• Land and Housing Review
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• v.14 no.2
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• pp.51-71
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• 2023

Based on the challenges encountered in the urban regeneration projects (general neighborhood-type) of Anjung District and Sinjang District in Pyeongtaek City, this study aims to explore the key factors of improvement measures for urban regeneration projects. Using the first survey conducted among shopping mall owners, employees, and residents, this study finds a significant level of awareness and very strong needs for urban regeneration projects. On the other hand, the overall satisfaction levels are observed to be low across the three factors: social, economic, and cultural. The second expert survey and the subsequent Analytic Hierarchy Process (AHP) analysis conducted for improvement measures reveal notable discrepancies in the prioritization of factors between administrative experts and practical experts. For the importance of administrative experts, the establishment of network-type regional governance was ranked first for the importance, followed by hosting events related to the U.S. military and long-term pre-market operations, and expanding exchange and cooperation between the U.S. Forces Korea and the community. For the importance of working-level experts, hosting U.S. military-related events and long-term pre-market operations was ranked first, while supporting U.S. military-related festivals and developing local natural landscape resources was ranked second. Our findings suggest the need for proactive measures such as attracting commercial facilities to stimulate demand from both the U.S. military and local residents, thereby revitalizing general neighborhood-type urban regeneration projects, developing programs for local tourism, and operating pre-market operations in the long run. Therefore, this study highlights the importance of regional government cooperation for urban regeneration projects.

• Journal of the Economic Geographical Society of Korea
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• v.24 no.4
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• pp.425-445
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• 2021

The purpose of this study is to present policy implications for future urban regeneration projects by deriving factors that affect residents' life satisfaction by region. To this end, a survey was conducted and multiple regression analysis was conducted using the Changsin/Sungin-dong, Haebangchon, and Garibong-dong regions where the general type of neighborhood regeneration project of Seoul was conducted. As a result of the analysis, it was found that life satisfaction increased when autonomy among the local governance factors in Changsin/Sungin-dong and autonomy and network in Garibong-dong increased. When examining the factors of regional regeneration capacity for each region, in terms of local identity, landscape characteristics in all three regions had a significant effect on the life satisfaction of residents. In the case of local communities, it was found that the satisfaction with life of residents increased when individual competence in Changsin/Sungin-dong and participation capacity and relationship competence in Garibong-dong increased. In terms of local assets, natural and social assets in all three regions had a significant effect on the life satisfaction of residents. Therefore, it is judged that the relationship between the influence of residents' life satisfaction in the general type of neighborhood regeneration project for neighborhood regeneration will be a meaningful data in promoting the urban regeneration project.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.273-283
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• 2020

The purpose of this study is to evaluate the user's evaluation of the street environment for the Urban Regeneration New Deal project, which maintains its original urban context and improves the urban environment, as opposed to the full-demolition projects such as urban redevelopment or reconstruction. A questionary survey was conducted on residents for the general neighborhood type of Urban Regeneration New Deal projects in Daegu. An IPA(Importance-Performance Analysis) was conducted to compare the user's satisfaction with the street environments. The main results are as follows. First, the importance and satisfaction of street environments were different for each project. Second, the cleanliness of sidewalks showed high importance and satisfaction at all projects. Third, residents' satisfaction with signs and buildings was high, but their importance was low. Last, residents' satisfaction with parking and the segregation of pedestrian and vehicle was low, but their importance was high. It is necessary to build differentiated plans and programs to meet the needs of residents in the Urban Regeneration New Deal projects.

• 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."