On Spatial Data Science

Data science is an emerging scientific discipline concerned with the development and application of theoretical and computational methods to work with and extract knowledge from Data. (Geo)statistics, geoinformatics and machine learning are basic and complementary methodological approaches contributing to the spatial data science (SDS). In the current presentation the main attention is paid to the analysis, modelling, prediction and visualisation of complex spatial (spatio-temporal) environmental data. A problem-oriented approach, which starts with the objectives of the study and quality and quantity of data, is adapted. It follows a generic data driven methodology: from data collection via intelligent exploratory data analysis and modelling with careful validation and testing to the interpretability/explainability of the results. SDS is considered as an experimental science, therefore experimentation with data by applying different methods, algorithms and tools is considered as very important. Such point of view helps in better understanding of data and phenomena, obtaining reliable and robust results and making intelligent decisions. The presentation is accompanied by simulated and real data case studies. In conclusion some general remarks and future perspectives are discussed.