Geocomputation with R
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11 Statistical learning

来源 Robinlovelace 浏览 8 扫码 打印 2026-10-24 11:33:15
  • 11 Statistical learning
    • 11 Statistical learning

11 Statistical learning

11 Statistical learning

  • Prerequisites
  • 11.1 Introduction
  • 11.2 Case study: Landslide susceptibility
  • 11.3 Conventional modeling approach in R
  • 11.4 Introduction to (spatial) cross-validation
  • 11.5 Spatial CV with mlr
  • 11.6 Conclusions
  • 11.7 Exercises
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  • Welcome
  • Foreword
  • Preface
  • 1 Introduction
    • 1.1 What is geocomputation?
    • 1.2 Why use R for geocomputation?
    • 1.3 Software for geocomputation
    • 1.4 R’s spatial ecosystem
    • 1.5 The history of R-spatial
    • 1.6 Exercises
  • 2 Geographic data in R
    • Prerequisites
    • 2.1 Introduction
    • 2.2 Vector data
    • 2.3 Raster data
    • 2.4 Coordinate Reference Systems
    • 2.5 Units
    • 2.6 Exercises
  • 3 Attribute data operations
    • Prerequisites
    • 3.1 Introduction
    • 3.2 Vector attribute manipulation
    • 3.3 Manipulating raster objects
    • 3.4 Exercises
  • 4 Spatial data operations
    • Prerequisites
    • 4.1 Introduction
    • 4.2 Spatial operations on vector data
    • 4.3 Spatial operations on raster data
    • 4.4 Exercises
  • 5 Geometry operations
    • Prerequisites
    • 5.1 Introduction
    • 5.2 Geometric operations on vector data
    • 5.3 Geometric operations on raster data
    • 5.4 Raster-vector interactions
    • 5.5 Exercises
  • 6 Reprojecting geographic data
    • Prerequisites
    • 6.1 Introduction
    • 6.2 When to reproject?
    • 6.3 Which CRS to use?
    • 6.4 Reprojecting vector geometries
    • 6.5 Modifying map projections
    • 6.6 Reprojecting raster geometries
    • 6.7 Exercises
  • 7 Geographic data I/O
    • Prerequisites
    • 7.1 Introduction
    • 7.2 Retrieving open data
    • 7.3 Geographic data packages
    • 7.4 Geographic web services
    • 7.5 File formats
    • 7.6 Data input (I)
    • 7.7 Data output (O)
    • 7.8 Visual outputs
    • 7.9 Exercises
  • 8 Making maps with R
    • Prerequisites
    • 8.1 Introduction
    • 8.2 Static maps
    • 8.3 Animated maps
    • 8.4 Interactive maps
    • 8.5 Mapping applications
    • 8.6 Other mapping packages
    • 8.7 Exercises
  • 9 Bridges to GIS software
    • Prerequisites
    • 9.1 Introduction
    • 9.2 ®QGIS
    • 9.3 ®SAGA
    • 9.4 GRASS through rgrass7
    • 9.5 When to use what?
    • 9.6 Other bridges
    • 9.7 Exercises
  • 10 Scripts, algorithms and functions
    • Prerequisites
    • 10.1 Introduction
    • 10.2 Scripts
    • 10.3 Geometric algorithms
    • 10.4 Functions
    • 10.5 Programming
    • 10.6 Exercises
  • 11 Statistical learning
    • Prerequisites
    • 11.1 Introduction
    • 11.2 Case study: Landslide susceptibility
    • 11.3 Conventional modeling approach in R
    • 11.4 Introduction to (spatial) cross-validation
    • 11.5 Spatial CV with mlr
    • 11.6 Conclusions
    • 11.7 Exercises
  • 12 Transportation
    • Prerequisites
    • 12.1 Introduction
    • 12.2 A case study of Bristol
    • 12.3 Transport zones
    • 12.4 Desire lines
    • 12.5 Routes
    • 12.6 Nodes
    • 12.7 Route networks
    • 12.8 Prioritizing new infrastructure
    • 12.9 Future directions of travel
    • 12.10 Exercises
  • 13 Geomarketing
    • Prerequisites
    • 13.1 Introduction
    • 13.2 Case study: bike shops in Germany
    • 13.3 Tidy the input data
    • 13.4 Create census rasters
    • 13.5 Define metropolitan areas
    • 13.6 Points of interest
    • 13.7 Identifying suitable locations
    • 13.8 Discussion and next steps
    • 13.9 Exercises
  • 14 Ecology
    • Prerequisites
    • 14.1 Introduction
    • 14.2 Data and data preparation
    • 14.3 Reducing dimensionality
    • 14.4 Modeling the floristic gradient
    • 14.5 Conclusions
    • 14.6 Exercises
  • 15 Conclusion
    • Prerequisites
    • 15.1 Introduction
    • 15.2 Package choice
    • 15.3 Gaps and overlaps
    • 15.4 Where to go next?
    • 15.5 The open source approach
  • References
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