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
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.