• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.174-189
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• 2015

The objective of this study was to reconstruct spatial information using the results of the investigation and evaluation of the health of the living organisms, habitat, and water quality at the investigation points for the aquatic ecosystem health of the Nakdong River basin, to support the rational decision making of the aquatic ecosystem preservation and restoration policies of the Nakdong River basin using spatial analysis techniques, and to present efficient management methods. To analyze the aquatic ecosystem health of the Nakdong River basin, punctiform data were constructed based on the position information of each point with the aquatic ecosystem health investigation and evaluation results of 250 investigation sections. To apply the spatial analysis technique, the data need to be reconstructed into areal data. For this purpose, spatial influence and trends were analyzed using the Kriging interpolation(ArcGIS 10.1, Geostatistical Analysis), and were reconstructed into areal data. To analyze the spatial distribution characteristics of the Nakdong River basin health based on these analytical results, hotspot(Getis-Ord Gi, $G^*_i$), LISA(Local Indicator of Spatial Association), and standard deviational ellipse analyses were used. The hotspot analysis results showed that the hotspot basins of the biotic indices(TDI, BMI, FAI) were the Andong Dam upstream, Wangpicheon, and the Imha Dam basin, and that the health grades of their biotic indices were good. The coldspot basins were Nakdong River Namhae, the Nakdong River mouth, and the Suyeong River basin. The LISA analysis results showed that the exceptional areas were Gahwacheon, the Hapcheon Dam, and the Yeong River upstream basin. These areas had high bio-health indices, but their surrounding basins were low and required management for aquatic ecosystem health. The hotspot basins of the physicochemical factor(BOD) were the Nakdong River downstream basin, Suyeong River, Hoeya River, and the Nakdong River Namhae basin, whereas the coldspot basins were the upstream basins of the Nakdong River tributaries, including Andong Dam, Imha Dam, and Yeong River. The hotspots of the habitat and riverside environment factor(HRI) were different from the hotspots and coldspots of each factor in the LISA analysis results. In general, the habitat and riverside environment of the Nakdong River mainstream and tributaries, including the Nakdong river upstream, Andong Dam, Imha Dam, and the Hapcheon Dam basin, had good health. The coldspot basins of the habitat and riverside environment also showed low health indices of the biotic indices and physicochemical factors, thus requiring management of the habitat and riverside environment. As a result of the time-series analysis with a standard deviation ellipsoid, the areas with good aquatic ecosystem health of the organisms, habitat, and riverside environment showed a tendency to move northward, and the BOD results showed different directions and concentrations by the year of investigation. These aquatic ecosystem health analysis results can provide not only the health management information for each investigation spot but also information for managing the aquatic ecosystem in the catchment unit for the working research staff as well as for the water environment researchers in the future, based on spatial information.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Korean Journal of Forensic Psychology
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• v.11 no.2
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• pp.135-153
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• 2020

Identifiable victim effect refers to the tendency of greater sympathy and helping behavior to identifiable victims than to abstract, unidentifiable ones. This research tested whether this tendency also affects third-party's punishment and compensation judgments in jury context for public's legal judgments. In addition, through the Identifiable victim effect in such legal judgment, we intended to explain the effect of 'the bill named for victim', putting the victim's real name and identity at the forefront, which is aimed at strengthening the punishment of related crimes by gaining public attention and support. To do so, we conducted experiments with hypothetical traffic accident scenarios that controlled legal components while manipulating victim's identifying information. In experiment 1, each participant read a scenario of an anonymous victim (unidentifiable condition) or a nonanonymous victim that included personal information such as name and age (identifiable condition) and made judgments on the degree of punishment and compensation. The results showed no effect of identifiability on third-party's punishment and compensation judgments, but moderation effect of BJW was obtained in the identifiable condition. That is, those with higher BJW showed greater tendency of punishment and compensation for identifiable victims. In Experiment 2, we compared an anonymous victim (unidentifiable condition) against a well-conducted victim (positive condition) and ill-conducted victim (negative condition) to test the effects of victim's characteristics on punishment for offender and compensation for victims. The results showed lower compensation for an ill-conducted victim than for an anonymous one. In addition, across all conditions except for negative condition, participants made punishment and compensation judgments higher than the average judicial precedents of 10-point presented in the rating scale. This research showed that victim's characteristics other than legal components affects third-party's legal decision making. Furthermore, we interpreted third-party's tendency to impose higher punishment and compensation with effect of 'the bill named for victim' and proposed social and legal discussion for and future research.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Journal of Korean Home Economics Education Association
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• v.24 no.2
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• pp.117-134
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• 2012

