Geocomputation with R Book Club
Welcome
Book club meetings
Pace
0.1
Quick round of introduction:
1
Introduction
1.1
Foreword 1st edition (R. Bivand)
1.2
Preface
1.3
What is geocomputation?
1.4
Why R
1.4.1
CLI vs GUI
1.5
Software for geocomputation
1.6
R spatial ecosystem
1.7
The history of R-spatial
1.8
Meeting Videos
1.8.1
Cohort 1
2
Geographic data in R
2.1
Introduction
2.2
Vector data:
2.3
Simple features (sf)
2.4
Why simple features, why not using {sp}
2.5
Basic map mapping
2.6
Geometry types
2.6.1
In sf: the workflow:
2.6.2
First is building geometry (
sfg
)
2.6.3
Simple feature columns (sfc)
2.6.4
sfheaders package
2.6.5
Spherical geometry with s2
2.7
Raster
2.7.1
R packages for raster data
2.7.2
Introduction to terra
2.7.3
Basic map making
2.7.4
Raster classes
2.8
Geographic and projected coordinate reference systems (CRS)
2.8.1
Geographic coordinate systems
2.8.2
Projected coordinate reference systems
2.8.3
Units
2.9
Meeting Videos
2.9.1
Cohort 1
3
Attribute Data Operations
3.1
Vector Objects
3.1.1
Subsetting
3.1.2
Chaining
3.1.3
Aggregation
3.1.4
Joining
3.1.5
Creating and removing attributes
3.2
Raster Objects
3.2.1
Subsetting
3.2.2
Summarizing
3.3
Meeting Videos
3.3.1
Cohort 1
4
Spatial data operations
4.1
Learning objectives Spatial Operations on Vectors
4.2
Learning Objectives Spatial Operations on Rasters
4.3
SLIDE Vectors Spatial Subsetting
4.4
SLIDE Vectors Topological Relations
4.5
SLIDE Vectors Spatial Joining
4.6
SLIDE Vectors - Spatial Aggregation
4.7
SLIDE Vectors - Spatial Congruence
4.8
SLIDE Vectors - Distance Relations
4.9
SLIDE Rasters 1 4.3
4.10
Slide Raster - Vector Processing Counterparts
4.11
Merging Rasters
4.12
Meeting Videos
4.12.1
Cohort 1
5
Geometry operations
5.1
Vector Data
5.2
Simplifying Lines
5.3
Simplifying Polygons
5.3.1
smoother algorithms
5.4
Centroids
5.5
Buffers
5.6
Shift
5.7
Scale
5.8
Rotate
5.9
Clipping
5.9.1
Intersection
5.9.2
Venn Diagrams
5.10
Geometry Unions
5.11
Type Transformations
5.11.1
New Objects
5.12
Attribute Creation
5.13
Raster Data
5.14
Extent
5.15
Origin
5.16
Aggregation
5.16.1
Disaggregation
5.17
Resampling
5.18
Meeting Videos
5.18.1
Cohort 1
6
Raster-vector interactions
6.1
Raster cropping/masking
6.2
Raster cropping/masking: Crop
6.3
Raster cropping/masking: mask
6.4
Raster extraction
6.4.1
Extracting value on points
6.4.2
Extracting value on lines
6.4.3
Extracting value on polygons
6.4.4
With quantitative data:
6.4.5
With qualitative data:
6.5
Rasterization
6.6
Spatial vectorization
6.7
Meeting Videos
6.7.1
Cohort 1
7
Reprojecting geographic data
7.1
Coordinate Reference System (CRS)
7.2
Querying and Setting CRS
7.2.1
For vector:
7.2.2
Raster:
7.3
Geometry operations on projected/unprojected data
7.4
When to project?
7.5
Which CRS to use?
7.5.1
Projected:
7.6
Reprojecting vector geometries:
7.7
Reprojecting raster:
7.8
My tl:dr
7.9
Resources:
7.10
Meeting Videos
7.10.1
Cohort 1
8
Geographic data I/O
8.1
Retrieving Open Data
8.2
Geographic Data Packages
8.2.1
Example 1:
rnaturalearth
8.2.2
Example 2:
geodata
8.2.3
Example 3:
osmdata
8.2.4
Built-in datasets
8.2.5
Geocoding
8.3
Geographic Web Services
8.4
File Formats
8.5
Data Input
8.5.1
Vector Data
8.5.2
Raster Data
8.6
Data Output
8.7
Visual Outputs
8.8
Exercises
8.9
Meeting Videos
8.9.1
Cohort 1
9
Making maps with R
9.1
Space and Time
9.1.1
Historical
9.1.2
Base R
9.1.3
Science Communication
9.1.4
Ease of Use
9.2
tmap Basics
9.2.1
Quick Thematic Maps
9.3
Map Objects
9.3.1
Layers
9.3.2
Pipeline
9.3.3
Arrange
9.4
Aesthetics
9.5
Colors
9.5.1
Breaks
9.5.2
Palettes
9.6
Elements
9.6.1
Compass and Scale Bar
9.6.2
Settings
9.6.3
Themes
9.7
Inset Maps
9.8
Facets
9.9
Animated Maps
9.10
Interactive Maps
9.10.1
Mapview
9.10.2
Mapdeck
9.10.3
Leaflet
9.11
Shiny
9.12
Other Mapping Packages
9.12.1
Specific Map Types
9.13
Meeting Videos
9.13.1
Cohort 1
10
Bridges to GIS software
10.1
SLIDE 1
10.2
Meeting Videos
10.2.1
Cohort 1
11
Scripts, algorithms and functions
11.1
SLIDE 1
11.2
Meeting Videos
11.2.1
Cohort 1
12
Statistical Learning
12.1
Terminology
12.1.1
Code Packages
12.2
Case Study: Landslide Susceptibility
12.2.1
Response Variable
12.2.2
Predictor Variables
12.3
Supervised Learning
12.3.1
AUROC
12.4
Spatial Cross-Validation
12.5
mlr3
12.6
GLM
12.7
Spatial Resampling
12.8
SVM
12.9
Tuning
12.10
Meeting Videos
12.10.1
Cohort 1
13
Transportation
13.1
Bristol Case Study
13.2
Transport Zones
13.3
Desire Lines
13.4
Nodes
13.5
Routes
13.6
Route Networks
13.7
Prioritizing New Infrastructure
13.8
Future Directions of Travel
13.9
Meeting Videos
13.9.1
Cohort 1
14
Geomarketing
14.1
Case Study: Bike Shops in Germany
14.2
Tidy input data
14.3
Create census rasters
14.4
Define metropolitan areas
14.5
Points of interest
14.6
Identifying suitable locations
14.7
Meeting Videos
14.7.1
Cohort 1
15
Ecology
15.1
Data
15.2
Preprocessing
15.2.1
Bridges to GIS Software
15.2.2
Site Locations
15.3
Reducing Dimensionality
15.4
NMDS
15.5
Floristic Gradient
15.6
Prediction
15.7
Meeting Videos
15.7.1
Cohort 1
16
Conclusion
16.1
Package Choice
16.2
Gaps and Overlaps
16.3
Getting Help
16.4
Where to go next
16.5
FOSS
16.6
Meeting Videos
16.6.1
Cohort 1
Published with bookdown
Geocomputation with R Book Club
Chapter 2
Geographic data in R
Learning objectives:
Learning the fundamental data models:
vector
and
raster
Exploring
sf
and
terra
for each one