Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning

Wu, J., Lu, Y., Gao, H. and Wang, M. (2022) Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning. Computers, Environment and Urban Systems, 91, 101716. (doi: 10.1016/j.compenvurbsys.2021.101716)

Abstract

The conservation of historical heritage can bring social benefits to cities by promoting community economic development and societal creativity. In the early stages of historical heritage conservation, the focus was on the museum-style concept for individual structures. At present, heritage area vitality is often adopted as a general conservation method to increase the vibrancy of such areas. However, it remains unclear whether urban morphological elements suitable for urban areas can be applied to heritage areas. This study uses ridge regression and LightGBM with multi-source big geospatial data to explore whether urban morphological elements that affect the vitality of heritage and urban areas are consistent or have different spatial distributions and daily variations. From a sample of 12 Chinese cities, our analysis shows the following results. First, factors affecting urban vitality differ from those influencing heritage areas. Second, factors influencing urban and heritage areas' vitality have diurnal variations and differ across cities. The overarching contribution of this study is to propose a quantitative and replicable framework for heritage adaptation, combining urban morphology and vitality measures derived from big geospatial data. This study also extends the understanding of forms of heritage areas and provides theoretical support for heritage conservation, urban construction, and economic development.

• Item Type: Articles
• Additional Information: This work was supported by the National Science Foundation of China (NO. 51908488), the Social Science Fund of Zhejiang Province (21NDJC034YB), the Fundamental Research Funds for the Central Universities, and Centre for Balance Architecture, Zhejiang University.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wang, Dr Mingshu
• Authors: Wu, J., Lu, Y., Gao, H., and Wang, M.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Computers, Environment and Urban Systems
• Publisher: Elsevier
• ISSN: 0198-9715
• ISSN (Online): 1873-7587
• Published Online: 30 September 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Computers, Environment and Urban Systems 91: 101716
• Publisher Policy: Reproduced under a Creative Commons License