author={Michael Steininger and Konstantin Kobs and Albin Zehe and Florian Lautenschlager and Martin Becker and Andreas Hotho},
  journal={ACM Transactions on Spatial Algorithms and Systems (TSAS)},
  pages={1 - 24}
Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this article, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that… 



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