코로나19 중기ㆍ이후 도시 물류 결정요인 분석: 서울시 사례 기반의 포아송 및 음이항 회귀 모형 적용 연구

초록

The COVID-19 pandemic brought significant changes to the structure of urban logistics demand, thereby increasing the need for quantitative research on the actual determinants of urban logistics. This study analyzes and compares the changes in urban logistics demand and its key influencing factors in Seoul at the administrative-dong level during the mid-pandemic period (2022) and the post-pandemic period (2023). Given the count data structure, Poisson regression and Negative Binomial regression models were employed, and due to the issue of overdispersion, the interpretation focused primarily on the results of the Negative Binomial model, which showed better model fitness. The analysis revealed that during the pandemic, the female population aged 20？39 and over 60 had significant negative effects on logistics demand, while the proportion of single-person households and fashion consumption indicators showed stronger positive effects. This study quantitatively identifies various socio-economic factors affecting urban logistics demand and provides empirical evidence to support policy design in preparation for future crises such as another pandemic. Furthermore, future research will apply geographically weighted regression models (GWPR, GWNBR) to analyze the spatial heterogeneity of explanatory power across regions in greater detail and extend the findings toward the development of locally tailored logistics strategies.

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