Spatial Data Science with applications in R Book Club
Welcome
Book club meetings
Pace
1
Getting Started
SLIDE 1
Meeting Videos
Cohort 1
2
Coordinates
SLIDE 1
Meeting Videos
Cohort 1
3
Geometries
Simple feature geometries
3.0.1
The big seven sf
3.0.2
Valid geometries
Z and M coordinates
Empty geometries
Ten additional geometry types
Operations on geometries
Unary
Unary measures
Unary transformers
Binary
Binary predicates
Binary measures
Binary transformers
N-aray
N-aray transformers
Tesselations and rasters
Tesselations
Networks
Meeting Videos
Cohort 1
4
Spherical Geometries
4.1
Straight Lines
4.2
Ring Direction
4.3
Bounding Boxes
4.3.1
Spherical Coordinates
4.4
Validity
4.4.1
Planar (Ellipsoidal Coordinates)
4.4.2
Spherical (Polar Stereographic Projection)
Meeting Videos
Cohort 1
5
Attributes and Support
SLIDE 1
Meeting Videos
Cohort 1
6
Data Cubes
A four-dimensional data cube
Dimensions, attributes, and support
Slicing a cube: filter
Applying functions to dimensions
Reducing dimensions
Aggregating raster to vector cubes
Switching dimension with attributes
Other dynamic spatial data
Spatiotemporal point patterns
Trajectory data
Meeting Videos
Cohort 1
7
Introduction to sf and stars
7.1
sf
7.1.1
Metadata
7.2
Example: North Carolina
7.2.1
Subsetting
7.3
tidyverse
7.3.1
summarise
7.3.2
filter
7.3.3
distinct
7.4
Spatial Joins
7.5
Creation
7.6
Ellipsoidal Coordinates
7.7
stars
7.8
Example: Olinda, Brazil
7.8.1
Plots
7.8.2
Subsetting
7.8.3
Cropping
7.9
Redimensioning
7.10
Extraction
7.11
Predictive Models
7.12
Computations
7.13
Aggregation
7.14
Example: Air Quality
7.15
Example: Transportation
7.15.1
Preprocessing
7.15.2
stars object
7.15.3
Normalization
7.16
Vector to Raster
Meeting Videos
Cohort 1
8
Plotting spatial data
SLIDE 1
Meeting Videos
Cohort 1
9
Large data and cloud native
SLIDE 1
Meeting Videos
Cohort 1
10
Statistical modelling of spatial data
10.1
Mapping with non-spatial regression and ML models
10.2
Support and statistical modelling
10.3
Time in predictive models
10.4
Design-based and model-based inference
10.5
Predictive models with coordinates
10.6
Exercises
10.7
Exercise 1
10.7.1
Exercise 2
10.7.2
Exercise 3
10.7.3
Exercise 4
Meeting Videos
Cohort 1
11
Point Pattern Analysis
SLIDE 1
Meeting Videos
Cohort 1
12
Spatial Interpolation
SLIDE 1
Meeting Videos
Cohort 1
13
Multivariate and Spatiotemporal Geostatistics
SLIDE 1
Meeting Videos
Cohort 1
14
Proximity and Areal Data
14.1
Areal Data
14.2
Proximity Data
14.3
Support
14.4
Representing Proximity
14.5
spdep package
14.6
Example: Poland 2015 Election
14.7
Contiguous Neighbors
14.7.1
Connected
14.8
Graph-Based Neighbors
14.8.1
How Far Away are the Neighbors?
14.8.2
Sphere of Influence
14.9
Distance-Based Neighbors
14.10
Weights Specification
14.10.1
Inverse Distance Weights
14.11
Higher-Order Neighbors
Meeting Videos
Cohort 1
15
Measures of spatial autocorrelation
15.1
Theory
15.1.1
Moran’s I
15.1.2
Case Study: Polish election data
15.1.3
Test the residuals
15.2
Global Measures
15.3
Local Measures
15.4
The rgeoda package
Meeting Videos
Cohort 1
16
Spatial Regression
SLIDE 1
Meeting Videos
Cohort 1
17
Spatial econometrics models
SLIDE 1
Meeting Videos
Cohort 1
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Spatial Data Science with applications in R Book Club
14.4
Representing Proximity
Ideas for spatial autocorrelation
(graph theory) undirected graph, and its neighbors, or
(geospatial) variogram
But what about
islands?
disconnected subgraphs?
sparse areas (cutoff by distance threshold)