Dr Stef De Sabbata

Stef De Sabbata is an Associate Professor of Geographical Information Science at the School of Geography Geology and the Environment, Senior Fellow at the Institute for Digital Culture, and Turing liaison (academic) of the University of Leicester. Her research focuses on geographical artificial intelligence (GeoAI), including the development of spatially-explicit approaches to urban analytics and the study and use of large language models, foundation models and generative artificial intelligence in geography and cultural analytics. One of her main current lines of research focuses on developing spatially-explicit approaches to urban analytics. In 2021, she organised a session at the Annual International Conference of the Royal Geographical Society with IBG, which sparked discussions and collaborations that finally led her to be the lead guest editor for a special issue of the International Journal of Geographical Information Science on GeoAI in Urban Analytics, including her paper on using graph neural networks in geodemographic classification. She also co-organised the International Workshop on Geospatial Knowledge Graphs and GeoAI at GIScience 2023, where she presented my work on graph neural networks to study urban form. 

Stef De Sabbata is the Chair of the Geographic Information Science Research Group of the Royal Geographical Society with IBG. She is also part of the steering committee of GIScience Research UK (GISRUK), the chair of the GISRUK 2018 conference, and a member of the Commission on Location-Based Services of the International Cartographic Association.

Before joining the University of Leicester in 2015, Stef De Sabbata was a Researcher at the Oxford Internet Institute of the University of Oxford (2013-2015) and a Junior Research Fellow at the Wolfson College of the University of Oxford (2014-2015), and thereafter, a Research Associate of the Oxford Internet Institute of the University of Oxford (2015-2021). She was awarded a PhD from the Department of Geography of the University of Zurich in 2013 and a BSc and an MSc in computer science from the Department of Mathematics and Computer Science of the University of Udine.

I am a geographic data scientist working at the intersection between human geography, artificial intelligence and internet studies. In the past few years I have focused my attention on developing new machine learning approaches to geographic information analysis - for instance applying deep neural networks to geodemographic classification and natural language processing to digital geographies. Studying internet platforms and their biases is one of my most long-standing research areas. Understanding the biases of user-generated content (volunteered geographic information) is crucial as such data feeds into a wide range of applications and they can reveal inequalities within our cities. My research also aims to leverage artificial intelligence to understand the emerging meaning we attach to geographic places through the content generated on internet platforms. I collaborate with colleagues on topics that span through the wide range of research topics represented in the School from the geographies of everyday multicultural living in Leicester to the study of the Anthropocene and the University from digital politics to artificial intelligence.

De Sabbata, S. and Liu, P. (2023). A graph neural network framework for spatial geodemographic classification. International Journal of Geographical Information Science, 37(12), pp. 2464–2486.

De Sabbata, S., Ballatore, A., Miller, H.J., et al. (2023). GeoAI in urban analytics. International Journal of Geographical Information Science, 37(12), 2455-2463.

De Sabbata, S., Bennett, K., and Gardner, Z. (2024). Towards a study of everyday geographic information: Bringing the everyday into view. Environment and Planning B: Urban Analytics and City Science, 51(6). 

Bennett, K., Gardner, Z. and De Sabbata, S., 2023. Digital geographies of everyday multiculturalism: ‘Let’s go Nando’s!’. Social & Cultural Geography, 24(8), pp.1458-1477.

Bennett, K. and De Sabbata, S., 2023. Introducing a more-than-quantitative approach to explore emerging structures of feeling in the everyday. Emotion, Space and Society, 49, p.100965.
Gardner, Z., Bennett, K. and De Sabbata, S., 2023. Virtual reality, place and affect. A Research Agenda for Digital Geographies, p.69.

Ballatore, A., and De Sabbata, S. (2020). Los Angeles as a digital place: The geographies of user-generated content. Transactions in GIS, 24(4), 880-902. doi:10.1111/tgis.12600

Gardner, Z., Mooney, P., De Sabbata, S., and Dowthwaite, L. (2020). Quantifying gendered participation in OpenStreetMap: responding to theories of female (under) representation in crowdsourced mapping. GeoJournal, 85(6), 1603-1620. doi:10.1007/s10708-019-10035-z

De Sabbata, S., and Liu, P. (2019). Deep learning geodemographics with autoencoders and geographic convolution. In 22nd AGILE Conference on Geo-information Science.

