Getting Started Spatial Data Processing in R

R has had an increasing number of contributed packages for handling and analyzing spatial data, from simple spatial data manipulation to advanced geospatial modeling with machine learning, for over two decades. There are many essential steps and packages for spatial data processing in R. Depending on your specific analysis requirements, you may explore more specialized packages or use a combination of these packages to efficiently process and analyze your geospatial data. This chapters consist following topics:

  • Reading and Writing Spatial Data

    • Vector data

    • Raster data

  • Map Projection and Coordinate Reference

    • Geographic coordinate system (GCS)

    • Projected coordinate system

    • Coordinate Reference System in R

  • Geoprocessing of Vector data

    • Clipping

    • Union

    • Dissolve

    • Intersect

    • Erase

    • Convex Hull

    • Buffer

  • Working with Spatial Point Data

    • Create a Spatial Point Data Frame

    • Extract Environmental Covariates to SPDF

    • Create a Prediction Grid

    • Exploratory Data Analysis

    • Plot Data on Web Map

  • Working with Spatial Polygon Data

    • Data Processing

    • Visualization

    • Animation of Time Series Data

  • Working with Raster Data

    • Basic Raster Operation

    • Clipping

    • Reclassification

    • Focal Statistics

    • Raster Algebra

    • Aggregation

    • Resample

    • Mosaic

    • Convert Raster to Point Data

    • Convert Point Data to Raster

    • Raster Stack and Raster Brick

    • Digital Terrain Modeling

      • Slope

      • Aspect

      • Hillshade

      • Terrain Ruggedness Index

      • Topographic Position Index

      • Roughness

      • Curvature

      • Flow Direction

  • netCDF Data Processing