Environmental data clustering analysis through wearable sensing techniques: New bottom‐up process aimed to identify intra‐urban granular morphologies from pedestrian transects

Abstract

The built environment modifies energy budget at its proximity and the main related effect is the so-called Urban Heat Island phenomenon consisting in higher air temperature detected in urban contexts with respect to rural surroundings. Many studies focus the attention on the existing correlations between cities morphology and Urban Heat Island intensity. Nevertheless, the urban environment is complex and heterogeneous mining that at a lower scale, distinctive microclimate conditions express intra-urban granularity. The intensity of such phenomenon is most commonly analysed by means of a network of weather stations or remote sensing. The current work proposes to analyse the intra-urban microclimate diversification by means of cluster analysis of environmental data gathered through mobile transects at pedestrian perspective. This methodology is applied to four different typologies of urban context, i.e. mainly open site, packed historical, packed low-rise buildings and packed high-rise buildings, where monitoring campaigns are carried out during both day-time and night-time. The obtained results demonstrate potentials of the method in identifying similar morphological structure on the base of row environmental data. The heterogeneity of the selected contexts demonstrates the replicability of the proposed method while suggests the selection of different number of final clusters as function of the monitored context as further development of the study.