The Use of Data Science and Earth Observation Data For Spatial Decision Making

Abstract: The past few years have seen a proliferation of both machine learning and spatial data. Many fields, notably ecology and conservation use both to build predictive models and inform decision making. Earth observation and spatial data have the potential to inform decision making in other fields as well, notably risk mapping, epidemiology to prioritization. Data science techniques, ranging from (spatial) data visualization to statistical modelling to both machine learning and deep learning can help us identify insights from spatial data which were previously not possible. Data science and spatial data make a formidable combination in our data driven world and can help build spatially relevant models. The session will introduce real life examples wherein data science techniques were used long with spatial data for addressing real life problems and demonstrating how appropriate use of spatial data can inform decision making. Examples range from building robust data visualizations (after obtaining and cleaning free earth observation data) to building predictive models of habitat suitability.

Bio: Minerva is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. In my PhD I focused on using machine learning models in conjunction with satellite data for predicting the impact of degradation on forest structure and biodiversity in SE Asia.

Thanks to my PhD training, she is also a Data Scientist on the side. As a part of my research, I have to carry out extensive data analysis, including spatial data analysis. For this purpose, I prefer to use a combination of freeware tools- R, QGIS and Python. She also holds an MPhil degree in Geography and Environment from Oxford University where she honed my remote sensing and spatial data analysis skills.

In addition to spatial data analysis, Minerva is also proficient in statistical analysis, machine learning and data mining. On the basis of her MPhil and PhD, Minerva published several peer-reviewed papers, including machine learning papers in PLOS One. The details of her research can be found at:

Open Data Science Conference