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GIB Lecture Series

Die GIB Lecture Series nimmt sich Themen geographischer Forschung zum Fokus. Gastdozierende tragen aus Ihrer Arbeit vor und stellen sich in einer anschließenden Runde den Fragen und Diskussionspunkten des Publikums.

Summer Term 2022: AG Geoinformatics and AG Social Geography

Geospatial Big Data and Societal Transformations

Zeit: Dienstags, 16:15 Uhr bis 17:45 Uhr
Ort: Hörsaal H33 (Gebäude der Angewandten Informatik INF/AI)


Die GIB Lecture Series wird Live vom Campus übertragen. ZUM LIVESTREAM


Referent*in/ Titel


Dr. Simon Scheider (Associate Professor – Department of Earth Sciences – Utrecht University)


Dr. Xiang Ye (Postdoctoral Researcher – Research Institute for Smart Cities – Shenzhen University)

Linear regression, model specification errors, and the modifiable areal unit problem

Dieser Vortrag findet über Zoom statt.


Dr. Jiong Wang (Assistant Professor – Digital Society Institute – Faculty of Geo-Information Science & Earth Observation (ITC) – University of Twente)

Deriving information from space for urban environmental risk management | Slides


Dr. Britta Ricker (Assistant Professor – Copernicus Institute of Sustainable Development Environmental Sciences – Utrecht University)


Dr. Fran Meissner (Assistant Professor – Critical Geodata Studies and Geodata Ethics – University of Twente)


Modern data science and the large volume and diversity of data stimulate a huge number of novel and ethical research questions and greatly influence our life. But what opportunities and challenges are presented for us to understand societal and environmental phenomena and processes in space and time? Compared to non-spatiotemporal data analysis, spatiotemporal data analysis additionally takes into concern spatio-temporal dependency, spatiotemporal heterogeneity, and the modifiable areal unit problem. What is state-of-the-art and does modern data science bring an evolution in spatiotemporal data analysis and contribute to our understanding in societal and environmental geography? Machine learning methods seem to suit well for predicting and analyzing commonly complex and non-gaussian environmental challenges, but how is the application of machine learning methods affected by the characteristics of spatiotemporal phenomena. How do we integrate data from multiple sources and how do we efficiently represent (possibly abstract) spatiotemporal phenomena (e.g. migration) or objects (e.g. slum mapping) in a computational system? 

The lecture series approaches these issues by bringing together speakers with a research background in geospatial data analysis and researchers who are looking into the societal questions of human-technology-interactions and the subsequent effects on how future societies may deal with the transformational issues. This bringing together of Geoinformatics with Social Geography makes room for critical questions such as: What are the societal consequences and implications of digitalization? How does big geospatial data influence – and how will it influence – our everyday lives? 

Verantwortlich für die Redaktion: Jasmin Samimi

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