• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.466-466
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• 2015

지역빈도해석은 수문학에서 오랜 역사를 갖고 있으며, 수년에 걸쳐 수문학적 변량의 정량적 추정을 위해 다양한 접근방법들이 제안되어 왔다. 그러나 제안된 방법들의 가설설정 수준이 높기 때문에 실제 적용에 제약이 많고, 적용 시에도 예측에 대한 불확실성이 높은 문제점이 있다. 본 연구에서는 이러한 문제점을 개선하기 위한 방법으로 계층적 베이지안 모델을 이용한 지역빈도해석 모형을 제안하고자 한다. 본 모형은 2개의 계층적 구조로 구성된다. 첫번째 계층은 재현기간별 GEV 분포의 매개변수를 정규화하여 주변분포로 설정하고, Kriging 기법을 이용하여 지형학적, 기상학적 정보들과 극치강수량 효과를 적합시켜 공간적 이질성과 미계측 유역에 대한 효과적인 보간을 가능하게 한다. 두번째 계층은 지점의 특성을 나타내는 매개변수들간의 공분산을 Bayesian 모델에 연계하여 매개변수들의 공간적 변동성을 나타낸다. 2개 계층의 결합확률분포는 MCMC 기법을 이용하여 예측값에 대한 불확실성을 정량적으로 분석하게 된다. 본 모형을 통해 홍수량 추정 시 필요한 시간 단위 극치강수량의 공간적 분포를 효과적으로 추정할 수 있을 것으로 판단된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.68-79
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• 2021

The recent occurrence of vacant houses in urban areas is a remarkable social problem. One of the physical declines, the occurrence of vacant houses, accelerates various social and economic declines, such as a decline in population and a slump in the commercial district. Vacant houses have regional characteristics and spatial influence, and it is necessary to approach them locally in order to grasp the exact status of vacant houses. Therefore, in this study, the effect of urban decline on the occurrence of vacant homes was examined by region using global Moran's I and Geographic Weighted Regression(GWR) model. As a result of the analysis, there were spatial autocorrelation and heterogeneity in the occurrence of vacant houses in each eup·myeon·dong, Busan metropolitan city. In addition, there is a difference in the influence of each variable of urban decline on the occurrence of vacant houses, and even the same variable of urban decline has different effects on the occurrence of vacant houses in different regions. Therefore, it is expected that a more efficient vacant home management plan can be presented if the GWR model is used to analyze the coefficient values differentiated by region and categorize the occurrence of vacant houses.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.151-160
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• 2019

The study of spiders that function as predators in agroecosystem can broaden the understanding of agroecosystems. This study investigated the effect of heterogeneity at different spatial scales on richness and abundance of spiders in upland and paddy fields. We collected 48 samples using pitfall traps at upland and paddy fields, respectively. The total species richness of spiders estimated by sample- and coverage-based rarefaction and extrapolation curves. The total species richness was high in the upland fields at the total study sites, whereas the average species richness per study site was high in the paddy fields. We confirmed that the diversity enhancement of spiders was influenced by the structural complexity of habitat at field-scale, and crop diversity at broader scale.

• The Korean Journal of Financial Management
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• v.8 no.2
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• pp.111-129
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• 1991

본 논문에서는 재정가격결정모형(裁定價格決定模型)(Arbitrage Pricing Model)을 기초로 우리나라 주식시장에 영향을 주는 거시경제변수가 무엇인가를 찾고자 하였다. 방법론면에서는 과거변수(過去變數)(lagged variables)에 의해서만 기대치를 형성시키는 AIRMA(Autoregressike Integrated with Moving Average) 방법을 이용하기보다는 마코프속성(屬性)(Markov Property)을 갖는 상태공간모형(狀態空間模型) (State Space Model)을 이용하여 보다 합리적인 거시경제 요인의 이노베이션을 하였다. 또한 단순한 요인분석(要因分析)(factor analysis)에 의한 요인추출은 요인의 표본의존성(標本依存性)(Sample dependency)이 심하므로 그룹간 요인분석(inter-battery factor analysis)을 행하여 추정(推定)된 요인(要因)(요인값 : factor score)과 요인수를 결정하여 관련 거시경제변수를 선택한다. 그룹간 요인분석을 위한 그룹을 형성할 때 그룹내에서는 동질성을 그룹간에는 이질성을 최대한 살리는 것이 필요한데, 이를 위해 군집분석(群集分析)(Cluster Analysis)을 사용한 것이 특징이다. 결론적으로 우리나라 주식시장에 영향을 미치는 거시경제요인(巨視經濟要因)으로 단위노동비율, 제조업제품재고지수, 채권프리미엄, 수출물가지수, 정부부문 통화공급, 회사채수익률, 종합주가지수 등 7가지가 있는 것으로 분석되고 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.34-34
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• 2018

