• Health Policy and Management
• /
• v.26 no.4
• /
• pp.271-278
• /
• 2016

Background: This study purposed to analyze the relationship between regional obesity rates and regional variables. Methods: Data was collected from the Korean Statistical Information Service (KOSIS) and Community Health Survey in 2012. The units of analysis were administrative districts such as city, county, and district. The dependent variable was the age-sex adjusted regional obesity rates. The independent variables were selected to represent four aspects of regions: health behaviour factor, psychological factor, socio-economic factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis model, this study applied geographically weighted regression (GWR) analysis to calculate the regression coefficients for each region. Results: The OLS results showed that there were significant differences in regional obesity rates in high-risk drinking, walking, depression, and financial independence. The GWR results showed that the size of regression coefficients in independent variables was differed by regions. Conclusion: Our results can help in providing useful information for health policy makers. Regional characteristics should be considered when allocating health resources and developing health-related programs.

• The Korean Journal of Health Service Management
• /
• v.8 no.2
• /
• pp.11-22
• /
• 2014

This study purposed to analyze the regional variation of the local-out rates of inpatient services. Multiple data sources collected from National Health Insurance Corporation and statistics Korea were merged to produce the analysis data set. The unit of analysis in this study was city, Gun, Gu, and all of them were included in analysis. The dependent variable measured the local-out rate of inpatient cost in study regions. Local environments were measured by variables in three dimensions: provider factors, socio-demographic factors, and health status. Along with the traditional ordinary least square (OLS) based regression model, geographically weighted regression (GWR) model were applied to test their effects. SPSS v21 and ArcMap v10.2 were applied for the statistical analysis. Results from OLS regression showed that most variables had significant relationships with the local-out rate of inpatient services. However, some variables had shown diverse directions in regression coefficients depending on regions in GWR. This implied that the study variables might not have consistent effects and they may varied depending the locations.

• Journal of Korean Society for Geospatial Information Science
• /
• v.23 no.3
• /
• pp.77-84
• /
• 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 Association of Geographic Information Studies
• /
• v.26 no.4
• /
• pp.218-236
• /
• 2023

This study attempted to analyze the environmental equity of fine dust(PM₁₀) in Daegu using MGWR(Multi-scale Geographically Weighted Regression) and KT(Korea Telecom Corporation) sensor data. Existing national monitoring network data for measuring fine dust are collected at a small number of ground-based stations that are sparsely distributed in a large area. To complement these drawbacks, KT sensor data with a large number of IoT(Internet of Things) stations densely distributed were used in this study. The MGWR model was used to deal with spatial heterogeneity and multi-scale contextual effects in the spatial relationships between fine dust concentration and socioeconomic variables. Results indicate that there existed an environmental inequity by land value and foreigner ratio in the spatial distribution of fine dust in Daegu metropolitan city. Also, the MGWR model showed better the explanatory power than Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) models in explaining the spatial relationships between the concentration of fine dust and socioeconomic variables. This study demonstrated the potential of KT sensor data as a supplement to the existing national monitoring network data for measuring fine dust.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.1
• /
• pp.173-183
• /
• 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 Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.1
• /
• pp.31-39
• /
• 2017

The goal of this study was to efficiently analyze the relationships of the number of thefts with related factors, considering the spatial patterns of theft crimes. Theft crime data for a 5-year period (2009-2013) were collected from Haeundae Police Station. A logarithmic transformation was performed to ensure an effective statistical analysis and the number of theft crimes was used as the dependent variable. Related factors were selected through a literature review and divided into social, environmental, and defensive factors. Seven factors, were selected as independent variables: the numbers of foreigners, aged persons, single households, companies, entertainment venues, community security centers, and CCTV (Closed-Circuit Television) systems. OLS (Ordinary Least Squares) and GWR (Geographically Weighted Regression) were used to analyze the relationship between the dependent variable and independent variables. In the GWR results, each independent variable had regression coefficients that differed by location over the study area. The GWR model calculated local values for, and could explain the relationships between, variables more efficiently than the OLS model. Additionally, the adjusted R square value of the GWR model was 10% higher than that of the OLS model, and the GWR model produced a AICc (Corrected Akaike Information Criterion) value that was lower by 230, as well as lower Moran's I values. From these results, it was concluded that the GWR model was more robust in explaining the relationship between the number of thefts and the factors related to theft crime.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.241-245
• /
• 2007

