• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.151-168
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• 2021

This study compared and analyzed the effect of the burden of household expenditure in the metropolitan and non-metropolitan areas on the intention to childbirth intention to married women using a panel logit model. To this end, this analysis targeted married women aged 25 to 39 who are highly likely to be fertile. The main results are as follows; First of all, it was confirmed that the economic power of spouse can be an important factor in the childbirth intention regardless of region. Second, it was found that the higher the satisfaction of marriage had a positive effect on the childbirth intention, and also higher the value that children must have, the higher the childbirth intention. Third, it was confirmed that the burden of household expenditure is the most important factor in the willingness to childbirth intention, excluding factors such as the number of existing children. In particular, the burden on education spending in both the capital region and non-capital region was found to be a key reason for the decrease in the childbirth intention. Lastly, the burden of household expenditure showed different effects on childbirth intention on depending on the region. Specifically, in the capital region, medical spending and loans had a greater impact, while, in the non-capital region, transportation and communication costs had a greater impact on childbirth intentions. Through the results, this study confirmed the implication that easing the burden on household expenditure is continuously necessary to enhance childbirth, and that discriminatory policy approaches are required depending on the area of residence.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.933-940
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• 2018

The conversion of the internal combustion engine vehicle to the electric vehicle is suggested as a solution to the problem of global climate change and environmental pollution. Accordingly, this study was started to promote the use of electric vehicles. The purpose of this study is to identify the basic background knowledge and current status of electric vehicles in Korea and abroad, and expand from previous understanding on which factors affect ones choice on electric vehicles by considering individual characteristics and context in detail. In the analysis, a set of demand forecasting models were constructed by grouping the respondents based on the household characteristics as well as the vehicle ownership. At the time in need for better understanding of the feasibility of electric vehicles, it is expected that the research can assist the promotion of electric vehicles. In the follow-up study, I would like to continue the research on the activation of electric vehicles.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.65-79
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• 2006

The ATIS(Advance Traveler Information System), as one part of ITS, is a system aiming to disperse traffic volume on transportation networks by providing traffic information to transportation users on pre-trip and en-route trips. One of tools in ATIS is usage of VMS(Variable Message Signs). It provides to the drivers with direct information about state of processing direction. which is considered as the most effective method in ATIS. The purposes of providing VMS information are classified two categories. One is to provide simple information to drivers for their convenience. The other is to manage traffic demand to improve transportation network performance. However, for more effective and reliable VMS information, several strategies should be taken into account. The main VMS management strategy is "Traffic Diversion Strategy for minimum delay" when traffic congestion or incident are occurred. For effective operation. firstly. reasonable diversion traffic volume is determined by network traffic condition Secondly, it is necessary to make providing information strategy which reflects driver response behavior for controling diversion traffic volume. This paper focuses on the providing real-time route guidance information by VMS when congestion is occurred by the incidents. This sturdy estimates time-dependent system optimal diversion rate that inflects travel time and queue lengths using traffic flow simulation model on base Cellular Automata. In addition, route choice behavior models are developed using binary logit model for traffic information variable by traffic system controller. Finally, this study provides time-dependent VMS massage contents and degree of providing information in order to optimize the traffic flow.

• Journal of Distribution Science
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• v.12 no.4
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• pp.31-39
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• 2014

Purpose - This study investigates the following two issues. First, we attempt to find the important determinants of housing investment and to identify their significance rank using survey panel data. Recently, the expansion of global uncertainty in the real estate market has directly and indirectly influenced the Korean housing market; households demonstrate a sensitive reaction to changes in that market. Therefore, this study aims to draw conclusions from understanding how the impact of financial strength of the household is related to house investment. Second, we attempt to verify the effectiveness of diverse indices of financial strength such as DTI, LTV, and PIR as measures to monitor the housing market. In the continuous housing market recession after the global crisis, the government places top priority on residence stability. However, the government still imposes forceful restraints on indices of financial strength. We believe this study verifies the utility of these regulations when used in the housing market. Research design, data, and methodology - The data source for this study is the "National Survey of Tax and Benefit" from 2007 (1st) to 2011 (5th) by the Korea Institute of Public Finance. Based on this survey data, we use panel data of 3,838 households that have been surveyed continuously for 5 years. We sort the base variables according to relevance of house investment criteria using the decision tree model (DTM), which is the standard decision-making model for data-mining techniques. The DTM method is known as a powerful methodology to identify contributory variables for predictive power. In addition, we analyze how important explanatory variables and the financial strength index of households affect housing investment with the binary logistic multi-regressive model. Based on the analyses, we conclude that the financial strength index has a significant role in house investment demand. Results - The results of this research are as follows: 1) The determinants of housing investment are age, consumption expenditures, income, total assets, rent deposit, housing price, habits satisfaction, housing scale, number of household members, and debt related to housing. 2) The impact power of these determinants has changed more or less annually due to economic situations and housing market conditions. The level of consumption expenditure and income are the main determinants before 2009; however, the determinants of housing investment changed to indices of the financial strength of households, i.e., DTI, LTV, and PIR, after 2009. 3) Most of all, since 2009, housing loans has been a more important variable than the level of consumption in making housing market decisions. Conclusions - The results of this research show that sound financing of households has a stronger effect on housing investment than reduced consumption expenditures. At the same time, the key indices that must be monitored by the government under economic emergency conditions differ from those requiring monitoring under normal market conditions; therefore, political indices to encourage and promote the housing market must be divided based on market conditions.

