CITY LOGISTICS AND CLUSTERING IMPACTS OF USING HDI AND TAXES
Cities have different characteristics, and policy measures applied in different urban areas can only result in different associated impacts. We applied a data-driven methodology to identify clusters to guide São Paulo’s urban logistics policy and practice decisions. The methodology uses relevant variables for urban logistics population, infrastructure (road capacity and road density), and regulation data in order to perform two statistical analysis, Principal Component Analysis and K-means clustering. The purpose of this paper is to study the impact of including new variables in the methodology proposed by Winkenbach et al. (2016), HDI and taxes. The results suggest adding new variables can help reaching a better undestanding of the city’s context.