Spatial econometrics using microdata

This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data.Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares appr.

Cover Page; Half-Title Page; Title Page; Copyright Page; Contents; Acknowledgements; Preface; P.1. Introduction; P.2. Who is this work aimed at?; P.3. Structure of the book; 1: Econometrics and Spatial Dimensions; 1.1. Introduction; 1.2. The types of data; 1.2.1. Cross-sectional data; 1.2.2. Time series; 1.2.3. Spatio-temporal data; 1.3. Spatial econometrics; 1.3.1. A picture is worth a thousand words; 1.3.2. The structure of the databases of spatial microdata; 1.4. History of spatial econometrics; 1.5. Conclusion; 2: Structuring Spatial Relations; 2.1. Introduction.

2.2. The spatial representation of data2.3. The distance matrix; 2.4. Spatial weights matrices; 2.4.1. Connectivity relations; 2.4.2. Relations of inverse distance; 2.4.3. Relations based on the inverse (or negative) exponential; 2.4.4. Relations based on Gaussian transformation; 2.4.5. The other spatial relation; 2.4.6. One choice in particular?; 2.4.7. To start; 2.5. Standardization of the spatial weights matrix; 2.6. Some examples; 2.7. Advantages/disadvantages of micro-data; 2.8. Conclusion; 3: Spatial Autocorrelation; 3.1. Introduction; 3.2. Statistics of global spatial autocorrelation.

3.2.1. Moran's I statist…