Modeling Correlations among Air Pollution-Related Data through Generalized Association Rules

摘要

Today's citizens and city administrations have an increasing interest in monitoring the air quality in urban areas. Studying the causes of air pollution entails analyzing the correlations between heterogeneous data, among which pollutant concentrations, traffic flow measurements, and meteorological data. To this end, innovative data analytics solutions able to acquire, integrate, and analyze very large amounts of data are needed. This paper presents a new data mining system, named GEneralized Correlation analyzer of pOllution data (GECKO), to discover interesting and multiple-level correlations among a large variety of open air pollution-related data. Specifically, correlations among pollutant levels and traffic and climate conditions are discovered and analyzed at different abstraction levels. The knowledge extraction process is driven by a taxonomy to generalize low-level measurement values as the corresponding categories. To ease the manual inspection of the result, the extracted correlations are classified into few classes based on the semantics of underlying data. The experiments, performed on real data acquired in a major Italian Smart City, demonstrate the effectiveness of the proposed analytics engine in discovering correlations among pollutant data that are potentially useful for supporting city administrators in decision-making.