• Clinics in Shoulder and Elbow
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• v.16 no.1
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• pp.63-72
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• 2013

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.27-38
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• 2002

VTIS(Value-added Traffic Information System), among the sub-systems of ATIS, is an Advanced Traffic System which innovates efficiency and safety. And this system, having marketability and publicness, is very important. Moreover, This system offers definite traffic information according to the demand of specified users. And it is expected to produce additional spread effects because of high participation rate of private sector. However, the VTIS service media are varied and there are varied optimal Prices and payment methods according to each medium. Because of that, there needs the study on these problems or optimal criteria. But because existing studies were devoted to estimate the optimal route, the study toward the optimal price which was considered part of user and service use demand do not exist. Accordingly, we surveyed under imaginary alternative pricing scenarios and forecasted the use demand of VTIS by using Binary Logit model. Also, for the users who answered that they would use VTIS service in survey, we classified their use's behaviors as four categories and estimated the use ratio to each category by using Ordered Probit model. Last, using sensitivity analysis for results form above, we derived the optimal price that is 2800won in monthly. 145won in payment per call. Then, VTIS service use rate is respectively 65%, 75%.

• Journal of the Korean Regional Science Association
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• v.33 no.3
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• pp.31-47
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• 2017

The purpose of this study is to identify determinants of economic aid regarding housing and cost of living for the newly-married households. This study applied the binary logit model to figure out the determinants of economic aid from their parents for the households. With utilizing the Newly-Married Housing Survey data in 2015, this study found that housing characteristics and level of housing expenditure leads to the economic aid from their parents. In particular, the housing price and transportation condition increase probability the financial aid from parents when the newly-married household starts their housing career. In addition, this study found that the items of housing expenditure increase the probability of economic aid for their cost of living. To improve the independence of newly-married household, the government should adopt the housing policy for stable housing price and alleviate the burden of housing expenditure. The significance of this study is analyzing the economic aid from their parents on newly-married household regarding housing economic issues and suggest the policy for independence of living from their parents.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.285-294
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• 2021

This study analyzes the factors that determine the attitude toward the electronic wristband(smartband) to check the position of self-quarantine subjects due to COVID-19. Furthermore, I analyze the interaction of class and age among the factors that determine attitudes toward the electronic wristband. In this study, the attitude toward self-quarantine electronic wristband is analyzed as a binary logit model, focusing on class and age. As a result of the analysis, the middle class significantly agreed with the self-quarantine electronic wristband compared to the lower class, and the older the person, the more in favor. On the other hand, the interaction between the class and the age shows that the age weakens the positive effect on the attitude of the self-quarantine electronic wristband in the middle and upper middle classes. The implication of this study is that it is necessary to push for mandatory electronic wristband in areas with high proportion of high-aged people with positive attitude toward self-quarantine electronic wristband and in the same age group, the approval rate is low, so it is necessary to promote mandatory electronic wristband in areas where the vulnerable class is dense.

• Land and Housing Review
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• v.13 no.1
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• pp.11-22
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• 2022

This paper estimates the demand of newly married couples for public rental housing in Chungnam. This research attempts to overcome data limitations by linking survey data with administrative data for analysis. First, the results of a binary logit model that analyzes newly married couples' intention to move into public rental housing, based on the Chungnam Social Survey 2019, reveal that residential location, educational level, housing type, and tenure type have a statistically significant effect. By combining the estimated coefficients with another dataset, the statistics of newly married couples for administration purposes acquired from Statistics Korea, this research estimates the demand for public rental housing among the newly married couples in Chungnam. The estimation results show that the total demand for public rental housing in Chungnam is 11,424 units among 43,705 newly married couples. The total demand of 21,685 newly married couples who occupy rental housing is estimated to be 9,436 units. The policy for providing public rental housing to newly married couples in Chungnam aims to increase their fertility rates. Hence, further research should be followed up to evaluate the effect of the supply of public rental housing on fertility rates. Also, a research method should be developed to control for possible endogeneity between the demand for public rental housing and childbirths.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.