Modeling Spatial Dimensions of Parcel Delivery Demand and Determinants

초록

This paper investigates the application of linear regression models in analyzing parcel demand at the nationwide level within South Korea. Using empirical delivery volume data, this paper explores the spatial dimensions of parcel demand as well as its socioeconomic features. The authors employ regression analysis to account for spatial autocorrelation latent in georeferenced parcel delivery volume data. The authors first conduct conventional ordinary least square regression, and then conduct spatial lag and error models to evaluate spatial regression variables. Furthermore, the authors separate analysis zones into two parts by each zone’s urbanization level to examine the distinction between two regions. The authors conclude that the spatial regression approaches show better fits for parcel demand at the national level, and parcel demand is positively associated with family types, gender ratio, young population, economic status, and degree of commercialization. Another key finding is that parcel delivery demand models for urban and non-urban area show different, and sometimes thoroughly contrasting, implications depending on their urbanization level. This result provides insights for future research on georeferenced variables and spatial dimensions in logistics.