• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.65-77
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• 2018

It is very important to understand the factors affecting the occurrence of vacant houses in research on them. The purpose of this study is to analyze the factors affecting the rural vacancy occurrence. This study set 121 research areas and selected eight independent variables (Aged house rate, housing transaction rate, house diffusion ratio, local extinction index, net migration rate, regional aging index, the ratio of the number of employees to population and financial independence rate) and one dependent variable (vacant house rate). As a result of the study, first, both Model 1 for the entire general agricultural fishing village area and Model 2 for the county (gun) area were statistically significant, there was no problem with the independence of residual. Second, local extinction index and aged house rate had a statistically significant positive (+) relationship in both Model 1 and Model 2. Third, diffusion ratio of house had a statistically significant positive (+) relationship only in Model 1, and housing transactions rate had a statistically significant negative (-) relationship in Model 2. The implications of the study were drawn as follows: First, the increase in the house diffusion ratio without growth in households and population suggests the increase of the probability of the vacancy occurrence in the area, and the higher the aged house rate, the higher the probability of the vacancy occurrence. Second, for the revitalization of housing transactions, it is necessary to have an investment inflow in the area for mid- to long-term development. Third, local extinction index has a significant relationship with vacant house rate, it is necessary to introduce a local revitalization policy from a long-term perspective for the permanence of the area.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.173-179
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• 2006

In order to explain a U-shape pattern of stock returns, Fama and French(1988) suggested the state-space model consisting of I(1) permanent component and AR(1) stationary component. They concluded the autoregression coefficient induced from the state-space model follow the U-shape pattern and the U-shape pattern of stock returns was due to both negative autocorrelation in returns beyond a year and substantial mean-reversion in stock market prices. However, we found negative autocorrelation is induced under the assumption that permanent and stationary noise component are independent in the state-space model. In this paper, we derive the autoregression coefficient based on ARIMA process equivalent to the state-space model without the assumption of independency. Based on the estimated parameters, we investigate the pattern of the time-varying autoregression coefficient and conclude the autoregression coefficient from the state-space model of ARIMA(1,1,1) process does not follow a U-shape pattern, but has always positive sign. We applied this result on the data of 1 month retums for all NYSE stocks for the 1926-85 period from the Center for Research in Security Prices.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.13-26
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• 2011

In order to explain the U-shaped pattern of autocorrelations of stock returns i.e., autocorrelations starting around 0 for short-term horizons and becoming negative and then moving toward 0 for long-term horizons, researchers suggested the use of a state-space model consisting of an I(1) permanent component and an AR(1) stationary component, where the two components are assumed to be independent. They concluded that auto-regression coefficients derived from the state-space model follow a U-shape pattern and thus there is mean-reversion in stock prices. In this paper, we show that only negative autocorrelations are feasible under the assumption that the permanent component and the stationary component are independent in the state-space model. When the two components are allowed to be correlated in the state-space model, we show that the sign of the auto-regression coefficients is not restricted as negative. Monthly return data for all NYSE stocks for the period from 1926 to 2007 support the state-space model with correlated noise processes. However, the auto-regression coefficients of the ARIMA process, equivalent to the state-space model with correlated noise processes, do not follow a U-shaped pattern, but are always positive.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.96-111
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• 2016

Effort to reduce energy consumptions or CO2 emissions is global trend. To follow this trend, spatial studies related to characteristics affecting energy consumptions or CO2 emissions have been conducted, but only with the focus on spatial dependence, not on spatial heterogeneity. The aim of this study is to investigate spatial heterogeneity patterns of CO2 emission based on socio-economic factors, land-use characteristics and traffic infrastructure of Seoul city. Geographically Weighted Regression (GWR) analysis was performed with 423 administrative district data in Seoul. The results suggest that population and employment densities, road density and railway length in most districts are found to have positive impact on the CO2 emissions. Residential and green area densities also have the highest positive impact on CO2 emissions in most districts of Gangnam-gu. The resulting model can be used to identify the spatial patterns of CO2 emissions at district level in Seoul. Eventually it can contribute to local energy policy and planning of metropolitan area.

• Land and Housing Review
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• v.11 no.1
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• pp.79-86
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• 2020

The number of business closure is one of key indicators diagnosing the status of local economy. The increases in closure are attributed to various endogenous/exogenous factors such as decreases in sales of stores, decline of local market, deterioration of global financial condition, but it is not trivial task to figure out the cause and effect mechanism among variables. The effects of those factors are expected to show geographical variations, which the empirical analysis results in this study presented. As such, the objective of this study is to estimate the effects of variables on increase in the number of business closure and examine the distributional properties of the geographic variations of the effects among spatial units of analysis. To this end, GWR (Geographically Weighted Regression) model was utilized to draw empirical analysis outcomes. It is expected that the outcomes of the sort in this research may be useful in aiding decision-making process of drafting locality-specific policies and/or deciding where to prioritize the limited public resources available.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.49-66
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• 2018

