This paper proposes a framework to define a zoning procedure using clustering taking into account socio-economic, spatial and logistics intensity variables to support urban logistics planning and management decision making. The methodology is centered around comparing two dimension reduction algorithms (PCA and UMAP) and four clustering algorithms (k-means, affinity propagation, HDBSCAN, and SOM). This comparison is based on combinations of dimension reduction and clustering techniques, assessing the results for geographic coherence, patterns that are captured and the statistical validity of the clustering results. The variables used in the clustering are defined from socio-economic, geographic and demographic data issued from standard sources, and a logistics intensity estimation via freight trip generation (FTG models). Within its application to Lima, Peru, results show that the choice of the FTG model, the main logistics intensity variable, has a strong impact on the final composition of the logistics profile and also on ensuring a geographical sense of the clustering results. Finally, research, policy, and practical implications are discussed, as well as future research stemming from these results.
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