pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data

Abstract

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 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 ST causal pathways are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present pg-Causality, a novel pattern-aided graphical causality analysis approach that combines the strengths of pattern mining and Bayesian learning to efficiently identify the ST causal pathways. First, 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, 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 128 cities, in three regions of China from 01-Jun-2013 to 31-Dec-2016. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.