PURPOSES : To prevent an increasing number of drowsiness-related accidents, considering driver fatigue is necessary, which is the main cause of drowsiness accidents. The purpose of this study is to propose a methodology for selecting drowsiness hotspots using continuous driving time, a variable that quantifies driver fatigue. METHODS : An analysis was conducted by dividing driver fatigue, which changes according to time and space, into temporal and spatiotemporal scenarios. The analysis technique derived four evaluation indicators (precision, recall, accuracy, and F1 score) using a random forest classification model that is effective for processing large amounts of data. RESULTS : Both the temporal and spatiotemporal scenarios performed better in models that reflected the characteristics of road sections with changes in time and space. Comparing the two scenarios, it was found that the spatiotemporal scenario showed a difference in precision of approximately 10% compared with the temporal scenarios. In addition, [Model 2-2] of the spatiotemporal scenario showed the best predictive power by assessing the model’s accuracy via a comparison of (1-recall) and precision. This shows better performance in predicting drowsy accidents by considering changes in time and space together rather than constructing only temporal changes. CONCLUSIONS : To classify hotspots of drowsiness, spatiotemporal factors must be considered. However, it is possible to develop a methodology with better performance if data on individuals driving vehicles can be collected.

국민기초생활보장수급 독거노인의 지역별 분포는 시𐩐군𐩐구 단위의 자료 부족으로 인하여, 이들에 대한 정보는 지역별 구분 없이 통합된 수치에 따라 해석되고 있다. 이에 본 연구는 기초생활수급 독거노인의 지역별 분포와 분포에 따른 핫스팟을 추적하였다. 시･군･구 단위에서의 분석이 가능한 2020년 지역사회건강조사 자료를 활용하여 250개의 기초지방자치단체를 대상으로 핫스팟 분석을 시행하였다. 분석 결과 전라남도에 기초생활수급 독거노인의 핫스팟이 집중되고 있는 것으로 나타났다. 본 연구는 기초생활 수급 독거노인 인구의 지역적 분포 특성을 정량적이고 가시적인 근거로 제시하고 있다. 더 나아가 지역별 기초생활수급 독거노인의 핫스팟을 추적하여 공간 불평등과 노인복지를 연계하는 시도를 선보인 연구로, 향후 중요한 참고자료로 활용될 수 있을 것으로 기대된다.

Objectives of this study were to identify the hotspot for displacement of the on-line water quality sensors, in order to detect illicit discharge of untreated wastewater. A total of twenty-six water quality parameters were measured in sewer networks of the industrial complex located in Daejeon city as a test-bed site of this study. For the water qualities measured on a daily basis by 2-hour interval, the self-organizing maps(SOMs), one of the artificial neural networks(ANNs), were applied to classify the catchments to the clusters in accordance with patterns of water qualities discharged, and to determine the hotspot for priority sensor allocation in the study. The results revealed that the catchments were classified into four clusters in terms of extent of water qualities, in which the grouping were validated by the Euclidean distance and Davies-Bouldin index. Of the on-line sensors, total organic carbon(TOC) sensor, selected to be suitable for organic pollutants monitoring, would be effective to be allocated in D and a part of E catchments. Pb sensor, of heavy metals, would be suitable to be displaced in A and a part of B catchments.

An understanding of the geographic distribution of highly pathogenic avian influenza (HPAI) is essential to assessing and managing the risk of introduction of HPAI virus (HPAIV). However, to date, local spatial clustering patterns of HPAI outbreaks in Korea has not been explicitly investigated. We compiled HPAI outbreak data (n=622 cases) from December 2003 to February 2016. Each reported case was geocoded and linked to a digital map of Korea according to its onset location using the geographic information system (GIS). Kernel density estimation was used to explore global patterns of the HPAI outbreaks. We also applied the Getis-Ord G local spatial statistic to identify significant hot spots of high and low abundance by calculating Z-scores. Hot spot analysis revealed that HPAI cases are likely to be distinct clusters of HPAI outbreaks, with the highest risk being in the southwest of the country, specifically in Jeonnam and Jeonbuk provinces, where there are high density poultry populations. More than 16 Si-Gun-Gu (administrative province unit with bandwidth of 30 km) were involved in these high risk areas, indicating that there is likely to be a spatial heterogeneity of HPAI outbreaks within the country. Because of the existence of apparent hot spots, particularly in western regions, along with the increased number of migratory birds in these areas, Korea is at high risk of HPAIV introduction. Taking this challenge into consideration, preemptive and effective targeted surveillance programs for wild birds and poultry farms are highly recommended. Future research should look at the risk factors related to the socio-economic, human and natural environments and the poultry production systems to explain the spatial heterogeneity of HPAI outbreaks.

생물 종의 분포를 파악하는 것은 진화생물학 뿐만 아니라 보전생물학에서 매우 중요한 연구 분야이다. 최근에는 직접적인 관찰 위주 결과의 한계를 극복하고자, 종 분포 모델을 적용한 멸종위기종의 보전에 대한 연구들이 다양한 분류군에서 이루어지고 있다. 본 연구에서는 개체들이 관찰된 좌표 자료와 종 분포 모델링 기법을 바탕으로 한국에 서식하고 있는 멸종위기양서·파충류 종들의 주요 분포지역을 예측하고 이들의 서식지 특성을 파악하였다. 분석에 이용 된 멸종위기양서·파충류는 이끼도롱뇽(Karsenia koreana)과 수원청개구리(Hyla suweonensis), 금개구리(Pelophylax chosenicus), 맹꽁이(Kaloula borealis), 구렁이(Elaphe schrenckii), 표범장지뱀(Eremias argus), 남생이(Mauremys reevesii), 자라(Pelodiscus sinensis)를 포함하며, 고리도롱뇽(Hynobius yangi)과 비바리뱀(Sibynophis chinensis)은 표 본수가 적어 분석에서 제외되었다. 그 결과 고도가 멸종위기종들의 분포에 가장 중요한 환경변수로 나타났으며, 그들이 분포한 고도는 그 지역의 기후와 상관관계를 나타냈다. 또한 종분포 모델링에서 예측된 분포지역은 본 연구의 관찰 결과 뿐만 아니라 다른 선행 조사의 관찰결과를 충분히 포함하고 있었다. 8종의 AUC 값은 평균 0.845±0.08로 비교적 높게 측정되었고, 오류 값은 0.087±0.01로 비교적 낮게 측정되었다. 따라서 생성된 멸종위기종들의 종 분포 모델은 성공적으로 생성되었다고 판단된다. 멸종위기양서·파충류들의 주요 분포지역을 확인하기 위해 분포 모델들을 중첩한 결과, 5 종은 한반도의 서쪽 지역인 경기도와 충청남도의 해안지역 주변에서 공통적으로 서식하고 있는 것으로 예측되 었다. 따라서 이와 같은 지역들을 우선적으로 보호지역으로 지정되어야 하며, 이러한 결과들은 멸종위기양서·파충류의 보호지역을 지정함에 있어 보호대책 수립에 중요한 기초자료가 될 수 있을 것이다.

This study was conducted to determine the micro-hotspot for bird habitats in Yeongheung Island. We analyzed the spatio-temporal changes in the distribution based on species diversity, species richness at the 13 focal areas, which was classified in five categories depending on the types of habitats, using Analysis of Variance Test for four years (2006~2009). The distribution of birds was different depending on areas, seasons but not years. The forests of two areas of 13 areas were determined as the micro-hospot in three season (spring, summer, autumn), which is consistent for 4 years. This study provides the new analytical method that habitats for birds are systematically characterized through micro-hotspot using the spatio-temporal analysis.