• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.4077-4085
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• 2013

The purpose of this study is to analyze relationships between compact city development and residents' shopping trips in Seoul. Compact city planning factors are classified into land use and traffic environment. The main data source used for this research is 2006 Household Travel Survey data, then a statistic analysis was carried out by applying random intercept logit model. Analysis shows that a high level of residential density increases residents' local shopping. Also, a high level of residential density and land use mix results in more uses of public transportation, bicycle and walking for shopping. Also, more access to public transportation leads to more use of public transportation for shopping. Therefore, compact city development will have a positive impact on activating the use of public transportation, bicycle and walking for shopping.

• Journal of the Korean Regional Science Association
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• v.31 no.2
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• pp.29-45
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• 2015

Urban household's interest in moving to rural area after retirement have been increasing. Most of them live in rural areal for the sake of pleasant natural environment such as fresh air, clean water. The purpose of the study is to analyse characteristics of them and factors affecting their decision. In 2010, about 27% of urban households wish to migrate to rural area after retirement. The results from the random intercept binary logit model implies that 40~50 age, less high-school graduate and middle-income households are more likely to move. And households are more concerned with residential environment-noise, air, water- than house condition. Also, more people have moved to rural in the region. more households wish to move. It implies that information about urban-to-rural migration and life in rural area affect people's positive attitude to move to rural after their retirement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4593-4601
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• 2013

The purpose of this study is to analyze neighborhood's land use and transit accessibility affecting residents' commuting trips through a case study of Seoul. The main data source used for this research is 2010 Household Travel Survey data from which 34,071 observations were selected as the final sample. Then a statistic analysis was carried out by applying random intercept logit model. Analysis shows that a high level of residential density, land use mix in neighborhood results in more use of subway for commuting. And higher access to subway station leads to more use of subway. Therefore, a high dense and mixed use development as well as a high accessibility to transit station can contribute to activating the use of public transportation for commuting. Finally, the walking range of subway station affecting transit mode for commuting is estimated at between 432 to 525m.

• 한국노년학
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• v.29 no.4
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• pp.1413-1425
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• 2009

The objective of the work is to analyze the factors affecting on changes in filial piety responsibility expectation. For the analysis, this study focuses on the two factors-aging effect and cohort effect. This work analyzes the 4 wave Hallym Aging Panel Data with random intercept model. In the study cohort is divided by the criteria of birth year 1940. and the former cohort is called colony-war cohort and the latter cohort is called industrialization-democratization cohort. The results are in following. First, older cohort shows higher filial piety responsibility expectation score than younger cohort. Second, age shows no relationship with filial responsibility expectation score. Third, male and resident in rural area shows higher score. Forth income, year of schooling, and subjective health show negative relationship with responsibility score.

• Journal of Korea Planning Association
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• v.54 no.5
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• pp.129-138
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• 2019

This study aims to empirically examine the relationship between building energy consumption and building and local factors in Seoul. Building energy issue is an important topic for low carbon and eco-friendly city development. Building physical, socio-economic and environmental factors effect to increasing or decreasing energy consumption. However, there are different characteristic in each area, and this kind of variable has a hierarchical structure. The multi-level model was used to consider the hierarchical structure of the variables. In this study, a multi-level model was applied to confirm the difference between areas. Spatial area is Seoul, Korea and the temporal scope is August, summer season. As the result, in Model 1 (Null Model), ICC is 0.817. This shows that the energy consumption differs by 8.174% due to factors at the Dong level. Model 2 (Random Intercept Model) suggests that building's physical factors and Average age, Household size and Land price in Dong level have significant effects on Building energy consumption. In Model 3 (Random Coefficient Model), random effect variables have intercepts and slopes to vary across groups. This study provides a perspective for policy makers that the building energy reduction policies to be applied for buildings should be differently applied on area. Furthermore, not only physical factors but also socio-economic and environmental factors are important when making energy reduction policy.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.35-48
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• 2018