본 연구는 다목적 유전자 알고리즘 Non-Dominated Sorting Genetic Algorithm II (NSGA-II)를 활용하여 자동 검보정 알고리즘을 개발하고, 이를 준분포형 수문모형인 SWAT (Soil and Water Assessment Tool) 모형에 적용하여 평가하고자 한다. 집중형 모형과 달리, 분포형 모형은 유역 내 다양한 물리적 변수와 공간 이질성(spatial heterogeneity)을 표현하기 위한 많은 매개변수를 포함하고 있고, 최근에는 기후 변화와 장기 가뭄과 같은 이상 기후에 따른 물 부족, 수질 오염 및 녹조 현상 등을 고려하기 위해 매개변수의 시간적인 변동성을 고려하기 위한 연구도 수행되고 있다. 이에 따라 본 연구에서 개발한 다목적 알고리즘은 다양한 매개변수의 시공간적 특성을 고려할 수 있도록 작성되었으며, Python으로 개발하여 타 모형으로의 확장성 및 범용성을 고려하였다. SWAT 모형의 유출 해석은 결정계수(Coefficient of determination, $R^2$), RMSE(Root mean square error), 모형 효율성 계수(Nash-Sutcliffe efficiency, NSE) 및 IOA(Index of agreement) 등을 활용해 기존 연구 결과와 비교분석할 수 있도록 하였으며, 사용자의 선택에 따라 다른 목적함수 또한 활용할 수 있도록 하였다. NSGA-II를 활용한 SWAT 모형의 유출 해석은 다목적 함수를 고려함에 따라 실측값과 높은 상관성을 보여줄 것으로 판단되며, 이상 기후 기간 설정에 따른 유동적인 매개변수 변화를 적용할 수 있을 것으로 기대된다.

• Journal of Korea Spatial Information System Society
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• v.2 no.2 s.4
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• pp.49-58
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• 2000

With the growing realization that the GIS data management becomes important more than ever, a great deal of the spatial data is being digitalized through various GIS projects, such as NGIS project. However, the integrated search and analysis for GIS data are highly constrained by the databases being heterogeneous and distributed among organizations. This paper is to introduce the system developed to solve the problem of interoperability in the heterogeneous and distributed databases environment. The system architecture is presented which is composed of spatial data servers, nodes, and a gateway. Also, the paper provides the implementation details of the client application to access and analyze the distributed and heterogeneous spatial data with the standardized interface. Finally, based on the technical architecture, the korea spatial data infrastructure is explained.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.77-84
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• 2015

The key objective of this research is to review the effectiveness of spatial regression to identify the influencing factors of spatial distribution patterns of floating population. To this end, global and local spatial autocorrelation test were performed using seoul floating population survey(2014) data. The result of Moran's I and Getis-Ord $Gi^*$ as used in the analysis derived spatial heterogeneity and spatial similarities of floating population patterns in a statistically significant range. Accordingly, Geographically Weighted Regression was applied to identify the relationship between land use attributes and population floating. Urbanization area, green tract of land of micro land cover data were aggregated in to $400m{\times}400m$ grid boundary of Seoul. Additionally public transportation variables such as intersection density transit accessibility, road density and pedestrian passage density were adopted as transit environmental factors. As a result, the GWR model derived more improved results than Ordinary Least Square(OLS) regression model. Furthermore, the spatial variation of applied local effect of independent variables for the floating population distributions.

• 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 the Korean association of regional geographers
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• v.7 no.3
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• pp.107-120
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• 2001

This study is concerned with providing a reliable procedure of calibrating a set of places specific distance parameters and with applying it to U.S. inter-State migration flows between 1985 and 1900. It attempts to conform to recent advances in quantitative geography that are characterized by an integration of ESDA(exploratory spatial data analysis) and local statistics. ESDA aims to detect the spatial clustering and heterogeneity by visualizing and exploring spatial patterns. A local statistic is defined as a statistically processed value given to each location as opposed to a global statistic that only captures an average trend across a whole study region. Whereas a global distance parameter estimates an averaged level of the friction of distance, place-specific distance parameters calibrate spatially varying effects of distance. It is presented that a poisson regression with an adequately specified design matrix yields a set of either origin-or destination-specific distance parameters. A case study demonstrates that the proposed model is a reliable device of measuring a spatial dimension of migration, and that place-specific distance parameters are spatially heterogeneous as well as spatially clustered.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.51-64
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• 2001

Schema integration is one of the most difficult issues in the heterogeneous and distributed geographic database systems (GDSs). As the use of spatial information in various application areas becomes increasingly popular, the integration of geographic information has become a crucial task for decision makers. Most existing schema integration techniques described in the database literature, however, do not address the problems of managing heterogeneities among complex objects that contain visual data and/or spatial and temporal information. The difficulties arise not only from the semantic conflicts, but also from the different representations of spatial models. Consequently, it is much more complex to achieve interoperability in the area of geographic databases. This research attempts to provide a solution to such problems. The research reported in this paper describes a schema integration methodology and a prototype toolkit developed to assist in schema integration activities for GDSs.