- I. OBJECTIVES
- II. DETERMINANTS OF LOCATION
- A. Technological Factors
- B. Economic and Geographic Factors
- C. Political Factors
- D. Social Factors
- III. THE ROLE OF ANALYSIS
- A. Airport Example
- B. Manufacturing Plant Example
- C. A Combined Example
- IV. ANALYTICAL TECHNIQUES
- V. CONCLUDING REMARKS
- VI. EXERCISES Self-Instructional Module: EMPIRICAL MODELING
- Problem 1: Further Discussions on Table 1.1
- Problem 2: Further Discussions on Airport Location
- ENDNOTE
- REFERENCES
- 2 Economic Methods of Analysis
- I. ECONOMIC CONSTRUCTS FOR ACTIVITY ALLOCATION AND FORECASTING
- A. Economic-Base Theory
- B. Location Theory
- C. Input-Output Models
- II. ECONOMETRIC MODELING: INTERREGIONAL DEMOGRAPHIC PROJECTIONS
- A. Population Projection Models
- B. Interregional Growth and Distribution
- C. Interregional Components of Change Model
- III. ECONOMIC CONSTRUCTS FOR COST-BENEFIT ESTIMATION
- A. Shift-Share Analysis
- B. Theory of Land Values
- C. Consumers' Surplus
- IV. UTILITY THEORY
- A. Estimating Bid-Rent via Utility Function
- B. Minimum-Cost Residential Location
- V. THE LOCATION DECISION
- A. Bid-Rent Curves
- B. Industrial Location
- C. Residential Location Models
- VI. SCALE AND NUMBER OF PUBLIC FACILITIES
- A. Static Short-Run Equilibrium
- B. Dynamic Long-Run Equilibrium
- VII. SPATIAL LOCATION OF A FACILITY
- A. Center of a Network
- B. Median of a Network
- C. Competitive Location and Games
- D. Imperfect Information
- VIII. ECONOMIC BASIS OF THE GRAVITY-BASED SPATIAL ALLOCATION MODEL
- A. The Singly Constrained Model
- B. The Doubly Constrained Model
- C. The Unconstrained Model
- D. The Intervening Opportunity Model
- IX. CONCLUDING REMARKS
- X. EXERCISES Self-Instructional Module: PROBABILITY
- Problem 1: Gravity Model
- Problem 2: Further Discussions on Forecasting
- ENDNOTES
- REFERENCES
- 3 Descriptive Tools for Analysis
- I. AN EXAMPLE
- II. DESCRIPTIVE TECHNIQUES: ANOTHER EXAMPLE
- III. SIMULATION
- IV. STOCHASTIC SIMULATION
- V. DISCRETE EVENT SIMULATION
- A. Stochastic Process
- B. Simulation
- VI. INVENTORY CONTROL USING MARGINAL ANALYSIS
- VII. BAYESIAN ANALYSIS
- A. Bayesian Update
- B. Bayesian Decisions
- C. Decision Tree
- D. Influence Diagram
- E. Bayesian Classifier
- VIII. ECONOMETRIC APPROACH
- A. Arrow Diagram and Path Analysis
- B. Econometric Models
- IX. CALIBRATION
- A. Ordinary Least Squares
- B. Two-Stage Least Squares
- C. Example of Two-Stage Least Squares
- D. Maximum Likelihood
- X. AGGREGATE VERSUS DISAGGREGATE MODELING
- XI. THE GRAVITY MODEL REVISITED
- A. Singly Constrained Gravity Model
- B. Doubly Constrained Model
- XII. SPATIAL INTERACTION
- A. Information Theory
- B. Entropy
- XIII. QUALITY OF A MODEL CALIBRATION
- A. Chi-Square Test
- B. Variance Reduction
- XIV. CONCLUDING REMARKS
- XV. EXERCISES Self-Instructional Module: PROBABILITY DISTRIBUTION AND QUEUING
- Problem 1: Decision Tree
- Problem 2: Simulation
- ENDNOTES
- REFERENCES
- 4 Prescriptive Tools for Analysis
- I. A TYPICAL PRESCRIPTIVE MODEL
- A. Goals and Objectives
- B. Representation of the System
- C. A Prescriptive Formulation of the Economic-Base Concept
- II. HEURISTIC SOLUTION TECHNIQUES
- A. Manual Approach
