Introduction to Spatial Analysis Using R

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


Sign up (price ticket depends on affiliation)

You will receive payment details later from the organiser by email.