• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.9-15
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• 2018

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, 'False alarm' and 'Miss' in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a 'False alarm' error. In addition, this study found that people's dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users' opinion in almost real time, which is impossible through survey or interview.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.2046-2055
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• 2021

This paper analyzed the primary concerns about radiation among the Korean public with a big data analysis of questions posted at the section of "Knowledge iN" on the portal site NAVER in Korea from January 2010 to August 2020. First, we extracted questions about radiation and categorized them into the three categories with TF-IDF analysis: "Medical," "Career Counseling," and "General Interest". The "Medical" category includes questions about radiation diagnosis or treatment. The "Career Counseling" category includes questions about entering college and the prospect of finding jobs in radiation-related fields. The "General Interest" category includes questions about terminology and the basic knowledge of radiation or radioisotopes. Second, we extracted common questions for each category. Finally, we analyzed the temporal change in the numbers of questions for each category to confirm whether there is any correlation between radiation-related events and the number of questions. The analysis results demonstrate that major radiation-related events have little relevance to the number of questions except during March 2011.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.497-514
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• 2023

Purpose: This study aims to estimate the demand for various public parking lots in Seoul by clustering similar demand types of parking lots and predicting the demand for new public parking lots. Methods: We examined real-time parking information data and used time series clustering analysis to cluster public parking lots with similar demand patterns. We also performed various regression analyses of parking demand based on diverse heterogeneous data that affect parking demand and proposed a parking demand prediction model. Results: As a result of cluster analysis, 68 public parking lots in Seoul were clustered into four types with similar demand patterns. We also identified key variables impacting parking demand and obtained a precise model for predicting parking demands. Conclusion: The proposed prediction model can be used to improve the efficiency and publicity of public parking lots in Seoul, and can be used as a basis for constructing new public parking lots that meet the actual demand. Future research could include studies on demand estimation models for each type of parking lot, and studies on the impact of parking lot usage patterns on demand.

• Journal of Preventive Medicine and Public Health
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• v.31 no.4 s.63
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• pp.875-884
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• 1998

Missing observations are common in medical research and health survey research. Several statistical methods to handle the missing data problem have been proposed. The EM algorithm (Expectation-Maximization algorithm) is one of the ways of efficiently handling the missing data problem based on sufficient statistics. In this paper, we developed statistical models and methods for survey data with multivariate missing observations. Especially, we adopted the EM algorithm to handle the multivariate missing observations. We assume that the multivariate observations follow a multivariate normal distribution, where the mean vector and the covariance matrix are primarily of interest. We applied the proposed statistical method to analyze data from a health survey. The data set we used came from a physician survey on Resource-Based Relative Value Scale(RBRVS). In addition to the EM algorithm, we applied the complete case analysis, which uses only completely observed cases, and the available case analysis, which utilizes all available information. The residual and normal probability plots were evaluated to access the assumption of normality. We found that the residual sum of squares from the EM algorithm was smaller than those of the complete-case and the available-case analyses.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.655-665
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• 2018

Social network services (SNS) have become the most common tool for the communication of public and private opinions as well as public issues; consequently, one may form or drive public opinions to advocate by spreading positive content using SNS. Controversy for survey data based opinion poll accuracy continues in relation to response rate or sampling methodology. This study suggests complementary measures that additionally consider the sentiment analysis results of unstructured data on a social network by data crawling and sentiment dictionary adjustment process. The suggested method shows the improvement of prediction accuracy by decreasing error rates.

• Asian Journal of Innovation and Policy
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• v.9 no.1
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• pp.12-35
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• 2020

Quality performance information has been regarded as a significant step toward managing public performance. Although a correlation between the quality of information and its actual usage among managers in high-accountability policy areas has been found, quality performance information has not been properly provided to practitioners. This study takes an Institutional Analysis and Development approach to assess an appropriate institutional framework that facilitates state agencies and academics to coproduce this information. Based on a conceptual framework, we analyze a public information system of the Workforce Data Quality Initiative in Ohio and carry out a content analysis with NVIVO. It is found that arrangements that can manage the incentive dynamic in this process may help to align heterogeneous stakeholders in a mutually supportive fashion. Also, the research agenda and information resulted from being coproduced for management and academic purposes, simultaneously. This use of administrative data sheds light on how quality performance information can be coproduced under an appropriate institutional arrangement between administration and research communities. It is suggested that accessibility to the information system among various stakeholders should be improved.

• Korean Institute of Interior Design Journal
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• no.20
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• pp.98-104
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• 1999

This study aims to offer fundamental design strategies for concerning public·service space in museums through comparative analysis between korean and foreign museums. In so doing, it deals with the spatial configuration of public·service space. It suggests that the careful consideration about the public·service space should be taken in order to make the museum public in real sense. Some design strategies suggested in this study can be used as fundamental data for a public museum design, in particular at the early stage of the design process.

• Korean Institute of Interior Design Journal
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• v.25 no.1
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• pp.124-131
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• 2016

This study focuses on the proposal of the basic data for the design guideline on the coming design, through the comparison analysis of the community facility's layout type inside housing complex, the space configuration and the characteristic of space program for the achievement and improvement of the physical space to cultivate the sense of community. This study investigate interviews with designers, administrators, residents, and site survey. The result of study shows that the community facilities in the public rental apartments have changed to include various programs and to increase the area like the public sales apartments, but there are spacial transformation if the community facilities do not apply the residents characteristic.

• Smart Media Journal
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• v.11 no.11
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• pp.49-62
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• 2022

Recently, with the development of Smart City Solutions such as Big data, AI, IoT, Autonomous driving, and Digital twins around the world, the proliferation of various smart devices and social media, and the record of the deeds that people have left everywhere, the construction of Smart Cities using the "Big Data" environment in which so much information and data is produced that it is impossible to gauge the scale is actively underway. The Purpose of this study is to construct an objective and systematic analysis Model based on Big Data to improve the transportation convenience of citizens and formulate efficient policies in Urban Information and Public Transportation accessibility in sustainable Smart Cities following the 4th Industrial Revolution. It is also to derive the methodology of developing a Big Data-Based public transport accessibility and policy management Platform using a sustainable Urban Public DB and a Private DB. To this end, Detailed Living Areas made a division and the accessibility of basic living amenities of Gwangju Metropolitan City, and the Public Transportation system based on Big Data were analyzed. As a result, it was Proposed to construct a Big Data-based Urban Information and Public Transportation accessibility Platform, such as 1) Using Big Data for public transportation network evaluation, 2) Supporting Transportation means/service decision-making based on Big Data, 3) Providing urban traffic network monitoring services, and 4) Analyzing parking demand sources and providing improvement measures.

• Journal of Information Management
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• v.41 no.3
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• pp.67-84
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• 2010

The purpose of this paper is to measure and analyse the relative efficiency of 17 public libraries in Daejeon metropolitan city using Data Envelopment Analysis. In this paper, total staffs, total area, total holdings are used as library inputs and total circulations, total user visits are used as library outputs. The estimated results show that libraries operate at 64.15% efficiency and only 4 libraries are relatively efficient. Inefficiency of the public libraries was due to small use of outputs compared to inputs.