• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.682-690
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• 2020

Purpose: This study builds a database by collecting and refining disaster occurrence data and real-time weather and atmospheric data. In conjunction with the public data provided by the API, we propose a service model for the Big Data-based Urban Safety Index. Method: The plan is to provide a way to collect various information related to disaster occurrence by utilizing public data and SNS, and to identify and cope with disaster situations in areas of interest by real-time dashboards. Result: Compared with the prediction model by extracting the characteristics of the local safety index and weather and air relationship by area, the regional safety index in the area of traffic accidents confirmed that there is a significant correlation with weather and atmospheric data. Conclusion: It proposed a system that generates a prediction model for safety index based on machine learning algorithm and displays safety index by sector on a map in areas of interest to users.

• Journal of KIBIM
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• v.7 no.1
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• pp.28-35
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• 2017

Cities have complex system composed diverse activities. The activities in cities have complex relationship that creates diverse urban phenomena. Big Data is emerging technology in order to understand such complex network. This research aims to understand such relations by analysing the diverse city indexes. 28 indexes were collected in 25 of districts in Seoul city and analysed to find a weighted correlation. By defining the correlation values of certain years, it tries to predict the missed index values, "happiness" of each districts in other years. The result presents that the overall prediction accuracy 70.25%. However, for further discussion, the result is considered that this methods may not enough to use in practice, since the data has inconstant accuracy by different learning years.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.2
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• pp.139-146
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• 2019

The purpose of this study is to derive an improved method for analyzing old buildings with risk of collapse using public big data. Previous studies on the risk of building collapse focused on internal factors such as building age and structural vulnerability. However, this study suggests a method to derive potentially collapsible buildings considering not only internal factors of buildings but also external factors such as nearby new construction data. Based on the big data analysis, this study develops a system to visualize vulnerable buildings that require safety diagnosis and proposed a future utilization plan.

• Construction Engineering and Management
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• v.24 no.2
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• pp.62-64
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• 2023

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.1
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• pp.35-45
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• 2020

This study focused on the park as a space to support the behavior of urban citizens in modern society. Modern city parks are not spaces that play a specific role but are used by many people, so their function and meaning may change depending on the user's behavior. In addition, current online data may determine the selection of parks to visit or the usage of parks. Therefore, this study analyzed the change of behavior in Yeouido Park, Yeouido Hangang Park, and Yangjae Citizen's Forest from 2000 to 2018 by utilizing a time series analysis. The analysis method used Big Data techniques such as text mining and social network analysis. The summary of the study is as follows. The usage behavior of Yeouido Park has changed over time to "Ride" (Dynamic Behavior) for the first period (I), "Take" (Information Communication Service Behavior) for the second period (II), "See" (Communicative Behavior) for the third period (III), and "Eat" (Energy Source Behavior) for the fourth period (IV). In the case of Yangjae Citizens' Forest, the usage behavior has changed over time to "Walk" (Dynamic Behavior) for the first, second, and third periods (I), (II), (III) and "Play" (Dynamic Behavior) for the fourth period (IV). Looking at the factors affecting behavior, Yeouido Park was had various factors related to sports, leisure, culture, art, and spare time compared to Yangjae Citizens' Forest. The differences in Yangjae Citizens' Forest that affected its main usage behavior were various elements of natural resources. Second, the behavior of the target areas was found to be focused on certain main behaviors over time and played a role in selecting or limiting future behaviors. These results indicate that the space and facilities of the target areas had not been utilized evenly, as various behaviors have not occurred, however, a certain main behavior has appeared in the target areas. This study has great significance in that it analyzes the usage of urban parks using Big Data techniques, and determined that urban parks are transformed into play spaces where consumption progressed beyond the role of rest and walking. The behavior occurring in modern urban parks is changing in quantity and content. Therefore, through various types of discussions based on the results of the behavior collected through Big Data, we can better understand how citizens are using city parks. This study found that the behavior associated with static behavior in both parks had a great impact on other behaviors.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.102-112
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• 2020

Smart City's goal is to solve urban problems through smart city's component technology, thereby developing eco-friendly and sustainable economies and improving citizens' quality of life. Until now, smart cities have evolved into component technologies, but it is time to focus attention on the needs and participation of citizens in smart cities. In this paper, we present a big data procedure for solving smart city problems based on citizens' needs and participation. To this end, we examine the smart city project market by region and major industry. We also examine the development stages of the smart city market area by sector. Additionally it understands the definition and necessity of each sector for citizen participation, and proposes a method to solve the problem through big data in the seven-step big data problem solving process. The seven-step big data process for solving problems is a method of deriving tasks after analyzing structured and unstructured data in each sector of smart cities and deriving policy programs accordingly. To attract citizen participation in these procedures, the empathy stage of the design thinking methodology is used in the unstructured data collection process. Also, as a method of identifying citizens' needs to solve urban problems in smart cities, the problem definition stage of the design sinking methodology was incorporated into the unstructured data analysis process.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.197-213
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• 2022

