Notes on Contributors xv Acknowledgements xxi About the Companion Website xxiii Part I Approaches 1 The Toolkit 3 Alan G. Wilson Part II Estimating Missing Data: Bi-proportional Fitting and Principal Components Analysis 2 The Effects of Economic and Labour Market Inequalities on Interregional Migration in Europe 9 Adam Dennett 2.1 Introduction 9 2.2 The Approach 12 2.3 Data 12 2.4 Preliminary Analysis 13 2.5 Multinomial Logit Regression Analysis 15 2.6 Discussion 22 2.

7 Conclusions 24 References 25 3 Test of Bi-Proportional Fitting Procedure Applied to International Trade 26 Simone Caschili and Alan G. Wilson 3.1 Introduction 26 3.2 Model 27 3.3 Notes of Implementation 28 3.4 Results 30 References 32 4 Estimating Services Flows 33 Robert G. Levy 4.1 Introduction 33 4.

2 Estimation Via Iterative Proportional Fitting 34 4.2.1 The Method 34 4.2.2 With All Initial Values Equal 35 4.2.3 Equivalence to Entropy Maximisation 36 4.2.

4 Estimation with Some Known Flows 37 4.2.5 Drawbacks to Estimating Services Flows with IPF 37 4.3 Estimating Services Flows Using Commodities Flows 37 4.3.1 The Gravity Model 37 4.3.2 Splitting Up Value Added 40 4.

4 A Comparison of The Methods 40 4.4.1 Unbalanced Row and Column Margins 42 4.4.2 Iterative Proportional Fitting 42 4.4.3 Gravity Model 42 4.4.

4 Gravity Model Followed by IPF 44 4.5 Results 45 4.5.1 Selecting a Representative Sector 45 4.5.2 Estimated in-Sample Flows 46 4.5.3 Estimated Export Totals 47 4.

6 Conclusion 49 References 50 5 A Method for Estimating Unknown National Input-Output Tables Using Limited Data 51 Thomas P. Oléron Evans and Robert G. Levy 5.1 Motivation and Aims 51 5.2 Obstacles to The Estimation of National Input-Output Tables 52 5.3 Vector Representation of Input-Output Tables 53 5.4 Method 54 5.4.

1 Concept 54 5.4.2 Estimation Procedure 55 5.4.3 Cross-Validation 57 5.5 In-Sample Assessment of The Estimates 58 5.5.1 Summary Statistics 58 5.

5.2 Visual Comparison 61 5.6 Out-of-Sample Discussion of The Estimates 63 5.6.1 Final Demand Closeness 63 5.6.2 Technical Coefficient Clustering 65 5.7 Conclusion 67 References 68 Part III Dynamics in Account-based Models 6 A Dynamic Global Trade Model With Four Sectors: Food, Natural Resources, Manufactured Goods and Labour 71 Hannah M.

Fry, Alan G. Wilson and Frank T. Smith 6.1 Introduction 71 6.2 Definition of Variables for System Description 73 6.3 The Pricing and Trade Flows Algorithm 73 6.4 Initial Setup 75 6.5 The Algorithm to Determine Farming Trade Flows 77 6.

5.1 The Accounts for the Farming Industry 79 6.5.2 A Final Point on The Farming Flows 79 6.6 The Algorithm to Determine The Natural Resources Trade Flows 80 6.6.1 The Accounts for The Natural Resources Sector 80 6.7 The Algorithm to Determine Manufacturing Trade Flows 81 6.

7.1 The Accounts for The Manufacturing Industry 82 6.8 The Dynamics 83 6.9 Experimental Results 84 6.9.1 Concluding Comments 88 References 90 7 Global Dynamical Input-Output Modelling 91 Anthony P. Korte and Alan G. Wilson 7.

1 Towards a Fully Dynamic Inter-country Input-Output Model 91 7.2 National Accounts 92 7.2.1 Definitions 92 7.2.2 The Production Account 94 7.2.3 The Commodity Markets Account 94 7.

2.4 The Household Account 94 7.2.5 The Capital Markets Account 94 7.2.6 The Rest of the World (RoW) Account 94 7.2.7 The Government Account 95 7.

2.8 The Net Worth of an Economy and Revaluations 95 7.2.9 Overview of the National Accounts 95 7.2.10 Closing the Model: Making Final Demand Endogenous 96 7.3 The Dynamical International Model 97 7.3.

1 Supply and Demand 97 7.3.2 The National Accounts Revisited 99 7.4 Investment: Modelling Production Capacity: The Capacity Planning Model 100 7.4.1 The Multi-region, Multi-sector Capacity Planning Model 100 7.5 Modelling Production Capacity: The Investment Growth Approach 103 7.5.

1 Multi-region, multi-sector Investment Growth Models with Reversibility 103 7.5.2 One-country, One-sector Investment Growth Model with Reversibility 104 7.5.3 Two-country, Two-sector Investment Growth Model with Reversibility 106 7.5.4 A Multi-region, Multi-sector, Investment Growth Model without Reversibility 108 7.5.

