  Recently, air pollution is one of the most concerns for big cities.
Predicting air quality for any regions and at any time is a critical
requirement of urban citizens. However, air pollution prediction for the whole
city is a challenging problem. The reason is, there are many spatiotemporal
factors affecting air pollution throughout the city. Collecting as many of them
could help us to forecast air pollution better. In this research, we present
many spatiotemporal datasets collected over Seoul city in Korea, which is
currently much suffered by air pollution problem as well. These datasets
include air pollution data, meteorological data, traffic volume, average
driving speed, and air pollution indexes of external areas which are known to
impact Seoul's air pollution. To the best of our knowledge, traffic volume and
average driving speed data are two new datasets in air pollution research. In
addition, recent research in air pollution has tried to build models to
interpolate and predict air pollution in the city. Nevertheless, they mostly
focused on predicting air quality in discrete locations or used hand-crafted
spatial and temporal features. In this paper, we propose the usage of
Convolutional Long Short-Term Memory (ConvLSTM) model \cite{b16}, a combination
of Convolutional Neural Networks and Long Short-Term Memory, which
automatically manipulates both the spatial and temporal features of the data.
Specially, we introduce how to transform the air pollution data into sequences
of images which leverages the using of ConvLSTM model to interpolate and
predict air quality for the entire city at the same time. We prove that our
approach is suitable for spatiotemporal air pollution problems and also
outperforms other related research.
