Earticle

본 논문에서는 소년범죄 현황 분석을 통한 경향을 확인하고, 이러한 자료를 바탕으로 소년범죄의 감소 및 대처방안 등을 논의하고자 하였다. 2003년부터 2012년까지의 법원․검찰단계에서의 사건처리경향, 범죄유형별 분류 및 범행동기․재범기간․가족관계 등과 관련된 자료를 바탕으로 소년법 개정 이전과 이후의 흐름과 사법당국의 소년사건 처리 경향을 분석한 결과, 전체 소년범 숫자 및 재비행율, 강력사건의 비중이 여전히 줄어들지 않고 있는 것으로 나타났다. 이와 같은 통계치 확인을 통한 청소년 범죄의 원인을 다양한 각도에서 모색, 이를 해결하기 위한 대처방안으로 ‘법무부 청소년꿈키움센터(비행예방센터)’와 같은 교육기관의 확충 및 유관기관과의 연계강화, 인성교육 및 상담기회의 제공 등을 제시했으며, 본 논문을 바탕으로 재비행 감소라는 명제 실현을 통해 범죄로 인한 사회적 비용 등을 감소하기 위한 대안을 제시하고자 하였다

This paper talks on the trend of juvenile delinquency throughout check about analysis of the present condition, on the basis of this material, debated on reduce of juvenile delinquency and respondence plan. As a result of the resolve the amendment ｢Juvenile Act｣ before and after this and about the tendency of treatment juvenile delinquency, classification on the type of crime, motivation of accident, period of a second offender, family relationship – on the foundation of statistical data from 2003 to 2012 on the court of justice and the Public Prosecutor’s Office –, appear that total of the number of juvenile delinquency and rate of repeat, portion of strong crimes are not declined. On the confirm of statistical source, the cause of juvenile delinquency seeks as a various aspect and suggest enlargement of educational institution such as ｢Youth Dream Up Center｣ in the ministry of Justice (Delinquency Prevention Center), strengthen connection to related organization, supply of human nature teach & consultation chance. Used this paper, propose alternation for the reduce social cost caused by juvenile delinquency and try to realization of proposition ‘Downsize repetitive juvenile delinquency’.

Oriental medicine statistics are essential in research planning, research evaluation, and policy decision based on objective data. However, integrated administration of such statistics is not presently possible in the oriental medicine field, which has been slow in incorporating information communication technology. In an effort to address this problem, the Korea Institute of Oriental Medicine (KIOM) developed an oriental medicine statistical system in 2009, and the system has been offered in the traditional medicine information portal of OASIS. However, according to a 2010 survey targeting OASIS users, those surveys reported that needs for a system where various statistical data can be extracted via an interactive approach to multidimensional data. As a result of an analysis of the functions of the existing system, it was found that it is necessary to array and arithmetically analyze Stats Value, Drill Up & Drill Down, and Pivot. To this end, the existing DB schema should be redesigned. Based on our analysis result, we redesigned the database into a structure that is applicable to the reverse pivot algorithm. We used J2EE/JSP and a Flex framework to design and develop an oriental medicine statistical system that can provide multidimensional statistical data. Considering that the improved oriental medicine statistical system is planned to be offered by OASIS of KIOM, utilization and value of oriental medicine statistical data are expected to be enhanced.

The high rate of urban crime is a main issue that needs to be dealt with in this high-tech society. With the rapid increase of urban crime, research has mainly focused on topics either on a global or a local scale, such as cities or communities and houses or buildings, without reliable observational data. This study makes the best use of the nationwide surveys carried out by Korean government agencies for the analysis of urban crime patterns and factors in major Korean cities. The aims of this research are threefold: understanding the relationship between urban crime patterns and socio-economic differences in cities, determining the effect of residence types on the urban crime patterns; and uncovering potential influential factors of a crime victim's individual characteristics. The statistical methods used for the analysis of social statistical data are as follows: simple regression, logistic regression, one-way ANOVA and post-hoc test. This research found that the patterns of urban crime rate in cities have a certain tendency toward the cities' socio-economic and geographical differences. The residence type is an influential factor showing a close relation to the crime rate. Personal issues, such as the types of occupation, education, marriage, etc., are directly relevant to victims of crime.

