• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.223-235
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• 2011

Although ordinary least squares (OLS) regression analysis can be used to examine the spatial relationships between environmental equity and urban quality of life, this global method may mask the local variations in the relationships between them. These geographical variations can not be captured without using local methods. In this context, this paper explores the spatially varying relationships between environmental equity and urban quality of life across the Atlanta metropolitan area by geographically weighted regression (GWR), a local method. Environmental equity and urban quality of life were quantified with an integrated approach of GIS and remote sensing. Results show that generally, there is a negatively significant relationship between them over the Atlanta metropolitan area. The results also suggest that the relationships between environmental equity and urban quality of life vary significantly over space and the GWR (local) model is a significant improvement on the OLS (global) model for the Atlanta metropolitan area.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.57-63
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• 2004

본 연구에서는 3차원 심근조직에서의 회귀성파동에 대한 수치적 해석결과를 제시한다. 심근 조직에서의 회귀성파동은 심실세동(ventricular fibrillation)의 원인으로 지목되고 있으며 심근세포 이온채널 또는 전기전도시스템 등과 같은 여러 가지 요소들이 관련된 복합적 현상으로 생각되고 있다. 지금까지 이에 관한 많은 연구가 전기생리학적 모델을 이용하여 이루어진바 있으며, 주로 동물 심근세포모델에 기반으로 균일한 2차원 또는 3차원 모델에서의 전기전도 현상 해석을 한 바 있다. 그러나 실제 심장조직의 경우, 두께를 가진 3차원적 형상을 지니고 있으며 층을 따라서 전기생리학적으로 상이한 특성을 가진 세포들로 구성된다. 즉 심근은 층을 가로질러 Epi-cardiac, mid-cardiac, endo-cardiac cell들로 구성되며 각기 다른 APD(action potential duration)을 가지고 있다. 따라서 본 연구에서는 이러한 세가지 종류의 인체 심근세포모델을 사용한 3차원 심근조직에서의 활동전위 전도현상에 대한 결과를 제시한다. 이를 위하여 기존의 인체 3가지 종류의 심근세포 모델을 구현하여 그 타당성을 검토한다. 그리고 이를 바탕으로 3차원 조직모델을 구현하는데, simplified bidomain방법을 사용하였다. 3차원 공간상에서 심근세포에 의한 활동전위 전달현상을 해석하기 위하여 유한요소법을 도입한다. 최종적으로는 3가지의 심근세포층을 가진 3차원 심근조직을 구성하고, 여기에 회귀성 파동을 유도한다. 그리고 단일층으로 이루어진 3차원조직에서의 결과와 비교 분석하여 다세포층에 의한 불균일 효과를 분석하였다.

• Annual Conference of KIPS
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• 2010.11a
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• pp.471-473
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• 2010

본 논문은 선형회귀(LR: Linear Regression)와 국부적인 방사기저함수 네트워크(RBFN: Radial Basis Function Networks)를 결합한 점진적인 모델(incremental model)의 설계와 관련되어진다. 전형적인 RBFN에 의한 모델링과는 달리, 제안된 방법의 근본적인 원리는 두 단계에 의해 고려되어진다. 첫째, 전체 모델의 설계과정에서 전역적인 모델로써 선형회귀에 의해 데이터의 선형부분을 구축한다. 다음으로, 모델링 오차는 오차가 존재하는 국부적인 공간에서 RBFN에 의해 보상되어진다. 여기서, 오차의 분포로부터 RBFN을 설계하기 위해 컨텍스트 기반 퍼지 클러스터링(CFC: Context-based Fuzzy Clustering)를 통해 정보입자의 형태로 구축되어진다. 실험은 자동차 mpg 연료소비량 예측과 부동산 가격예측문제를 통해 제안된 방법의 우수성을 증명한다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.173-183
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• 2017

We investigate the factors affecting the price of apartments using the spatial and temporal data of private real estate prices. The factors affecting the price of apartment were analyzed using geographical and temporal weighted regression (GTWR) model which incorporates the temporal and spatial variation. In contrast to the OLS, a general approach used in previous studies, and GWR method which is most widely used for analyzing spatial data, GTWR considers both temporal and spatial characteristics of the house price, and leads to better description of the house price determination. Year of construction and floor area are selected as the significant factors from the analysis, and the house price are affected by them temporally and geographically.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.65-76
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• 2017

