• Journal of Korean Society of Rural Planning
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• v.30 no.2
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• pp.13-24
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• 2024

This study conducts an analysis of social media big data pertaining to island tourism resources, aiming to discern the diverse forms and categories of island tourism favored by consumers, ascertain predominant resources, and facilitate objective decision-making grounded in scientific methodologies. To achieve this objective, an examination of blog posts published on Naver from 2022 to 2023 was undertaken, utilizing keywords such as 'Island tourism', 'Island travel', and 'Island backpacking' as focal points for analysis. Text mining techniques were applied to sift through the data. Among the resources identified, the port emerged as a significant asset, serving as a pivotal conduit linking the island and mainland and holding substantial importance as a focal point and resource for tourist access to the island. Furthermore, an analysis of the disparity between existing island tourism resources and those acknowledged by tourists who actively engage with and appreciate island destinations led to the identification of 186 newly emerging resources. These nascent resources predominantly clustered within five regions: Incheon Metropolitan City, Tongyeong/Geoje City, Jeju Island, Ulleung-gun, and Shinan-gun. A scrutiny of these resources, categorized according to the tourism resource classification system, revealed a notable presence of new resources, chiefly in the domains of 'rural landscape', 'tourist resort/training facility', 'transportation facility', and 'natural resource'. Notably, many of these emerging resources were previously overlooked in official management targets or resource inventories pertaining to existing island tourism resources. Noteworthy examples include ports, beaches, and mountains, which, despite constituting a substantial proportion of the newly identified tourist resources, were not accorded prominence in spatial information datasets. This study holds significance in its ability to unearth novel tourism resources recognized by island tourism consumers through a gap analysis approach that juxtaposes the existing status of island tourism resource data with techniques utilizing social media big data. Furthermore, the methodology delineated in this research offers a valuable framework for domestic local governments to gauge local tourism demand and embark on initiatives for tourism development or regional revitalization.

• Journal of Conservation Science
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• v.26 no.3
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• pp.341-348
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• 2010

After excavation work of relics, historical site which has historically meaningful, are preserved using the various methods. There are three method in the relocation methods that are original relocation method, the remaining structure-peel method, and soil peel method. The original relocation method is restored after relocating in historical site such as residential site, iron foundry site, kiln site, old mound. The remaining structure-peel method are restored only the feature of exposed remaining structure using polymeric resin, when it is difficult to relocate the entire remaining structure. And soil-peel method is exhibited after peeling in case when soil layers such as grave of old mound, foundation of building site, sedimentary deposit layer, shell heap, and etc. Soil-peel method becomes important historical data of changes according to environment at that time, that is, flooding by storm, traces of fire and living features of that time such as heaps of shells discarded after eating shellfish and living wastes. In particular, in case of soil layer for preparing foundation sites of building by compacting in turn soils with different components such as clay soil, rough sand soil and etc, it becomes important data which can judge foundation technology of that time. It can be said to be an important data preservation method for utilizing these historical data as historical data as well as for the purpose of education, exhibitions and public relations which can be shared not only by experts but also by general public. In this paper, we present the reliable definition of soil-peel method in various preservation methods and explain the using polymer in this method. So, we will come up with the accurate index about this method that is used the eminent analysis method for soil layer.

• Journal of the Korean Society of Costume
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• v.57 no.2 s.111
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• pp.69-85
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• 2007

Despite the recovery of consumer expenditure and retailing in the Korean economy after 2001, the domestic apparel industry has been aggravated by negative growth in both productivity and production. The purpose of the stud? is to diagnose the develop competitive of the Korean apparel industry and derive implications for this after estimating the efficiency of the Korean apparel companies with Data Envelopment Analysis. Data Envelopment Analysis(DEA) is a methodology based in non-parametric analysis and linear programming. It was developed for measuring the relative efficiency of a set of firms that use inputs to produce outputs. Data used fer input and output variables in the analysis are drawn from financial statement recorded by the Korean Financial Supervisory Service. The initial input data comprise the number fo the employees, fixed assets, general management and selling expenses, and cost of sales. The initial outputs are the operating profit and the gross margin. To summary the results, the efficiencies of the Korean apparel companies has increased yearly in spite of being overabundance of investment in Labour and Capital. According to correlation between input and output variables, the Korean apparel industry has been revamping gradually from labor intensive industries to the capital. The companies need to reduce costs in the results from the number of employees, fixed asset and cost of sales to transform into an efficiently enterprise. The companies owning or obtaining a brand had bitter establish an outsourcing strategic in production, while OEM corporations are called far setting up a manufactory in domestic or abroad. Although the paper is derived some implications with production efficiencies, the relation between apparel companies and brand power, consumption level of consumer, and social trend is remained on a limitation to the study. The next research necessitates a topic with Fashion industry or examining the correlation between brand value, social propensity and profit margin.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.364-378
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• 2008

