• Land and Housing Review
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• v.13 no.2
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• pp.81-90
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• 2022

This study is based on social needs for green healing spaces assumed to enhance mental health in a city. This study proposes development directions through the analysis of modern social recognition factors for neighborhood gardens. As a research method, web information data was collected using Textom among big data tools. Text Mining was conducted to extract elements and analyze their relationship through keyword analysis, network analysis, and cluster analysis. As a result, first, the healing space and the healing environment were creating an eco-friendly healthy environment in a space close to the neighborhood within the city. Second, neighborhood gardens included projects and activities that involved government, local administration, and citizens by linking facilities as well as living culture and urban environments. These gardens have been reinforced through green welfare and service programs. In conclusion, friendly gardens in the neighborhood for the purpose of public interest, which are beneficial to mental health, are green infrastructures as a healing environment that can produce positive effects.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.386-386
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• 2020

가뭄은 강수, 증발산, 대기온도, 토양수분 등 다양한 수문기상학적 인자들이 복합적으로 작용하여 발생되기 때문에 가뭄의 정확한 사상을 분석하는 것은 매우 어렵다. 또한 어떤 요인을 중심으로 고려하느냐에 따라 가뭄은 다양한 시각으로 정의되고 있다. 일정기간 평균 강수량보다 적은 강수로 인해 건조한 날이 지속되는 것, 즉 기상요소를 중심으로 가뭄을 정의하는 것을 기상학적 가뭄이라 하며, 작물의 생육에 필요한 수분을 중심으로 고려하는 것을 농업적 가뭄이라 한다. 또한 하천유량, 댐 저수량 등 전반적인 수자원 공급원의 부족을 수문학적 가뭄이라 한다. 이와 같이 다양하게 나타는 가뭄의 발생특성을 정량적으로 해석하기 위해 다양한 가뭄지수가 개발되어 왔다. 그러나 현재까지 개발된 가뭄지수들은 공통적으로 정형데이터를 활용하여 산정한다. 하지만 최근에는 비정형데이터를 활용하여 지수(Index)를 산정하거나, 재난관리에 적용하는 등 비정형 데이터의 활용이 급증하고 있다. 따라서 본 연구에서는 비정형 데이터(뉴스 데이터)를 활용하여 가뭄지수를 산정하고 기존의 가뭄지수들과의 상관성 분석을 실시 한 뒤, 지수결합을 통해 가뭄사상 분석의 새로운 방안을 제시하고자 하였다. 본 연구의 공간적범위는 2014~2015 충남서북부가뭄 지역 중 가장 큰 피해를 입었던 보령지역으로 선정하였으며 시간적범위는 2013~2016년으로 설정하였다. 비정형 데이터의 구축은 크롤링(Crawling)을 활용하여 네이버 뉴스의 기사를 수집하였으며 자료의 신뢰성을 위해 URL이 동일한 중복기사 및 '보령', '가뭄' 단어가 없는 기사는 제거하였다. 구축된 데이터를 기반으로 월별 빈도를 산출하고 표준점수(Z-score)로 환산하여 가뭄지수를 산정하였다. 산정된 가뭄지수가 어떤 가뭄의 유형(기상학적, 농업적, 수문학적)을 보이는지 확인하기 위해 기존의 가뭄지수들과 상관성분석을 실시하였으며, 가장 높은 상관성을 보이는 가뭄지수와 결합을 통해 새로운 가뭄 사상을 분석하였다. 본 연구에서 진행한 가뭄사상 분석은 향후 가뭄만이 아니라 다양한 재난분야에서 비정형 데이터를 활용한 분석의 기초로자료로 활용될 수 있을 것이다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.705-717
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• 2020

Cities in the world are rushing to develop smart cities to create a sustainable and happy city by solving many problems in cities using information and communication technologies such as big data and IoT. However, in Korea's smart cities and smart city certification systems, the focus is on platform-oriented hardware infrastructure, and the information security aspect is first considered to build and authenticate. It is a situation in which a response system for the risk of leakage of big data containing personal information is needed through policy research on the aspect of personal information protection for smart city operation. This paper analyzes the types of personal information in smart cities, problems associated with the construction and operation of smart cities, and the limitations of the current smart city law and personal information protection management system. As a solution, I would like to present a model of a personal information protection management system in the smart city field and propose a plan to strengthen personal information protection through this. Since the management system model of this paper is applied and operated in the national smart city pilot cities, demonstration cities, and CCTV integrated control centers, it is expected that citizens' personal information can be safely managed.

