Cross-sectional Associations between Lifestyle Factors and Obesity among Students in a University at Gyeonggi Province

Objectives: This study examined the associations between lifestyle factors and obesity among university students.Methods: In this cross-sectional correlational study, 671 students completed measures of lifestyle factors (dietary habit, exercise, smoking, and alcohol drinking), health concern, academic stress, and anthropometric indices. Obesity was classified by body mass index (BMI) and abdominal circumference (AC). The data were analyzed with χ2 test, Mann-Whitney U test and logistic regression.Results: The frequencies of obesity and abdominal obesity were higher in male students than in female students (17.6% vs. 3.4% by BMI, 18.9% vs. 5.9% by AC). Abdominal obesity in students with diabetic parent was higher (16.8%) than in students without diabetic family history (9.2%). Obesity of BMI ≥ 25 kg/m2 associated with breakfast absence of 5-7 times/week (odds ratio, OR: 1.98, 95% confidence interval, CI: 1.05-3.71). Abdominal obesity associated with instant foods intake of 5-7 times/week (OR: 2.65, 95% CI: 1.14-6.17), fast foods intake of 3-4 times/week (OR: 2.87, 95% CI: 1.48-5.56), snacks of 3-4 times/wk (OR: 2.15, 95% CI: 1.15-4.00), and consumed alcohol of ≥ 1 glass a day (OR: 2.11, 95% CI: 1.19-3.73).Conclusions: Breakfast absence is associated with obesity. Instant foods, fast foods, snacks, and alcohol consumption are the associated factors with abdominal obesity among university students.

Clustering Analysis with Embedding Vectors: An Application to Real Estate Market Delineation

Although clustering analysis is a popular tool in unsupervised learning, it is inefficient for the datasets dominated by categorical variables, e.g., real estate datasets. To apply clustering analysis to real estate datasets, this study proposes an entity embedding approach that transforms categorical variables into vector representations. Three variants of a clustering algorithm, i.e., the clustering based on the traditional Euclidean distance, the Gower distance, and the embedding vectors, are applied to the land sales records to delineate the real estate market in Gwacheon-si, Gyeonggi province, South Korea. Then, the relevance of the resultant submarkets is evaluated using the root mean squared errors (RMSE) obtained from a hedonic pricing model. The results show that the RMSE in the embedding vector-based algorithm decreases substantially from 0.076-0.077 to 0.069. This study shows that the clustering algorithm empowered by embedding vectors outperforms the conventional algorithms, thereby enhancing the relevance of the delineated submarkets.

Understanding Chinese Independent Travel Market To South Korea: a Segmentation Approach

The rapid growth of the Chinese travel market has gained attention in the tourism industry. However, very few studies have been conducted to examine travel constraints that prevent Chinese outbound travelers from going somewhere quite accessible to their major destination from a multidestination perspective. Drawing upon the leisure constraint model (LCM), this study explored Chinese independent tourists' perceived travel constraints in selecting second-tier destinations in their destination choice and analyzed the market segments. A self-administered survey was collected from 393 Chinese travelers who did not visit Gyeonggi Province close to Seoul during their travels in South Korea. Based on the findings, four distinct groups were formed. The findings provide important insights into destinations that desire to attract more Chinese independent travelers.