The purpose of this study was to present a prediction model that reflects crime risk area analysis, including factors and spatial characteristics, as a precursor to preparing an alternative plan for crime prevention and design. This analysis of criminal cases in high-risk areas revealed clusters in which approximately 25% of the cases within the study area occurred, distributed evenly throughout the region. This means that using a multiple linear regression model might overestimate the crime rate in some regions and underestimate in others. It also suggests that the number of deserted houses in an analyzed region has a negative relationship with the dependent variable, based on the multiple linear regression model results, and can also have different influences depending on the region. These results reveal that closure signs in a study area affect the dependent variable differently, depending on the region, rather than a simple or direct relationship with the dependent variable, as indicated by the results of the multiple linear regression model.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.917-934
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• 2014

Geographically weighted regression(GWR) model has been widely used to estimate spatially heterogeneous real estate prices. The GWR model, however, has some limitations of the selection of different price determinants over space and the restricted number of observations for local estimation. Alternatively, the geographically weighted LASSO(GWL) model has been recently introduced and received a growing interest. In this paper, we attempt to explore various local price determinants for the real estate by utilizing the GWL and its applicability to forecasting the real estate price. To do this, we developed the three hedonic models of OLS, GWR, and GWL focusing on the sales price of apartments in Seoul and compared those models in terms of model fit, prediction, and multicollinearity. As a result, local models appeared to be better than the global OLS on the whole, and in particular, the GWL appeared to be more explanatory and predictable than other models. Moreover, the GWL enabled to provide spatially different sets of price determinants which no multicollinearity exists. The GWL helps select the significant sets of independent variables from a high dimensional dataset, and hence will be a useful technique for large and complex spatial big data.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.39-48
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• 2011

Global warming causes the climate change and makes severe damage to ecosystem and civilization Carbon dioxide greatly contributes to global warming, thus many studies have been conducted to estimate the forest biomass carbon stock as an important carbon storage. However, more studies are required for the selection and use of technique and remotely sensed data suitable for the carbon stock estimation in Korea In this study, the aboveground forest biomass carbon stocks of Danyang-Gun in South Korea was estimated using $k$NN($k$-Nearest Neighbor) algorithm and regression model, then the results were compared. The Landsat TM and 5th NFI(National Forest Inventory) data were prepared, and ratio images, which are effective in topographic effect correction and distinction of forest biomass, were also used. Consequently, it was found that $k$NN algorithm was better than regression model to estimate the forest carbon stocks in Danyang-Gun, and there was no significant improvement in terms of accuracy for the use of ratio images.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.195-211
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• 2004

The innovation of information and communication technology has brought the emergence of the digital economy in which the growing importance of the Internet for the production and consumption of information has caused a rapid increase of commercial domains. Domains are basic form of Internet address for the delivery of information, but the location of registered commercial domains is not free from a spatial context. Utilizing a database of commercial domain registrations, spatial statistical methods and GIS, the spatial dimensions of the commercial domains are explored for the city of Seoul. Through this research, it was found that the commercial domains were unevenly distributed, namely 44% of commercial domains are located at 3 Gus in Seoul. The locations of commercial domains by themselves represented a strong spatial autocorrelation among adjacent places. In order to identify factors affecting spatial variation in the development of the commercial domains among Dongs, a conditional spatial autoregressive model which effectively eliminates a spatial autocorrelation was used. As a result of this research, it is clearly shown that the selective location of firms having commercial domains and their role in economic activities are influencing the spatial structure of urban with dynamic mix of spatial characteristic.

• Korean Institute of Interior Design Journal
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• v.14 no.2 s.49
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• pp.217-224
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• 2005

The lack of objective decisive criteria and the absence of analyzing tools accrued from the experiments on various types developed from space design process makes it difficult to select and execute alternatives for them. As an attempt of coping with these problems, the aims of this study is to establish space analysis' models and to propose possibility of analyzing models by utilizing the technology of image process. It is now under study in the field of artificial intelligence based on the accomplishment of digital images. This study focused on establishment an analysis model based on accomplished digital images and image processing framework. It helps utilize various processing technologies that are currently in use of image processes, and problems of the study can be supplemented through further follow-up studies. Finally, analysis model can be constructed gradually huge design data in the analogue data to the digital image database and be proposed with index in design or evaluation step.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.85-91
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• 2006

Recently, slope failures are happen to natural disastrous when they occur in mountainous areas adjoining highways in Korea. The accidents associated with slope failures have increased due to rapid urbanization of mountainous areas. Therefore, Regular maintenance is essential for all slope and needs maintenance of road safety as well as road function. In this study, we take priority of making a database of risk factor of the failure of a slope before assesment and analysis. The purpose of this paper is to recommend a standard of Slope Management Information Sheet(SMIS) like as Hazard Map. The next research, we suggest to pre-estimated model of a road slope using Logistic Regression Model.