## 15 Apr Introduction to Spatial Analysis Using R

- 17/05/2021 - 18/05/2021

1:00 pm - 5:00 pm - 20/05/2021

1:00 pm - 5:00 pm - 21/05/2021

9:00 am - 5:00 pm

#### Course details

data science course | level: beginner |
register now

for questions related to this event, contact ugent@flames-statistics.com

affiliation: Ghent University

#### Abstract

Spatial Analysis using R – syllabus

Day 1 (17th May: 13h-17h)

1. Introduction to R and RStudio:

i. Menus

ii. Scripts

iii. Basic Data Values

iv. Numeric and integer, logical, factors, missing values and time.

v. Basic Data Structures

vi. Vectors, matrices, lists and data.frames

vii. Functions, Loops and conditions

2. Tidyverse – Part I

i. Pipes, tibbles.

Day 2 (18th May: 13h-17h)

3. Tidyverse – Part II

i. dplyr, ggplot2, lubridate, forcats.

4. Data.Table

i. DT[i, j, by], order, filter, arrange

5. Exercise:

i. Working with large datasets the cab data example

ii. Differences in speed, memory and when to use Tidyverse or Data.Table.

Day 3 (20th May : 13h-17h)

6. Spatial Packages

i. Rgdal, sf/sp, rgeos, ggmap, igraph

7. Coordinate systems/Spatial Projections

8. Plotting Data (Introduction) – Part I

i. Leaflet, ggplot2 (part II), ggmap (part II)

9. Spatial Statistics – Part I

i. Spatial interpolation

a. Introduction to Thiessen polygons, IDW

b. Kriging

10. Exercise: Meuse Zinc data

Day 4 (21st May : 9h-17h)

11. Spatial Statistics – Part II

i. Point Pattern Analysis

a. Density based analysis (i.e. global density, local density, quadrat

density, kernel density)

b. Distance based analysis (i.e. average nearest neighbour, K, L and

G functions)

ii. Clustering algorithms and analysis in R

12. Network Analysis in R.

i. Introduction to graphs (e.g. directed networks, undirected networks)

ii. Centrality indicators (e.g. degree, closeness, betweenness, eigenvector,

density)

13. Exercise:

i. Japanese pines data

ii. Air transport data

14. Spatial Statistics - part III

i. DBSCAN

ii. OPTICS

iii. Challenges in Spatial Statistics

15. Plotting Data – Part II

i. Covid-19 data for Belgium (https://www.sciensano.be/en/covid-19-

data).

16. Exercise: Bring your own data

#### Prerequisites

#### Background readings

#### Fee

Phd’s and post-docs of a Flemish University: free Other Academics: €150 Non-profit/social sector: €250 Private sector: €500

#### Venue

UGent-Online

#### Instructor

Dr. Filipe Alberto Marques Teixeira