The sftime class | Functions to get or set the time column of an sftime object | Conversion to class sftime | Subsetting | Plotting | User-defined time columns

Introduction | Spatio-temporal dependence modelling and kriging | Application and illustration | Results and discussion | Conclusions | Acknowledgements | Appendix

Preamble: the starsdata package | Reading chunks, change resolution, select bands | Reading a particular chunk | Reading at a different resolution | Stars proxy objects | Methods for stars proxy objects | Select attributes | Select an area | Lazy evaluation, changing evaluation order | Fetching the data | Plotting with changed evaluation order | Multi-resolution proxy objects

slice | filter | pull | mutate | select | geom_stars

Rasterizing an sf vector object | Vectorizing a raster object to an sf object | Polygonizing | Exporting to points | Exporting to polygons | Switching between vector and raster in stars objects | Reprojecting a raster | Warping a raster

COMMENT LEGEND | Creating objects | Changing spatial extent and/or resolution of objects | Cell based computation | Spatial contextual computation | Model predictions | Data type conversion | Summarizing | Accessing values of objects | Plotting | Getting and setting dimensions | Computing row, column, cell numbers and coordinates

Training and prediction with stars objects | stars objects as data.frames | Predict for stars objects | models fitted for every pixel | Linear regression on pixel time series | Unsupervised learners | Principal components | K-means clustering | Supervised learners | Random Forest land use classification | Parallel processing

Reading and writing through GDAL | Using st_read | Using st_write | Guessing a driver for output | Dataset and layer reading or creation options | Reading and writing directly to and from spatial databases | Conversion to other formats: WKT, WKB, sp | Conversion to and from well-known text | Conversion to and from well-known binary | Conversion to and from sp

Type transformations | For single geometries | For collections of geometry (sfc) and simple feature collections (sf) | Affine transformations | Coordinate reference systems conversion and transformation | Getting and setting coordinate reference systems of sf objects | Coordinate reference system transformations | Geometrical operations | Unary operations | Binary operations: distance and relate | Binary logical operations: | Operations returning a geometry

Subsetting feature sets | Aggregating or summarizing feature sets | Joining two feature sets based on attributes | Joining two feature sets based on geometries

Plot methods for sf and sfc objects | Geometry only: sfc | Geometry with attributes: sf | Color key place and size | Class intervals | How does sf project geographic coordinates? | Graticules | Plotting sf objects with other packages | grid: st_as_grob | ggplot2 | mapview | tmap

What is a feature? | Dimensions | Simple feature geometry types | Coordinate reference system | How simple features in R are organized | sf: objects with simple features | sfc: simple feature geometry list-column | Mixed geometry types | sfg: simple feature geometry | Well-known text, well-known binary, precision | WKT and WKB | Precision | Reading and writing | Driver-specific options | Create, read, update and delete | Connection to spatial databases | Coordinate reference systems and transformations | Conversion, including to and from sp | Geometrical operations | Non-simple and non-valid geometries | Units | How attributes relate to geometries

Introduction | Variography | Spatio-temporal prediction

Introduction | Projected and geographic coordinates | Fundamental differences | Polygons on $S^2$ divide the sphere in two parts | Semi-open polygon boundaries | Bounding cap, bounding rectangle | Switching between S2 and GEOS | Measures | Area | Length | Distances | Predicates | Transformations | Buffers | st_buffer or st_is_within_distance? | References

Two APIs: legacy vs. Self-describing | Legacy API (parseOTBFunction() / runOTB()) | Self-describing API (recommended) | Minimal workflow (Self-describing) | Typical transparent workflow (Self-describing) | Example: DimensionalityReduction (PCA) | Legacy workflow (supported but not recommended) | What changed (Self-describing) | Compatibility and deprecations | Compatibility | Deprecations (explicit list)

Vignette scope | Windows | Recommended baseline: OSGeo4W | Express installation (minimal) | Advanced installation (recommended) | Important note: starting R/RStudio | Orfeo Toolbox (Windows) | Linux | Baseline: use distribution packages where possible | Ubuntu / Debian | Orfeo Toolbox (Linux) | Note on SAGA versions | Arch Linux | Fedora | macOS | GDAL | GRASS | SAGA | QGIS | Homebrew | Practical guidance: “default” vs. “anything else”

What is link2GI? | Why link2GI? | What does “linking” mean? | GRASS GIS | SAGA GIS | Orfeo Toolbox (OTB) | GDAL

Motivation | Design Goals | Core Concept: The GDAL Context | Creating a Context from linkGDAL() | Inspecting Capabilities (Fingerprinting) | Running GDAL Commands Deterministically | Help-Driven Skeleton Generation | Building Arguments Programmatically | NDJSON Logging for Reproducibility | Relationship to Existing Packages | When Is This Useful? | Summary

Brute force search | Setting up project structures | linkSAGA - Find and set up 'SAGA' API bindings | Find and set up GRASS 7/8 API bindings | Important note: GRASS runtime environment | Windows (OSGeo4W) | Linux | macOS | linkGRASS | Standard full search usage | Typical for specified search paths and OS | Specific examples | Manual version selection | Creating a permanent gisdbase folder | Using a permanent gisdbase folder | Manual Setup of the spatial attributes | A typical use case for the Orfeo Toolbox wrapper | Usecases presented on the GEOSTAT August 2018 | Find slides and materials | The examples

Real world example | Downloading census data | Preprocessing the data | Setting up a GRASS project | Importing raster data into GRASS | Importing data as GRASS vector points

