Rough set-based regionalisation in air quality monitoring

Abstract

Regionalisation is to organise a large set of spatial objects into spatially contiguous regions despite optimising the homogeneity of the derived regions, while representing social and economic geography. To confront this problem, it is necessary to classify the regions to form groups that are homogeneous in air quality attributes. It is to develop a system that applies data mining techniques to study the distribution of air pollutants in Chennai, a metro city in India using vehicular ad hoc networking and map the distribution on the geographical map for effective policy making. In conventional regionalisation methods, the data points are assigned to a single region in a multidimensional attribute space affecting air pollution response. However, some data points, having distributed membership to more than one region, could not be justified and allocated to a single region. Rough set-based clustering technique is applied to regionalisation problem to resolve vague or overlapping regions. The overlapping regions are restructured to guarantee the homogeneity of the regions formed or altered. The investigations of the cluster validity tests confirm the effectiveness of rough set-based regionalisation in air quality modelling.