Infusing our understanding of Cities with Geographic Data Science - Dani Arribas-Bel

"Infusing our understanding of Cities with Geographic Data Science"

Talk given by Dani Arribas-Bel as a virtual colloquium at the Department of Geography of the University of Zurich. April 27th 2021.

In this talk, we introduce the notion of Geographic Data Science (GDS) as a cross-fertilisation space between Geography, GIS and Data Science. The recent explosion in availability of new forms of data poses significant opportunities to how we analyse cities. This presentation walks through several examples where new forms of data are applied to tackle new questions or obtain new perspectives on long-standing challenges of regional and urban analysis. As part of this whirlwind tour, we also spend some time trying to understand what the main challenges, methodological advances, and risks that "accidental data” pose are, and emphasise the opportunities they unleash.

References
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The following publications are referred to in the talk:

Arribas-Bel, D. (2019) “A course on Geographic Data Science”. The Journal of Open Source Education, 2(14), 42. doi: 10.21105/jose.00042

Arribas-Bel, D.; Reades, J. (2018) “Geography and Computers: Past, present, and future”. Geography Compass.

Arribas-Bel, D.; Garcia-Lopez, M.-A.; Viladecans-Marsal, E. (2021) “Building(s and) cities: Delineating urban areas with a machine learning algorithm”. Journal of Urban Economics. 10.1016/j.jue.2019.103217

Calafiore, A.; Palmer, G.; Comber, S.; Arribas-Bel, D.; Singleton, A. (2021) “A geographic data science framework for the functional and contextual analysis of human dynamics within global cities”. Computers, Environment and Urban Systems, 85. j.compenvurbsys.2020.101539

Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745-766.

Rey, S; Arribas-Bel, D.; Wolf, L. (in progress) “Geographic Data Science in Python”. CRC Press. https://geographicdata.science/book

Singleton, A.; Arribas-Bel, D. (2019) “Geographic Data Science”. Geographical Analysis. 10.1111/gean.12194

Stubbings, P.; Peskett, J.; Rowe, F.; Arribas-Bel, D. (2019) “A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning”. Remote Sensing, 11(12). 10.3390/rs11121395