Giacomo Titti , Liwei Hu, Pietro Festi, Letizia Elia, Lisa Borgatti, Luigi Lombardo 

The geospatial community usually makes use of GIS environments to handle databases and pre-process their information. Actual analyses, especially data-driven ones, are performed outside GIS platforms. This interrupts the flow of information and the processing chain in a number of I/O operations that inevitably slow down the overall analytical protocols. The first version of the SZ-plugin attempted to mitigate this issue by offering a modeling solution from within QGIS. However, the available models in the SZ-plugin essentially boiled down to binary classifiers, whose dimensionality was constrained to address pure spatial problems. In this updated version, we focused on two major aspects: 1) a space-time extension and 2) the inclusion of a regression option in addition to the already existing classification one. These two aspects have been introduced as part of two new models, namely, a Generalized Additive Modeling and a Multi-Layer Perceptron. In short, these would allow users to obtain susceptibility and intensity estimates in space and time. An improved graphical reporting tool has also been implemented. This makes it possible to produce relevant statistical summaries as well as cartographic outputs for users to directly integrate into their technical reports or scientific documents. The problem of landslide prediction is taken as a reference in Taiwan, but the same plugin can be used to perform regressions or classifications for any other phenomenon associated with (e.g.) digital soil mapping, wildfire and gully erosion modeling, land-use or tree species detection, etc.