Forke, Chris-MarianTropmann-Frick, Marina2022-01-272022-01-2720212021http://dx.doi.org/10.1007/s13222-021-00391-xhttps://dl.gi.de/handle/20.500.12116/38052More and more applications nowadays use spatio-temporal data for different purposes. In order to be processed and used efficiently, this unique type of data requires special handling. This paper summarizes methods and approaches for feature selection of spatio-temporal data and machine learning algorithms for spatio-temporal data engineering. Furthermore, it highlights relevant work in specific domains. The range of possible approaches for data processing is quite wide. However, in order to use these approaches with the spatio-temporal data in a meaningful and practical way, individual data processing steps need to be adapted. One of the most important steps is feature engineering.Feature engineeringFeature selectionSpatio-temporal dataText/Journal Article10.1007/s13222-021-00391-x1610-1995