Big Data Campus

Seoul

To establish communication with this city regarding
their project, please contact secretariat@we-gov.org

  • Fast Facts

    City :

    $600,000

    Project Cost (by 2020)

    10.29million

    City Population

    2016

    Year Implemented

    Region : Asia

    National GDP Per Capita (USD) : 32,046 (IMF, 2018)

    City Population: 10.29 million

    Year Implemented : 2016

    National Gini Index : 31.6 (2012)

    Tags : ECONOMY DATA

    Technologies Utilized : PCs with Virtual Desktop Interface (VDI), Toad, Python, R, QGIS, Hadoop, cloud, closed network, Office Software.

    Funding Source : Municipal Government

    Project Cost : $600,000 (in total by 2020)

    Project Savings : NA

    Planned Project Duration : Indefinite

    KPIs : Number of analyses made using data sets. Number of data set types analyzed. Number of data set users.

  • Project Context and Overview

    Seoul Metropolitan Government has long prioritized social innovation for identifying urban problems and creating solutions.. New technologies, particularly IoT, are generating data that can be utilized towards innovations. Some of the data has been open while other data was not readily available due to privacy and ownership. To bridge these complications and to provide a greater amount of data to the public, the Big Data Campus was implemented under Mayor Won-soon Park, through the Seoul Data & Statistics Division. The campus is an offline space that allows stakeholders from the public and private sectors and academia to access data sets, public and private, open and proprietary, in a controlled setting. The aim of the Big Data Campus is to allow cross-sectoral analysis on urban solutions, utilizing advanced analytical technologies free-of-charge. The project has performed well by the key performance indicators set out by the city, and further expansions are planned. Popular datasets include credit card usage patterns, sales analysis by locations, and particulate air pollution measurements. As the size and scope of the datasets become broader and as usage increases, the city hopes that crowd-sourced urban solutions will become more and more common. Additionally, not only can the project reduce the information gap between government and citizens, it also aims to provide a resource for small and medium-sized enterprises to make them more competitive and to stimulate local economy.

  • Project Planning and Implementation

    Key contributors to the big data campus included private sector stakeholders that allowed usage of proprietary datasets. This encompassed SAS Institute, Intel Korea, SK Telecom Co., Open Mate, KB Card, KT Corporation, and Shinhan Card. While negotiation was necessary, the city was able to secure the use of this data without purchasing it. Five government staff are assigned to the Big Data Campus, though Seoul outsourced construction of the two facilities and some aspects of the development. Users may register to access the data on-site at one of two facilities. Types of data considered sensitive and therefore controlled include credit card usage patterns and vehicle data; much of it is contributed by private companies. Data is anonymized and analyzed over Virtual Desktop Interface (VDI). The advantage of VDI for these datasets is that the operating system and data for each computer is centrally managed by city administrators in the data center, giving them control over usage of this sensitive data. An advantage for users, aside from having access to sets which normally would be beyond their reach, is that users may receive professional education on how to use the software and analyze the data. The program was made to comply with existing legislation, including privacy legislation, ensuring that no modification of laws would be necessary.

  • Project Results

    As innovations are less measurable and may take time to be realized, the city considers the primary KPI to be the number of datasets analyzed by users. Since its implementation, 393 analyses of data have already occurred by users from academia, NGOs, and individuals. Seoul Metropolitan Government deems this amount to be a success, and expects numbers to steadily increase, as the scope of datasets will increase as well. University students have also taken advantage of the data analysis education offered by the campus. Some categories analyzed so far include education, traffic, healthcare, the economy, and safety. In future expansions to the project, the city also plans to utilize the campus for human resource development in training professional data analysts.

  • Recommendations for Transfer

    This sort of project is most suited for a highly-populated ciy with capabilities already in place for collecting data. Companies which collect these sorts of data can be advantageous, and it is ideal if the city government has a close, cooperative working relationship with these companies.

    As the project’s success depends on the willingness of the private sector to share proprietary data, these relationships are helpful. In Seoul’s case, this gave them access to datasets without any additional cost. Such sets may also be purchased, but this would need to be factored into budgeting. In Seoul’s case, carefully laid-out memoranda of understanding helped to create greater understanding and cooperation between the private and public sectors.

    Given the careful nature of this cooperative relationship, the security of the data sets is paramount. Their unauthorized distribution could potentially be very damaging to the relationship between cities and partner companies and the ability for the city to form such relationships in the future. The physical nature of the campus is what separates this project from more traditional open data projects. While it could be feasible to monitor and control distribution of data by other means, the use of the physical campus with a VDI has given Seoul administrators very careful control to ensure the safety of the data. In terms of privacy, data anonymity was necessary in Korea, and in other national legal contexts, requirements may be even stricter.

    Additionally, for budget savings, open source solutions can be employed for much of the analytical software.

  • Figures and Images



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