• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.99-107
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• 2010

Analyzing mode choice among transportation demand estimate procedures is complicated and understanding characteristics of travelers is also difficult. Generally, it is well known that traveler choose mode considering psychometric factors and characteristic besides socio-demographic indicators. Accordingly, many researches has investigated on methodology that can be applied in mode choice to reflect psychometric factor or specific preference. Latent Class Analysis among various studies is recognized as the theoretically potential approach. This study focuses on class segmented using latent class cluster to analyze impact that included psychometric factors and characteristics on mode choice. It also provides evidence that mode choice model for each class and mode choice model not considering latent class are different. This study based on citizen's stated preference and revealed preference on a new transit on the Han river shows that latent class cluster analysis is the potential approach considering latent preference.

• Korean Journal of Agricultural Science
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• v.45 no.3
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• pp.547-559
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• 2018

Consumers are becoming increasingly interested in what kind of value their food has. Many studies have focused on consumers' preferences and willingness to pay for specific food values. However, few studies have asked consumers to consider or rank the importance of different food values. This paper determined consumers' food values by implementing the best-worst scaling approach and segmented consumers based on the relative importance of general food values that consumers place on them. Among a list of eleven food values (taste, safety, origin, appearance, price, environmental impact, naturalness, convenience, nutrition, fairness, and habit) which was compiled from previous studies on food preferences, on average, safety, nutrition, taste, and price were the most important values to consumers, whereas fairness, habit, appearance, convenience, origin, and environmental impact were the least important values. However, significant variation exists among consumers in terms of the relative importance of food values. To investigate the heterogeneity among consumers, a Latent Class Analysis was performed to classify consumers into subgroups based on responses to questions. Two latent classes were found and characterized as 'safety-nutrition' and 'taste-price'. The 'safety-nutrition' cluster represents 61% of the sample and a group of people who find safety and nutrition centered values to be the most important. Another cluster represents about 39% of the sample, and relative to the first group, this group finds price and taste values to be more important.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.18-27
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• 2022

Construction accidents are difficult to prevent because several different types of activities occur simultaneously. The current method of accident analysis only indicates the number of occurrences for one or two variables and accidents have not reduced as a result of safety measures that focus solely on individual variables. Even if accident data is analyzed to establish appropriate safety measures, it is difficult to derive significant results due to a large number of data variables, elements, and qualitative records. In this study, in order to simplify the analysis and approach this complex problem logically, data preprocessing techniques, such as latent class cluster analysis (LCCA) and predictor importance were used to discover the most influential variables. Finally, the correlation was analyzed using an alluvial flow diagram consisting of seven variables and fourteen elements based on accident data. The alluvial diagram analysis using reduced variables and elements enabled the identification of accident trends into four categories. The findings of this study demonstrate that complex and diverse construction accident data can yield relevant analysis results, assisting in the prevention of accidents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.324-333
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• 2018

The aim of this study was to investigate the latent classes and predictors of chronic diseases such as hypertension, dyslipidemia, arthritis, thyroid disease, depression, atopy, allergy, and diabetes. The subjects of this study were Korean citizens who participated in the Korea National Health and Nutrition Examination Survey in 2014. Stratified cluster sampling method was used with a sample size of 7,550. Latent hierarchy analysis was applied to this data. Four classes were identified. Class 1 consisted of participants with hypertension and diabetes. Class 2 consisted of participants with atopy and allergies. Class 3 consisted of participants with dyslipidemia, arthritis, thyroid disease, and depression. Class 4 consisted of participants without any chronic diseases. In comparing Class 1 to Class 4, age, physical activity, self-management, obesity, and presence of high cholesterol were found to be significant. In comparing Class 2 to Class 4, gender, age, and education level were significant. When Class 3 was compared to Class 4, gender, age, pain and discomfort, as well as high cholesterol were found to be significant. Diabetes and hypertension should be treated as comorbid conditions, applying integrated treatments involving effective drug treatment, diet, and physical activity programs. Atopy was found to be strongly correlated with allergies. Thyroid disease was found to coexist with dyslipidemia and arthritis, along with having a strong correlation to depression. Age-appropriate preventive measures can help reduce the risk of chronic diseases.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.194-210
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• 2022

The user-centered EV charging infrastructure construction policy the government is aiming for can increase convenience for electric vehicle users and bring new electric vehicle users into the market. This study was conducted to provide an in-depth understanding of the charging behaviors of actual electric vehicle users, which can be used as basic information for the electric vehicle charging infrastructure. Based on charging diary data collected for a week, the charging of electric vehicles was analyzed on a daily and weekly basis, and sequence analysis and latent class analysis were used. As a result, five daily charging profiles and four weekly charging profiles were identified, which are expected to contribute to revitalizing the electric vehicle market by providing key information for decision-making by potential electric vehicle users as well for establishing user-centered charging infrastructure policies in the future.