Objectives: This study investigated the average number of drinkers in Korea, the number of high-risk drinkers, the average amount of alcohol consumed by high-risk drinkers, and the types of alcohol consumed according to the characteristics of the group of dependent drinkers. Methods: The results were obtained by analyzing the following data: The Global Status Report on Alcohol and Health; Country Profile 2014; WHO Country Profile 2014; Korea National Health and Nutrition Examination Survey 2014, Korean Statistical Information Service; National Tax Statistics-Liquor Tax; Gallup Drinking Frequency Survey 2015 Results: This study found that a large proportion of drinkers in Korea are already high-risk drinkers, and even among drinkers, alcohol consumption was highly biased. It was reported that 49.8% of men in the problem, abuse, and dependence groups accounted for 92.4% of total alcohol consumption among the male population. Notably, the 9.6% of men making up the dependent group consumed more than 30% of the alcohol ingested among males. Women had significant variations within groups that were considered high-risk and exhibited a large share of alcohol consumption in the problem (10.0% of the female population), abuse (1.8% of the female population), and dependence (1.5% of the female population) groups, constituting 72.8% of total alcohol consumption. The average amount of alcohol consumed by drinkers in Korea seems to have exceeded the level of intake by high-risk groups. Alcohol-dependent groups consumed 900.7 mL of soju, 405.2 mL of table wine, and 2,043.8 mL of beer, which is very similar to the consumption average of 2,031 mL of beer and 895.2 mL of soju in the drinking group. Conclusion: It has been shown that men's dependence on alcohol is serious, and it is possible to infer that alcohol consumption in some vulnerable groups is very high. As the average alcohol intake among alcohol-dependent groups and ordinary drinkers is very similar, it is highly likely that the drinker is an alcohol-dependent consumer in Korea.

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.

This paper presents a dynamic development of an ontology-based statistical information system supporting the collection, storage, processing, analysis, and the presentation of statistical knowledge at the national scale. To accomplish this, we propose a data mining technique to dynamically collect data relating to citizens from publicly available data sources; the collected data will then be structured, classified, categorized, and integrated into an ontology. Moreover, an intelligent platform is proposed in order to generate quantitative and qualitative statistical information based on the knowledge stored in the ontology. The main aims of our proposed system are to digitize administrative tasks and to provide reliable statistical information to governmental, economic, and social actors. The authorities will use the ontology-based statistical information system for strategic decision-making as it easily collects, produces, analyzes, and provides both quantitative and qualitative knowledge that will help to improve the administration and management of national political, social, and economic life.

최근 이상기후로 자연재해가 증가하는 가운데, 국립공원은 생태 보전 가치와 함께 연간 약 4천만 명이 방문하는 주요 관광자원으로서 산사태 발생 시 인명·사회경제적 피해가 우려된다. 본 연구는 관리주체가 다른 한라산국립공원을 제외한 22개 국립공원 중 산사태가 발생한 19개 공원을 대상으로 발생 특성을 분석하였다. 현장 조사를 통해 고도, 사면 경사, 임상, 모암, 행정구역 등 지형·지질 조건을 정리하고, 전국 산사태 통계와 비교하였다. 분석 결과, 국립공원은 전국 평균보다 고도가 높고 급경사 지형 비율이 큰 특징을 보였다. 또한 특정 임상과 지질 유형에서 차별적인 분포가 나타났다. 일부 공원에 피해가 집중되는 공간적 불균형도 확인되었다. 이는 국립공원에 특화된 사면 안정성 평가와 맞춤형 재해 대응 전략이 필요함을 시사한다. 연구 결과는 향후 국립공원 산사태 대응 정책 수립의 기초자료로 활용될 수 있다.

Amid intensifying climate driven disasters, Korean national parks attracting 40 million annual visitors face significant landslide risks that threaten both ecological conservation and human safety. This study analyzed landslide characteristics across 19 Korean national parks, excluding Hallasan, by conducting field surveys on elevation, slope, forest type, and lithology to compare with nationwide landslide statistics. The analysis revealed that Korean national parks feature significantly higher elevations and steeper slopes than the national average, with distinct distributions in specific forest and geological compositions. Furthermore, a clear spatial imbalance was identified, with landslide damages being heavily concentrated in certain specific parks. These findings emphasize the critical necessity for slope stability assessments and disaster response strategies specifically customized for the unique environments of Korean national parks. Ultimately, this research serves as a foundational resource for establishing future landslide mitigation and management policies within the Korean national park system.