- B. Enumerative Method
- C. Direct Search Technique
- D. The Golden Section Algorithm
- E. Fibonacci Search Procedure
- III. ANALYTICAL SOLUTION TECHNIQUES
- A. Calculus
- B. Linear Programming
- C. Primal and Dual Linear Programs
- D. Solution of Linear Programs
- E. Nonlinear Programming
- F. Solution of a Nonlinear Program
- IV. INTEGER OR MIXED-INTEGER PROGRAMMING
- A. Total Unimodularity
- B. Network Software
- C. Network with Gains
- V. DECOMPOSITION METHODS IN FACILITY LOCATION
- A. Resource Directive Decomposition
- B. Price Directive Decomposition
- VI. SPATIAL INTERACTIONS: THE QUADRATIC ASSIGNMENT PROBLEM
- A. Nonlinear Formation
- B. Linear Formulation
- C. Comments
- VII. PRESCRIPTIVE ANALYSIS IN FACILITY LOCATION: DATA ENVELOPMENT ANALYSIS
- VIII. PRESCRIPTIVE TECHNIQUES IN LAND USE
- A. Entropy Maximization Model
- B. Relationship to the Allocation Model
- C. Optimal Control Models of Spatial Interaction
- IX. CONCLUDING REMARKS
- X. EXERCISES Self-Instructional Module: GRAPH OPTIMIZATION
- Problem 1: Properties of a Facility-Location Model
- Problem 2: Maximal-Coverage Facility-Location Model
- ENDNOTES
- REFERENCES
- 5 Multicriteria Decision Making
- I. PREFERENCE STRUCTURE
- A. The Importance of Preference Structure
- B. Paired versus Simultaneous Comparison
- II. SIMPLE ORDERING
- III. EXPLORING THE EFFICIENT FRONTIER
- IV. MULTICRITERIA SIMPLEX (MC-SIMPLEX)
- A. The MC-Simplex Algorithm
- B. Nonlinear and Integer Programming
- C. An Interactive Frank-Wolfe Example
- D. Comments
- V. GOAL SETTING
- A. Compromise Programming
- B. Deviational Measures
- C. Goal-Setting Example
- VI. VALUE FUNCTIONS
- A. Additive versus Multiplicative Form
- B. Univariate Utility Function Construction
- C. Independence Among Criterion Functions
- D. Summary
- VII. VALUE-FUNCTION MEASUREMENT STEPS
- A. Preferential, Utility and Additive Independence
- B. Examples of Utility Function Calibration
- C. Validation
- VIII. MULTICRITERIA DECISION MAKING AND FACILITY LOCATION
- A. The X, Y', and Z' Spaces in Facility Location
- B. Multi-Attribute Utility and Optimization
- IX. A TAXONOMY OF METHODS
- A. Prior Articulation of Alternatives
- B. Prior Articulation of Preferences
- C. Progressive Articulation of Alternatives
- D. Progressive Articulation of Preferences
- X. DOMINATION STRUCTURES
- XI. COLLECTIVE DECISION MAKING
- A. Arrow's Paradox
- B. Game Theory
- C. Recommended Procedure
- XII. CONCLUDING REMARKS
- XIII. EXERCISES Self-Instructional Module: RISK ASSESSMENT
- Problem 1: Multicriteria Optimization
- Problem 2: Multi-attribute Decision Analysis
- ENDNOTES
- REFERENCES
- 6 Remote Sensing and Geographic Information Systems
- I. DATA IN SPATIAL-TEMPORAL ANALYSIS
- A. Resource Requirement
- B. Assembly of Data Sources
- C. Use and Display of Information
- II. GEOGRAPHIC CODING SYSTEMS
- A. Central Place Theory
- B. Concentric Zone, Sector, and Multi-Nuclei City Structures
- C. Dual Independent Map Encoding System
- D. Topologically Integrated Geographic Encoding and Referencing
- E. Other Data Sources
- III. GEOGRAPHIC INFORMATION SYSTEMS (GIS)
- A. Data Organization and Structure
- B. Location Reference System and Data Structure
- C. Geospatial Metadata
