• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.253-268
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• 2020

This study examined the effectiveness of policy measures in a growth management plan by analyzing empirically the influence of regulations and incentives in a non-urban growth management plan of Sejong City using the binomial logistic model. The parcel unit data related development location of Sejong City from 2012 to 2017 was used in the model. The analysis showed that time regulation in the growth management plan has a negative (-) impact on the spread of development, which means it is effective in slowing urban sprawl by lowering the profits of developers. The time regulation applied in Sejong City needs to be used actively in other cities in Korea to prevent urban sprawl. Nevertheless, floor ratio incentives had no influence in inducing development within the growth management area, which means a new incentive policy to meet the local characteristics is needed to strengthen the effectiveness of the growth management plan. This study is meaningful because it attempted an empirical analysis of the effects of the growth management plan at The National Territory Act, and this study could encourage further studies.

• Journal of Global Scholars of Marketing Science
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• v.11
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• pp.129-146
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• 2003

This study explores whether a NBD type of model can be applied to characterize the underlying frequency distribution of online consumer's visit behavior. In this study, the following two research questions are addressed: (1) How can we characterize the underlying distribution pattern(s) of the number of repeat i i visits to a site? (2) How can consumer's Internet usages and his/her demographics affect the average number of visits to the site? Through the empirical investigation, this study found that NBD models are directly applicable to characterize the underlying distribution of visit frequency on the Internet. Furthermore, this study addresses some managerial implications for understanding how site visits are determined. Especially this study highlights the relationship between repeated visits and the visitors' Internet Usages and demographics. The proposed models are estimated and validated by online panel data that covers more than 1000 different sites and has 800,000 observations.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.84-92
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• 2019

The deterioration of the urban heat environment due to climate change and the occurrence of heat-related diseases have emerged as one of the major social problems. This has led to more research on climate change, including heat waves, but it is mainly focused on climate factors. However, the urban heat island phenomenon accelerates the summer heat wave, and the increasing trend of heat-related patients in urban areas suggests the impact of the city's environment. Thus, this study analyzed the effects of physical and social characteristics of urban areas on heat-related patients in Seoul and Gyeonggi-do. The analysis showed that the ratio of the total area of residential, commercial and industrial facilities, the main source of heat energy locality, among the land use statuses, was not statistically significant, but the road area and the green area were found to have a positive and negative The population density and the percentage of people aged 65 or older, the percentage of people living alone and the proportion of people receiving basic living were all shown to be significant, with only the ratio of elderly living alone and the ratio of population density having negative effects. The results of the study can be used to develop urban policy alternatives related to local warming patients.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2829-2840
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• 2018

The random variable with an arbitrary value or more is called semi-continuous variable or zero-inflated one in case that its boundary value is more frequently observed than expected. This means the boundary value is likely to be practically observed more than it should be theoretically under certain probability distribution. When the distribution considered is continuous, the variable is defined as semi-continuous and when one of discrete distribution is assumed for the variable, we regard it as zero-inflated. In this study, we introduce the two-part model, which consists of one part for modelling the binary response and the other part for modelling the variable greater than the boundary value. Especially, the zero-inflated regression models are explained by using Poisson distribution and negative binomial distribution. In real data analysis, we employ the zero-inflated regression models to estimate the number of days under extreme heat-wave circumstances during the last 10 years in South Korea. Based on the estimation results, we create prediction maps for the estimated number of days under heat-wave advisory and heat-wave warning by using the universal kriging, which is one of the spatial prediction methods.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.19-29
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• 2008

The main purpose of this study is to introduce Hauer's(2004) approach that overcomes current accident prediction models' limitation and to apply this approach to Korean situation using fatal accident data on motorways. After developing accident prediction models according to this approach, it is found that AADT and vertical grade could improve fitness of the model, whereas a radius of roads is not related to the number of accidents. The advantage of Hauer's approach is to reduce possibility to eliminate critical variables and to keep uncritical variables when we consider many variables to develop accident prediction models.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.100-111
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• 2015

After empirically investigating the vehicle accident risks by age groups, various programs and policies have been imposed to reduce the old's risks in other countries. In Korea, it is little known the risk level by age groups and no policy changes has been implemented even if the number of vehicle accidents occurred by the old has been rapidly rising while the total number of vehicle accidents has been decreasing. This study empirically investigates the vehicle accident risks by age groups and the results show that the old drivers over age 65 has the highest accident risk except for the young drivers below age 25. Thus, we emphasize the necessity of reinforcing the qualifications for reissuing the drive licence and programs for educating the old drivers in Korea which is facing the most rapid population aging in the world. On the other hand, various changes are needed reflecting the old drivers such as reforming the road signs, issuing a sticker and providing them incentives such that the old drivers use the public transportation instead of self-driving.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.427-434
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• 2018

As noted by Pavitt (1984) and Malerbar (2002), previous studies have focused on identifying differences in industry characteristics between the machinery industry and other manufacturing industries. This study considered quantitative and qualitative aspects of performance of product innovation in analyzing what factors determine those outcomes. In particular, this study examined stepwise selection processes embedded in innovation activities by applying a hurdle negative binomial model as well as the Heckman two-step selection model. Results show that factors affecting performance improvement and patents differ, and the threshold effect and the intensity effect of innovation were also distinguished. These results imply that the R&D capability should be enhanced and external innovation is required to be effectively embodied in the organization. Furthermore, motivating employees plays a pivotal role in this technology and skill-intensive sector.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.56-65
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• 2019

This study empirically analyzes the effect of weather on pedestrian volume in an urban space. We used data from the 2009 Seoul Flow Population Survey and constructed a model with the pedestrian volume as a dependent variable and the weather and physical environment as independent variables. We constructed 28 models and compared the results to determine the effects of weather on pedestrian volume by season, land use, and time zone. A negative binomial regression model was used because the dependent variable did not have a normal distribution. The results show that weather affects the volume of walking. Rain reduced walking volume in most models, and snow and thunderstorms reduced the volume in a small number of models. The effects of the weather depended on the season and land use, and the effects of environmental factors depended on the season. The results have various policy implications. First, it is necessary to provide semi-outdoor urban spaces that can cope with snow or rain. Second, it is necessary to have different policies to encourage walking for each season.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.