국내 홍수빈도 분포의 매개변수 추정에서 지점추정(at-site estimate) 방법은 유량 자료의 부족으로 발생하는 표본오차(sampling error)가 크기 때문에 충분한 유량 자료를 보유한 지점에 한하여 제한적으로 사용되고 있다. 대안으로 동질성을 가진 유역의 유량 자료를 모아 지역 매개변수를 추정하는 지수홍수법(Index Flood Method)이 제안되기도 하였으나, 이질성이 큰 우리나라의 유역특성 때문에 적용이 쉽지 않다. Stedinger와 Tasker가 1986년 제안한 GLS(Generalized Least Square) 기법은 유역을 동질지역으로 구분할 필요가 없으며 지점들간의 상관관계와 이분산성을 고려할 수 있어, 국내 홍수빈도 해석을 위해서 꼭 도입해야할 기법으로 생각된다. 본 연구에서는 기존의 GLS 기법의 단점을 보완한 Bayesian-GLS 기법을 이용하여, 국내 대유역에 골고루 위치하며 댐의 영향을 받지 않는 31개 지점의 연최대 일유량 시계열의 L-변동계수(L-moment coefficient variation)와 L-왜도계수(L-moment coefficient skewness)를 추정할 수 있는 회귀모형을 제안하였다. 위 회귀모형을 구성하기 위한 유역특성으로는 유역면적, 유역경사, 유역평균강우 등을 사용하였다. Bayesian-GLS (B-GLS) 적용 결과를 OLS(Ordinary Least Square) 및 Bayesian-GLS 기법에서 지점간의 상관관계를 고려하지 않는 Bayesian-WLS(Weighted Least Square)와 비교 평가하여 그 우수성을 입증하였다. 따라서 본 연구에서 제안된 B-GLS에 의한 지역회귀모형은 국내의 미계측유역이나 또는 관측 길이가 짧은 계측유역의 홍수빈도분석을 위해 매우 유용할 것으로 기대된다.년 홍수 피해가 발생하고 있지만, 다른 한편 인구밀도가 높고 1인당 가용 수자원이 상대적으로 적기 때문에 국지적 물 부족 문제를 경험하고 있다. 최근 국제적으로도 농업용수의 물 낭비 최소화와 절약 노력 및 타 분야 물 수요 증대에 대한 대응 능력 제고가 매우 중요한 과제로 부각되고 있다. 2006년 3월 멕시코에서 개최된 제4차 세계 물 포럼에서 국제 강 네트워크는 "세계 물 위기의 주범은 농경지", "농민들은 모든 물 위기 논의에서 핵심"이라고 주장하고, 전 프랑스 총리 미셀 로카르는 "...관개시설에 큰 문제점이 있고 덜 조방적 농업을 하도록 농민들을 설득해야 한다. 이는 전체 농경법을 바꾸는 문제..."(segye.com, 2006. 3. 19)라고 주장하는 등 세계 물 문제 해결을 위해서는 농업용수의 효율적 이용 관리가 중요함을 강조하였다. 본 연구는 이러한 국내외 여건 및 정책 환경 변화에 적극적으로 대처하고 물 분쟁에 따른 갈등해소 전략 수립과 효율적인 물 배분 및 이용을 위한 기초연구로서 농업용수 수리권과 관련된 법 및 제도를 분석하였다.. 삼요소의 시용 시험결과 그 적량은 10a당 질소 10kg, 인산 5kg, 및 가리 6kg 정도였으며 질소는 8kg 이상의 경우에는 분시할수록 비효가 높았으며 특히 벼의 후기 중점시비에 의하여 1수영화수와 결실율의 증대가 크게 이루어졌다. 3. 파종기와 파종량에 관한 시험결과는 공시품종선단의 파종적기는 4월 25일부터 5월 10일경까지 인데 이 기간중 일찍 파종하는 경우에 파종적량은 10a당 약 8${\ell}$이고 늦은 경우에는 12${\ell}$ 정도였다. 여기서 늦게 파종한 경우 감수의 가장 큰 원인은 1수영화수가 적어지기 때문이었다. 4. 건답직파에 대한 담수상태로 관수를 시작하는 적기는 파종후

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.4_1
• /
• pp.343-351
• /
• 2014

The correlation between regional Walkability Index and their physical socio-economic characteristics has evaluated by the spatial statistical analysis to understand the urban pedestrian environments, where has been emerging the significance, recently. Following to the study, the Walkability Indexes were calculated quantitatively from two administrative districts of Busan and measured Global Local spatial autocorrelation indices. Additionally, the Geographically Weighted Regression model was applied to define the correlation between Walkability Indexes and urban environmental variables. The spatial autocorrelation values and clusters on the Walkability Indexes were derived in statistically significant level. Furthermore, the Geographically Weighted Regression model has been derived more improved inference than the OLS regression model, so as the influence of local level pedestrian environment was identified. The results of this study suggest that the spatial statistical approach can be effective on quantitative assessing the pedestrian environment and navigating their associated factors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.15 no.1
• /
• pp.29-39
• /
• 1997

The need of transformation parameters from local geodetic datums to a geocentric coordinate system is becoming more common, with the increasing application of satellite positioning techniques to LIS/GIS survey with cadastral management. In this paper, the national transformation parameters between the Korean geodetic coordinates which is based on the Bessel 1841 ellipsoid and the WGS84 ellipsoid are determined by the least square methods with weighted parameter constraints. Three-dimensional geocentric coordinates are based on GPS observation at 31 stations in the geodetic network, the datum parameters are computed within a standard deviation of less than 1 meter. In South Korea, the national transformation parameters with Bessel geoid-heights are useful for GPS baseline processing and for middle-scale map/database transformation.

• Health Policy and Management
• /
• v.26 no.1
• /
• pp.30-38
• /
• 2016

Background: This study purposed to analyze the relationship between spatial distribution of Diabetes prevalence rates and regional variables. Methods: The unit of analysis was administrative districts of city gun gu. Dependent variable was the age- and sex- adjusted diabetes prevalence rates and regional variables were selected to represent three aspects: demographic and socioeconomic factor, health and medical factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis, geographically weighted regression (GWR) was applied for the spatial analysis. Results: Analysis results showed that age- and sex-adjusted diabetes prevalence rates were varied depending on regions. OLS regression showed that diabetes prevalence rates had significant relationships with percent of population over age 65 and financial independence rate. In GWR, the effects of regional variables were not consistent. These results provide information to health policy makers. Conclusion: Regional characteristics should be considered in allocating health resources and developing health related programs for the regional disease management.