Linked Data Explorer

"Introdução a temas de análise dados referenciados espacializados, modelos paramétricos para variação espacial, coleta de dados espaciais e análise preliminar e modelagem de dados espaciais."
"Within both the social and environmental sciences, much of the data collected is within a spatial context and requires statistical analysis for interpretation. The purpose of this book is to describe current methods for the analysis of spatial data. Methods described include data description, map interpolation, and exploratory and explanatory analyses. The book also examines spatial referencing, and methods for detecting problems, assessing their seriousness and taking appropriate action are discussed. This is an important text for any discipline requiring a broad overview of current theoretical and applied work for the analysis of spatial data sets. It will be of particular use to research workers and final year undergraduates in the fields of geography, environmental sciences and social sciences."
"Spatial data are data about the world where both the attribute of interest, and its location on the earth are recorded. Are there geographic clusters of disease cases, or hotspots of crime, for example? This comprehensive overview explains all for students and researchers in geography, social science and environmental science."@en
"Spatial Data Analysis: Theory and Practice provides a broad ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy related research. Covering fundamental problems concerning how attributes in geographical space are represented to the latest methods of exploratory spatial data analysis and spatial modeling, it is designed to take the reader through the key areas that underpin the analysis of spatial data, providing a platform from which to view and critically appreciate many of the key areas of the field. Parts of the text are accessible to undergraduate and master's level students, but it also contains sufficient challenging material that it will be of interest to geographers, social and economic scientists, environmental scientists and statisticians, whose research takes them into the area of spatial analysis."
"Introduction. Aboutthe book. What is spatial data analysis?. Motivation for the book. Organization. The spatial data matrix. Part A: The context for spatial data analysis. Spatial data analysis: scientific and policy context. Spatial data analysis in science. Generic issues of place, context and space in scientific explanation. Location as place and context. Location and spatial relationships. Spatial processes. Place and space in specific areas of scientific explanation. Defining spatial subdisciplines. Examples: selected research areas. Environmental criminology. Geographical and environmental (spatial) epidemiology. Regional economics and the new economic geography. Urban studies. Environmental sciences. Spatial data analysis in problem solving. Spatial data analysis in the policy area. Some examples of problems that arise in analysing spatial data. Description and map interpretation. Information redundancy. Modelling. Concluding remarks. 2 The nature of spatial data. The spatial data matrix: conceptualization and representation issues. Geographic space: objects, fields and geometric representations. Geographic space: spatial dependence in attribute values. Variables. Classifying variables. Levels of measurement. Sample or population?. The spatial data matrix: its form. The spatial data matrix: its quality. Model quality. Attribute representation. Spatial representation: general considerations. Spatial representation: resolution and aggregation. Data quality. Accuracy. Resolution. Consistency. Completeness. Quantifying spatial dependence. Fields: data from two-dimensional continuous space. Objects: data from two-dimensional discrete space. Concluding remarks. Part B: Spatial data: obtaining data and quality issues. Obtaining spatial data through sampling. Sources of spatial data. Spatial sampling. The purpose and conduct of spatial sampling. Design- and model-based approaches to spatial sampling. Design-based approach to sampling. Model-based approach to sampling. Comparative comments. Sampling plans. Selected sampling problems. Design-based estimation of the population mean 103 (b) Model-based estimation of means. Spatial prediction. Sampling to identify extreme values or detect rare events. Maps through simulation. Data quality: implications for spatial data analysis. Errors in data and spatial data analysis. Models for measurement error. Independent error models. Spatially correlated error models. Gross errors. Distributionaloutliers. Spatial outliers. Testing for outliers in large data sets. Error propagation. Data resolution and spatial data analysis. Variable precision and tests of significance . The change of support problem. Change of support in geostatistics. Areal interpolation. Analysing relationships using aggregate data. Ecological inference: parameter estimation. Ecological inference in environmental epidemiology: identifying valid hypotheses. The modifiable areal units