TUM Library

Description based upon print version of record.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- What Are the Aims of the Book? -- What Are the Key Features of the Book? -- The Structure of the Book -- Acknowledgements -- Part I Fundamentals for Modelling Spatial and Spatial-Temporal Data -- 1 Challenges and Opportunities Analysing Spatial and Spatial-Temporal Data -- 1.1 Introduction -- 1.2 Four Main Challenges When Analysing Spatial and Spatial-Temporal Data -- 1.2.1 Dependency -- 1.2.2 Heterogeneity -- 1.2.3 Data Sparsity -- 1.2.4 Uncertainty -- 1.2.4.1 Data Uncertainty

1.2.4.2 Model (or Process) Uncertainty -- 1.2.4.3 Parameter Uncertainty -- 1.3 Opportunities Arising from Modelling Spatial and Spatial-Temporal Data -- 1.3.1 Improving Statistical Precision -- 1.3.2 Explaining Variation in Space and Time -- 1.3.2.1 Example 1: Modelling Exposure-Outcome Relationships -- 1.3.2.2 Example 2: Testing a Conceptual Model at the Small Area Level -- 1.3.2.3 Example 3: Testing for Spatial Spillover (Local Competition) Effects -- 1.3.2.4 Example 4: Assessing the Effects of an Intervention -- 1.3.3 Investigating Space-Time Dynamics

1.4 Spatial and Spatial-Temporal Models: Bridging between Challenges and Opportunities -- 1.4.1 Statistical Thinking in Analysing Spatial and Spatial-Temporal Data: The Big Picture -- 1.4.2 Bayesian Thinking in a Statistical Analysis -- 1.4.3 Bayesian Hierarchical Models -- 1.4.3.1 Thinking Hierarchically -- 1.4.3.2 Incorporating Spatial and Spatial-Temporal Dependence Structures in a Bayesian Hierarchical Model Using Random Effects -- 1.4.3.3 Information Sharing in a Bayesian Hierarchical Model through Random Effects -- 1.4.4 Bayesian Spatial Econometrics -- 1.5 Concluding Remarks

1.6 The Datasets Used in the Book -- 1.7 Exercises -- 2 Concepts for Modelling Spatial and Spatial-Temporal Data: An Introduction to "Spatial Thinking" -- 2.1 Introduction -- 2.2 Mapping Data and Why It Matters -- 2.3 Thinking Spatially -- 2.3.1 Explaining Spatial Variation -- 2.3.2 Spatial Interpolation and Small Area Estimation -- 2.4 Thinking Spatially and Temporally -- 2.4.1 Explaining Space-Time Variation -- 2.4.2 Estimating Parameters for Spatial-Temporal Units -- 2.5 Concluding Remarks -- 2.6 Exercises -- Appendix: Geographic Information Systems

3 The Nature of Spatial and Spatial-Temporal Attribute Data -- 3.1 Introduction -- 3.2 Data Collection Processes in the Social Sciences -- 3.2.1 Natural Experiments -- 3.2.2 Quasi-Experiments -- 3.2.3 Non-Experimental Observational Studies -- 3.3 Spatial and Spatial-Temporal Data: Properties -- 3.3.1 From Geographical Reality to the Spatial Database -- 3.3.2 Fundamental Properties of Spatial and Spatial-Temporal Data -- 3.3.2.1 Spatial and Temporal Dependence -- 3.3.2.2 Spatial and Temporal Heterogeneity -- 3.3.3 Properties Induced by Representational Choices

3.3.4 Properties Induced by Measurement Processes

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.

OCLC-licensed vendor bibliographic record.