• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.446-446
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• 2021

본 연구에서는 수질오염 관리 측면에서 유역의 취약성을 평가하고 우선 관리가 필요한 지역을 선정하는 기법을 수립하고자 한다. 수질은 다양한 요인에 의해 영향을 받기 때문에 오염의 취약성을 평가하기 위해 다기준 분석 기법을 적용하였다. 다기준 평가기법은 다양한 평가 항목이 포함되는 의사결정 문제에 유용하다. 연구 절차는 평가 항목 및 가중치 결정, 항목별 평가자료 구축, 수질오염에 대한 취약성 평가 후 수질오염 관리가 필요한 유역 선정의 단계로 구성하였다. 평가 대상은 814개 소유역이다. 평가 프레임워크는 오염원, 확산 과정, 수자원 현황의 3개 그룹으로 구성되며, 각 그룹에 대한 하위 평가 항목을 선정하였다. 오염원 그룹은 중앙 및 지방 정부에서 제공하는 오염원 조사 결과, 농업 분야 자료, 토지 사용 현황 등을 적용하였다. 확산 과정 그룹은 강우, 토지 피복, 토양 등의 데이터를 사용하였으며, 수자원 현황은 하천의 흐름, 수질 및 수생 생태계 현황 등이 반영되었다. 유역 단위로 모든 항목에 대하여 가중치를 반영한 점수를 집계하고 취약지역을 선정하였다.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.2947-2959
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• 1997

Temporal data model is able to handle the time varying information, which is to add temporal attributes to conventional data model. The temporal data model is classified into three models depending upon supporting time dimension, that are the valid time model to support valid time, the transaction time model to support transaction model, and the bitemporal data model to support valid time and transaction time. Most temporal data models are designed to process the temporal data by extending the relational model. There are two types or temporal data model, which are the tuple timestamping and the attribute timestamping depending on time dimension. In this research, a concepts of temporal data model, the time dimension, types of thc data model, and a consideration for the data model design are discussed Also, temporal data models in terms of the time dimension are compared. And the aggregate function model of valid time model is proposed, and then logical analysis for its computing consts has been done.

• Journal of the Korean Geographical Society
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• v.51 no.3
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• pp.367-380
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• 2016

This study reviewed and compared methods for mapping aggregated low birth weight (LBW) and geographic variations in LBW in South Korea. Based on this review, we produced LBW maps in South Korea. Standardized mortality/morbidity ratios (SMRs) and crude mortality rates have been widely used for many years in epidemiological research. However, SMR-based maps are likely to be affected by sample size of unit area. Therefore, this study adopted a model-based approach using Bayesian estimates to reduce noisy variability in the SMR. By using a Bayesian model, we can calculate a statistically reliable RR values. We used the full Bayes estimator, as well as empirical Bayes estimators. As a result, variations in the two Bayes models were similar. The SMR-based statistics had the largest variation. The result maps can be used to identify regions with a high risk of LBW in South Korea.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.40-53
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• 2021

Recently, shared dockless e-scooter usage has rapidly increased, rather than the station-based shared mobility service, because of convenience. This transition leads to new social problems in urban areas such as increased traffic accidents and hindrance of pedestrian environments. In this study, we analyze the usage characteristics of shared e-scooters in Seoul, and identify factors influencing demand for shared e-scooters by developing a negative binomial regression model. As a result, the usage characteristics show that the average trip distance, the average trip duration, and the average trip speed were 1.5km, 9.4min, and 10.3km/h, respectively. Demographic factor, transport facility factors, land use factors, and weather factors have statistically significant impacts on demand for shared e-scooters. The results of this study will be used as basic data for suggesting effective operation strategies for areas with higher shared e-scooter demand and for establishing transport policies for facilitating shared e-scooter usage.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.3
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• pp.21-28
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• 2008

In MCDA (Multi-Criteria Decision Analysis) based on GIS, building the CM(Criteria Map) which represents the space phenomenon properly is important process to deduce precise and efficient site analysis result. The CM using administrative district data is widely used for site analysis process. But, there are not enough studies on site analysis using dasymetric mapping technique. For MCDA, this study suggests building the CM by using dasymetric mapping technique, which re-assigns the social-economic attribute value to more detail space unit. The suggested method is used for industrial site analysis. The criteria map for workforce and criteria map for the distance to the city were built and criteria map which represents attribute's space distribution pattern is documented. The criteria map is successfully applied to multi-criteria decision making process and eventually the analysis result of proposed suitable industrial site is derived.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.141-156
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• 2023