5 A Multi-region, Multi-sector, Investment Growth Model without Reversibility, with Variable Trade Coefficients 111 7.5.6 Dynamical Final Demand 114 7.5.7 Labour 115 7.5.8 The Price Model 118 7.6 Conclusions 121 References 122 Appendix 123 A.

1 Proof of Linearity of the Static Model and the Equivalence of Two Modelling Approaches 123 Part IV Space-Time Statistical Analysis 8 Space-Time Analysis of Point Patterns in Crime and Security Events 127 Toby P. Davies, Shane D. Johnson, Alex Braithwaite and Elio Marchione 8.1 Introduction 127 8.1.1 Clustering 127 8.1.2 Clustering of Urban Crime 129 8.

1.3 The Knox Test 130 8.2 Application in Novel Areas 132 8.2.1 Maritime Piracy 132 8.2.2 Space-Time Clustering of Piracy 134 8.2.

3 Insurgency and Counterinsurgency in Iraq 136 8.3 Motif Analysis 138 8.3.1 Introduction 138 8.3.2 Event Networks 140 8.3.3 Network Motifs 140 8.

3.4 Statistical Analysis 141 8.3.5 Random Network Generation 142 8.3.6 Results 143 8.4 Discussion 147 References 148 Part V Real-Time Response Models 9 The London Riots -1: Epidemiology, Spatial Interaction and Probability of Arrest 153 Toby P. Davies, Hannah M.

Fry, Alan G. Wilson and Steven R. Bishop 9.1 Introduction 153 9.2 Characteristics of Disorder 156 9.3 The Model 158 9.3.1 Outline 158 9.

3.2 General Concepts 158 9.3.3 Riot Participation 159 9.3.4 Spatial Assignment 160 9.3.5 Interaction between Police and Rioters 162 9.

4 Demonstration Case 162 9.5 Concluding Comments 166 References 166 Appendix 168 A.1 Note on Methods: Data 168 A.2 Numerical Simulations 169 10 The London Riots -2: A Discrete Choice Model 170 Peter Baudains, Alex Braithwaite and Shane D. Johnson 10.1 Introduction 170 10.2 Model Setup 170 10.3 Modelling the Observed Utility 172 10.

4 Results 176 10.5 Simulating the 2011 London Riots: Towards a Policy Tool 181 10.6 Modelling Optimal Police Deployment 187 References 190 Part VI The Mathematics of War 11 Richardson Models with Space 195 Peter Baudains 11.1 Introduction 195 11.2 The Richardson Model 196 11.3 Empirical Applications of Richardson''s Model 202 11.4 A Global Arms Race Model 204 11.5 Relationship to a Spatial Conflict Model 206 11.

6 An Empirical Application 207 11.6.1 Two Models of Global Military Expenditure 207 11.6.2 The Alliance Measure C ij 208 11.6.3 A Spatial Richardson Model of Global Military Expenditure 210 11.6.

4 Results 211 11.7 Conclusion 212 References 213 Part VII Agent-based Models 12 Agent-based Models of Piracy 217 Elio Marchione, Shane D. Johnson and Alan G. Wilson 12.1 Introduction 217 12.2 Data 219 12.3 An Agent-based Model 221 12.3.

1 Defining Maritime Piracy Maps 221 12.3.2 Defining Vessel Route Maps 222 12.3.3 Defining Pirates'', Naval Units'' and Vessels'' Behaviours 224 12.3.4 Comparing Risk Maps 227 12.4 Model Calibration 232 12.

5 Discussion 232 References 235 13 A Simple Approach for the Prediction of Extinction Events in Multi-agent Models 237 Thomas P. Oléron Evans, Steven R. Bishop and Frank T. Smith 13.1 Introduction 237 13.2 Key Concepts 238 13.2.1 Binary Classification 238 13.

2.2 Measures of Classifier Performance 238 13.2.3 Stochastic Processes 240 13.3 The NANIA Predator-prey Model 241 13.3.1 Background 241 13.3.

2 An ODD Description of the NANIA Model 241 13.3.3 Behaviour of the NANIA Model 245 13.3.4 Extinctions in the NANIA Model 246 13.4 Computer Simulation 247 13.4.1 Data Generation 247 13.

4.2 Categorisation of the Data 249 13.5 Period Detection 249 13.6 A Monte Carlo Approach to Prediction 252 13.6.1 Binned Data 252 13.6.2 Confidence Intervals 257 13.

6.3 Predicting Extinctions using Binned Population Data 257 13.6.4 ROC and Precision-recall Curves for Monte Carlo Prediction of Predator Extinctions 260 13.7 Conclusions 263 References 264 Part VIII Diffusion Models 14 Urban Agglomeration Through the Diffusion of Investment Impacts 269 Minette D''Lima, Francesca R. Medda and Alan G. Wilson 14.1 Introduction 269 14.

2 The Model 270 14.3 Mathematical Analysis for Agglomeration Conditions 272 14.3.1 Introduction 272 14.3.2 Case: r < c 274 14.3.3 Case: r ≥ c 274 14.

4 Simulation Results 275 1.