• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.2
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• pp.11-18
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• 2023

This study aims to distinguish what can be used in consideration of the national situation with Korea for Chinese Scenic and Historic Interest Areas, and the results are as follows; First, the Chinese Scenic and Historic Interest Areas expanded to the existing scenic cruise culture, travel, and tourism culture in the process of the influx of Western culture in the modern and contemporary era, and became popular as a travel destination. Accordingly, the Chinese government developed the tourism industry around the scenic sites, and thanks to the development of transportation and communication, the Scenic and Historic Interest Areas has become an important national heritage. This influenced the establishment of the system related to Scenic and Historic Interest Areas, and today, it is operated around the Scenic and Historic Interest Areas ordinance. Second, the designation of the Scenic and Historic Interest Areas is divided into the size of the site according to the area, and the process of selecting the Scenic and Historic Interest Areas classification, rating evaluation, and comprehensive value evaluation according to evaluation indicators and rating standards is carried out. Accordingly, according to the results of the classification, it is subdivided from the national level to the Scenic and Historic Interest Areas at the local level. Third, the central government is in charge of managing and supervising Scenic and Historic Interest Areas across the country, and the local government's construction department is in charge of supervising Scenic and Historic Interest Areas in the region. The management organization of Scenic and Historic Interest Areas established by local governments above the county level has a system that actually protects, utilizes, and manages Scenic and Historic Interest Areas. In addition, 14 detailed indicators are used to monitor Scenic and Historic Interest Areas. Based on these results, considering the application of the domestic scenic site policy, the method of developing the policy that has established the system from the perspective of the utilization of the people is worth considering. On the other hand, the evaluation of the designation and management system through the setting of various indicators has limitations in that it is difficult to secure objectivity in impressing or evaluating the landscape. Therefore, rather than blindly introducing quantified evaluation, it seems that guidance and promotion on how to expand consensus on scenic values and enjoy heritage should be prioritized.

• Korea Trade Review
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• v.48 no.3
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• pp.217-242
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• 2023

This study aims to analyze the factors influencing the logistics demand of inland ports along the Yangtze River and predict future port logistics demand based on these factors. The logistics demand prediction using system dynamics techniques was conducted for a total of six ports, including Chongqing and Yibin ports in the upper reaches, Jingzhou and Wuhan ports in the middle reaches, and Nanjing and Suzhou ports in the lower reaches of the Yangtze River. The logistics demand for all ports showed an increasing trend in the mid-term prediction until 2026. The logistics demand of Chongqing port was mainly influenced by the scale of the hinterland economy, while Yibin port appeared to heavily rely on the level of port automation. In the case of the upper and middle reach ports, logistics demand increased as the energy consumption of the hinterland increased and the air pollution situation worsened. The logistics demand of the middle reach ports was greatly influenced by the hinterland infrastructure, while the lower reach ports were sensitive to changes in the urban construction area. According to the sensitivity analysis, the logistics demand of ports relying on large cities was relatively stable against the increase and decrease of influential factors, while ports with smaller hinterland city scales reacted sensitively to changes in influential factors. Therefore, a strategy should be established to strengthen policy support for Chongqing port as the core port of the upper Yangtze River and have surrounding ports play a supporting role for Chongqing port. The upper reach ports need to play a supporting role for Chongqing port and consider measures to enhance connections with middle and lower reach ports and promote the port industry. The development strategy for inland ports along the Yangtze River suggests the establishment of direct routes and expansion of the transportation network for South Korean ports and stakeholders. It can suggest expanding the hinterland network and building an efficient transportation system linked with the logistics hub. Through cooperation, logistics efficiency can be enhanced in both regions, which will contribute to strengthening the international position and competitiveness of each port.

• Economic and Environmental Geology
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• v.57 no.2
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• pp.161-175
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• 2024

Aggregate typically refers to sand and gravel formed by the transportation of rocks in rivers or artificially crushed, constituting a core resource in the construction industry. Gyeongsangbuk-do, the largest administrative area in South Korea, produces various sources of gravel, including forest, land (excluding other sources), river, and crushed stone. As of 2022, it has extracted approximately 6.96 million cubic meters of aggregate, with permitted production totaling around 4.07 million cubic meters and reported production of about 2.88 million cubic meters. The aggregate demand in Gyeongsangbuk-do is estimated to be 12.39 million cubic meters according to the estimation method in Ready-Mix Concrete. From the supply perspective, about 120 extraction sites are operational, with most municipalities maintaining an appropriate balance between aggregate demand and supply. However, in some areas, there is inbound and outbound transportation of aggregate to neighboring regions. Regions with significant inbound and outbound aggregate transportation in Gyeongsangbuk-do are areas connected to Daegu Metropolitan City and Pohang City along the Gyeongbu rail line, showing a high correlation with population distribution. Gyeongsangbuk-do faces challenges such as population decline, aging rural areas, and insufficient balanced regional development. Analysis using GIS reveals these trends in gravel demand and supply. Currently in this study, Gyeongsangbuk-do meets its demand for aggregate through the supply of various aggregate sources, maintaining stable aggregate procurement. River and terrestrial aggregates may be sustained as short-term supply strategies due to the difficulty of longterm development. Considering the reliance on raw material supply for selective crushing, it suggests the need for raw material management to maintain stability. Gyeongsangbuk-do highlights quarries in the forest as an important resource for sustainable aggregate supply, advocating for the development of large-scale aggregate quarries as a long-term alternative. These research findings are expected to provide valuable insights for formulating strategies for sustainable management and stable utilization of aggregate resources.

• 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 Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.