• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.4
• /
• pp.281-298
• /
• 2015

From the complexity system theory, City is dynamic system which has evolved through evolution and adaptation in initial conditions and different situation. So people's active should involve in decision-making processes in the urban planning. And this suggests that responding to the demands of its citizens are important factors influencing the process of urban planning. The implications of this study are following: using big data helps people understand current social phenomena. Specifically, it figured out latent needs of citizens that traditional survey methods could not before. we can make the most of new opportunities given by digital data and prevent potential dangers in advance. They are complementary and do not replace one another.

• Journal of the Korean Regional Science Association
• /
• v.39 no.3
• /
• pp.35-48
• /
• 2023

This study analyzed the inflow factors of Yangjae Citizen's Forest Park using social big data generated online. To this end, the applicability of the emotional information analysis method is to be confirmed as a method of analyzing the perception of the city park and confirming the difference in the characteristics and use of the park. The analysis is based on big data, and as the core of the study is keyword network analysis, the methodology of the 'emotional information analysis method' patented by the author was applied. As a result of the analysis, among the influx factors of Yangjae Citizens' Forest recognized by citizens, the most positive emotional factor was derived as a factor related to 'park contents', and the negative emotional factor was derived as a factor related to 'park management'. These research results suggest that more in-depth program development and operation are needed to discover 'park contents' when implementing urban park revitalization support projects in the future

• Journal of Information Processing Systems
• /
• v.19 no.2
• /
• pp.267-274
• /
• 2023

As the Internet has expanded and the continuous expansion of online shopping in China, many rural areas also have sales outlets. Due to the impact of economic conditions, rural locations have inadequate e-commerce logistical infrastructure, the number of outlets is small, and each other is in a decentralized state. For various reasons, the advancement of rural e-commerce logistics lags far behind that in urban areas. As the Internet of Things with big data grow in popularity, we can create and enhance the assurance system for the booming ecommerce in rural areas by building the support system of rural online shopping platform, and strengthening the joint distribution of logistics terminals based on data mining, so as to encourage the quick and healthy growth of rural online shopping.

• Journal of the Korean Institute of Rural Architecture
• /
• v.22 no.4
• /
• pp.43-50
• /
• 2020

The purpose of this study is to extract deficiency areas as basic data of policies and projects in the future Living SOC introduction and planning. In order to extract living SOC deficient areas, accessibility data for living SOC and density data for main users by facility were overlapped, focusing on the living SOC indicators presented in the National Urban Regeneration Basic Policy. According to the analysis of accessibility of the Iksan-si Living SOC, the gap between deficiency in urban and township areas was large in common with the accessibility of the village and local base units. As a result of overlapping life SOC accessibility data and density data analysis of the main users by facility, areas where accessibility is weak but not inhabited by the main users of each facility were extracted. It is meaningful that more accurate deficient areas can be extracted by simultaneously utilizing the density distribution of the main users, rather than simply accessing the facilities.

• Journal of KIBIM
• /
• v.10 no.4
• /
• pp.11-21
• /
• 2020

With increasing interest and research on smart cities, there is also an increasing number of studies on urban facilities that can be built within smart cities. According to these studies, smart cities' urban facilities are likely to become high value-added industries. However, the concept of smart city is not clear because it involves various fields. Therefore, in this study, the definition of Next-Generation(N.G) Smart City Facilities with Digital Twin and Digital Chain is carried out through a multidisciplinary approach. Based on this, Next-Generation Smart City Facilities will be divided into High Value-Added Products and Big Data Platforms. Subsequently, the definition of the Digital Chain containing the data flow of the entire process built through the construction of the Digital Twin proceeds. The definitions derived are applied to the Next-Generation Noise Barrier Tunnel to ensure that data is exchanged at the Digital Twin stage, and to review the proposed configuration of the Digital Chain and Data Flow in this study. The platform definition and Digital Chain of Next-Generation Smart City Facilities proposed in this study suggest that it can affect not only the aspects of data management that are currently in the spotlight, but also the manufacturing industry as a whole.

• Journal of Korea Multimedia Society
• /
• v.21 no.5
• /
• pp.599-608
• /
• 2018

As natural disasters are increasing due to the unusual weather and the modern society is getting complicated, the rapid change of the urban environment has increased human disasters. Thus, citizens are becoming more anxious about social safety. The importance of preparation for safety has been suggested by providing the disaster safety services such as regional safety index, life safety map, and disaster safety portal application. In this paper, we propose an application framework to predict the urban safety index based on user's location with realtime weather/atmosphere data after creating a predication model based on the machine learning using number of occurrence cases and weather/atmosphere history data. Also, we implement an application to provide traffic safety index with executing preprocessing occurrence cases of traffic and weather/atmosphere data. The existing regional safety index, which is displayed on the Si-gun-gu area, has been mainly utilized to establish safety plans for districts vulnerable to national policies on safety. The proposed system has an advantage to service useful information to citizens by providing urban safety index based on location of interests and current position with realtime related data.

• Journal of Environmental Science International
• /
• v.30 no.12
• /
• pp.1101-1111
• /
• 2021

In this study, among the W-S-R(Wiper-Signal-Rainfall) relationship methods used to produce sensor-based rain information in real time, we sought to produce actual rainfall information by applying machine learning techniques to account for the effects of wiper operation. To this end, we used the gradient descent and threshold map methods for pre-processing the cumulative value of the difference before and after wiper operation by utilizing four sensitive channels for optical sensors which collected rain sensor data produced by five rain conditions in indoor artificial rainfall experiments. These methods produced rainfall information by calculating the average value of the threshold according to the rainfall conditions and channels, creating a threshold map corresponding to the 4 (channel) × 5 (considering rainfall information) grid and applying Optima Rainfall Intensity among the big data processing techniques. To verify these proposed results, the application was evaluated by comparing rainfall observations.

• Journal of the Korean Institute of Rural Architecture
• /
• v.23 no.2
• /
• pp.9-16
• /
• 2021

The purpose of this study is to maintain the existing characteristics of the city by utilizing the physical decline status and floating population in small and medium cities residential areas. In addition, it intends to present the direction of flexible urban regeneration and maintenance by reflecting regional characteristics and current status. A total of three data were used in this study. Building data, floating population data, and census output area data were used. Building data and floating population data were classified into five classes. The graded data were joined to the census output area data and analyzed by overlapping the two data. As a result of analysis of 17 residential areas in 5 small and medium cities in Jeollanam-do, 4 types, 2 management models, and 4 indicators could be presented by grade and regional characteristics. This study is meaningful in that it is possible to plan regionally customized urban regeneration/maintenance management plans and projects through the typology of the current status and characteristics of the region, which is an important step in the bottom-up form.

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

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