Bright, J., De Sabbata, S., Lee, S., Ganesh, B., and Humphreys, D. K. (2018). OpenStreetMap data for alcohol research: Reliability assessment and quality indicators. Health & Place, 50, 130-136. doi:10.1016/j.healthplace.2018.01.009

Acheson, E., De Sabbata, S., and Purves, R. S. (2017). A quantitative analysis of global gazetteers: Patterns of coverage for common feature types. Computers, Environment and Urban Systems, 64, 309-320. doi:10.1016/j.compenvurbsys.2017.03.007

Reichenbacher, T., De Sabbata, S., Purves, R. S., and Fabrikant, S. I. (2016). Assessing geographic relevance for mobile search: A computational model and its validation via crowdsourcing. Journal of the Association for Information Science and Technology, 67(11), 2620-2634. doi:10.1002/asi.23625

Graham, M., De Sabbata, S., and Zook, M. A. (2015). Towards a study of information geographies: (im)mutable augmentations and a mapping of the geographies of information. Geo: Geography and Environment, 2 (1), 88-105. doi:10.1002/geo2.8

De Sabbata, S., Mizzaro, S., and Reichenbacher, T. (2015). Geographic dimensions of relevance. Journal of Documentation, 71(4), 650-666. doi:10.1108/JD-12-2013-0167

De Sabbata, S., and Reichenbacher, T. (2012). Criteria of geographic relevance: An experimental study. International Journal of Geographical Information Science, 26(8), 1495-1520. doi:10.1080/13658816.2011.639303

Geospatial artificial intelligence (GeoAI)

Geographic data science

Geographic information science

GIS

Geocomputation

Artificial intelligence

Machine learning

Deep learning

Geographic relevance

Geographic information retrieval

Natural language processing

Information geographies

Digital geographies

Internet geographies

Information visualisation

Cartography

Data science is a crucial aspect of my teaching. I teach a number of modules that focus on applying data science approaches in geography. I am particularly excited about the approach I took in creating the new GY7702 R for Data Science R and GY7708 Geospatial Artificial Intelligence modules for the MSc in GIScience and the MSc in Satellite Data Science. The GY7702 module covers the basics of programming in R and machine learning but it puts a significant emphasis on reproducibility which I think is a key aspect of data science. One of the ten teaching weeks focuses entirely on reproducibility and both pieces of coursework are expected to be reproducible analysis documents. Following the principle of teaching by example I created all materials (including lecture slides and practical session instructions) in R and Markdown and are thus fully human-readable reproducible using free and open-source software and accessible as webpages (https://sdesabbata.github.io/granolarr/). My undergraduate GY3421 Information Visualisation module takes a systematic approach to visualising data based on the grammar of graphics and using Tableau.

Geographic information science

Geographic data science

Geographic artificial intelligence

Geocomputation 

GIS

Location-based services

Geographic information retrieval

Natural language processing

Digital geographies 

Volunteered geographic information

Information visualisation

Cartography

Urban geography

Chair of the GIScience Research Group of the Royal Geographical Society

Member of the steering committee of GIScience Research UK (GISRUK)

Chair of the 26th Annual GIScience Research UK (GISRUK) Conference 2018

Member of the commission of the Commission on Location Based Services of the International Cartographic Association (ICA/ACI)

Journal of Documentation Highly Commended Paper Award 2016 for: De Sabbata S. Mizzaro S. & Reichenbacher T. (2015). Geographic dimensions of relevance. Journal of Documentation 71(4) 650-666.

Leicester Students’ Union Superstar Award nomination for teaching (2016-17) best personal tutor (2017-18 and 2020-21) and best supervisor (2020-21).

Fellow of the Royal Geographical Society (with IBG)

Fellow of the Higher Education Academy