Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.
As China's pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.
Empowering communities through art: Unleashing the potential of public art in urban micro-renewal
Vol 6, Issue 1, 2025
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Abstract
As urban space available for new construction continues to diminish, urban renewal strategies have increasingly shifted toward micro-renewal approaches. Within this framework, public art serves as a vital medium for urban cultural development and has been effectively integrated into community spaces under the paradigm of human-centered urban micro-renewal. This study systematically explores the forms, principles, and mechanisms of community public art. First, it establishes the synergistic relationship between urban micro-renewal and community public art. It then identifies six distinct artistic forms: spatial transformation, sculptural installations, murals and wall paintings, public facilities, digital media, and artistic events. Building upon this classification, the study delineates four core principles that community public art should adhere to: the participatory principle of human-centered engagement, the principle of locality emphasizing cultural distinctiveness, the principle of gradual progression ensuring sustainable development, and the social principle of art education. Through comparative case analysis, the study reveals the multidimensional role of community public art in restructuring power dynamics, driving community transformation, and reshaping cultural identity. The findings demonstrate that community public art not only enhances the spatial environment and improves community quality but also fosters social integration and strengthens cultural identity, providing an innovative pathway for urban governance. Finally, the study acknowledges its limitations and outlines future research directions, aiming to contribute to both theoretical discourse and practical applications in community public art.
Keywords
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