Using the RStudio GUI | Console Best-Practice Workflow | Minimal recommended lifecycle | Ad 1 - Project creation | Ad 2 - Project entry point | Comprehensive Workflow Description and Design Principles | Scope of initProj() in link2GI | Separation of responsibilities: initProj() vs setupProj() | initProj() | setupProj() | Default setups and configuration-driven structure | Creating a project from the console | Customising a project at creation time | Location tags (loc_name) | The role of src/functions/000_setup.R | Template system and file generation | Common failure modes

What is an EE user token (EEtk)? | Deploying a simple rgee shiny app on shinyapps.io

1. The problem | 2. A tentative workaround | 3. Show me the code!

Reading a satellite image | Switching attributes to dimensions and back | Subsetting | Overviews | Reading a raster time series: NetCDF | Reading datasets from multiple files | Writing stars objects to disk | Cropping a raster's extent | Vector data cube example | OD: space x space x travel mode x time x time | Extracting at point locations, aggregating over polygons

Stars objects | Grid type | Regular grids | Raster attributes, rotated and sheared grids | Rectilinear grids | Curvilinear grids

Introduction | Summary | Data set | nb and listw objects (copied from the nb_igraph vignette) | Comparison of sp and sf approaches | Contiguity neighbours for polygon support | Contiguity neighbours from invalid polygons | Planar point-based neighbours | Finding points for polygon objects | Graph-based neighbours | K-nearest neighbours | Distance neighbours | Spherical point-based neighbours | Contiguity neighbours for spherical polygon support

Introduction | No-neighbour observations | Subgraphs | Per-session control of function behaviour | Unintentional disconnected graphs | References

Introduction | The RSAGA Package | Initializing the RSAGA Geoprocessing Environment | RSAGA Function Calls | Getting Help with Modules | Accessing Data with RSAGA | RSAGA Terrain Analysis | Grid Calculus & Functions | Extracting Grid Data to a Data Frame | Generalized Additive Modeling with Grids | Summary | A Note on Versions | References

Introduction | Spatial weights | Measures of spatial autocorrelation | Simulating measures of spatial autocorrelation | Odds and ends $\ldots$ | References

Introduction | Getting the data into R | CAR model fitting | References

Introduction | Getting the data into R | Probability mapping | Exploration and modelling of the data | Median polish smoothing | References

Abstract | Introduction | Breaking method | Step by step example | Implementation in classInt package | Case study | Testing and benchmark | References

Introduction | Creating Contiguity Neighbours | Creating Graph-Based Neighbours | Distance-Based Neighbours | Higher-Order Neighbours | Grid Neighbours | References

Overview | Descriptions

Introduction | spatialreg depends on Matrix | Getting some data | Finding contiguous neighbours and droppping links | Using sparse matrices to represent spatial weights | Symmetric sparse matrices | Log determinants (symmetric weights) used in spatial regression | Asymmetric sparse matrices | Log determinants (asymmetric weights) used in spatial regression | Log determinants: symmetric by similarity | Using eigs in RSpectra for finding some eigenvalues | Using igraph for spatial weights as graphs | Converting from symmetric adjacency matrix to graph | Converting from graph to symmetric adjacency matrix | Graph components in spdep | Graph components in igraph | Shortest paths in weights matrices: igraph | Shortest paths in weights matrices: spdep | Smirnov/Anselin (2009) cyclical matrices

Introduction [^1] | References

What is this EPSG code all about? | Why should we use OGC:CRS84 instead of EPSG:4326? | How does sf deal with secondary geometry columns? | Does st_simplify preserve topology? | Why do my dplyr verbs not work for sf objects?

1. What is Google Earth Engine ? | 2. The purpose of Earth Engine is to: | 3. Components: | 4. Meet Earth Engine | 5. Why rgee instead of code editor (Javascript)? | 6. Installation | 7. Authentication | 8. Hello World | 9. Checking

Create a Service Account Key | 1. Create a Google Cloud Platform (GCP) account | 2. Create a new project | 3. Activate GCS API | 4: Set up a service account | 5: Create and download a SaK as a json file. | Copy the SaK in your system | ERROR: Cannot insert legacy ACL for an object when uniform bucket-level access is enabled | ERROR in Earth Engine servers: Unable to write to bucket demo_0001 (permission denied). | Conclusion

1. The map message error: | 2. Do not forget the L | 3. Be careful with ee$Date | 4. Take into consideration reserved words in R

Avoid mixing client functions and objects with server functions and objects | Avoid converting to list unnecessarily | Avoid ee.Algorithms.If() | Avoid reproject() | Filter and select() first | Use updateMask() instead of mask() | Combine reducers | Use Export | **If you don't need to clip, don't use clip() ** | If you need to clip with a complex collection, use clipToCollection() | Don't use a complex collection as the region for a reducer | Use a non-zero errorMargin | Don't use a ridiculously small scale with reduceToVectors() | Don't use reduceToVectors() with reduceRegions() | Use fastDistanceTransform() for neighborhood operations | Use the optimizations in reduceNeighborhood() | Don't sample more data than you need | Export intermediate results | Join vs. map-filter | reduceRegion() vs. reduceRegions() vs. for-loop | Use forward differencing for neighbors in time

Introduction | R geostatistics packages | Spatial data frames | Spatial data on a regular grid | Variograms | Kriging | Conditional simulation | Directional variograms | Variogram maps | Cross variography

Introduction | Tutorial | Establishing the ArcPy Python interface in R | Find out about ArcPy functionality | Geoprocessing with ArcPy | Map algebra | Conclusions | References

Introduction | Verification with plain R code | Verification with GSLIB