• Safety and Health at Work
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• v.11 no.1
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• pp.26-32
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• 2020

Background: Hong Kong's construction industry currently faces a manpower crisis. Blue-collar workers are a disadvantaged group and suffer higher levels of chronic diseases, for example, cancer, than the wider population. Cancer risk factors are likely to cluster together. We documented prevalence of cancer-associated lifestyle risk behaviors and their correlates among Hong Kong construction workers. Methods: Data were collected from workers at 37 railway-related construction worksites throughout Hong Kong during May 2014. Tobacco use, alcohol consumption, unbalanced nutrition intake, and physical inactivity were included in the analysis. Latent class analysis and multivariable logistic regression were performed to identify the patterns of risk behaviors related to cancer, as well as their impact factors among construction workers in Hong Kong. Results: Overall, 1,443 workers participated. Latent class analysis identified four different behavioral classes in the sample. Fully adjusted multiple logistic regression identified age, gender, years of Hong Kong residency, ethnicity, educational level, and living status differentiated behavioral classes. Conclusion: High levels of lifestyle-related cancer-risk behaviors were found in most of the Hong Kong construction workers studied. The present study contributes to understanding how cancer-related lifestyle risk behaviors cluster among construction workers and relative impact factors of risk behaviors. It is essential to tailor health behavior interventions focused on multiple risk behaviors among different groups for further enlarging the effects on cancer prevention.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.12
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• pp.1440-1453
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• 2011

Proper consumer segmentation is receiving more attention from industry professionals as markets become more diverse and consumer-centered. Researchers have recognized the limitations of the traditional cluster analysis technique and this research study analyzes market segmentation using Mixture-model or latent-class segmentation. This study used a questionnaire to determine the characteristics of clothing shoppers using a new technique that proved its superiority over traditional techniques. Questions included items measuring fashion shopping behavior, store choice criteria, apparel consumption styles, price perception by product type, and demographic characteristics. Data were collected from 1074 males and females in their 20s and 30s through an online survey. SPSS 16.0 and Latent GOLD 4.0 were used to analyze the data. The ideal typology of clothing shoppers using the Mixture-model were: 'brand loyalty orientated group', 'group of conservative late 30s', 'group of pleasure-emotion early 20s', 'value oriented consumer product with high-income group', 'group of eco/symbol oriented consumer', and 'group of utility/goal oriented male consumer'. This study showed differences in fashion product purchasing behavior by conducting market segmentation for clothing shoppers using the Mixture-model.

• Clinical and Experimental Pediatrics
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• v.62 no.9
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• pp.325-333
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• 2019

Allergic diseases, including allergic rhinitis, asthma, and atopic dermatitis, are common heterogeneous diseases that encompass diverse phenotypes and different pathogeneses. Phenotype studies of allergic diseases can facilitate the identification of risk factors and their underlying pathophysiology, resulting in the application of more effective treatment, selection of better treatment responses, and prediction of prognosis for each phenotype. In the early phase of phenotype studies in allergic diseases, artificial classifications were usually performed based on clinical features, such as triggering factors or the presence of atopy, which can result in the biased classification of phenotypes and limit the characterization of heterogeneous allergic diseases. Subsequent phenotype studies have suggested more diverse phenotypes for each allergic disease using relatively unbiased statistical methods, such as cluster analysis or latent class analysis. The classifications of phenotypes in allergic diseases may overlap or be unstable over time due to their complex interactions with genetic and encountered environmental factors during the illness, which may affect the disease course and pathophysiology. In this review, diverse phenotype classifications of allergic diseases, including atopic dermatitis, asthma, and wheezing in children, allergic rhinitis, and atopy, are described. The review also discusses the applications of the results obtained from phenotype studies performed in other countries to Korean children. Consideration of changes in the characteristics of each phenotype over time in an individual's lifespan is needed in future studies.

• Journal of Korean Academy of Nursing
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• v.35 no.8
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• pp.1500-1507
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• 2005

Purpose. The purpose of this study was to identify subgroups of the physical and behavioral risk profiles for cardiovascular disease among industrial workers, and to examine predicting factors for the subgroups. Sample and Method. Health records of 2,616 male and female workers aged 19-56 years who were employed in an airplane manufacturing industry were analyzed. Data were analyzed using the Latent class cluster analysis. Results. Four different clusters (two high-risk groups, one low-risk group, and one normal group) were found and these clusters were significantly different by age, gender, and work type (p < .05 ). The two high-risk groups had higher chances of drinking alcohol, elevated BMI, FBS, total cholesterol, having hypertension, and were significantly older, and had relatively high chances of being day workers rather than other groups. The low-risk group had higher chances of drinking alcohol, higher BMI and total cholesterols compared to normal group, and highest portions of current smokers and shift workers in the four clusters and their mean BP was within prehypertension criteria. Conclusion. Industrial nurses should guide the lifestyle behaviors and risk factors of the high risk groups for CVD and need to intervene early for behavioral change for the low-risk group who are young and shift workers. Age, and work environment should be considered in planning for targeted preventive interventions for industrial workers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.21-35
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• 2022

With the support of local and central governments, various incentive policies for "green" cars have been established, and the number of electric vehicle users has been rapidly increasing in recent years. As a result, much attention is being given to establishing a user-centered charging infrastructure. A standard for the number of electric vehicle chargers to be supplied is being prepared based on building characteristics, but there is quite limited research on the appropriate ratio of slow and fast chargers based on the characteristics of each place. Therefore, this study derived an appropriate penetration ratio based on data about the distribution ratio of common slow chargers. These data were collected using a survey of actual electric vehicle users. Next, an analysis was done on how to categorize the needs of charging environments and to determine what criteria or characteristics to use for categorization. Based on the results of the survey analysis, three types of places were derived. Type-1 places require 10% of chargers to be slow chargers, Type-2 places require 40-60% of chargers to be slow chargers (i.e., around equal distribution of slow and fast chargers), and Type-3 places require more than 80% of chargers to be slow chargers. The required levels of slow chargers were classified by place type and by individual using latent class cluster analysis, which made it possible to categorize them into five clusters related to socioeconomic variables, vehicle characteristics, traffic, and charging behaviors. It was found that there was a high correlation between charging behavior, weekend travel behavior, gender, and income. The results and insights from this study could be used to establish charging infrastructure policies in the future and to prepare standards for supplying charging infrastructure according to changes in the electric vehicle market.