This study aims to explore the public perception of sports welfare by employing big data analysis techniques and to analyze it through a multi-layered lens grounded in Bronfenbrenner’s ecological systems theory. To this end, text mining software Textom and Ucinet 6 were utilized to examine online textual data related to “sports welfare” collected from May 2017 to February 2025. frequency analysis, TF-IDF analysis, degree centrality analysis, and CONCOR analysis were conducted. The results identified keywords such as “physical education.” “fitness.” “citizens.” “society.” “support.” “disability.” “voucher.” “university.” and “center.” as having high co-occurrence with sports welfare. CONCOR analysis revealed six major clusters: National Fitness 100 Service, Sports Voucher Program, Health Programs at Public Sports Centers, Community-Based Sports Welfare Environment, Training of Sports Welfare Professionals, and Support System Centered on the Korea Sports Promotion Foundation. This study suggests that the level of individual sports welfare can be enhanced through dynamic and interactive relationships between the individual and various environmental systems. Furthermore, to realize sustainable sports welfare, it is essential to develop long-term national strategies that holistically integrate all levels of the ecological systems from the micro system to the chrono system.

중앙버스전용차로는 일반 도로 대비 높은 교통량과 반복적인 축하중이 작용하는 구간으로, 정차 및 출발 과정에서 발생 하는 국부적인 응력 집중으로 인해 포장 파손이 빈번하게 발생한다. 그러나 기존 도로 설계에서는 정적인 교통량을 기준 으로 축하중을 산정하여, 실제 교통 환경에서의 버스 유형별 차이, 재차 인원, 시간대별 하중 변화 등 동적인 요소를 충 분히 반영하지 못하는 한계가 존재한다. 이에 본 연구에서는 대중교통 빅데이터를 활용하여 중앙버스전용차로의 버스 유 형 및 시간대별 재차 인원을 반영한 새로운 축하중 산정 모델을 개발하였다. 이를 위해 서울시 열린 데이터 광장의 교통 정보를 활용하여 버스 유형 및 시간대별 재차 인원 데이터를 수집하고, 카카오맵 및 네이버 로드뷰 데이터를 이용해 결 측치를 보완하여 데이터셋을 구축하였다. 구축된 데이터셋을 활용하여 기존 ESAL(Equivalent Single Axle Load) 방식과 비교 분석한 결과, 새로운 축하중 모델에서는 기존 방식 대비 평균 111.8% 높은 축하중이 산정되었으며, 일부 구간에서 는 최대 128.9%까지 차이가 발생하는 것으로 나타났다. 이는 기존 포장 설계가 중앙버스전용차로의 실질적인 교통 하중 을 충분히 반영하지 못하고 있음을 시사하며, 추가적으로 버스 중하중의 가·감속의 영향을 고려한다면, 시간대별·노선별 실시간 축하중 변화를 보다 정밀하게 분석할 수 있으며, 이를 통해 과소 산정된 설계 하중을 보완하고 포장 공용성을 향 상시킬 수 있는 최적의 설계 및 유지보수 전략 수립이 가능할 것으로 기대된다.

PURPOSES : This study aimed to predict the number of future COVID-19 confirmed cases more accurately using public and transportation big data and suggested priorities for introducing major policies by region. METHODS : Prediction analysis was performed using a long short-term memory (LSTM) model with excellent prediction accuracy for time-series data. Random forest (RF) classification analysis was used to derive regional priorities and major influencing factors. RESULTS : Based on the daily number of COVID-19 confirmed cases from January 26 to December 12, 2020, as well as the daily number of confirmed cases in Gyeonggi Province, which was expected to occur on December 24 and 25, depending on social distancing, the accuracy of the LSTM artificial neural network was approximately 95.8%. In addition, as a result of deriving the major influencing factors of COVID-19 through random forest classification analysis, according to the number of people, social distancing stages, and masks worn, Bucheon, Yongin, and Pyeongtaek were identified as regions expected to be at high risk in the future. CONCLUSIONS : The results of this study can help predict pandemics such as COVID-19.

This study examines the concept of Korean ethnicity by looking into how the Choseonjok, ethnic return migrants from China, are perceived in online news comments. Data were collected from 3,109 comments, which were written in response to 28 news articles regarding the Choseonjok, and analyzed using a Python-based text-mining technique and critical discourse analysis. The findings show that the commenters perceived the Choseonjok negatively, including as potential criminals or social vice. They not only placed the Choseonjok in an inferior position to pure Koreans but also excluded them from the category of compatriots, arguing that speaking the same language and sharing a similar appearance were not enough to make the ethnic return migrants Korean compatriots. This study critically demonstrates how a group of ethnic return migrants is depicted in online public discourses and how this portrayal can shed light on the conceptualization of Korean ethnicity in this era of multiculturalism.

This study had two main objectives. We first investigated which weather phenomena people were most concerned about in the context of climate change or global warming. Then, we conducted content analysis to find which words were more commonly used with climate change or global warming. For this, we collected web data from Twitter, Naver, and Daum from April to October 2019 in the Republic of Korea. The results suggested that people were more concerned about air quality, followed by typhoons and heat waves. Because this study only considered one warm period in the year of 2019, winter-related weather phenomena such as cold wave and snowfall were not well captured. From Twitter, we were able to find wider range of terminologies and thoughts/opinions than Naver and Daum. Also, more life-relevant weather events such as typhoons and heat waves in Twitter were commonly mentioned compared to Naver and Daum. On the other hand, the comments from Naver and Daum showed relatively narrower and limited terms and thoughts/ opinions. Especially, most of the comments were influenced by headlines of articles. We found many comments about air quality and energy/economic policy. We hope this paper could provide background information about how to promote the climate change education and public awareness and how to efficiently interact with general audiences.

최근 공공시설물의 노후화에 따른 사회문제가 빈번하게 발생하고 있으며, 이에 따라 시설물에 대한 국민의 불안감도 증가 하고 있다. 향후 10년 내에 급증하게 되는 시설물 노후화 문제의 효과적인 대응을 위해 현재의 사후적인 유지관리에서 예측을 통한 선제적 유지관리로의 패러다임 전환이 시급한 실정이다. 본 연구에서는 빅데이터 시범분석을 통해서 교량, 터널, 공공건축물 일부에 대해 FMS 축적된 유지관리 데이터를 활용하여 지역별·환경별·공용년수별 취약요소를 도출하였고, Social Media의 비정형 데이터 분석을 통해 국민이 체감하는 불안/불편요소를 도출하였다. 또한 교량 취약요소의 손상발생패턴 분석을 통해 향후 선제적으로 관리해야 하는 유지관리 항목 및 추가적으로 확보해야 하는 디지털 정보 등을 제안하였다.