A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

Abstract

Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting cooccurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus, it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance. Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting co-occurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance.

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

“…These lead to poor wind circulation with low wind speed, and to western winds being trapped so the pollutants remain in the city. Another reason is temperature inversion, especially in these regions [40]. Figure 4 shows the monthly and seasonal numbers of CO and PM 2.5 clusters obtained by each method.…”

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

“…This step defines PCE's neighborhood by using initialized R 1 and R 2 values and then indexes all features participating in mining process to the buffer which was created by the upper bound. As mentioned in (Akbari et al 2015), it is necessary to find co-occurrence patterns with a local view; in this regard, a Voronoi diagram as a spatial indexing structure was used. However, in this method, to increase execution efficiency, buffer of upper bound was used instead of Voronoi diagram.…”

“…All the aforementioned studies tried to detect time-prevalent patterns, but none of these methods allows identifying how a spatio-temporal co-occurrence pattern evolves over time. Recently, authors have developed a crisp method (Akbari et al 2015) to handle this lack. They proposed a new spatio-temporal measure to consider evolution of patterns simultaneously in space and time.…”

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining

“…Spatio-temporal pattern mining offers computationally efficient approaches to identify frequent spatial and temporal patterns from large databases (Aggarwal 2014;Akbari, Samadzadegan, and Weibel 2015). In particular, sequential pattern mining (SPM) can be used to detect frequent sequential patterns.…”