• Title/Summary/Keyword: Alley Market Area

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An Analysis of the Effects of Customer Characteristics on Sales of Alley Market Area Using Geographically Weighted Regression (지리가중회귀분석을 이용한 고객특성별 골목상권 매출액 영향 연구)

  • Kang, Hyun Mo;Lee, Sang-Kyeong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.6
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    • pp.611-620
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    • 2018
  • With the revitalization of alley market area becoming a major goal of the urban regeneration project, an understanding on customer characteristics that affect the sales of alley market areas is needed. As spatial heterogeneity appears to exist in alley market areas, the use of GWR (Geographically Weighted Regression) is required as an alternative to OLS (Ordinary Least Squares) regression. This study analyzes effects of customer characteristics on sales of 1007 alley market areas in Seoul. Comparing R squared and AICc, results show that GWR is better than OLS regression. According to OLS regression, the ratio of female, the ratio of 40's and 50's, the number of employees, the opening rate of establishment, the density of building and the size of alley market area have positive effects on sales, while the ratio of 20's and 30's, the distance of bus stop and that of subway station have negative effects. As a result of comparing local regression coefficients of geographically weighted regression analysis, the ratio of female customers has the greatest effect on the northwestern region, followed by the southwestern region, the central region and the northeastern region. The ratio of 20's and 30's and that of 40's and 50's effect on the southeastern and northeastern regions, and then the southwestern region. It is expected that this study will help to identify marketing target for each alley market area.

Development for establishing Big Data-based alley commercial area (빅데이터 기반 골목상권 영역설정 방법론 개발)

  • Hwang, Dong-Hyun;Ko, Kyeong-Seok;Park, Sang-June;Kim, Wan-Su
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.6
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    • pp.784-792
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    • 2018
  • In this study, we designed the area except the development market and the traditional market, where large scale shops were concentrated by realizing the real estate center of the alley commercial area. In addition, we have developed an area setting method for the alley area where reliability and rationality can be ensured by utilizing the actual data such as the business statistics, the survey data of the business, and the store business DB, which are managed by the local government or the state. The alley commercial areas were classified into five groups according to density. It is thought that users can distinguish the commercial areas from dense commercial areas to the commercial areas in order to utilize various commercial areas.

Seoul Local Brand Alley Commercial Area Recommendation System Design Using Machine Learning (머신러닝 기반 서울시 로컬브랜드 골목상권 추천시스템 설계)

  • Jiyeon, Kim;Hyoseon, Jang;Minseo, Park
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.1
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    • pp.101-109
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    • 2023
  • According to data released by the Covid 19 Self-Employed Emergency Response Committee, 95.6% of small business sales due to Covid 19 have decreased over the past two years, and the damage has further increased due to social distancing for quarantine. However, as all social distancing guidelines have rebeen lifted, and the commercial district has been revitalized, the Seoul Metropolitan Government is pushing for a project to foster local brand commercial districts so that small business owners or prospective founders who have closed their businesses due to the prolonged COVID-19. Therefore, this study propose the model that recommends alley commercial districts suitable for founders among the five alley commercial districts selected for the project to foster local brand commercial districts in Seoul. The Seoul Metropolitan Government's local brand alley commercial recommendation system recommends major population age groups and major industries in the commercial district by combining the population perspective model using Xgboost and the commercial district characteristic model using Decision Tree.