• Title/Summary/Keyword: 소비중심지

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A Study on the Influence of Commercial Facility Diversity on the Formation of Consumption Centre: Application of Spatial Regression Models (상업시설의 다양성이 소비중심지 형성에 미치는 영향에 관한 연구: 공간회귀모형의 적용)

  • Sul-Hee Kim;Heung-Soon Kim
    • Land and Housing Review
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    • v.15 no.1
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    • pp.57-75
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    • 2024
  • To create dynamic and bustling urban environments, a diverse array of commercial facilities is indispensable. These facilities are recognised as pivotal in attracting and accommodating a larger floating population, thereby suggesting that a greater diversity of commercial establishments fosters heightened consumer expenditure. With this premise, our study endeavours to explore the influence of commercial facility diversity on the Consumer Centre Index. Focused on the temporal context of 2021 and the spatial context of Seoul, our analysis utilizes the Consumer Centre Index, derived from Kernel Density analysis, as the dependent variable. Independent variables encompass factors reflecting commercial attributes and urban characteristics. Employing spatial regression analysis at the administrative district level, we discern that the clustering of similar industries exerts a more pronounced positive effect on consumer activation compared to the clustering of disparate industries. Additionally, the findings underscore the importance of concentrating industries that bolster consumer activation. Anticipated outcomes of this study include insights beneficial for optimizing commercial facility location policies within the consumer market.

Assessment of Busan City Central Area System and Service Area Using Machine Learning and Spatial Analysis (머신러닝과 공간분석을 활용한 부산시 중심지 체계 및 영향권 분석)

  • Ji Yoon CHOI;Minyeong PARK;Jung Eun KANG
    • Journal of the Korean Association of Geographic Information Studies
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    • v.26 no.3
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    • pp.65-84
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    • 2023
  • In order to establish a balanced development plan at the local government level, it is necessary to understand the current urban spatial structure. In particular, since the central area is a key element of balanced development, it is necessary to accurately identify its location and size. Therefore, the purpose of this study was to identify the central area system for Busan and to derive underprivileged areas that were alienated from the service areas where the functions of the central area could be used. To identify the central area system, four indicators(De facto Population, Land Price, Commercial Buildings, Credit Card Consumption) were used to calculate the central area index, and Getis-Ord Gi* and DBSCAN analysis were performed. Next, the hierarchy of the central areas were classified and the service areas were derived through network analysis by using it. As a result of the analysis, a total of 12 central areas were found in Seomyeon, Jungang, Yeonsan, Jangsan, Haeundae, Deokcheon, Dongnae, Daeyeon, Sasang, Pusan National University, Busan Station, and Sajik. Most of the underprivileged areas affected by the central area appeared in the Eastern area of Busan and the Western area of Busan, and were derived from old industrial areas, residential areas, and some new cities. Based on the results of the study, we can find three meanings. First, we have made a new attempt to apply a machine learning methodology that has not been covered in previous studies. Second, our data show the difference between the actual data and the existing planned central areas. Third, we not only found the location of the central areas, but also identified the underprivileged areas.

Development of Machine Learning Model to Predict the Ground Subsidence Risk Grade According to the Characteristics of Underground Facility (지하매설물 속성을 활용한 기계학습 기반 지반함몰 위험도 예측모델 개발)

  • Lee, Sungyeol;Kang, Jaemo;Kim, Jinyoung
    • Journal of the Korean GEO-environmental Society
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    • v.23 no.8
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    • pp.5-10
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    • 2022
  • Ground Subsidence has been continuously occurring in densely populated downtown. The main cause of ground subsidence is the damaged underground facility like sewer. Currently, ground subsidence is being dealt with by discovering cavities in ground using GPR. However, this consumes large amount of manpower and cost, so it is necessary to predict hazardous area for efficient operation of GPR. In this study, ◯◯city is divided into 500 m×500 m grids. Then, data set was constructed using the characteristics of the underground facility and ground subsidence in grids. Data set used to machine learning model for ground subsidence risk grade prediction. The purposed model would be used to present a ground subsidence risk map of target area.

A Study on Chinese Corporate Social Responsibility Management Mode in Economic Transition Age A Case Study of Beijing Retailing Industry (경제전환시대 중국 소매기업의 사회적 책임에 관한 연구 : 베이징(北京)의 소매기업을 중심으로)

  • Li, Dong?xin;Kang, Tae?won;Lee, Yong?Ki
    • The Korean Journal of Franchise Management
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    • v.2 no.2
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    • pp.134-149
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    • 2011
  • For decades, corporate social responsibility (CSR) has been a subject of intense debate among scholars and practitioners. Discussions have generally focused on the role of business in society and the nature of an enterprise's social responsibilities. The International Organization for Standardization (ISO) announced the implication of the ISO 26000 as the new guidance standard for social responsibility, which is built on the intellectual and practical infrastructure of ISO 9000 and ISO 14000. Although the enthusiasm for corporate social responsibility (CSR) has been echoed in the Chinese marketing literature, with the very low rate and level of CSR implementation in China's enterprises based on 2011 report of Chinese Academy of Social Sciences, this paper will give a general statement on the current status and future management mode of CSR in China.