• 한국대기환경학회지
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• 제27권5호
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• pp.545-557
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• 2011

The objective of this study was to estimate the trends of each pollutant using the air pollution monitoring networks data from January 2005 to December 2008 in Daegu area. Also, the spatial characteristics of each pollutant were determined using the Pearson correlation coefficients and COD (coefficients of divergence). In this study, the trends of hourly, monthly, seasonal, and total average concentrations of each pollutant for the 10 sites were analyzed. The Ihyeon site showed highest concentration for the $SO_2$, $NO_2$, and PM10}. In the case of $O_3$, the Jisan site showed highest concentration among the other sites. Also, industrial area presented highest concentration for the $SO_2$, CO, and PM10. On the other hand, $NO_2$ showed highest in commercial area. The IDW (inverse distance weighting) method was used to estimate characteristics of spatial distribution. The results provide identify spatial distribution for each pollutant. Also, the Pearson correlation coefficients and COD values provide spatial variability among the monitoring sites. The COD of each pollutant showed very low values for all of the sites pairs. On the other hand, the Pearson correlation coefficients showed high values for all of the sites pairs. Finally, analysis of spatial variability can be used to characterize the spatial uniformity and similarity of concentrations from each pollutant.

• 재활치료과학
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• 제6권1호
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• pp.11-23
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• 2017

편측 공간무시는 주로 우측 대뇌반구의 병변으로 인한 뇌졸중 이후 발생하는 신경학적 질환으로, 병변 반대 측 신체와 공간에 대한 처리 기능과 주의집중에 문제가 발생한다. 기능적 신경이미지 연구에서 편측 공간무시는 큰 수준의 중대뇌 동맥, 페리실비안 연결망, 주의집중 연결망의 손상과 연관성이 있다고 보고하였다. 편측 공간무시는 부정적인 예후와 관련이 있기 때문에 정확한 진단과 중재를 위해 편측 공간무시의 유형과 이론 그리고 전통적 평가와 기능적 평가를 포함한 임상적 평가가 고려되어야 한다. 편측 공간무시의 치료는 하향식 접근방법과 상향식 접근방법으로 구분하며, 두 접근방법을 결합하는 형태가 가장 효과적일 수 있다. 편측 공간무시의 모든 최신 중재방법 중에서 프리즘 적응이 가장 적절한 중재법으로 연구되고 있으나, 편측 공간무시의 유형과 병변 위치 등을 고려하여 환자에게 적절한 중재를 적용하는 것이 중요하다.

• 대한공간정보학회지
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• 제24권4호
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• pp.115-123
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• 2016

지난 2012년부터 도로명주소가 본격 시행 및 활용되고 있지만, 아직도 상당 부분에서는 기존의 지번주소가 많이 통용되고 있다. 이는 일반인들의 공간 인식체계와 도로명주소의 공간 구조화 방식의 간극으로 인한 문제이다. 따라서 도로명주소 기반의 공간 인식 체계가 자리잡기 위해서는 생활 속에서 활용되는 각종 공간정보들이 도로명에 의하여 주소정보를 부여받을 수 있어야 한다. 본 연구는 공간 인식 과정이 가장 중요하게 나타나는 길찾기 과정에서의 도로명주소 체계의 연계를 목적으로, 이를 위하여 필요한 공간데이터의 기하학적 매칭 방법론을 설계 및 구현하였다. 도로명주소 기본도의 도로구간 레이어와 보행자용 도로 네트워크에 대하여, 개별 도로 객체를 중심으로 네트워크 이웃을 생성하였다. 그 다음, 생성된 이웃 집합 간의 기하학적 유사도 비교를 통하여, 네트워크 데이터의 각 객체에 매칭되는 도로구간을 탐색하였다. 매칭 성능은 F0.5 값을 기준으로 0.936의 결과를 얻었으며, 유사도 값을 기준으로 10% 수동 검사를 수행한 결과 이 값을 0.978까지 향상시킬 수 있었다. 이렇게 생성된 매칭 대응관계를 이용하여, 보행자용 도로 네트워크 데이터에 도로명 정보를 부여하였다. 이러한 방법론을 통하여, 도로명주소를 기반으로 한 길찾기 서비스 제공 및 공간 인식체계 정착에 도움을 줄 수 있다.

• 보건행정학회지
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• 제27권4호
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• pp.315-323
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• 2017

Background: This study was purposed to analyze the effect of spatial accessibility to the psychiatry department in general hospital on the outpatient visit of mental patients. Methods: Data was provided from the Statistics Korea and Statistical Geographic Information Service, National Health Insurance Service, Health Insurance Review and Assessment Service, and Korea Transport Institute in 2015. The study regions were 103 administrative regions such as Si and Gu. The 103 regions had at least one general hospitals with a psychiatry department. The number of outpatient visit of mental patients in regions was used as the dependent variable. Spatial accessibility to mental general hospital was used as the independent variable. Control variables included such as demographic, economic, and health medical factors. This study used network analysis and multi-variate regression analysis. Network analysis by ArcGIS ver. 10.0 (ESRI, Redlands, CA, USA) was used to evaluate the average travel time and travel distance in Korea. Multi-variate regression analysis was conducted by SAS ver. 9.4 (SAS Institute Inc., Cary, NC, USA). Results: Travel distance and time had significant effects on the number of outpatient visits in mental patients in general hospital. Average travel time and travel distance had negative effects on the number of visits. Variables such as (number of total population, percentage of aged population over 65, and number of mental general hospital) had significant effects on the number of visit in mental patients. Conclusion: Health policy makers will need to consider the spatial accessibility to the mental healthcare organization in conducting regional health planning.