The purpose of this study is to analyze neighborhood environmental factors affecting bicycle accidents and accidental severity in Seoul, Korea. The use of bicycles has increased rapidly as daily transportation means in recent years. As a result, bicycle accidents are also steadily increasing. Using Traffic Accident Analysis System (TAAS) data from 2015 to 2017, this study uses negative binomial regression analysis to identify neighborhood environmental factors affecting bicycle accidents and accidential severity. The main results are as follows. First, bicycle accidents are more likely to occur in commercial and mixed land use areas where pedestrians, bicycle and vehicles are moving together. Second, bicycle accidents are positively associated with road structures such as four-way intersection. In contrast, three-way intersection is negatively associated with serious bicycle accidents. The density of speed hump or street tree is negatively associated with bicycle accidents and accidential severity. This finding indicates the effect of speed limit or street trees on bicycle safety. Fourth, bicycle infrastructures are also important factors affecting bicycle accidents and accidential severity. Bicycle-exclusive roads or bicycle-pedestrian mixed roads are positively associated with bicycle accidents and accidential severity. Finally, this study suggests policy implications to improve bicycle safety.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.224-224
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• 2022

물부족, 수질오염, 조류발생 등 효율적 물관리를 위해서는 물정보 통합이 필요하지만 부처별/목적별로 개별 생산·관리되어 물관리 현안에 효과적으로 대응하기 어려운 실정이다. 물관리 현안 대응 의사결정을 위해서는 현재 상황에 대한 정확한 인식과 장래(1,3개월) 수자원 상황을 고려한 예측·분석체계 구축 필요하며, 이를 위해서는 수원별 가용수량, 지역별 물사용량 및 회귀수량 등 지자체, 유역, 하천을 연계한 실제 물이용 정보 기반의 물배분 현황 분석체계 구축이 필요하다. 본 연구에서는 물수급 관련 수요·공급 시설의 위치를 연결하는 물수급 분석 알고리즘 개발을 통해 지형공간정보의 위상(topology) 관계를 설정하여 물수급 분석의 계산순서를 선정하고, 시계열 DB를 입력하여 전국 약 40만개 이상의 일단위 물수급 분석 정보생산체계를 구축하였다. 본 연구에서 개발된 물수급 분석 모형은 향후 물관련 이슈 지역의 용수공급능력 평가 및 디지털트윈 등 다양한 수자원 정책평가에 활용될 것으로 기대된다.

• Korea Trade Review
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• v.47 no.2
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• pp.175-193
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• 2022

This study analyzes the effects of road transportation networks on the local economy in korea. The analysis methods are SNA and spatial panel regression model. The subjects of this study are inland areas of Korea, and the research period is from 2010 to 2019. The network analysis showed that the connection centrality of Gyeongg-do was high internally and externally. Gyeonggi-do has played a central role in the domestic road freight transportation industry. The results of spatial panel regression analysis showed that there was economic competition between regions. Domestic road transportation industry has been competitive among regions and has economic ripple effect. And Internal cargo has been shown to boost the economy of the region. But internal cargo has been shown to lower the economy of surrounding regions, but external cargo has been shown to increase the economy. In order to revitalize the local economy, it is necessary to increase road cargo.

• Korea Trade Review
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• v.47 no.3
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• pp.157-175
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• 2022

This study calculates agglomeration indices of manufacturing specialization and diversification in different regions of South Korea. Two types of agglomeration indices are introduced into the spatial durbin model (SDM) to analyzes the effects of manufacturing agglomeration in Korea on CO₂ emission intensity. The subjects of this study are 17 regions of South Korea , and the research period is from 2013 to 2019. This study also uses partial differential to analyze the direct and spillover effect of specialization and diversification agglomeration on CO₂ emission intensity. From the perspective of direct effect, the results reveal that specialization agglomeration is an important factor contributing to Korea's CO₂ emissions. However, diversification agglomeration has an obvious CO₂ emission reduction effect. From the perspective of spillover effect, this study finds that specialization agglomeration in one region can also contribute to CO₂ emissions in nearby regions. However, the development of diversification agglomeration in one region can have CO₂ emission reduction spillover effect on neighboring regions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1D
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• pp.61-69
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• 2012

The purpose of this study is to draw the development direction of subway adjacent area by distance through an analysis on influence factors of land value by urban spatial constitution. The method of this study is analyzing influence factors of land value by the distance of subway adjacent area by using regression analysis method with urban spatial constitution variables from advanced research and drew the difference and extent of land value influence factors by distance of subway adjacent area. The result of analyzed the influence factors of land value by distance to draw the purpose of this study, which is the direction of development by distance of subway adjacent area, the connected area has shown the development direction as high density development centrally on commercial and improvement of accessibility and decrepit status, the directly influence area shows as complexity with high density centrally on commercial and business and lastly, the in directly influence area has shown the development direction as complexity of residential and commercial, improvement of accessibility and decrepit status. This study has used land value as a dependent variable to verify the speculation of this study based on the contents, the results of the analyze and the critical points of precedent studies to draw the development direction of subway adjacent influence area by using autonomous variables from advanced research. Upon this, drew a conclusion of this study as analyze of influence factors of land value could be a reference material for development of subway adjacent area.