- IV. REMOTE SENSING SYSTEMS
- A. Interface between Remote Sensing Data and GIS
- B. An Assessment
- C. Remote Sensing Technology
- V. DIGITAL IMAGE PROCESSING
- A. Image Rectification and Restoration
- B. Image Enhancement
- C. Image Classification
- D. Data Merging
- VI. DIGITAL IMAGE PROCESSING SOFTWARE AND HARDWARE
- VII. APPLICATIONS OF REMOTE SENSING
- VIII. SPECTRAL VERSUS SPATIAL PATTERN RECOGNITION
- A. Spectral Pattern Recognition
- B. Contextual Allocation of Pixels
- IX. A DISTRICT CLUSTERING MODEL
- A. A Single Subregion Model
- B. Multiple Subregion Model
- C. Demand Equity
- D. Extensions
- X. CASE STUDY OF IMAGE CLASSIFICATION
- A. Digital Image Data
- B. Image Classification
- C. Lessons Learned
- XI. REMOTE SENSING, GIS, AND SPATIAL ANALYSIS
- XII. CONCLUDING REMARKS
- XIII. EXERCISES Self-Instructional Module: LINEAR PROGRAMMING PART 1: MODEL FORMULATION
- Problem 1: Bayesian and Contextual Classification
- Problem 2: TS-IP Image-Processing Software
- ENDNOTES
- REFERENCES
- 7 Analytics and Spatial Information Technology: Retrospect and Prospects
- I. ANALYTICS
- A. Statistical Modeling
- B. Optimization
- C. Multicriteria Decision-Making
- D. Location-Based Analysis
- II. SPATIAL ANALYTICS
- A. Spatial Association
- B. Spatial Clustering
- C. Facility or Site Location
- D. Routing
- III. SOFTWARE
- A. Commercial/Licensed Software
- B. Developmental Geospatial Software in the Public Domain
- C. Selecting a Software: The Case of GIS
- IV. SPATIAL INFORMATION TECHNOLOGY: LOOKING AHEAD
- A. Spatial Information Technology
- B. Going Beyond
- V. EXERCISES Self-Instructional Module: LINEAR PROGRAMMING PART 2 SOLUTION ALGORITHM
- ENDNOTES
- REFERENCES
- 8 A Software Survey of Analytics and Spatial Information Technology
- I. GENERAL ANALYTIC SOFTWARE
- A. Spreadsheet Modeling
- B. Applied Mathematics
- C. Statistics
- D. Simulation
- E. Optimization
- F. Decision Analysis
- II. SPATIAL ANALYTICS SOFTWARE
- A. GIS
- B. Image Processing
- C. Routing
- III. CONCLUDING COMMENTS
- ENDNOTES
- REFERENCES
- Synthesis Exercises and Problems
- I. REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS
- A. Bayesian Classifier
- B. Iterative Conditional Mode Algorithm
- C. Weighted Iterative Conditional Mode Algorithm
- D. District Clustering Model
- E. Combined Classification Scheme
- F. Histogram Processing
- II. FACILITY LOCATION
- A. Nodal Optimality Conditions
- B. Solid Waste Facility
- C. Quadratic Assignment Problem
- III. LOCATION-ROUTING
- A. Districting
- B. Minkowski's Metric
- IV. ACTIVITY DERIVATION, ALLOCATION AND COMPETITION
- A. Multicriteria Game
- B. Gravity versus Transportation Model
- C. Calibration of a Doubly Constrained Model
- V. LAND USE MODELS
- A. Economic-Base and Activity Allocation
- B. Forecasting Airbase Housing Requirements
- VI. SPATIAL-TEMPORAL INFORMATION
- A. Cohort Survival Method
- VII. TERM PROJECT
- REFERENCES
- Appendix 1 Control, Dynamics, and System Stability
- I. CONTROL THEORY
- II. CALCULUS OF VARIATIONS
- III. VARIATIONAL INEQUALITY
- A. Fundamentals
- B. Existence and Uniqueness
- IV. CATASTROPHE THEORY
- A. Basic Concepts
- B. Elementary Catastrophes
- C. The Fold Catastrophe as an Example