In this study, text mining analysis was performed on online big data for recognition and assessment of urban brand I Seoul U. To this end, TEXTOM, a processing program for data acquisition and analysis was used, and the 'I SEOUL U' keyword was selected as an analysis keyword. Keyword analysis shows the keywords associated with I Seoul U to be as follows: First, as a business and marketing term, keywords include pop-up store, gallery, co-branding, (festival, etc.), commodities, private companies and online. Second, as an event-related term, keywords include Han River, tree-planting day, tree planting, Hongdae, Christmas, Mapo, Jung-gu, Sejong University, and festival. Third, as a promotional term, keywords include robotics engineer Dr. Dennis Hong, Government, Art and Korea. In the N Gram analysis, as the city brand of Seoul, I Seoul U, in the public interest, was found to contribute to the commercial activities of private companies. In connection-oriented analysis, business and marketing, events, and promotions have been derived as categories. In matrix analysis, it was found that the products of the pop-up store are mainly developed, and products in the form of co-branding were being developed. In the topic modeling, a total of 10 topics were extracted and needs for commercial utilization and information for event festivals were mostly found.

• Journal of Urban Science
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• v.12 no.1
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• pp.35-48
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• 2023

Recently increasing in marketing or policy decision is the trend of reflecting big data, which, however, has yet to be used directly for the location analysis of public facilities in terms of urban planning. This study examined how the local currency big data, issued often recently by municipalities throughout the country, can be used for the decision-making to select the location of public facilities more rationally. It is such an interesting attempt to acquire the big data of local currency payments by local residents and directly apply it to analyzing the location analysis of public facilities they use. The big data of local currencies which are issued by most municipalities now in Korea will continue to extend its role as the public data. Relatively easily available for municipalities with low cost, it is expected to be used for various policy decisions in future. Although the analysis of big data can make more accurate results than conventional survey methods, however, local residents' participation should not be scaled down in policy decisions. Rather, they should be given the findings of this kind of scientific survey so as to extend the citizen-participatory decision-making model.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.67-78
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• 2016

Recently many countries around the world are actively promoting smart city projects to address various urban problems such as traffic congestion, housing shortage, and energy scarcity. Due to development of the Internet of Things (IoT), the development of a smart city with sustainability, convenience, and environment-friendliness was enabled through the effective control and reuse of urban resources. The purpose of this study is to analyze the technical trends of IoT and present a development plan for smart city which is one of the applications of the IoT. To this end, the news articles of the Electronic Times between 2013 and 2015were analyzed using the text mining technique and smart city development cases of other countries were investigated. The analysis results revealed the close relationships of big data, cloud, platforms, and sensors with smart city. For the successful development of a smart city, first, all the interested parties in the city must work together to create new values throughout the entire process of value chain. Second, they must utilize big data and disclose public data more actively than they are doing now. This study has made academic contribution in that it has presented a big data analysis method and stimulated follow-up studies. For the practical contribution, the results of this study provided useful data for the policy making of local governments and administrative agencies for smart city development. This study may have limitations in the incorporation of the total trends because only the news articles of the Electronic Times were selected to analyze the technical trends of the IoT.

• Annual Conference of KIPS
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• 2020.11a
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• pp.773-776
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• 2020

웹 뉴스 기사는 태풍과 같은 재해 발생상황에 대한 신속하고 정확한 정보를 포함하고 있다. 예를 들어, 태풍의 발생시점, 이동·예측경로, 피해·사고 현황 등 유용한 정보를 텍스트, 이미지, 동영상의 형태로 관련 상황정보를 전달한다. 그러나 대부분의 재해재난 관련 뉴스 기사는 특정 시점의 정보만을 웹페이지 형태로 제공하므로, 시계열 측면의 연결성을 지니는 기사들에 대한 정보를 전달하기 어렵다. 또한 시간적 변화에 따라 기사 내용에 포함된 장소, 지역, 건물 등의 지명에 대한 공간적 정보를 지도와 연계하여 정보를 전달하는데 한계가 있어, 시공간적 변화에 따른 특정 재해재난 상황정보에 대한 전체적인 현황파악이 어렵다. 따라서, 본 논문에서는 데이터 시각화 측면에서 이러한 한계를 극복하기 위해, 1) 웹크롤링을 통해 구축된 뉴스 빅데이터를 자연어 처리를 통해 태풍과 관련된 뉴스 기사들을 추출하였고, 2) 시공간적 관련 정보를 지식그래프로 구축하였고, 이를 통해 최근 발생한 태풍 사건들과 관련된 뉴스 정보를 시계열 특성을 고려하여 3) 인터렉티브 지도 기반의 태풍 상황정보를 시각화하는 방안을 연구하였다.