• The Korean Journal of Applied Statistics
• /
• v.36 no.5
• /
• pp.473-488
• /
• 2023

Gangwon-do is one of Korea's most popular tourist destinations, with varying tourism demands and trends across its subregions. It is crucial to identify the characteristics of tourism in each area and compare the tourism patterns over time to devise policies that revitalize tourism in each local government and promote balanced development across regions. In this paper, we classify the regions in Gangwon-do based on tourism data from the last four years and analyze the tourism pattern of each region using the non-Euclidean additive model proposed by Jeon et al. (2021). The model incorporates the proportions of visitors by age groups and the proportions of navigation searches by destination types as two covariates, and the proportions of tourism expenditure types as a response variable. We estimate the model using the smooth-backfitting method and coordinate-wise bandwidth selection. The results are visualized in ternary plots, and changes in tourism patterns over time are analyzed by comparing the ratios of prediction errors to fitting errors.

• The Korean Journal of Applied Statistics
• /
• v.34 no.5
• /
• pp.773-794
• /
• 2021

Prediction of vehicle traffic volume is very important in planning municipal administration. It may help promote social and economic interests and also prevent traffic congestion costs. Traffic volume as a time-varying trajectory is considered as functional data. In this paper we study three functional regression models that can be used to predict an unseen trajectory of traffic volume based on already observed trajectories. We apply the methods to highway tollgate traffic volume data collected at some tollgates in Seoul, Chuncheon and Gangneung. We compare the prediction errors of the three models to find the best one for each of the three tollgate traffic volumes.