• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.5
• /
• pp.13-29
• /
• 2023

Urban parks are social infrastructure supporting citizens' health, quality of life, and community formation. As the proportion of urban parks that have been established for more than 20 years is increasing, the need for refurbishment to improve the physical space environment and enhance the functions of aging urban parks is increasing. Since the government's refurbishment of aging urban parks has limitations in securing financial resources and promoting attractiveness, they must be promoted through public-private partnerships. Japan, which suffered from the problem of aging urban parks, has successfully promoted several park refurbishment projects by introducing the Park-PFI through the revision of the 「Urban Park Act」 in 2017. This study examines and analyzes the characteristics of the Japan Park-PFI as an alternative to improving the quality of aging domestic urban park services through public-private partnerships and the validity of the aging urban park refurbishment projects through Park-PFI. The main findings are as follows. First, it is necessary to start discussions on introducing Japan's Park-PFI according to the domestic conditions as a means of public-private partnership to improve the service quality and diversify the functions of aging urban parks. In order to introduce Park-PFI social discussions and follow-up studies on the deterioration of urban parks. Must be conducted. The installation of private capital and profit facilities and improvements of related regulations, such as the 「Parks and Green Spaces Act」 and the 「Public Property Act」, is required. Second, it is judged that the Park-PFI project is a policy alternative that can enhance the benefits to citizens, local governments, and private operators under the premise that the need to refurbish aging urban parks is high and the location is suitable for promoting the project. As a result of a pilot application of the Park-PFI project to Seyeong Park, an aging urban park located in Bupyeong-gu, Incheon, it was analyzed to be profitable in terms of the profitability index (PI), net present value (FNPV), and internal rate of return (FIRR). It is considered possible to participate in the business sector. At the local government level, private capital is used to improve the physical space environment of aging urban parks, as well as the refurbishment of the urban parks by utilizing financial resources generated by returning a portion of the facility usage fees and profits (0.5% of annual sales) of private operators. It was found that management budgets could be secured.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.3
• /
• pp.70-82
• /
• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.