A growing literature examines the effects of poverty, inequality and polarization on civil war but analyses typically use spatially aggregated data. Many civil wars exhibit great spatial heterogeneity so very local effects may be obscured. We study Nepal's civil war that escalated sharply from...

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...

Censuses are fundamental building blocks of most modern‐day societies, yet collected every 10 years at best. We propose an extension of the widely popular census updating technique structure‐preserving estimation by incorporating auxiliary information in order to take ongoing subnational...

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...