Dr Stefano De Sabbata

I am an Associate Professor of Geographical Information Science at the School of Geography Geology and the Environment of the University of Leicester. My research focuses on geographic data science and the application of machine learning to human geography and internet studies. I teach data science information visualization and GIS. I am the secretary of the Geographic Information Science Research Group of the Royal Geographical Society with IBG. I am also part of the steering committee of GIScience Research UK (GISRUK) and I was the chair of the GISRUK 2018 conference. I am a member of the Commission on Location-Based Services of the International Cartographic Association. Before joining the University of Leicester I was a Researcher at the Oxford Internet Institute of the University of Oxford (2013-2015) and Junior Research Fellow at the Wolfson College of the University of Oxford (2014-2015) and I am now a Research Associate of the Oxford Internet Institute of the University of Oxford. I was awarded a PhD from the Department of Geography of the University of Zurich in 2013 and a BSc and a 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.

Publications

(0) Liu, P., & De Sabbata, S. (2021). A graph-based semi-supervised approach to classification learning in digital geographies. Computers, Environment and Urban Systems, 86, 101583. doi:10.1016/j.compenvurbsys.2020.101583
Ballatore, A., & 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., & 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., & 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., & 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., & 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., & 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., & 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., & Reichenbacher, T. (2015). Geographic dimensions of relevance. Journal of Documentation, 71(4), 650-666. doi:10.1108/JD-12-2013-0167
De Sabbata, S., & 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.

Geographic data science

Geographic information science

GIS

Geocomputation

Artificial intelligence

Machine learning

Deep learning

Geographic relevance

Location-based services

Geographic information retrieval

Natural language processing

Information geographies

Digital geographies

Internet geographies

Volunteered geographic information

User-generated content

Big data

Citizen science

Public participation GIS

Critical GIS

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

Secretary 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