Scholars and researchers from different German universities gathered in Darmstadt at the 15th DGK Seminar for Postdoctoral and Ph.D. Candidates. Organized by the German Geodetic Commission’s Department of Land and Real Estate Management (DGK), the goal of the seminar is to provide a platform for young researchers – specializing in land and real estate management – to present their ongoing work.

For the first time, some special workshops were added to the seminar program, including the one named “Bias in Research and Urban Studies.” This workshop was led by Dr. Felipe Francisco De Souza, a postdoctoral researcher and lecturer, and M.Sc. Jan Schmid, a research associate and doctoral candidate from the Department of Land Management from TU Darmstadt.

With 15 participants from leading universities such as Bonn, TU Dresden, and TU Munich, the seminar focused initially on the concept of bias, presenting well-known psychological experiments, such as the Asch Conformity Experiment to illustrate how group dynamics can distort individual judgment. A literature review was presented and discussed, including Daniel Kahneman’s “Thinking, Fast and Slow,” which outlines the cognitive shortcuts that lead to bias, and Daniel Levitin’s “A Field Guide to Lies,” which emphasizes the importance of critically evaluating data sources.

The session then transitioned into discussions on how biases manifest in urban studies, using a case study from Andheri, Mumbai, India, to demonstrate how statistical biases can affect the interpretation of crime data in urban areas. Throughout the seminar, dynamic discussions were encouraged aiming at participants reflecting on their personal experiences with bias, both in daily life and in their own research.

As a special guest, Dr.-Ing. Matthias Soot, a postdoctoral researcher from TU Dresden, contributed to the workshop by examining the technical aspects of bias in research, questioning the generalization of models, and the reliability of observational data. His insights prompted participants to think critically about how outliers and missing data could mislead urban studies and how important it is to ensure that statistical models reflect real-world conditions accurately.

The workshop provided a comprehensive overview of bias and equipped participants with analytical tools, such as declustering and regression techniques to minimize biases in their future research. Through interactive discussions and practical examples, the session offered valuable insights into the complexities of bias, ultimately enriching the participants’ approaches to their respective fields of study.