Identification of air pollution patterns using a modified fuzzy co-occurrence pattern mining method

Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Discovering spatio-temporal co-occurrence patterns is an important task having many application domains. There are a number of developed methods to mine co-occurrence patterns; however, using them needs a unique parameter to define the neighborhood. Identification of a unique optimum k-value or neighborhood radius is a challenging issue in different application domains. The developed method of this research defines a new fuzzy neighborhood and new fuzzy metrics to be applicable for real applications such as air pollution, especially when the researchers have no comprehensive knowledge regarding the application domain; in addition, it mines patterns based on the fuzzy nature of environmental phenomena. The new method mines patterns locally without localization step to speed up the mining process and considers all feature types (point, line and polygon) to handle all applications. Subsequently, it is applied to a real data set of Tehran city for air pollution to discover significant co-occurrence patterns of air pollution and influencing environmental parameters such as meteorological, topography and traffic. The case study results showed seven meaningful patterns among air pollution classes 2 and 3 and wind speed class 1, topography class 1 and traffic classes 1 and 2. The evaluation confirmed the accuracy and applicability of the new developed method for air pollution case. Furthermore, two performance tests for the method itself and a performance test against a crisp method were done, where the results exhibited an efficient computational performance.