Patrick Schratz (M.Sc.)

Research assistant
Patrick Schratz
Patrick Schratz
+49 3641 9-48868
+49 3641 9-48852
Room 126
Grietgasse 6
07743 Jena
Overview Show content

I am a PhD candidate within the GIScience group at the Department of Geography. I focus on the application of statistical- and machine learning techniques to address research questions related to environmental issues.
At the same time I am working for the Department of Statistics at LMU Munich to develop the R package framework mlr3.
Besides the scientific part my interests lie in the development of programming solutions to simplify todays data science challenges. I am working in a reproducible manner and actively working on promoting the idea of reproducible research in science. I like to write about the Linux world, the R programming world and other technical topics that I find interesting.

Curriculum vitae Show content

Since 05/2019
Researcher at the Department of Geography, GIScience group

Since 05/2019
Researcher at the Department of Statistics, Ludwig-Maximilian-Universität München

Since 10/2016
Researcher in the EU project "LIFE Healthy Forest"

10/2014 - 09/2016
Master of Science in Geoinformatics.
Thesis: Modeling the Spatial Distribution of Hail Damage in Pine Plantations of Northern Spain as a Major Risk Factor for Forest Disease

10/2011 - 09/2014
Bachelor of Science in Geography.
Thesis: Investigation of the ASAR BIOMASAR GSV maps from 2005 and 2010 using optical satellite data at different temporal and spatial resolution in Northeast China

Research focus Show content
  • Application of statistical and machine-learning techniques in environmental modelling
  • Supporting use of remote sensing data for solving environmental research questions
  • R package development
Homepage Show content

Für weitere und aktuelle Informationen besuchen Sie bitte meine persönliche Website:

Publications Show content
  • Schratz, P., J. Muenchow, E. Iturritxa, J. Richter, A. Brenning (2019): Hyperparameter tuning and performance assessment of statistical and
    machine-learning algorithms using spatial data. Ecological Modelling, 406: 109-120.
  • Muenchow, J., Schratz, P. & Brenning, A. (2017). RQGIS: Integrating R with QGIS for statistical geocomputing. The R Journal, 9, 2, 409-428,
  • Schmullius, C., Balling, J., Schratz, P., Thiel, C., Santoro, M., Wegmuller, U., Li, Z., Yong, P. (2016): Forest DRAGON-3: Decadal trends of Northeastern Forests in China from Earth Observation Synergy. Proc. 'Dragon 3 Final Results & Dragon 4 Kick-Off Symposium', Wuhan, PR China, 4-8 July 2016 (ESA SP-739, August 2016)
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