• 한국측량학회지
• /
• 제34권6호
• /
• pp.579-589
• /
• 2016

This research focuses on accomplishing analysis problems and issues by examining the policies and systems related to geo-spatial big data which have recently arisen, and suggests political and systemic improvement plan for service activation. To do this, problems and probable issues concerning geo-spatial big data service activation should be analyzed through the examination of precedent studies, policies and planning, pilot projects, the current legislative situation regarding geo-spatial big data, both domestic and abroad. Therefore, eight political and systematical improvement plan proposals are suggested for geo-spatial big data service activation: legislative-related issues regarding geo-spatial big data, establishing an exclusive organization in charge of geospatial big data, setting up systems for cooperative governance, establishing subsequent systems, preparing non-identifying standards for personal information, providing measures for activating civil information, data standardization on geo-spatial big data analysis, developing analysis techniques for geo-spatial big data, etc. Consistent governmental problem-solving approaches should be required to make these suggestions effectively proceed.

• 한국BIM학회 논문집
• /
• 제13권4호
• /
• pp.106-114
• /
• 2023

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

• 한국BIM학회 논문집
• /
• 제8권3호
• /
• pp.12-19
• /
• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• Spatial Information Research
• /
• 제23권6호
• /
• pp.19-29
• /
• 2015

본 연구는 공간 빅데이터 서비스 활성화를 위한 정책과제 도출을 목적으로 수행하였다. 이를 위해 관련 선행연구를 검토하고, 국내 외 공간 빅데이터 관련 추진체계 및 정책현황을 분석하였다. 그 결과 미래 공간정보 융 복합 대응정책 미흡, 개인정보 보호 및 서비스 활성화 제도적 기반 미흡, 관련 기술 정책 마련 미흡, 공간 빅데이터 구축 활용을 위한 추진체계 미흡, 공공정보의 품질저하와 공유체계 미흡 등의 문제점이 도출되었다. 다음으로 도출된 문제점을 해결하기 위해 정책 추진방향을 설정하고, 공간 빅데이터 추진체계 마련, 관련 법 제도 개선, 공간 빅데이터 관련 기술 개발, 공간 빅데이터 지원 사업 추진, 공공DB 융 복합 공유체계 마련 총 5가지의 정책과제를 제시하였다.

• 한국BIM학회 논문집
• /
• 제6권3호
• /
• pp.24-33
• /
• 2016

Planning occurs from day-to-day, small-scale decisions to large-scale infrastructure investment decisions. For that reason, various attempts have been made to appropriately assist decision-making process and its optimization. Lately, initiation of a large amount of data, also known as big data has received great attention from diverse disciplines because of versatility and adoptability in its use and possibility to generate new information. Accordingly, implementation of big data and other information management systems, such as geographic information systems (GIS) and building information modeling (BIM) have received enough attention to establish each of its own profession and other associated activities. In this extent, this study illustrates a series of big data implementation cases that can provide a lesson to urban planning domain. In specific, case studies analyze how data was used to extract the most optimized solution and what aspects could be helpful in relation to planning decisions. Also, important notions about GIS and its application in various urban cases are examined.

• 지식경영연구
• /
• 제20권3호
• /
• pp.73-89
• /
• 2019

The purpose of this study is to investigate the current state of public Big Data in Seoul and suggest policy directions for the revitalization of Seoul's public Big Data. Big Data is perceived as innovation resources under the era of 4th Industrial revolution and Data economy. Especially, public Big Data serves a significant role in terms of universal access for citizens, startup, and enterprise compared with the private sector. Seoul reorganized a substructure of government's focus on Big Data and established organizations such as Big Data Campus and Urban Data Science Lab. Although the number of public open Data has increased in Seoul, there exists not much Data with characteristics similar to Big Data, such as volume, velocity, and value. In order to present the direction of Big Data policy in Seoul, we investigate the current status of Big Data Campus and Urban Data Science Lab operated by Seoul City. Considering the results of this study, we have proposed several directions that Seoul can use in establishing big data related strategies.

• 도시과학
• /
• 제12권1호
• /
• pp.35-48
• /
• 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.

• 전기학회논문지
• /
• 제67권3호
• /
• pp.460-466
• /
• 2018

For the efficient energy consumption in an urban railway station, it is necessary to know the patterns of electrical loads for each usage in detail. The electrical loads in an urban railway station have different characteristics from other normal electrical load, such as the peak load timing during a day. The lighting, HVAC, communication, and commercial loads make up large amount of electrical load for equipment in an urban railway station, and each of them has the unique specificity. These loads for each usage were estimated without measuring device by the polynomial regression method with big data such as total amount of electrical load and weather data. In the simulation with real data, the optimal polynomial regression model was third order polynomial regression model with 9 or 10 independent variables.

• 국제학술발표논문집
• /
• The 6th International Conference on Construction Engineering and Project Management
• /
• pp.41-43
• /
• 2015

Urban Informatics, the application of data science methodologies to the urban development and planning domain, has been increasingly adopted to improve the management and efficiency of cities. This paper introduces state of the art use cases in major cities including New York, London, Seoul and Amsterdam. It also introduces recent advances in using Big Data by multi-lateral institutions for poverty reduction, and startups utilizing open data initiatives to create new value and insights. Preliminary research performed on using Seoul's open data such as building permit data and health code violations are also introduced to demonstrate opportunities in this relatively new but promising area of research.

• Journal of Information Processing Systems
• /
• 제17권3호
• /
• pp.645-657
• /
• 2021

Along with the rapid development of the economy, the urban scale has extended rapidly, leading to the formation of different types of urban function districts (UFDs), such as central business, residential and industrial districts. Recognizing the spatial distributions of these districts is of great significance to manage the evolving role of urban planning and further help in developing reliable urban planning programs. In this paper, we propose an automatic UFD division method based on big data analysis of point of interest (POI) data. Considering that the distribution of POI data is unbalanced in a geographic space, a dichotomy-based data retrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial feature analysis method based on the mean shift algorithm is proposed, where data points with similar attributive characteristics are clustered to form the function districts. The proposed method was thoroughly tested in an actual urban case scenario and the results show its superior performance. Further, the suitability of fit to practical situations reaches 88.4%, demonstrating a reasonable UFD division result.