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

Jiayu Wu, Yutian Lu, Hei Gao, Mingshu Wang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
58 Downloads (Pure)


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.

Original languageEnglish
Article number101716
Pages (from-to)1-12
Number of pages12
JournalComputers, environment and urban systems
Early online date30 Sep 2021
Publication statusPublished - Jan 2022


  • Heritage conservation
  • Historical heritage
  • Urban morphology
  • Urban planning
  • Vitality
  • UT-Hybrid-D


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