Regression modelling wih spatial and spatial-temporal data : a Bayesian approach /: a Bayesian approach. (2019)

• Record Type: Book
• Further Information: Note: Robert P. Haining, Guangquan Li.
• Authors: Haining, Robert P; Li, Guangquan, 1982-
• Contents: Preface Section I. Fundamentals for modelling spatial and spatial-temporal data 1. Challenges and opportunities analysing spatial and spatial-temporal data Introduction Four main challenges when analysing spatial and spatial-temporal data Dependency Heterogeneity Data sparsity Uncertainty Data uncertainty Model (or process) uncertainty Parameter uncertainty Opportunities arising from modelling spatial and spatial-temporal data Improving statistical precision Explaining variation in space and time Example 1: Modelling exposure-outcome relationships Example 2: Testing a conceptual model at the small area level Example 3: Testing for spatial spillover (local competition) effects Example 4: Assessing the effects of an intervention Investigating space-time dynamics Spatial and spatial-temporal models: bridging between challenges and opportunities Statistical thinking in analysing spatial and spatial-temporal data: the big picture Bayesian thinking in a statistical analysis Bayesian hierarchical models Thinking hierarchically The data model The process model The parameter model Incorporating spatial and spatial-temporal dependence structures in a Bayesian hierarchical model using random effects Information sharing in a Bayesian hierarchical model through random effects Bayesian spatial econometrics Concluding remarks The datasets used in the book Exercises 2. Concepts for modelling spatial and spatial-temporal data: an introduction to "spatial thinking" Introduction Mapping data and why it matters Thinking spatially Explaining spatial variation Spatial interpolation and small area estimation Thinking spatially and temporally Explaining space-time variation Estimating parameters for spatial-temporal units Concluding remarks Exercises Appendix: Geographic Information Systems 3. The nature of spatial and spatial-temporal attribute data Introduction Data collection processes in the social sciences Natural experiments Quasi-experiments Non-experimental observational studies Spatial and spatial-temporal data: properties From geographical reality to the spatial database Fundamental properties of spatial and spatial-temporal data Spatial and temporal dependence. Spatial and temporal heterogeneity Properties induced by representational choices Properties induced by measurement processes Concluding remarks Exercises 4. Specifying spatial relationships on the map: the weights matrix Introduction Specifying weights based on contiguity Specifying weights based on geographical distance Specifying weights based on the graph structure associated with a set of points Specifying weights based on attribute values Specifying weights based on evidence about interactions Row standardisation Higher order weights matrices Choice of W and statistical implications Implications for small area estimation Implications for spatial econometric modelling Implications for estimating the effects of observable covariates on the outcome Estimating the W matrix Concluding remarks Exercises Appendices Appendix: Building a geodatabase in R Appendix: Constructing the W matrix and accessing data stored in a shapefile 5. Introduction to the Bayesian approach to regression modelling with spatial and spatial-temporal data Introduction Introducing Bayesian analysis Prior, likelihood and posterior: what do these terms refer to? Example: modelling high-intensity crime areas Bayesian computation Summarizing the posterior distribution Integration and Monte Carlo integration Markov chain Monte Carlo with Gibbs sampling Introduction to WinBUGS Practical considerations when fitting models in WinBUGS Setting the initial values Checking convergence Checking efficiency Bayesian regression models Example I: modelling household-level income Example II: modelling annual burglary rates in small areas Bayesian model comparison and model evaluation Prior specifications When we have little prior information Towards more informative priors for spatial and spatial-temporal data Concluding remarks Exercises Section II Modelling spatial data 6. Exploratory analysis of spatial data Introduction Techniques for the exploratory analysis of univariate spatial data Mapping Checking for spatial trend Checking for spatial heterogeneity in the mean Count data A Monte Carlo test Continuous-valued data Checking for global spatial dependence (spatial autocorrelation) The Moran scatterplot The global Moran’s I statistic Other test statistics for assessing global spatial autocorrelation The join-count test for categorical data The global Moran’s I applied to regression residuals Checking for spatial heterogeneity in the spatial dependence structure: detecting local spatial clusters The Local Moran’s I The multiple testing problem when using local Moran’s I Kulldorff’s spatial scan statistic Exploring relationships between variables: Scatterplots and the bivariate Moran scatterplot Quantifying bivariate association The Clifford-Richardson test of bivariate correlation in the presence of spatial autocorrelation Testing for association "at a distance" and the global bivariate Moran’s I Checking for spatial heterogeneity in the outcome-covariate relationship: Geographically weighted regression (GWR) Overdispersion and zero-inflation in spatial count data Testing for overdispersion Testing for zero-inflation Concluding remarks Exercises Appendix: An R function to perform the zero-inflation test by van den Broek (1995) 7. Bayesian models for spatial data I: Non-hierarchical and exchangeable hierarchical models Introduction Estimating small area income: a motivating example and different modelling strategies Modelling the 109 parameters non-hierarchically Modelling the 109 parameters hierarchically Modelling the Newcastle income data using non-hierarchical models An identical parameter model based on Strategy 1 An independent parameters model based on Strategy 2 An exchangeable hierarchical model based on Strategy 3 The logic of information borrowing and shrinkage Explaining the nature of global smoothing due to exchangeability The variance partition coefficient (VPC) Applying an exchangeable hierarchical model to the Newcastle income data Concluding remarks Exercises Appendix: Obtaining the simulated household income data 8. Bayesian models for spatial data II: hierarchical models with spatial dependence Introduction The intrinsic conditional autoregressive (ICAR) model The ICAR model using a spatial weights matrix with binary entries The WinBUGS implementation of the ICAR model Applying the ICAR model using spatial contiguity to the Newcastle income data Results A summary of the properties of the ICAR model using a binary spatial weights matrix The ICAR model with a general weights matrix Expressing the ICAR model as a joint distribution and the implied restriction on W The sum-to-zero constraint Applying the ICAR model using general weigh
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2019
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 519.5; Spatial analysis (Statistics); Regression analysis; Bayesian statistical decision theory
• Languages: English
• ISBNs: 9780429529108
• Related ISBNs: 9780429543807; 9780429088933
• Notes: Note: Description based on CIP data; resource not viewed.
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