  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 paper, we advocate a paradigm shift for LUR models: We propose the
Data-driven, Open, Global (DOG) paradigm that entails models based on purely
data-driven approaches using only openly and globally available data. Progress
within this paradigm will alleviate the need for experts to adapt models to the
local characteristics of the available data sources and thus facilitate the
generalizability of air pollution models to new areas on a global scale. In
order to illustrate the feasibility of the DOG paradigm for LUR, we introduce a
deep learning model called MapLUR. It is based on a convolutional neural
network architecture and is trained exclusively on globally and openly
available map data without requiring manual feature engineering. We compare our
model to state-of-the-art baselines like linear regression, random forests and
multi-layer perceptrons using a large data set of modeled $\text{NO}_2$
concentrations in Central London. Our results show that MapLUR significantly
outperforms these approaches even though they are provided with manually
tailored features. Furthermore, we illustrate that the automatic feature
extraction inherent to models based on the DOG paradigm can learn features that
are readily interpretable and closely resemble those commonly used in
traditional LUR approaches.
