Description

SpatialLab@UP is a fast-growing, dynamic research group that specialises in spatial statistics and geostatistics. We analyse data with a locational aspect, such as crime locations, incidences of disease, distributions of roads and buildings, and more. Our theoretical areas include point pattern analysis, spatial linear networks, nonconvex spatial domains, spatial dynamic processes, and AI for spatial image analysis. Our research applications target relevant problems within South Africa and the developing world, including criminology, disease modelling, urban development and agriculture.

Collaborators

The UP staff acting as PIs for the group are:

Prof Inger Fabris-Rotelli (Department of Statistics)

Prof Gregory Breetzke (Department of Geography, Geoinformatics and Meteorology)

Dr Kabelo Mahloromela (Department of Statistics)

Dr Renate Thiede (Department of Statistics)

Local collaborators include:

SEPIMOD: https://raymandoc.quarto.pub/sepimod/

Dr Jaco Kemp (SU)

SDGs

Our research links to the following SDGs:

SDG 3: Good health and well-being

SDG 9: Industry, innovation and infrastructure

SDG 11: Sustainable cities and communities

Recent published works of the group

• Mahloromela, K., Fabris-Rotelli, I., and Kraamwinkel, C. "Covariate construction of nonconvex windows for spatial point patterns." South African Statistical Journal 57.2 (2023): 65-87. 
• Stander, R., Fabris-Rotelli, I., and Chen, D. "Multiscale decomposition of spatial lattice data for hotspot detection." South African Statistical Journal 58.1 (2024): 57-79. 
• Thiede, R., et al. "A Markov chain model for geographical accessibility." Spatial Statistics 55 (2023): 100748. 
• Fabris-Rotelli, I., et al. "A Spatial SEIR Model for COVID-19 in South Africa." Journal of Data Science, Statistics, and Visualisation 2.7 (2022): 14-45. 
• Fabris-Rotelli, I., et al. "An informal road detection neural network for societal impact in developing countries." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4 (2022): 267-274.

Banner image accreditation:

Martin Grandjean, CC BY-SA 4.0, via Wikimedia Commons