Data Envelopment Analysis(DEA) models can be used for performance evaluation on governmental funding projects for IT small and medium-sized enterprises associated with multiple-outputs/multiple-inputs. In order to enhance the accuracy of DEA efficiency scores, DEA models with exogenously fixed variables are required where the corporate competency is taken into account. Additionally, it is necessary to use multiple DEA basic as well as extended models so as to relax the restriction on the performance evaluation to relying on a single DEA model. In this study; 1)a DEA data structure is designed including exogenously fixed variables representing corporate asset, revenue and the number of employees at the point in time that the governmental funding project concerned is initiated; 2)DEA basic as well as extended models are established according to the DEA data structure presented abovementioned; and 3)a case study is illustrated with an empirical testbed dataset. As for the DEA basic models, CCR, BCC, Super-efficiency model are adopted. The DEA extended models are developed based on the models associated with noncontrollable and nondiscretionary variables. In the case study, it is explained a comparison of DEA models and also major numerical outcomes such as efficiency scores, ranks derived from each DEA model are integrated using Analytic Hierarchy Process(AHP) weights. Performance significance with DEA efficiency scores between technical categories are tested based not only on parametric but also nonparametric single-factor analysis of variance method.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.4
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• pp.49-65
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• 2022

Since trees in the city are green assets that create a healthy environment for the city, systematic management of trees improves urban ecosystem services. The sporadic urban tree information centered on the site is vast, and it is difficult to manage the data, so efforts to increase efficiency are needed. This paper summarizes tree data inventory based on data constructed by Seoul Green Trust activists and constructs and discloses online database maps using Tableau Software. In order to verify the utilization of the map, we divided into consumer and supplier aspects to collect various opinions and reflect feedback to implement tree database maps for each area and species of Seoul Forest. As a result, the utilization value of tree database in urban parks was presented. The technical significance of this study is to systematically record the process of constructing and implementing a dashboard directly using the Mapbox platform and Tableau Software in the field of landscaping for the first time in Korea. In addition, the implications and supplements of landscape information were derived by collecting user opinions on the results. This can be used as an exploratory basis in the process of developing online-based services such as web and apps by utilizing landscaping tree information in the future. Although the visualization database currently constructed has limitations that ordinary users cannot interact in both directions because it utilizes business intelligence tools in terms of service provision it has affirmed both the database construction and its usability in web public format. In the future it is essential to investigate the assets of the trees in the city park and to build a database as a public asset of the city. The survey participants positively recognized that information is intuitively presented based on the map and responded that it is necessary to provide information on the overall urban assets such as small parks and roadside trees by using open source maps in the future.

• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.657-668
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• 2022

With the occurrence of localized heavy rain while river flow has increased, both flow and rainfall cause riverside flood damages. As the degree of damage varies according to the level of social and economic impact, it is required to secure sufficient forecast lead time for flood response in areas with high population and asset density. In this study, the author established a flood risk matrix using ensemble rainfall runoff modeling and evaluated its applicability in order to increase the damage reduction effect by securing the time required for flood response. The flood risk matrix constructs the flood damage impact level (X-axis) using flood damage data and predicts the likelihood of flood occurrence (Y-axis) according to the result of ensemble rainfall runoff modeling using LENS rainfall data and as well as probabilistic forecasting. Therefore, the author introduced a method for determining the impact level of flood damage using historical flood damage data and quantitative flood damage assessment methods. It was compared with the existing flood warning data and the damage situation at the flood warning points in the Taehwa River Basin and the Hyeongsan River Basin in the Nakdong River Region. As a result, the analysis showed that it was possible to predict the time and degree of flood risk from up to three days in advance. Hence, it will be helpful for damage reduction activities by securing the lead time for flood response.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.19-31
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• 2022

Currently, the role of public accelerators in the domestic accelerator market is gradually expanding. Accordingly, in order to establish relevant policies properly, it is necessary to check the effect and validity of public accelerators' investment. However, there is no quantitative research conducted on domestic accelerators, using their financial data, as domestic accelerators have a short history and quantitative data on them are not disclosed. Therefore, this study conducted an empirical analysis with financial data of the startups that received equity investments from public accelerators to confirm the effect of public accelerators'investment in startups. A regression analysis was conducted with financial data from 112 startups that acquired investments from public accelerators in the period of 2016~2020. And the findings are as follows: First, it was found that the initial investment of public accelerators had an effect on the growth and profitability of startups. Specifically, it was confirmed that the initial investment of public accelerators had a positive (+) effect on sales growth rates and total asset growth rates, which are growth indicators. Second, it was found that the joint investment of public accelerators had a significant positive (+) effect on profit margin, an indicator of profitability, rather than on growth. Therefore, it is deemed that it will be a great force for growth if investment in the early-stage startups that showed significant investment results in this study is continuously expanded in combination with support projects, which are a strength of public accelerators. Since this study has confirmed the investment effect of public accelerators, it is deemed necessary to actively promote policies that direct public accelerators' projects toward improving the performance of startups through joint investment with the private sector and supplementing private accelerators' deficiencies.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Journal of Digital Convergence
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• v.12 no.9
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• pp.75-81
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• 2014

This study is analyzed using the Shanghai stock market and Shenzhen stock market data in order to analyze the usefulness of accounting information to appear from the introduction of international accounting standards in China. Summarized the relevant previous researches for objective study approach, studied the hypothesis based on the empirical analysis and set a hypothesis as adjusting the stuffs to fix in this research model. In this study, Analyzed the hypothesis to input of detailed variables for analyzing the value relevance between periods before fair value and after fair value. Also, in this hypothesis study, analyzed and estimated to affect the quality of information the acceptant period when compare with acceptant periods and before periods of fair value. These results suggested that impact the net asset value per share and earnings per share of the company because the value of the relationship had statistically significant at the level of relevance. Therefore, in the future studies about fair value assessment, will be expected that usefulness of the enterprise value evaluation method enable to discuss it such as critical sucess factors.