• Korean Association of Arts Management
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• no.50
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• pp.147-175
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• 2019

Gentrification is the process by which a working class or other disadvantaged area of a city changes into a middle class residential or commercial district. Gentrification, which has received much attention in arts management in recent years as part of a concern with urban regeneration, carries a generally negative connotation. In this paper, we interrogate this negative view of gentrification to explore ways arts entrepreneurship can convert the perceived threat of gentrification into opportunity. To this end, we examine the Mullae Cultural Salon in the gentrifying district of the Mullae Creative Village. Through a literature review of gentrification and arts entrepreneurship, we propose seven elements of art entrepreneurs responding to gentrification as an analytic framework for research. Our findings indicate that arts entrepreneurs were able to extend the maturity phase of gentrification and thus enhance the cultural and artistic value of the region for other artists and arts entrepreneurs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.187-194
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• 2019

The purpose of this study was to review the Gentrification and Development Index in terms of domestic and foreign gentrification. Based on the important indicators of the Gentrification index through previous research, the four evaluation areas were divided into structure and subject, production and consumption, supply and demand, and capital and culture. Looking at the importance of each area, the production and consumption aspects were highest as the important index of the occurrence of gentrification, followed in order by the supply and demand, the structure and subject, and the capital and culture order. From the detailed factors, the report revealed the changes in sales to structure and subject matter, increases in franchises to production and consumption, rises in rent to supply and demand, and transient population to capital and culture to be important items. In addition, an analysis of the gentrification occurrence indicators in urban regeneration project areas revealed high weight in terms of production and consumption, supply, and demand, including the increased franchises, one-person start-ups, higher rents and higher real estate values. In other words, the occurrence of gentrification in urban regeneration areas produces the largest portion of the increases in franchises and rent. Therefore, step-by-step measures are needed through monitoring.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.175-186
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• 2023

In this paper, we aim to build a gentrification analysis model and examine its characteristics, focusing on the point at which rents rose sharply alongside the recovery of commercial districts after the gradual resumption of daily life. Recently, in Korea, the influence of social distancing measures after the pandemic has led to the formation of small-scale commercial districts, known as 'hot places', rather than large-scale ones. These hot places have gained popularity by leveraging various media and social networking services to attract customers effectively. As a result, with an increase in the floating population, commercial districts have become active, leading to a rapid surge in rents. However, for small business owners, coping with the sudden rise in rent even with increased sales can lead to gentrification, where they might be forced to leave the area. Therefore, in this study, we seek to analyze the periods before and after by identifying points where rents rise sharply as commercial districts experience revitalization. Firstly, we collect text data to explore topics related to gentrification, utilizing LDA topic modeling. Based on this, we gather data at the commercial district level and build a gentrification analysis model to examine its characteristics. We hope that the analysis of gentrification through this model during a time when commercial districts are being revitalized after facing challenges due to the pandemic can contribute to policies supporting small businesses.

• Journal of Korea Planning Association
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• v.54 no.1
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• pp.40-51
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• 2019

The ultimate aim of this study is to diagnose the process stage and look at the step change of transition process to see how the step changes. Therefore, in this study, cluster analysis was conducted by examining four types of commercial characteristics such as Retail Homogeneity, Share of Neighbourhood store, Share of chain store, and Share of cafe & Western food store. Through the cluster analysis, three types have been identified. Type1 is the first step which can explain the time before gentrification occurs and when the ratio of neighborhood facilities is the highest. Type2 is the second step that can explain boutique stage where the gentrification occurs. At this time, the ratio of Cafes & Western food restaurant increased and the proportion of neighborhood shops decreased. And Type3, third step is when the mature gentrification occurs. In the analysis of the transition period, it is necessary to monitor the change of the industry in the period from the first stage to the second stage. In the transition period from the second stage to the third stage, It is necessary to constantly monitor such factors as the increase of shops.

• Journal of the Society of Disaster Information
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• v.20 no.3
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• pp.601-608
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• 2024

Purpose: The purpose of this study is to predict the criminal patterns and risks of conflicts caused by inequality such as weakening regional ties and social exclusion caused by climate change and present basic policy data to solve them. Method: This study was mainly conducted through analysis of contents and cases through the use of media information such as the Internet and newspapers, and some literature research. Result: The crime patterns and characteristics of climate gentrification are as follows. First, rising sea levels caused by climate change will temporarily increase crimes related to real estate speculation. Second, social exclusion due to public service and environmental inequality will intensify, leading to terrorist crimes such as riots and hate crimes. Third, due to the weakening of regional ties, young people in poverty in the region will participate in organized violence crimes such as drugs and gangs or become crime victims. Conclusion: Therefore, it is necessary to prepare policy countermeasures through cooperation with institutions. In particular, it is necessary to explore ESG policy measures in police activities in consideration of environmental factors in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."