A Bíblia Sagrada Contendo O Velho E O Novo Testamento, Traduzida Em Português Segundo A Vulgata Latina. Antônio Pereira De Figueiredo. E Algumas Palavras Marginais Traduzidas Segundo O Texto Hebraico E Grego.

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online. The solutions to the exercises can be found at the site. Data Science is concerned with finding answers to questions on the basis of available data, and communicating that effort. Besides showing the results, this communication involves sharing the data used, but also exposing the path that led to the answers in a comprehensive and reproducible way. It also acknowledges the fact that available data may not be sufficient to answer questions, and that any answers are conditional on the data collection or sampling protocols employed. This book introduces and explains the concepts underlying spatial dаta: points, lines, polygons, rasters, coverages, geometry attributes, data cubes, reference systems, as well as higher-level concepts including how attributes relate to geometries and how this affects analysis. The relationship of attributes to geometries is known as support, and changing support also changes the characteristics of attributes. Some data generation processes are continuous in space, and may be observed everywhere. Others are discrete, observed in tesselated containers. In modern spatial data analysis, tesellated methods are often used for all data, extending across the legacy partition into point process, geostatistical and lattice models. It is support (and the understanding of support) that underlies the importance of spatial representation. The book aims at data scientists who want to get a grip on using spatial data in their analysis. To exemplify how to do things, it uses R. In future editions we hope to extend this with examples using Python and Julia. Cover Half Title Series Page Title Page Copyright Page Table of contents Preface I. Spatial Data 1. Getting Started 1.1. A first map 1.2. Coordinate reference systems 1.3. Raster and vector data 1.4. Raster types 1.5. Time series, arrays, data cubes 1.6. Support 1.7. Spatial data science software 1.7.1. GDAL 1.7.2. PROJ 1.7.3. GEOS and s2geometry 1.7.4. NetCDF, udunits2, liblwgeom 1.8. Exercises 2. Coordinates 2.1. Quantities, units, datum 2.2. Ellipsoidal coordinates 2.2.1. Spherical or ellipsoidal coordinates 2.2.2. Projected coordinates, distances 2.2.3. Bounded and unbounded spaces 2.3. Coordinate reference systems 2.4. PROJ and mapping accuracy 2.5. WKT-2 2.6. Exercises 3. Geometries 3.1. Simple feature geometries 3.1.1. The big seven 3.1.2. Simple and valid geometries, ring direction 3.1.3. Z and M coordinates 3.1.4. Empty geometries 3.1.5. Ten further geometry types 3.1.6. Text and binary encodings 3.2. Operations on geometries 3.2.1. Unary predicates 3.2.2. Binary predicates and DE-9IM 3.2.3. Unary measures 3.2.4. Binary measures 3.2.5. Unary transformers 3.2.6. Binary transformers 3.2.7. N-ary transformers 3.3. Precision 3.4. Coverages: tessellations and rasters 3.4.1. Topological models 3.4.2. Raster tessellations 3.5. Networks 3.6. Exercises 4. Spherical Geometries 4.1. Straight lines 4.2. Ring direction and full polygon 4.3. Bounding box, rectangle, and cap 4.4. Validity on the sphere 4.5. Exercises 5. Attributes and Support 5.1. Attribute-geometry relationships and support 5.2. Aggregating and summarising 5.3. Area-weighted interpolation 5.3.1. Spatially extensive and intensive variables 5.3.2. Dasymetric mapping 5.3.3. Support in file formats 5.4. Up- and Downscaling 5.5. Exercises 6. Data Cubes 6.1. A four-dimensional data cube 6.2. Dimensions, attributes, and support 6.2.1. Regular dimensions, GDAL’s geotransform 6.2.2. Support along cube dimensions 6.3. Operations on data cubes 6.3.1. Slicing a cube: filter 6.3.2. Applying functions to dimensions 6.3.3. Reducing dimensions 6.4. Aggregating raster to vector cubes 6.5. Switching dimension with attributes 6.6. Other dynamic spatial data 6.7. Exercises II. R for Spatial Data Science 7. Introduction to sf and stars 7.1. Package sf 7.1.1. Creation 7.1.2. Reading and writing 7.1.3. Subsetting 7.1.4. Binary predicates 7.1.5. tidyverse 7.2. Spatial joins 7.2.1. Sampling, gridding, interpolating 7.3. Ellipsoidal coordinates 7.4. Package stars 7.4.1. Reading and writing raster data 7.4.2. Subsetting stars data cubes 7.4.3. Cropping 7.4.4. Redimensioning and combining stars objects 7.4.5. Extracting point samples, aggregating 7.4.6. Predictive models 7.4.7. Plotting raster data 7.4.8. Analysing raster data 7.4.9. Curvilinear rasters 7.4.10. GDAL utils 7.5. Vector data cube examples 7.5.1. Example: aggregating air quality