• Informatization Policy
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• v.27 no.2
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• pp.66-83
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• 2020

Local issues that occur in cities typically garner great attention from the public. While local governments strive to resolve these issues, it is often difficult to effectively eliminate them all, which leads to complaints. In tackling these issues, it is imperative for local governments to use big data to identify the nature of complaints, and proactively provide solutions. This study applies the LDA topic modeling technique to research and analyze trends and patterns in complaints filed online. To this end, 9,625 cases of online complaints submitted to the city of Busan from 2015 to 2017 were analyzed, and 20 topics were identified. From these topics, key topics were singled out, and through analysis of quarterly weighting trends, four "hot" topics(Bus stops, Taxi drivers, Praises, and Administrative handling) and four "cold" topics(CCTV installation, Bus routes, Park facilities including parking, and Festivities issues) were highlighted. The study conducted big data analysis for the identification of trends and patterns in civil affairs and makes an academic impact by encouraging follow-up research. Moreover, the text mining technique used for complaint analysis can be used for other projects requiring big data processing.

• Information Systems Review
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• v.21 no.2
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• pp.1-27
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• 2019

It is possible to provide Smart Tourism Service through the development of information technology. It is necessary for the tourism industry to understand and utilize Big Data that has tourists' consumption patterns and service usage patterns in order to continuously create a new business model by converging with other industries. This study suggests to activate Jeju Smart Tourism by analyzing Big Data based on credit card usage records and location of tourists in Jeju. The results of the study show that First, the percentage of Chinese tourists visiting Jeju has decreased because of the effect of THAAD. Second, Consumption pattern of Chinese tourists is mostly occurring in the northern areas where airports and duty-free shops are located, while one in other regions is very low. The regional economy of Jeju City and Seogwipo City shows a overall stagnation, without changes in policy, existing consumption trends and growth rates will continue in line with regional characteristics. Third, we need a policy that young people flow into by building Jeju Multi-complex Mall where they can eat, drink, and go shopping at once because the number of young tourists and the price they spend are increasing. Furthermore, it is necessary to provide services for life-support related to weather, shopping, traffic, and facilities etc. through analyzing Wi-Fi usage location. Based on the results, we suggests the marketing strategies and public policies for understanding Jeju tourists' patterns and stimulating Jeju tourism industry.

• Journal of the Korean Regional Science Association
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• v.39 no.4
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• pp.19-32
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• 2023

Using extensive job posting big data, this study investigates the industrial and locational characteristics of decent work from the supply side. The analysis revealed that manufacturing is pivotal in supplying decent work, accompanied by a stark regional disparity, most notable in the Seoul Metropolitan Statistical Area (MSA), which constitutes nearly half of all decent work opportunities. The study further uncovered that the distribution of decent work varies significantly across MSAs, with a pronounced inclination towards a higher supply in peripheral rather than central areas. These findings bring to light the critical need for policies that bolster manufacturing, aiming to enhance the availability of high-quality jobs and to bridge the job quality gap between the Seoul MSA and other regions. Moreover, the results emphasize the necessity for customized job supply strategies in each MSA, prioritizing strategies that account for the proximity between workplaces and living areas in the job supply process.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.89-101
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• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.1-12
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• 2021

With the recent 4th Industrial Revolution, the growth and value of big data are continuously increasing, and the government is also actively making efforts to open and utilize public data. However, the situation still does not reach the level of demand for public data use by citizens, At this point, it is necessary to identify research trends in the public data field and seek directions for development. In this study, in order to understand the research trends related to public data, the analysis was performed using topic modeling, which is mainly used in text mining techniques. To this end, we collected papers containing keywords of 'Public data' among domestic and foreign research papers (1,437 domestically, 9,607 overseas) and performed topic modeling based on the LDA algorithm, and compared domestic and foreign public data research trends. After analysis, policy implications were presented. Looking at the time series by topic, research in the fields of 'personal information protection', 'public data management', and 'urban environment' has increased in Korea. Overseas, it was confirmed that research in the fields of 'urban policy', 'cell biology', 'deep learning', and 'cloud·security' is active.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.282-285
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• 2018

The purpose of this study is to investigate the effect of personal factors and community factors on the quality of life based on the presence of chronic diseases based on the Big Data Platform. The research methodology was the matching of the 2017 Community Health Survey data and the National Statistical Office data to the health center units. In the study, The higher the age, the higher the education level, the higher the monthly household income, the economic activity, the spouse, the higher the quality of life. In the case of community factors, the lower the population density, the lower the elderly population ratio, the more doctors engaged in medical institutions, the higher the financial independence, the higher the quality of life.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.53-58
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• 2020

The purpose of this study is to build a database of Spatial information Bigdata of cities using satellite images and spatial information, and to examine the correlations with the surface temperature. Using architectural structure and usage in building information, DEM and Slope topographical information for constructed with 300 × 300 mesh grids for Busan. The satellite image is used to prepare the Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BI), and Land Surface Temperature (LST). In addition, the building area in the grid was calculated and the building ratio was constructed to build the urban environment DB. In architectural structure, positive correlation was found in masonry and concrete structures. On the terrain, negative correlations were observed between DEM and slope. NDBI and BI were positively correlated, and NDVI was negatively correlated. The higher the Building ratio, the higher the surface temperature. It was found that the urban environment DB could be used as a basic data for urban environment analysis, and it was possible to quantitatively grasp the impact on the architecture and urban environment by adding local meteorological factors. This result is expected to be used as basic data for future urban environment planning and disaster prevention data construction.