Program

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

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

coffe break

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).

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

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

Follow this link for a fortaste 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.