The purpose of this study was to identify the stage of concern, the level of use, and the innovation configuration of Home Economics teachers regarding creativity and personality education in Home Economics(HE) classes. The survey questionnaires were sent through mails and e-mails to middle-school HE teachers in the whole country selected by systematic sampling and convenience sampling. Questionnaires of the stages of concern and the levels of use developed by Hall(1987) were used in this study. 187 data were used for the final analysis by using SPSS/window(12.0) program. The results of the study were as following: First, for the stage of concerns of HE teachers on creativity and personality education, the information stage of concerns(85.51) was the one with the highest response rate and the next high in the following order: the management stage of concerns(81.88), the awareness stage of concerns(82.15), the refocusing stage of concerns(68.80), the collaboration stage of concerns(61.97), and the consequence stage of concerns(59.76). Second, the levels of use of HE teachers on creativity and personality education was highest with the mechanical levels(level 3; 21.4%) and the next high in the following order: the orientation levels of use(level 1; 20.9%), the refinement levels(level 5; 17.1%), the non-use levels(level 0; 15.0%), the preparation levels(level 2; 10.2%), the integration levels(level 6; 5.9%), the renewal levels(level 7; 4.8%), the routine levels(level 4; 4.8%). Third, for the innovation configuration of HE teachers on creativity and personality education, more than half of the HE teachers(56.1%) mainly focused on personality education in their HE classes; 31.0% of the HE teachers performed both creativity and personality education; a small number of teachers(6.4%) focused on creativity education; the same number of teachers(6.4%) responded that they do not focus on neither of the two. Examining the level and type of performance HE teachers applied, the average score on the performance of creativity and personality education was 3.76 out of 5.00 and the mean of creativity component was 3.59 and of personality component was 3.94, higher than standard. For the creativity education, openness/sensitivity(3.97) education was performed most and the next most in the following order: problem-solving skill(3.79), curiosity/interest(3.73), critical thinking(3.63), problem-finding skill(3.61), originality(3.57), analogy(3.47), fluency/adaptability(3.46), precision(3.46), imagination(3.37), and focus/sympathy(3.37). For the personality education, the following components were performed in order from most to least: power of execution(4.07), cooperation/consideration/just(4.06), self-management skill(4.04), civic consciousness(4.04), career development ability(4.03), environment adaptability(3.95), responsibility/ownership(3.94), decision making(3.89), trust/honesty/promise(3.88), autonomy(3.86), and global competency(3.55). Regarding what makes performing creativity and personality education difficult, most HE teachers(64.71%) chose the lack of instructional materials and 40.11% of participants chose the lack of seminar and workshop opportunity. 38.5% chose the difficulty of developing an evaluation criteria or an evaluation tool while 25.67% responded that they do not know any means of performing creativity and personality education. Regarding the better way to support for creativity and personality education, the HE teachers chose in order from most to least: 'expansion of hands-on activities for students related to education on creativity and personality'(4.34), 'development of HE classroom culture putting emphasis on creativity and personality'(4.29), 'a proper curriculum on creativity and personality education that goes along with students' developmental stages'(4.27), 'securing enough human resource and number of professors who will conduct creativity and personality education'(4.21), 'establishment of the concept and value of the education on creativity and personality'(4.09), and 'educational promotion on creativity and personality education supported by local communities and companies'(3.94).