problem (MAUP). Data consistency and spatial data analysis. Data completeness and spatial data analysis. The missing-data problem. Approaches to analysis when data are missing. Approaches to analysis when spatial data are missing. Spatial interpolation, spatial prediction. Boundaries, weights matrices and data completeness. Concluding remarks. Part C: The exploratory analysis of spatial data. Exploratory spatial data analysis: conceptual models. EDA and ESDA. Conceptual models of spatial variation 183 (a) The regional model. Spatial 'rough' and 'smooth'. Scales of spatial variation. Exploratory spatial data analysis: visualization methods. Data visualization and exploratory data analysis. Data visualization: approaches and tasks. Data visualization: developments through computers. Data visualization: selected techniques. Visualizing spatial data. Data preparation issues for aggregated data: variable values. Data preparation issues for aggregated data: the spatial framework. Non-spatial approaches to region building. Spatial approaches to region building. Design criteria for region building. Special issues in the visualization of spatial data. Data visualization and exploratory spatial data analysis. Spatial data visualization: selected techniques for univariate data. Methods for data associated with point or area objects. Methods for data from a continuous surface. Spatial data visualization: selected techniques for bi- and multi-variate data. Uptake of breast cancer screening in Sheffield. Concluding remarks. Exploratory spatial data analysis: numerical methods. Smoothing methods. Resistant smoothing of graph plots. Resistant description of spatial dependencies. Map smoothing. Simple mean and median smoothers. Introducing distance weighting. Smoothing rates. Non-linear smoothing: headbanging. Non-linear smoothing: median polishing. Some comparative examples. The exploratory identification of global map properties: overall clustering. Clustering in area data. Clustering in a marked point pattern. The exploratory identification oflocal map properties. Cluster detection. Area data. Inhomogeneous point data. Focused tests. Map comparison. Bivariate association. Spatial association. Part D: Hypothesis testing and spatial autocorrelation. Hypothesis testing in the presence of spatial dependence. Spatial autocorrelation and testing the mean of a spatial data set. Spatial autocorrelation and tests of bivariate Association. Pearson's product moment correlation coefficient. Chi-square tests for contingency tables. Part E: Modelling spatial data. Models for the statistical analysis of spatial data. Descriptive models. Models for large-scale spatial variation. Models for small-scale spatial variation. Models for data from a surface. Models for continuous-valued area data. Models for discrete-valued area data. Models with several scales of spatial variation. Hierarchical Bayesian models. Explanatory models. Models for continuous-valued response variables: normal regression models. Models for discrete-valued area data: generalized linear models. Hierarchical models. Adding covariates to hierarchical Bayesian models. Modelling spatial context: multi-level models. Statistical modelling of spatial variation: descriptive modelling. Models for representing spatial variation. Models for continuous-valued variables. Trend surface models with independent errors. Semi-variogram and covariance models. Trend surface models with spatially correlated errors. Models for discrete-valued variables. Some general problems in modelling spatial variation 338 10.3 Hierarchical Bayesian models. Statistical modelling of spatial variation: explanatory modelling. Methodologies for spatial data modelling. The 'classical' approach. The econometric approach. A general spatial specification. Two models of spatial pricing. A 'data-driven' methodology. Some applications oflinear modelling of spatial data. Testing for regional income convergence. Models for binary responses. A logistic model with spatial lags on the covariates. Autologistic models with covariates. Multi-level modelling. Bayesian modelling of burglaries in Sheffield. Bayesian modelling of children excluded from school. Concluding comments. Appendix I Software. Appendix II Cambridgeshire lung cancer data. Appendix III Sheffield burglary data. Appendix IV Children excluded from school: Sheffield."

genre

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"Livres électroniques"
"Electronic books"
"Electronic books"@en
"Matériel didactique"
"Lehrbuch"

name

http://schema.org/name

"Spatial data analysis : Theory and practice"
"Spatial data analysis"
"Spatial data analysis theory and practice"@en
"Spatial data analysis theory and practice"
"Spatial data analysis : theory and practice"
"Spatial Data Analysis in the Social and Environmental Sciences"
"Spatial data analysis in the social and environmental science"
"Spatial data analysis in the social and environmental sciences"
"Spatial data analysis in the social and environmental sciences"@en

workExample

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