• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.57-70
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• 2018

The advancement and the spread of information and communication technology (ICT) changes the way we live and act. Computer and ICT devices become smaller and invisible, and they are now virtually everywhere in the world. Many socio-economic activities are now subject to the use of computer and ICT devices although we don't really recognize it. Various socio economic activities supported by digital devices leave digital records, and a myriad of these records becomes what we call'big data'. Big data differ from conventional data we have collected and managed in that it holds more detailed information of socio-economic activities. Thus, they offer not only new insight for our society and but also new opportunity for policy analysis. However, the use of big data requires development of new methods and tools as well as consideration of institutional issues such as privacy. The goals of this research are twofold. Firstly, it aims to understand the opportunities and challenges of using big data for planning support. Big data indeed is a big sum of microscopic and dynamic data, and this challenges conventional analytical methods and planning support tools. Secondly, it seeks to suggest ways of visualizing such spatial big data for planning support. In this regards, this study attempts to develop a dynamic visualization model and conducts an experimental case study with mobile phone big data for the Jeju island. Since the off-the-shelf commercial software for the analysis of spatial big data is not yet commonly available, the roles of open source software and computer programming are important. This research presents a pilot model of dynamic visualization for spatial big data, as well as results from them. Then, the study concludes with future studies and implications to promote the use of spatial big data in urban planning field.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.10-10
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• 2020

하천은 최근 주민에게 휴식과 레저의 기회를 제공하고 지역활기 창출의 자원으로 가치가 부상함에 따라 하천공간의 관리와 운영이 한층 더 중요해 졌다. 즉 하천공간의 개발과 보전은 지역의 문화관광과 복지 등의 지역 정책과 함께 하천이용의 수요를 고려하여 신중하게 운영해야 한다. 이에 본 연구에서는 하천공간의 체계적인 관리를 위해 통신 빅데이터를 활용하여 이용객 수를 추정하고 검증 한 뒤, 이용도 지표를 산정하였으며, 하천공간의 상세 유형화 방안을 마련하고 유형별 특성분석 등을 실시하고자 하였다. 현장표본조사를 통한 검증결과 통신 빅데이터는 하천공간에서의 이용객 수 추정에 활용 가능성을 보였으며, 이용도 지표 산정결과를 통해 친구지구를 이용객들이 어떻게 활용하는지 판단할 수 있었다. 또한 상세 유형화 방안을 마련하고 적용한 결과 이용객들이 하천공간을 근린의 성격과 거점의 성격으로 이용하고 있는지 판단할 수 있었다. 본 연구의 결과를 종합할 때, 친수지구 조성 및 관리를 위한 자료 활용방안을 제시할 수 있었으며, 국가하천 점용허가 시 통신 빅데이터 활용방안을 마련할 수 있었다. 통신 빅데이터는 친수지구 이용도 조사에 크게 유용한 방법을 제공하며, 하천계획, 유지·보수 등 관련 실무활용 및 정책수립에 큰 도움이 될 것으로 판단된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.811-812
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• 2016

최근 공간 정보는 생산 되는 양과 데이터의 생성 빈도 및 다양성으로 인해 기존의 공간 데이터베이스 시스템에서 처리하기 어렵다. 그래서 공간 정보는 빅데이터와 연계에 관한 시도가 활발히 진행되고 있다. 그러나 효율적인 단일할당, 다중할당 색인기반 공간 연산에 대한 연구는 거의 없다. 이 논문에서는 공간 연산 중 비용이 매우 큰 공간 조인을 빅데이터 시스템에서 처리하기 위한 고려요소를 제시하고자 한다. 구체적으로 맵리듀스 시스템의 태스크 할당을 위한 단일 할당 공간 색인방안을 설명하고, 불균일 분포가 심한 공간 정보의 특성을 고려한 부하 균등화 시 고려 요소를 제시하고자 한다. 맵리듀스와 같은 병렬 공간 데이터베이스 시스템에서의 두 가지 문제인 데이터 불균일 분포 문제와 경계 겹침 색인의 문제와의 연관성을 기술한다.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.29-36
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• 2017