Background: It is very difficult to distinguish between a radioactive contamination source and background radiation from natural radionuclides in the marine environment by means of online monitoring system. The objective of this study was to investigate a statistical process for triggering abnormal level of count rate data measured from our on-line seawater radioactivity monitoring. Materials and Methods: Count rate data sets in time series were collected from 9 monitoring posts. All of the count rate data were measured every 15 minutes from the region of interest (ROI) for 137Cs (Eγ = 661.6 keV) on the gamma-ray energy spectrum. The Shewhart (3σ), CUSUM, and Bayesian S-R control chart methods were evaluated and the comparative analysis of determination methods for count rate data was carried out in terms of the false positive incidence rate. All statistical algorithms were developed using R Programming by the authors. Results and Discussion: The 3σ, CUSUM, and S-R analyses resulted in the average false positive incidence rate of 0.164 ±0.047%, 0.064 ±0.0367%, and 0.030 ±0.018%, respectively. The S-R method has a lower value than that of the 3σ and CUSUM method, because the Bayesian S-R method use the information to evaluate a posterior distribution, even though the CUSUM control chart accumulate information from recent data points. As the result of comparison between net count rate and gross count rate measured in time series all the year at a monitoring post using the 3σ control charts, the two methods resulted in the false positive incidence rate of 0.142% and 0.219%, respectively. Conclusion: Bayesian S-R and CUSUM control charts are better suited for on-line seawater radioactivity monitoring with an count rate data in time series than 3σ control chart. However, it requires a continuous increasing trend to differentiate between a false positive and actual radioactive contamination. For the determination of count rate, the net count method is better than the gross count method because of relatively a small variation in the data points.

항공산업은 기술적인 혁신을 통해 안전성을 향상했으며, 항공 당국의 안전 규제와 감독을 통해 비행안전을 강화해 왔다. 항공산업의 안전 접근 방식이 항공기 시스템 전체에 대한 체계적인 접근 방식으로 발전함으로써 항공사는 새로운 안전 관리시스템을 구축하게 되었다. 항공기의 기술적 결함이나 비정상적인 데이터는 사고로 이어질 수 있는 전조 징후가 될 수 있으며, 이러한 징후를 조기에 식별하고 대처함으로써 사고 발생의 위험을 감소시킬 수 있다. 따라서 비정상적인 전조 징후의 관리는 데이터 기반 의사결정을 촉진하고, 항공사의 운영 효율성 및 안전수준을 강화하는 데 있어 필수적인 요소이다. 본 연구에서는 항공기 착륙 시에 활주로 이탈로 이어질 수 있는 크랩랜딩 이벤트의 패턴과 원인 분석을 위한 사전적 분석 단계에서 QAR (quick access recorder) 비행 데이터 통계 분석 모델을 제시하여 착륙 이벤트의 전조 징후와 원인을 식별 및 제거하는 안전관리의 효율성을 제고하고자 한다.

The aviation has improved safety through technological innovation and strengthened flight safety through safety regulations and supervision by aviation authorities. As the industry's safety approach has evolved into a systematic approach to the aircraft system, airlines have established a safety management system. Technical defects or abnormal data in an aircraft can be warning signs that could lead to an accident, and the risk of an accident can be reduced by identifying and responding to these signs early. Therefore, management of abnormal warning signs is an essential element in promoting data-based decision-making and enhancing the operational efficiency and safety level of airlines. In this study, we present a model to statistically analyze quick access recorder (QAR) flight data in the preliminary analysis stage to analyze the patterns and causes of crab landing events that can lead to runway departures when landing an aircraft, and provide a precursor to a landing event. We aim to identify signs and causes and contribute to increasing the efficiency of safety management.

