Program

Figure 1: Spatial prediction of landslide hazards along roads in Ecuador (Brenning et al., 2015).

Please note that the program may still be subject to change.

Monday

Spatial Data and Visualization

day	time	lecture/tutorial	lecturer
Monday	8:15-9:45	Welcome Reception and 1-min presentations	A. Brenning
	coffee break
	10:15-11:45	Geodata and -algorithms in R	J. Muenchow
	lunch break
	14:00-15:30	Visualizing spatial data in R, part I	T. Appelhans
	coffee break
	16:00-17:30	Visualizing spatial data in R, part II	T. Appelhans

Tuesday

Introduction to Modeling

day	time	lecture/tutorial	lecturer
Tuesday	8:15-9:45	R as a GIS	J. Muenchow
	coffee break
	10:15-11:45	Research data management	R. Gerlach
	lunch break
	14:00-14:30	Research opportunities	International Office
	14:30-15:30	PS: Intro to modeling natural hazards/Environmental modeling and remote sensing	H. Goetz/H. Meyer
	coffee break
	16:00-17:30	PS: Intro to modeling natural hazards/Environmental modeling and remote sensing	H. Goetz/H. Meyer
Abbreviation: PS = Parallel Sessions.

Wednesday

Statistical and Machine Learning

day	time	lecture/tutorial	lecturer
Wednesday	8:15-9:45	Assignment of modeling tasks and working groups	A. Brenning
	coffee break
	10:15-11:45	Overview of statistical and machine-learning techniques	A. Brenning/P. Schratz
	lunch break
	14:00-15:30	Statistical learning II: Model assessment	A. Brenning/P. Schratz
	coffee break
	16:00-17:30	Parallel session: Hyperspectral data analysis or ordination techniques	P. Schratz/J. Muenchow
Abbreviation: PS = Parallel Sessions.

Thursday

Data Science and Modeling challenges

day	time	lecture/tutorial	lecturer
Thursday	8:15-9:45	Working on assigned tasks	participants
	coffee break
	10:15-11:45	Working on assigned tasks	participants
	lunch break		lunch
	14:00-15:30	Poster presentation of the participants	participants
	coffee break
	16:00-17:30	Group discussion regarding methodological challenges	all

Friday

Spatio-temporal Patterns and Trends

day	time	lecture/tutorial	lecturer
Friday	8:15-9:45	Introduction to the Earth System Data Cube	M. Mahecha
	coffee break
	10:15-11:45	Dealing with multiple testing problems in spatial trend analysis	J. Cortés
	lunch break
	14:00-15:30	Closing discussion	all
	Barbecue

Saturday

Field trip related to natural hazards and/or environmental monitoring, e.g., excursion to the Thuringian Forest or to the Kyffhäuser Region).

Figure 2: Spatial and random partitioning. Taken from Lovelace et al. (2019).

Follow this link for a foretaste to our summer school. This is a recoding of a talk given by Prof. Dr. Alexander Brenning on “Geospatial data science perspectives on physical geography” held on 27.06.2018 at the University of Klagenfurt.

Recommended literature

Bischl, Bernd, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, and Zachary M. Jones. 2016. “Mlr: Machine Learning in R.” Journal of Machine Learning Research 17 (170): 1–5. http://jmlr.org/papers/v17/15-066.html.

Bivand, Roger S., Edzer Pebesma, and Virgilio Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. 2nd ed. New York: Springer.

Brenning, Alexander. 2012. “Spatial Cross-Validation and Bootstrap for the Assessment of Prediction Rules in Remote Sensing: The R Package Sperrorest.” In, 5372–5. IEEE. https://doi.org/10.1109/IGARSS.2012.6352393.

Brenning, A., M. Schwinn, A. P. Ruiz-Páez, and J. Muenchow. 2015. “Landslide Susceptibility Near Highways Is Increased by 1 Order of Magnitude in the Andes of Southern Ecuador, Loja Province.” Natural Hazards and Earth System Sciences 15 (1): 45–57. https://doi.org/10.5194/nhess-15-45-2015.

Gareth, James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics 103. New York: Springer.

Longley, Paul, Michael Goodchild, David Maguire, and David Rhind. 2015. Geographic Information Science & Systems. Fourth edition. Hoboken, NJ: Wiley.

Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2019. Geocomputation with R. The R Series. CRC Press. https://geocompr.robinlovelace.net/.

Muenchow, Jannes, Patrick Schratz, and Alexander Brenning. 2017. “RQGIS: Integrating R with QGIS for Statistical Geocomputing.” The R Journal 9 (2): 409–28. https://journal.r-project.org/archive/2017/RJ-2017-067/index.html.

Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal. https://journal.r-project.org/archive/2018/RJ-2018-009/index.html.

Pebesma, Edzer, and Roger Bivand. 2020. Spatial Data Science. CRC Press. https://keen-swartz-3146c4.netlify.com.