In this paper, we implement a spatial big data analysis prototype based on Spark which is an in-memory system and compares the performance by the spatial split algorithm on this basis. In cluster computing environments, big data is divided into blocks of a certain size order to balance the computing load of big data. Existing research showed that in the case of the Hadoop based spatial big data system, the split method by spatial is more effective than the general sequential split method. Hadoop based spatial data system stores raw data as it is in spatial-divided blocks. However, in the proposed Spark-based spatial analysis system, there is a difference that spatial data is converted into a memory data structure and stored in a spatial block for search efficiency. Therefore, in this paper, we propose an in-memory spatial big data prototype and a spatial split block storage method. Also, we compare the performance of existing spatial split algorithms in the proposed prototype. We presented an appropriate spatial split strategy with the Spark based big data system. In the experiment, we compared the query execution time of the spatial split algorithm, and confirmed that the BSP algorithm shows the best performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.800-802
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• 2016

In these days, the profitability of cadastral survey for national spatial information is getting worse. In order to reinforce the structure of the profitability, there exists the necessity to launch new and various businesses except the cadastral survey. In manipulating national spatial data effectively, it is necessary to design a platform for spatial information. Against this backdrop, we propose a platform for spatial data on the basis of Big Data in this paper.

• Information and Communications Magazine
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• v.32 no.11
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• pp.3-7
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• 2015

최근 활발히 연구되고 있는 빅데이터와 의료 영역이 융합되면서, 보건의료서비스 분야에서는 데이터 집약적이고 공간을 초월한 새로운 서비스패러다임의 움직임이 진행되고 있다. 본고에서는 이러한 빅데이터를 활용하여 건강증진 및 예방을 위하여 생활 속에서 제공되고 있는 생활환경 및 보건 데이터 기반의 라이프케어 서비스동향과 기술에 관하여 알아본다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.1-8
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• 2017

In this paper we present a spatial big data query processing system that can store spatial data in Hadoop and query the data with SQL-based query language. The system stores large-scale spatial data in HDFS-based storage system, and supports spatial queries expressed in SQL-based query language extended for spatial data processing. It supports standard spatial data types and functions defined in OGC simple feature model in the query language. This paper presents the development of core functions of the system including query language parsing, query validation, query planning, and connection with storage system. We compares the performance of the suggested system with an existing system, and our experiments show that the system shows about 58% performance improvement of query execution time over the existing system when executing region query for spatial data stored in Hadoop.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.100-112
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• 2021

With the recent growth of the big data technology market, interest in visualization technology has steadily increased over the past few years. Data visualization is currently used in a wide range of disciplines such as information science, computer science, human-computer interaction, statistics, data mining, cartography, and journalism, each with a slightly different meaning. Big data visualization in smart cities that require multidisciplinary research enables an objective and scientific approach to developing user-centered smart city services and related policies. In particular, spatial-based data visualization enables efficient collaboration of various stakeholders through visualization data in the process of establishing city policy. In this paper, a user-centered spatial big data visualization expression request method was derived by examining the spatial-based big data visualization expression process and principle from the viewpoint of effective information delivery, not just a visualization tool.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.101-115
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• 2020

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.385-396
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• 2015

The purpose of this study are in selecting a core service and drawing an analysis functions and service sector, based on contents of geo-spatial big data. For the study, the demand survey in the methodology has to be done by reviewing of preceding geo-spatial big data service. The survey has conducted by targeting on those experts in Industry-Academy-Research cooperation. From the survey, we could draw out requirements for the analysis function and the geo-spatial big data service sector. Also, order of priorities in service of four fields(Society, Environment, Economy, Humanities) has been utilized by a QFD(Quality Function Deployment). With the data, the first two priorities and required sectors for each field were selected for the analysis functions. From the result, we could suggest the core service model(plan), and also expect developments following each sectoral core service in the future.