[英文] Geocomputation with R
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12 Transportation

来源 1 浏览 620 扫码 打印 2019-03-20 21:25:00

    12 Transportation

    • Prerequisites
    • 12.2 A case study of Bristol
    • 12.5 Routes
    • 12.8 Prioritizing new infrastructure
    • 12.10 Exercises
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    • Welcome
    • Foreword
    • 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
      • 12.9 Future directions of travel
      • 12.10 Exercises
      • 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
    • 13 Geomarketing
      • Prerequisites
      • 13.9 Exercises
      • 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
    • 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
    • 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
      • 7.9 Exercises
      • 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
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