Objectives: According to the central limit theorem, the samples in population might be considered to follow normal distribution if a large number of samples are available. Once we assume that toxicity dataset follow normal distribution, we can treat and process data statistically to calculate genus or species mean value with standard deviation. However, little is known and only limited studies are conducted to investigate whether toxicity dataset follows normal distribution or not. Therefore, the purpose of study is to evaluate the generally accepted normality hypothesis of aquatic toxicity dataset Methods: We selected the 8 chemicals, which consist of 4 organic and 4 inorganic chemical compounds considering data availability for the development of species sensitivity distribution. Toxicity data were collected at the US EPA ECOTOX Knowledgebase by simple search with target chemicals. Toxicity data were re-arranged to a proper format based on the endpoint and test duration, where we conducted normality test according to the Shapiro-Wilk test. Also we investigated the degree of normality by simple log transformation of toxicity data Results: Despite of the central limit theorem, only one large dataset (n>25) follow normal distribution out of 25 large dataset. By log transforming, more 7 large dataset show normality. As a result of normality test on small dataset (n<25), log transformation of toxicity value generally increases normality. Both organic and inorganic chemicals show normality growth for 26 species and 30 species, respectively. Those 56 species shows normality growth by log transformation in the taxonomic groups such as amphibian (1), crustacean (21), fish (22), insect (5), rotifer (2), and worm (5). In contrast, mollusca shows normality decrease at 1 species out of 23 that originally show normality. Conclusions: The normality of large toxicity dataset was not always satisfactory to the central limit theorem. Normality of those data could be improved through log transformation. Therefore, care should be taken when using toxicity data to induce, for example, mean value for risk assessment.

본 연구에서는 개인정보 비식별화 데이터의 통계적 유용성에 대한 품질 측정 방안에 대하여 통계 모형화에 따른 예측 정확도 측면에서 고찰하였다. 4차 산업혁명 시대에서 정보통신기술을 통한 혁신에는 반드시 빅데이터의 효과적인 활용이 필수적이지만, 개인정보 이슈는 적극적인 빅데이터 활용에 제약이 되고 있다. 이를 해결하기 위해 비식별화 가이드라인이 제정되었으며 다양한 개인정보 비식별화 방법이 활용되면서 개인정보의 실질적인 재식별 가능성은 매우 낮아졌다. 반면에 강력한 비식별화는 데이터의 유용성을 떨어뜨리는 부작용이 나타날 수 있다. 그 동안은 재식별 불가능한 비식별화 방법이 연구의 주를 이루어 왔다면 본 연구에서는 대표적인 비식별 방법인 KLT 모형에 의한 비식별화 데이터에 대한 통계적 유용성 측면의 품질 측정에 대하여 연구하였다. 비식별화 데이터에 대한 통계적 예측모형의 정확도에 기반하여 비식별화 된 데이터의 통계적 유용성이 어느 정도 훼손되는지에 대하여 사례분석을 수행하였다. 또한, 비식별 자료에 어느 정도의 비식별화 되지 않은 자료가 추가되어야 예측모형의 정확도를 회복하는 지를 살펴봄으로써 비식별화된 자료의 데이터 유용성 정도에 대한 새로운 측정지표를 제안하였다.

In this study, the method of quality measurement for the statistical usefulness of de-identified data was examined in terms of prediction accuracy by statistical modeling. In the era of the 4th industrial revolution, effective use of big data is essential to innovation through information and communication technology, but personal information issues are constrained to actively utilize big data. In order to solve this problem, de-identification guidelines have been established and the possibility of actual re-identification of personal information has become very low due to the utilization of various de-identification methods. On the other hand, strong de-identification can have side effects that degrade the usefulness of the data. We have studied the quality of statistical usefulness of the de-identified data by KLT model which is a representative de-identification method, A case study was conducted to see how statistical accuracy of prediction is degraded by de-identification. We also proposed a new measure of data usefulness of the de-identified data by quantifying how much data is added to the de-identified data to restore the accuracy of the predictive model.

