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  • 빅데이터 관련 사례 및 연구 | Cases and Studies on Big Data | 大数据的研究和最佳实践

    date : 2018-01-14 22:49|hit : 2485
    Study] Analysis of market district hinterland in Seoul by ‘Big Data’ analysis
    Analysis of market district hinterland in Seoul by ‘Big Data’ analysis

    HeungRok Oh, heungrokoh@gmail.com Bachlor of Mathematics, Thejoeun IT Academy
    BumSeok Bae, qoqjatjr10@naver.com Bachlor of Computer Communication Information, Thejoeun IT Academy
    HyeJung Moon, hyejung.moon@gmail.com Corresponding Author, Ph D. of Public Policy, Adjunct Professor

    The Seoul government has opened the commercial data and analysis results of the
    Seoul market districts. Most of the data are consists of statistics. The
    researchers hope to derive new analysis results from the data. Therefore, the
    subject of research is 1008 defined commercial districts and hinterland from 2015
    to 2016. Topics are apartment buildings, housing, facilities, incomes, sales of
    industries, population (floating, resident, employee), etc.
    The research procedure follows 1. Collecting the public data in Seoul, 2.
    Building the database system for analysis using MySQL, 3. Data analysis using
    MsExcel 4. Performing correlation & regression analysis by R studio, 5. Using
    Tableau tool for visualization.
    The analysis results show that Sales per month is associated with apartments
    size, population(resident, floating, employee), daily sales, sales per ages and
    expenditures. 25 administrative districts of Seoul were devided into 5 sectors by
    clustering using correlation coefficients. The community consisted mainly of
    neighborhood districts, and the sale of community stores in Gangnam was highly
    related with the number of lower priced apartments in the apartment complex. On
    the other hand, Dobong-gu is kind of balanced market.
    Hence, we could identify the components of the Seoul metropolitan county, which
    is formed differently according to the type of industry, and identify
    characteristics and sales correlation factors of each community.

    Key words: Big Data, Correlation Analysis, Cluster Analysis, Market district,
    reply : 0
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