The study of air defense against North Korean tactical ballistic missiles (TBM) should consider the rapidly changing battlefield environment. The study for target re-designation for intercept missiles enables effective operation of friendly defensive assets as well as responses to dynamic battlefield. The researches that have been conducted so far do not represent real-time dynamic battlefield situation because the hit probability for the TBM, which plays an important role in the decision making process, is fixed. Therefore, this study proposes a target re-designation algorithm that makes decision based on hit probability which considers real-time field environment. The proposed method contains a trajectory prediction model that predicts the expected trajectory of the TBM from the current position and velocity information by using random forest and moving window. The predicted hit probability can be calculated through the trajectory prediction model and the simulator of the intercept missile, and the calculated hit probability becomes the decision criterion of the target re-designation algorithm for the missile. In the experiment, the validity of the methodology used in the TBM trajectory prediction model was verified and the superiority of using the hit probability through the proposed model in the target re-designation decision making process was validated.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.7
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• pp.931-936
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• 2009

Body weights of 862 Angora goats between birth and 36 months of age, recorded on a semiyearly basis from 1988 to 2000, were used to estimate genetic, permanent environmental and phenotypic covariance functions. These functions were estimated by fitting a random regression model with 6th order polynomial for direct additive genetic and animal permanent environmental effects and 4th and 5th order polynomial for maternal genetic and permanent environmental effects, respectively. A phenotypic covariance function was estimated by modelling overall animal and maternal effects. The results showed that the most variable coefficient was the intercept for both direct and maternal additive genetic effects. The direct additive genetic (co)variances increased with age and reached a maximum at about 30 months, whereas the maternal additive genetic (co)variances increased rapidly from birth and reached a maximum at weaning, and then decreased with age. Animal permanent environmental (co)variances increased with age from birth to 30 months with lower rate before 12 months and higher rate between 12 and 30 months. Maternal permanent environmental (co)variances changed little before 6 months but then increased slowly and reached a maximum at about 30 months. These results suggested that the contribution of maternal additive genetic and permanent environmental effects to growth variation differed from those of direct additive genetic and animal permanent environmental effects not only in expression time, but also in action magnitude. The phenotypic (co)variance estimates increased with age from birth to 36 months of age.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.291-309
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• 2023

Longitudinal count data has been widely collected in biomedical research, public health, and clinical trials. These repeated measurements over time on the same subjects need to account for an appropriate dependency. The Poisson regression model is the first choice to model the expected count of interest, however, this may not be an appropriate when data exhibit over-dispersion or under-dispersion. Recently, Conway-Maxwell-Poisson (CMP) distribution is popularly used as the distribution offers a flexibility to capture a wide range of dispersion in the data. In this article, we propose a Bayesian CMP regression model to accommodate over and under-dispersion in modeling longitudinal count data. Specifically, we develop a regression model with random intercept and slope to capture subject heterogeneity and estimate covariate effects to be different across subjects. We implement a Bayesian computation via Hamiltonian MCMC (HMCMC) algorithm for posterior sampling. We then compute Bayesian model assessment measures for model comparison. Simulation studies are conducted to assess the accuracy and effectiveness of our methodology. The usefulness of the proposed methodology is demonstrated by a well-known example of epilepsy data.

• Restorative Dentistry and Endodontics
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• v.43 no.3
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• pp.34.1-34.5
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• 2018

In the previous section, we established a simple linear regression line by finding the slope and intercept using the least square method as: ${\hat{Y}}=30.79+0.71X$. Finding the regression line was a mathematical procedure. After that we need to evaluate the usefulness or effectiveness of the regression line, whether the regression model helps explain the variability of the dependent variable. Also, statistical inference of the regression line is required to make a conclusion at the population level, because practically, we work with a sample, which is a small part of population. Basic assumption of sampling method is simple random sampling.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.12-28
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• 2019

Road geometry is one of the many factors that cause crashes, but the effect on traffic accident depends on weather conditions even under the same road geometry. This study identifies the variables affecting the crash severity by matching the highway accident data and weather data for 14 years from 2001 to 2014. A hierarchical ordered Logit model is used to reflect the effects of road geometry and weather condition interactions on crash severity, as well as the correlation between individual crashes in a region. Among the hierarchical models, we apply a random intercept model including interaction variables between road geometry and weather condition and a random coefficient model including regional weather characteristics as upper-level variables. As a result, it is confirmed that the effects of toll, ramp, downhill slope of 3% or more, and concrete barrier on the crash severity vary depending on weather conditions. It also shows that the combined effects of road geometry and weather conditions may not be linear depending on rainfall or snowfall levels. Finally, we suggest safety improvement measures based on the results of this study, which are expected to reduce the severity of traffic accidents in the future.