Geospatial data science is a subset of data science concerned specifically with location-based data, i.e., data about phenomena implicitly or explicitly associated with a location relative to the Earth. This interdisciplinary field draws on other disciplines such as statistics, informatics, computing, communication and management to study geospatial data and its applications, and to transform data into insights. In our department, we sometimes study geospatial data and its visualisation per se, e.g.,

• Facilitating geospatial data availability and access through an infrastructure 
• Geospatial data needs for informal settlement upgrading 
• Geospatial visualisation in extended reality, such as virtual or augmented reality 
• How OpenStreetMap is crowdsourced and used in Africa 
• Usability studies for maps and geospatial visualisations in planning, education, archaeology and spatial accessibility 

We also apply geospatial data science methods to create knowledge and insights in specific domains, such as the other research foci in our department:

• The application of geospatial technologies to study landscape processes
• The application of geospatial tools for long-term vegetation changes due anthropogenic and natural factors, vegetation structure, quality or condition assessments, land use and land cover change mapping, and crop type and condition mapping
• Geospatial analytics to understand socio-economic phenomena, such as poverty, inequality and racial residential segregation
• Geospatial suitability analysis and renewable energy resource assessment.
• Integrating geospatial technologies to improve human health and access to open spaces 
• Using machine learning to collect data on landforms
• Web mapping for renewable energy potential assessment.

Staff involved include Dr Adedayo Adeleke, Prof Gregory Breetzke, Prof Serena Coetzee, Dr Christel Hansen, Mr Samy Katumba, Dr Victoria Rautenbach, Mr Lourens Snyman, Mr Barend van der Merwe