The main objective of SDS2020 is to initiate a dialogue about the different issues we face when performing and developing innovative methodologies of mining, analysis, modelling and visualization of geo-spatial data.
The recent development of quantitative methods allowing to perform intelligent data reduction and suitable analysis is a central issue in environmental and socio-economic sciences. In both these fields, geo-referenced numerical data are nowadays massively available and can be further enhanced by other sources of information numerically transformed, but this information is often complex and sometimes unstructured or noisy. Thus, discovering interesting spatial or intrinsic patterns is a challenging task that led scientists to search for new tools. With this in mind, innovative techniques based on clustering, pattern recognition and data mining, can be employed to extract knowledge and insights from data.
These theoretical developments proved to be very helpful in various applications. Here just some examples: natural hazard susceptibility assessment (e.g. flood, landslides, earthquakes, wildfires); multivariate time series analyses, for both environmental risks (e.g. pollution time series) and renewable energy potential assessments (e.g. meteorological data, such as wind speed, rainfall, solar radiation); understanding network flows (such as commuter traffic).
The main theoretical topics of the workshop include, but are not limited, to three principal axes:
The main applications will be closely related to the research in environmental sciences, quantitative geography and spatial statistics, in particular: