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
Published with bookdown
Spatial Data Science with applications in R Book Club
Chapter 15
Measures of spatial autocorrelation
Learning objectives:
What is spatial autocorrelation
Tools for measuring spatial autocorrelation
{rgeoda} package