pg-Causality: Identifying Spatiotemporal Causal Pathways for Air
  Pollutants with Urban Big Data
  Many countries are suffering from severe air pollution. Understanding how
different air pollutants accumulate and propagate is critical to making
relevant public policies. In this paper, we use urban big data (air quality
data and meteorological data) to identify the \emph{spatiotemporal (ST) causal
pathways} for air pollutants. This problem is challenging because: (1) there
are numerous noisy and low-pollution periods in the raw air quality data, which
may lead to unreliable causality analysis, (2) for large-scale data in the ST
space, the computational complexity of constructing a causal structure is very
high, and (3) the \emph{ST causal pathways} are complex due to the interactions
of multiple pollutants and the influence of environmental factors. Therefore,
we present \emph{p-Causality}, a novel pattern-aided causality analysis
approach that combines the strengths of \emph{pattern mining} and
\emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST
causal pathways}. First, \emph{Pattern mining} helps suppress the noise by
capturing frequent evolving patterns (FEPs) of each monitoring sensor, and
greatly reduce the complexity by selecting the pattern-matched sensors as
"causers". Then, \emph{Bayesian learning} carefully encodes the local and ST
causal relations with a Gaussian Bayesian network (GBN)-based graphical model,
which also integrates environmental influences to minimize biases in the final
results. We evaluate our approach with three real-world data sets containing
982 air quality sensors, in three regions of China from 01-Jun-2013 to
19-Dec-2015. Results show that our approach outperforms the traditional causal
structure learning methods in time efficiency, inference accuracy and
interpretability.
