Application of Association Rule Mining for Exploring the Relationship between Urban Land Surface Temperature and Biophysical/Social Parameters

This paper explores the relationship between remote sensing measurements of land surface temperature and biophysical/socioeconomic data by utilizing the association rule mining technique. The surfaces associated with urban uses typically radiate more heat as compared to its rural counterparts. There is a need to quantitatively analyze this contrast in temperature and the biophysical and social characteristics which influence it. Furthermore, in order to consider the urban heat island (UHI) effect, a parameterization is required to account for the urban surface characteristics impacts on the magnitude of land surface temperature (LST). The association rule mining model has demonstrated to bring in additional quantitative information concerning the relationship;; among urban parameters. The ASTER data from 2000 was used for the selection of appropriate variables to be used in the model. This information was then used for generating association rules between land-use land-cover (LULC) and LST information from 2000, 2001, and 2004. The results thus obtained quantitatively described the relationships between various urban parameters. It was found that there was little change in the percentage area of the LULC types from 2000 to 2004. This made the comparison of the results possible, In the case of the 2000 data, it was found that forest and impervious surfaces had strong association with temperature and scaled normalized difference vegetation index (SNDVI). Specific zones such as hospitals and universities had negative association with water. The comparison of data from 2000, 2001, and 2004 suggests that impervious surface and the zoning category of airport had a strong association. Nevertheless, the information extracted needs to be analyzed in greater detail in order to arrive at robust decision rules. Overall, the model so developed has demonstrated to be effective in predicting associations between urban EST and pertinent factors. This model could be useful for urban planners and environmental managers in quantifying rules that characterize a particular urban