time series 7.5.2. Example: Bristol origin-destination data cube 7.5.3. Tidy array data 7.5.4. File formats for vector data cubes 7.6. Raster-to-vector, vector-to-raster 7.6.1. Vector-to-raster 7.7. Coordinate transformations and conversions 7.7.1. st_crs 7.7.2. st_transform, sf_project 7.7.3. sf_proj_info 7.7.4. Datum grids, proj.db, cdn.proj.org, local cache 7.7.5. Transformation pipelines 7.7.6. Axis order and direction 7.8. Transforming and warping rasters 7.9. Exercises 8. Plotting spatial data 8.1. Every plot is a projection 8.1.1. What is a good projection for my data? 8.2. Plotting points, lines, polygons, grid cells 8.2.1. Colours 8.2.2. Colour breaks: classInt 8.2.3. Graticule and other navigation aids 8.3. Base plot 8.3.1. Adding to plots with legends 8.3.2. Projections in base plots 8.3.3. Colours and colour breaks 8.4. Maps with ggplot2 8.5. Maps with tmap 8.6. Interactive maps: leaflet, mapview, tmap 8.7. Exercises 9. Large data and cloud native 9.1. Vector data: sf 9.1.1. Reading from local disk 9.1.2. Reading from databases, dbplyr 9.1.3. Reading from online resources or web services 9.1.4. APIs, OpenStreetMap 9.1.5. GeoParquet and GeoArrow 9.2. Raster data: stars 9.2.1. stars proxy objects 9.2.2. Operations on proxy objects 9.2.3. Remote raster resources 9.3. Very large data cubes 9.3.1. Finding and processing assets 9.3.2. Cloud native storage: Zarr 9.3.3. APIs for data: GEE, openEO 9.4. Exercises III. Models for Spatial Data 10. Statistical modelling of spatial data 10.1. Mapping with non-spatial regression and ML models 10.2. Support and statistical modelling 10.3. Time in predictive models 10.4. Design-based and model-based inference 10.5. Predictive models with coordinates 10.6. Exercises 11. Point Pattern Analysis 11.1. Observation window 11.2. Coordinate reference systems 11.3. Marked point patterns, points on linear networks 11.4. Spatial sampling and simulating a point process 11.5. Simulating points on the sphere 11.6. Exercises 12. Spatial Interpolation 12.1. A first dataset 12.2. Sample variogram 12.3. Fitting variogram models 12.4. Kriging interpolation 12.5. Areal means: block kriging 12.6. Conditional simulation 12.7. Trend models 12.7.1. A population grid 12.8. Exercises 13. Multivariate and Spatiotemporal Geostatistics 13.1. Preparing the air quality dataset 13.2. Multivariable geostatistics 13.3. Spatiotemporal geostatistics 13.3.1. A spatiotemporal variogram model 13.3.2. Irregular space time data 13.4. Exercises 14. Proximity and Areal Data 14.1. Representing proximity in spdep 14.2. Contiguous neighbours 14.3. Graph-based neighbours 14.4. Distance-based neighbours 14.5. Weights specification 14.6. Higher order neighbours 14.7. Exercises 15. Measures of Spatial Autocorrelation 15.1. Measures and process misspecification 15.2. Global measures 15.2.1. Join-count tests for categorical data 15.2.2. Moran’s I 15.3. Local measures 15.3.1. Local Moran’s Ii 15.3.2. Local Getis-Ord Gi 15.3.3. Local Geary’s Ci 15.3.4. The rgeoda package 15.4. Exercises 16. Spatial Regression 16.1. Markov random field and multilevel models 16.1.1. Boston house value dataset 16.2. Multilevel models of the Boston dataset 16.2.1. IID random effects with lme4 16.2.2. IID and CAR random effects with hglm 16.2.3. IID and ICAR random effects with R2BayesX 16.2.4. IID, ICAR and Leroux random effects with INLA 16.2.5. ICAR random effects with mgcv::gam() 16.2.6. Upper-level random effects: summary 16.3. Exercises 17. Spatial Econometrics Models 17.1. Spatial econometric models: definitions 17.2. Maximum likelihood estimation in spatialreg 17.2.1. Boston house value dataset examples 17.3. Impacts 17.4. Predictions 17.5. Exercises A. Older R Spatial Packages A.1. Retiring rgdal and rgeos A.2. Links and differences between sf and sp A.3. Migration code and packages A.4. Package raster and terra B. R Basics B.1. Pipes B.2. Data structures B.2.1. Homogeneous vectors B.2.2. Heterogeneous vectors: list B.2.3. NULL and removing list elements B.2.4. Attributes B.2.5. The names attributes B.2.6. Using structure B.3. Dissecting a MULTIPOLYGON References Index Index of functions Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors.The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online from https://r-spatial.org/book/. The solutions to the exercises can be found here: https://edzer.github.io/sdsr_exercises/. "Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, a number of major spatial data analysis errors should no longer be made because of lack of knowledge. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes - array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud"-- Provided by publisher