• 한국공간정보시스템학회 논문지
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• 제9권3호
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• pp.67-80
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• 2007

최근 LBS(location-based service) 및 텔레매틱스(telematics) 응용의 효율적인 지원을 위해, 기존 유클리디언(Euclidean) 공간 대신, 실제 도로나 철도와 같은 공간 네트워크(network)를 고려한 다수의 연구가 수행되었다. 그러나 Closest Pairs 질의 및 e-distance 조인 질의는, 하나의 POI(Point Of Interest)를 다루는 대신 POI 집합에 대하여 질의처리를 수행하기 때문에 매우 비용이 많이 든다. 아울러, k 값 및 범위의 증가에 따라 질의처리에 필요한 노드 검색 및 거리 계산의 비용이 매우 크게 증가한다. 따라서 본 논문에서는 공간 네트워크를 위한 효율적인 Closest Pairs 질의 및 e-distance 조인 질의 처리를 위해, POI 기반의 실체화 기법을 이용한 효율적인 질의처리 알고리즘을 제안한다. 아울러 기존 질의처리 알고리즘과의 성능 비교를 통하여 제안하는 알고리즘이 검색 성능이 우수함을 보인다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.82-84
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• 2003

Watershed boundaries and flow paths within the watershed are the most important factors required in watershed analysis. Most often the derivation of watershed boundaries and stream network and flow paths is based on topographical maps but spatial variation of flow direction is not clearly understandable using this method. Water resources projects currently use 1: 50, 000-scale ground survey or aerial photography-based topographical maps to derive watershed boundary and stream network. In basins, where these maps are not available or not accessible it creates a real barrier to watershed geo-spatial analysis. Such situations require the use of global datasets, like GTOPO30. Global data sets like ETOPO5, GTOPO30 are the only data sets, which can be used to derive basin boundaries and stream network and other terrain variations like slope aspects and flow direction and flow accumulation of the watershed in the absence of topographic maps. Approximately 1-km grid-based GTOPO 30 data sets can derive better outputs for larger basins, but they fail in flat areas like the Karkheh basin in Iran and the Amudarya in Uzbekistan. A new window in geo-spatial hydrology has opened after the launching of the space-borne satellite stereo pair of the Terra ASTER sensor. ASTER data sets are available at very low cost for most areas of the world and global coverage is expected within the next four years. The DEM generated from ASTER data has a reasonably good accuracy, which can be used effectively for hydrology application, even in small basins. This paper demonstrates the use of stereo pairs in the generation of ASTER DEMs, the application of ASTER DEM for watershed boundary delineation, sub-watershed delineation and explores the possibility of understanding the drainage flow paths in irrigation command areas. All the ASTER derived products were compared with GTOPO and 1:50,000-based topographic map products and this comparison showed that ASTER stereo pairs can derive very good data sets for all the basins with good spatial variation, which are equal in quality to 1:50,000 scale maps-based products.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.256-272
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and to apply the newly developed techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs, field survey data, and a spatial database of the topography, soil type, timber cover, geology and land use. The landslide-related factors (slope, aspect, curvature, topographic type, soil texture, soil material, soil drainage, soil effective thickness, timber type, timber age, and timber diameter, timber density, geology and land use) were extracted from the spatial database. Using those factors, landslide susceptibility was analyzed by artificial neural network methods. For this, the weights of each factor were determinated in 3 cases by the backpropagation method, which is a type of artificial neural network method. Then the landslide susceptibility indexes were calculated and the susceptibility maps were made with a GIS program. The results of the landslide susceptibility maps were verified and compared using landslide location data. A GIS was used to efficiently analyze the vast amount of data, and an artificial neural network was turned out be an effective tool to maintain precision and accuracy.

• 전기학회논문지
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• 제63권12호
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• pp.1690-1696
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• 2014

Network RTK generates spatial corrections by using differenced measurements from reference stations in the network, and the corrections are then provided to a rover. The rover, generally, uses linear interpolation, which assumes that the corrections at each reference station are spatially correlated, to obtain a precise correction of its location. However, an irregularity of the tropospheric delay is a real-world factor that violates this assumption. Tropospheric delay is a result of weather conditions, such as humidity, temperature and pressure, and it can cause spatial decorrelation when there are changes in the local climate. In this paper, we have defined the non-linear characteristics of the tropospheric delay between reference stations or user within a region as the "irregularity of tropospheric delay". Such an irregularity can negatively impact the network RTK performance. Therefore, we analyze the influence of the irregularity of tropospheric delay in network RTK based on meteorological data.

• ETRI Journal
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• 제42권5호
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

• 한국환경보건학회지
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• 제38권5호
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• pp.369-379
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• 2012

Objectives: Spatial analysis is useful for understanding complicated causal relationships. This paper focuses trends and appling methods for spatial analysis associated with environmental asbestos exposure. Methods: Literature review and reflection of experience of authors were conducted to know academic background of spatial analysis, appling methods on epidemiology and asbestos exposure. Results: Spatial analysis based on spatial autocorrelation provides a variety of methods through which to conduct mapping, cluster analysis, diffusion, interpolation, and identification. Cause of disease occurrence can be investigated through spatial analysis. Appropriate methods can be applied according to contagiousness and continuity. Spatial analysis for asbestos exposure source is needed to study asbestos related diseases. Although a great amount of research has used spatial analysis to study exposure assessment and distribution of disease occurrence, these studies tend to focus on the construction of a thematic map without different forms of analysis. Recently, spatial analysis has been advanced by merging with web tools, mobile computing, statistical packages, social network analysis, and big data. Conclusions: Because the trend in spatial analysis has evolved from simple marking into a variety of forms of analyses, environmental researchers including asbestos exposure study are required to be aware of recent trends.