Jean DUBÉ is Professor in regional development at Laval University, Canada.Diègo LEGROS is a lecturer in economics and management at the University of Burgundy, France.

• Acknowledgments ixPreface xiChapter 1. Econometrics and Spatial Dimensions 11.1 Introduction 11.2 The types of data 61.2.1 Cross-sectional data 71.2.2 Time series 81.2.3 Spatio-temporal data 91.3 Spatial econometrics 111.3.1 A picture is worth a thousand words 131.3.2. The structure of the databases of spatial microdata 151.4 History of spatial econometrics 161.5 Conclusion 21Chapter 2 Structuring Spatial Relations 292.1 Introduction 292.2 The spatial representation of data 302.3 The distance matrix 342.4 Spatial weights matrices 372.4.1 Connectivity relations 402.4.2 Relations of inverse distance 422.4.3 Relations based on the inverse (or negative) exponential 452.4.4 Relations based on Gaussian transformation 472.4.5 The other spatial relation 472.4.6 One choice in particular? 482.4.7 To start 492.5 Standardization of the spatial weights matrix 502.6 Some examples 512.7 Advantages/disadvantages of micro-data 552.8 Conclusion 56Chapter 3 Spatial Autocorrelation 593.1 Introduction 593.2 Statistics of global spatial autocorrelation 653.2.1 Moran’s I statistic 683.2.2 Another way of testing significance 723.2.3 Advantages of Moran’s I statistic in modeling 743.2.4 Moran’s I for determining the optimal form of W 753.3 Local spatial autocorrelation 773.3.1 The LISA indices 793.4 Some numerical examples of the detection tests 863.5 Conclusion 89Chapter 4 Spatial Econometric Models 934.1 Introduction 934.2 Linear regression models 954.2.1 The different multiple linear regression model types 994.3 Link between spatial and temporal models 1024.3.1 Temporal autoregressive models 1034.3.2 Spatial autoregressive models 1104.4 Spatial autocorrelation sources 1154.4.1 Spatial externalities 1174.4.2 Spillover effect 1194.4.3 Omission of variables or spatial heterogeneity 1234.4.4 Mixed effects 1274.5 Statistical tests 1294.5.1 LM tests in spatial econometrics 1344.6 Conclusion 140Chapter 5 Spatio-temporal Modeling 1455.1 Introduction 1455.2 The impact of the two dimensions on the structure of the links: structuring of spatio-temporal links 1485.3 Spatial representation of spatio-temporal data 1505.4 Graphic representation of the spatial data generating processes pooled over time 1545.5 Impacts on the shape of the weights matrix 1595.6 The structuring of temporal links: a temporal weights matrix 1625.7 Creation of spatio-temporal weights matrices 1675.8 Applications of autocorrelation tests and of autoregressive models 1705.9 Some spatio-temporal applications 1725.10 Conclusion 173Conclusion 177Glossary 185Appendix 189Bibliography 215Index 227