본 연구는 공개된 수질측정 자료를 이용하여 담수호의 수질변화추이를 분석하고 수질항목의 이상여부의 판단기준을 마련하며, 자료로부터 부영양화의 지표인 Chlorophyll-a를 예측할 수 있는 회귀모형을 구성하여 담수호 관리에 이용할 수 있는 방안을 검토하고자 하였다. 이에 따라 서해안 담수호 3개소를 선정하여 약 20년간의 수질항목자료를 회귀분석 방법으로 분석하고, 각 수질항목의 연중 주기적인 변화를 나타내는 회귀식과 신뢰도 95%에서의 표준편차를 산정함으로서 이상 여부의 판단방법을 제시하였다. 또한 불규칙한 관측일로부터 Chlorophyll-a의 시간적 변화율을 산정하고, 다른 수질항목간의 상관관계 분석 및 회귀모형을 구성하여 분석함으로서 수질측정 자료만을 이용하여 Chlorophyll-a의 변화를 예상할 수 있는 방법을 제시하였다. 본 연구결과는 통계학적 모형에 의한 근사적인 수질예측방법으로서 향후 수질측정 자료의 양적·질적 개선이 이루어진다면 담수호 수질관리에 기여할 것으로 기대된다.

As using public monitoring data, analysing a trends of water quality change, establishing a criteria to determine abnormal status and constructing a regression model that can predict Chlorophyll-a, an indicator of eutrophication, was studied. Accordingly, the three freshwater lakes were selected, approximately 20 years of water quality monitoring data were analyzed for periodic changes in water quality each year using regression analysis, and a method for determining abnormalities was presented by the standard deviation at confidence level 95%. By calculating the temporal change rate of Chlorophyll-a from irregular observed data, analyzing correlations between the rate and other water quality items, and constructing regression models, a method to predict changes in Chlorophyll-a was presented. The results of this study are expected to contribute to freshwater lake water quality management as an approximate water quality prediction method using the statistical model.

Purpose: This study aimed to compare and analyze statistical data on 119 ambulance runs and ambulance crew, which are the components of the emergency medical services system in Korea and Japan. Methods: Data from National Fire Agencies of both Korea and Japan were collected and statistically compared. Results: With regard to the ratio of 119 ambulance runs, Korea's ratio has been gradually and continuously growing beyond that of Japan (Korea 4708.11, Japan 4706.47) since 2014. The ratio of firefighting ambulances in Korea was 2.59 (2.59±0.10), and was 4.76 (4.76±0.12) in Japan. The ratio of 119 ambulance crews in Korea was 15.55 (15.55±2.03), and was 47.24 (47.24±1.06) in Japan. Among the ambulance crews, the ratio of paramedics was 33.81 (33.81±5.85) in Korea and was 38.86(38.86±4.10) in Japan. Conclusion: The ratio of 119 ambulance runs in Korea has already exceeded that of Japan, but the numbers of 119 ambulance crews and paramedics qualified for special emergency treatment are still insufficient. Therefore, supply and demand policy that promotes the development of the firefighting ambulance service system is necessary.

최근 합성데이터는 실제 데이터의 패턴과 특성을 유지하면서도 프라이버시 유출을 예방할 수 있는 기술로 각광받고 있다. 그에 따라 합성데이터 활용에 대한 기술적 및 제도적 연구가 활발하게 진행되고 있지만, 명확한 표준과 지침이 부재하여 합성데이터 기술의 적극적 활용이 어렵다. 본 연구는 합성데이터의 개인정보 노출 위험 정량화를 위한 전초적 연구로 통계적 방법론을 활용하여 개인정보 노출 위험 정량화 지수를 도출하고 유럽 개인정보 보호 규정(General Data Protection Regulation)를 준수하기 위한 구체적인 적용 방안을 제시한다. 오픈 데이터 환경에서 본 연구의 개인정보 노출 위험 지수를 통해 개인정보 노출 위험을 인식하고 데이터 활용성과의 균형을 조절할 수 있을 것으로 기대한다.

Recently, Syntheic data has been in the spotlight as a technology that can protect personal information while maintaining the patterns and characteristics of actual data. Accordingly, technical and institutional research on synthetic data is actively being conducted, but it is difficult to actively use synthetic data due to the lack of clear standards and guidelines. This study is a preliminary study for quantifying the disclosure risk of synthetic data, and derives a privacy disclosure risk index through statistical methodology and suggests specific application measures to comply with the General Data Protection Regulation(GDPR). It is expected that the disclosure risk and the balance of data utility can be controlled through the privacy disclosure risk index of this study in an open data environment.