Temporal and Spatial Analysis of Pedestrian Count Data for Thermal Environmental Planning in Street Canyons

In this study, we analyzed the spatiotemporal characteristics of pedestrian behavior in street spaces using pedestrian count data—specifically, the number of pedestrians passing in front of infrared sensors installed throughout the downtown area. The analysis focused on three main questions: (1) whether the thermal environment affects pedestrian behavior, (2) how to characterize the spatiotemporal patterns of pedestrian activity, and (3) how to effectively present the results to urban planners and designers. A temporal and spatial analysis method was examined using hourly pedestrian count data over one year at more than 100 locations in the street canyon. The temporal characteristics of the pedestrian count data were classified into weekday and weekend clusters according to the peak hours within a day. The spatial characteristics of the pedestrian count data were clearly defined by distance from the station, office district, and commercial district, according to peak commuting, shopping, etc. Results from principal component analysis and cluster analysis did not reveal a significant influence of the thermal environment on the temporal variation in pedestrian counts. Instead, the data suggested that weekday versus weekend distinctions were the primary determinants of daily and annual patterns, while seasonal and weather-related factors had relatively minor effects. The analytical approach developed in this study represents a valuable and practical contribution that may be applicable to other urban contexts as well.