This paper utilises distributional random forests as a flexible machine learning method for analysing income distributions. Distributional random forests avoid parametric assumptions, capture complex interactions among covariates, and, once trained, provide full estimates of conditional income...

This paper utilises distributional random forests as a flexible machine learning method for analysing income distributions. Distributional random forests avoid parametric assumptions, capture complex interactions among covariates, and, once trained, provide full estimates of conditional income...

Für Deutschland und andere entwickelte Länder werden Methoden zur Schätzung von sozioökonomischen Indikatoren auf räumlich disaggregierter Ebene benötigt, ohne dabei Populations-Mikrodaten zu verwenden, die meist nicht öffentlich verfügbar sind. Viele sozioökonomische Indikatoren, zum...

Climate change is among the biggest challenges of our time. Climate impacts and socioeconomic vulnerability are spatially heterogeneous. Hence, adaptation policies are more effective when designed at lower administrative levels. However, spatially-detailed social vulnerability profiles are often...

Climate change is among the biggest challenges of our time. Climate impacts and socioeconomic vulnerability are spatially heterogeneous. Hence, adaptation policies are more effective when designed at lower administrative levels. However, spatially-detailed social vulnerability profiles are often...