- D. Higher Order Catastrophes
- E. Remarks
- V. COMPARTMENTAL MODELS
- A. Basics
- B. Stochastic Models
- C. Deterministic Models
- D. Deterministic Example
- E. Stochastic Example
- F. Discrete Time Models
- G. Example of a Quasi-Deterministic Analysis
- VI. SYSTEM STABILITY
- A. Basic Types of Trajectory
- B. Bifurcation Theory
- C. Comments
- VII. CONCLUDING REMARKS
- ENDNOTES
- REFERENCES
- Appendix 2 Review of Some Pertinent Statistical Tools
- I. STATISTICAL ANALYSIS: BASIC CONCEPTS
- II. GOODNESS-OF-FIT MEASURES
- III. LINEAR REGRESSION
- IV. ANALYSIS OF VARIANCE
- V. USING THE REGRESSION EQUATION
- A. Confidence Interval
- B. Prediction Interval
- C. Summary
- VI. STEPWISE REGRESSION
- A. Backward and Forward Regression
- B. Goodness-of-Fit Parameters for Stepwise Regression
- VII. MATRIX APPROACH TO LINEAR REGRESSION
- VIII. NONLINEAR REGRESSION
- IX. CONCLUDING REMARKS
- ENDNOTES
- REFERENCES
- Appendix 3 Review of Pertinent Markovian Processes
- I. POISSON PROCESS
- A. State Transition Equations
- B. Solution to Random Process
- II. FIELD DATA FROM AIR TERMINAL
- A. Exponential Distribution
- B. Poisson Distribution
- III. M/M/1 QUEUE
- IV. QUEUING SYSTEMS
- A. Basic Theory
- B. Queuing Formulas
- C. Choosing a Queuing Discipline
- V. MARKOVIAN PROPERTIES
- VI. MARKOVIAN PROPERTIES OF DYNAMIC PROGRAMMING
- A. Vehicle Dispatching Example
- B. Principle of Optimality
- VII. MARKOVIAN DECISION PROCESSES
- A. Policy Iteration
- B. Reward Per Period
- VIII. RECURSIVE PROGRAMMING
- A. Existence of Solutions
- B. Phase Solutions
- IX. CONCLUDING REMARKS
- ENDNOTES
- REFERENCES
- Appendix 4 Review of Some Pertinent Optimization Schemes
- I. LINEAR PROGRAMMING
- A. Simplex Algorithm
- B. Some Other Key Concepts
- C. Theory of Simplex
- II. NETWORK-WITH-SIDE-CONSTRAINTS
- A. Multicommodity-Flow Problem
- B. The Network-with-Side-Constraints Algorithm
- III. LAGRANGIAN RELAXATION
- A. Illustration of Basic Concepts
- B. Underlying Theory
- C. Subgradient Optimization
- D. Branch-and-Bound (B&B) Solution
- IV. BENDERS' DECOMPOSITION
- A. Example
- B. Convergence
- C. Extension
- V. ALGORITHMS AND COMPLEXITY
- VI. CONCLUDING REMARKS
- ENDNOTES
- REFERENCES
- Appendix 5 Discussion of Technical Concepts
- Appendix 6 Abbreviation and Mathematical Symbols
- List of Symbols
- Solutions to Exercises and Problems
- I. SOLUTIONS TO SELF-INSTRUCTIONAL MODULES
- A. Empirical Modeling Module: Answers to Illustrative Exercises
- B. Probability Module: Answers to Illustrative Exercises
- C. Probability Distribution & Queuing Module: Answers to Illustrative Exercises
- D. Graph Theory Module: Answers to Illustrative Exercises
- E. Risk Assessment Module: Answers To Illustrstive Exercises
- F. Linear Programming Module: Part 1 Modeling Answers To Illustrative Exercises
- G. Linear Programming Module: Part 2 Solution Algorithm Answers To Illustrative Exercises
- II. SOLUTIONS TO REGULAR PROBLEMS
- III. SOLUTIONS TO SYNTHESIS EXERCISES AND PROBLEMS
- A. Remote Sensing and Geographic Information Systems
- B. Facility Location
- C. Location-Routing
- D. Activity Derivation, Competition and Allocation
- E. Land-Use Models
- F. Spatial-Temporal Information
- REFERENCES
- Index