This study aims to improve understanding of freight movement, crucial for logistics facility investment and policy making. It addresses the limitations of traditional freight truck traffic data, aggregated only at city and county levels, by developing a new methodology. This method uses trip chain data for more detailed, facility-level analysis of freight truck movements. It employs DTG (Digital Tachograph) data to identify individual truck visit locations and creates H3 system-based polygons to represent these visits spatially. The study also involves an algorithm to dynamically determine the optimal spatial resolution of these polygons. Tested nationally, the approach resulted in polygons with 81.26% spatial fit and 14.8% error rate, offering insights into freight characteristics and enabling clustering based on traffic chain characteristics of freight trucks and visited facility types.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.118-129
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• 2016

Many epidemiological studies, relying on administrative air pollution monitoring data, have reported the association between particulate matter ($PM_{10}$) air pollution and human health. These monitoring data were collected at a limited number of fixed sites, whereas government-generated health data are aggregated at the area level. To link these two data types for assessing health effects, it is necessary to estimate area-level concentrations of $PM_{10}$. In this study, we estimated district (Gu)-level $PM_{10}$ concentrations using a previously developed pointwise exposure prediction model for $PM_{10}$ and three types of point locations in Seoul, Korea. These points included 16,230 centroids of the largest census output residential areas, 422 community service centers, and 610 centroids on the 1km grid. After creating three types of points, we predicted $PM_{10}$ annual average concentrations at all locations and calculated Gu averages of predicted $PM_{10}$ concentrations as representative Gu-estimates. Then, we compared estimates to each other and to measurements. Prediction-based Gu-level estimates showed higher correlations with measurement-based estimates as prediction locations became more population representative ($R^2=0.06-0.59$). Among the three estimates, grid-based estimates gave lowest correlations compared to the other two(0.35-0.47). This study provides an approach for estimating area-level air pollution concentrations and assesses air pollution health effects using national-scale administrative health data.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.97-106
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• 2011

Mode choice model is an essential element for estimating- the demand of new means of transportation in the planning stage as well as in the establishment phase. In general, current demand analysis model developed for the mode choice analysis applies common parameters of utility function in each region which causes inaccuracy in forecasting mode choice behavior. Several critical problems from using common parameters are: a common parameter set can not reflect different distribution of coefficient for travel time and travel cost by different population. Consequently, the resulting model fails to accurately explain policy variables such as travel time and travel cost. In particular, the nonlinear logit model applied to aggregation data is vulnerable to the aggregation error. The purpose of this paper is to consider the regional characteristics by adopting the parameters fitted to each area, so as to reduce prediction errors and enhance accuracy of the resulting mode choice model. In order to estimate parameter of each area, this study used Household Travel Survey Data of Metropolitan Transportation Authority. For the verification of the model, the value of time by marginal rate of substitution is evaluated and statistical test for resulting coefficients is also carried out. In order to crosscheck the applicability and reliability of the model, changes in mode choice are analyzed when Seoul subway line 9 is newly opened and the results are compared with those from the existing model developed without considering the regional characteristics.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.50-60
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• 2014

The purpose of this study is to identify characteristics and improvements of the climate change vulnerability analysis methods to build a safe city from disasters. For this, an empirical analysis on sea level rise disasters was performed focusing on Heaundae-gu in Busan. For the analysis, Census output areas and Dongs were set as analysis unit and their disaster vulnerability was analyzed. Improvements were reviewed through the comparison and review of analysis process and results. According to analysis results, Modifiable Areal Unit Problem(MAUP) which gives different results according to aggregate unit occurs. Improvements were induced by analysis process, and it was found that in spatial unit setting stage that becomes the base of analysis, analysis unit adjustment, score computation method adjustment, and clearer analysis method for each disaster type would be needed. In analysis execution stage, it was thought that weighting according to variables, diversification of variables, and exclusion of subjective analysis selection method would be needed. It is expected that accurate the total disaster vulnerability analysis will be the base for the improvement of efficiency in urban resilience responding to future weather changes.

• Journal of the Korean Society for Railway
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• v.18 no.2
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• pp.166-174
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• 2015

A direct demand model requires relatively little analysis time and incurs a low cost. It is also known to be useful for the preliminary screening of promising configurations or concepts. This study reviews direct demand models of 12 existing urban railways using demographic data based on a block group level which is approximately 1/24 of a traditional zone area. However, direct demand models are limited. Therefore, a new approach is suggested. The proposed method is based on a field study and an empirical analysis. The study finds factors that affect ridership at the station level. As a case study, the proposed approach is tested using 54 light railway transit stations. The results of this empirical study demonstrate its applicability to improve the error rates of the predicted ridership at the station level.