Exploratory and Explanatory Statistical Analysis of Spatial Data

1: Introduction.- 1. General Introduction.- 2. Operational Statistical Methods for Analysing Spatial Data.- 2.1. Introduction.- 2.1 The structure of spatial data.- 2.3. Methods based on simple correlations between cross-regional data.- 2.4. Time-series analysis applied to spatial data.- 2.5 Adaptations of time-series analysis to the spatial context.- 2.6. Single equation explanatory models.- 2.7. Simultaneous equation models with spatial data.- 2.8. Some remaining topics.- 2.9. Final remarks.- References.- 2: Exploratory statistical analysis.- 3. The Analysis of Geographical Maps.- 3.1. Introduction.- 3.2. Methods of analysis.- 3.3. Models.- 3.4. Tests for randomness.- 3.5. Examples.- 3.6. Conclusions.- References.- 4 Construction of Interregional Input-Output Tables by Efficient Information Adding.- 4.1. Introduction.- 4.2. Regional and national accounts.- 4.3. Generation of survey-tired transaction tables.- 4.4. Results of the statistical estimations.- 4.5. Results of the minimum information estimations.- 4.6. Some conclusions.- References.- 5. Further Evidence on Alternative Procedures for Testing of Spatial Auto-Correlation among Regression Disturbances.- 5.1. Introduction.- 5.2. Formulation of the statistical decision problem.- 5.3. Moran's test statistic.- 5.4. Moments of the Moran statistic using OLS and LUS estimators.- 5.5. The likelihood ratio test.- 5.6. Simulation study of the Moran and likelihood ratio tests.- 5.7. Results.- 5.8. Conclusions.- References.- 3: Explanatory statistical analysis.- 6. Multivariate Models of Dependent Spatial Data.- 6.1. Introduction.- 6.2. Decomposable covariance structures.- 6.3. Linear models.- 6.4. Principal components.- 6.5. Conclusion.- References.- 7. Bayesian Analysis of the Linear Model with Spatial Dependence.- 7.1. Introduction.- 7.2. The nature of Bayesian inference.- 7.3. Linear regression model with spatially auto-correlated disturbances.- 7.4. An empirical application.- 7.5. Concluding remarks.- References.- 8. Alternative Methods of Estimating Spatial Interaction Models and Their Performance in Short-Term Forecasting.- 8.1. Introduction.- 8.2. Description of data and models.- 8.3. Parameter estimation and model calibration in terms of 1966 and 1971 data.- 8.4. On the accuracy of short-term forecasts made by spatial interaction models.- 8.5. An evaluation of tome alternatives designed to improve model performance.- 8.6. Conclusions.- References.- 9. Two Estimation Methods for Singly Constrained Spatial Distribution Models.- 9.1. Introduction.- 9.2. The calibration of a model.- 9.3. The maximum likelihood method.- 9.4. The least-squares method for the singly constrained model.- 9.5. Numerical results.- 9.6. Conclusions.- References.- 4: The introduction of stochastics in regional control.- 10. Stochastic Control of Regional Economies.- 10.1. Introduction.- 10.2. Mathematical representation of regional systems.- 10.3. Optimal control models of regional systems.- 104 Interaction of optimal control of regional economies with national governments.- 10.5. Problems in applying optimal control to regional systems.- 106. Conclusion.- References.