Speaker
Description
Background
Heritage communities possess the dual attributes of both cultural heritage and residential communities. In Shanghai, traditional lane-house-based heritage communities face building decay and social decline, as well as risk dilemmas during renewal. The bonds among community residents and their sense of community identity are increasingly being challenged. Against this backdrop, enhancing the social cohesion of community residents is of particular importance for the dynamic protection and sustainable development of heritage communities. However, current research rarely quantitatively explored the relationship between the relevant attributes and community cohesion, lacking an exploration of the degree to which multiple factors influence social cohesion in heritage communities.
Methods
This study aims to explore the relationships between social cohesion and 23 related factors from 5 dimensions: community conditions, facilities, heritage conditions, residents' socioeconomic status, and heritage engagement. By analyzing survey data from 362 respondents in different heritage communities, we used the Gradient Boosting Decision Tree (GBDT) machine learning model to measure the contribution degrees of different influencing factors to social cohesion. Then, we used the Partial Dependency Plot (PDP) to depict the nonlinear relationships and threshold effects between the independent variables and the dependent variable (social cohesion). The GBDT model has advantages in handling non-linear relationships and measuring the importance of variables, and the PDP can help visualize the threshold effect.
Results
The results show diverse impacts of different factors on social cohesion. Firstly, in terms of the influence degrees in different dimensions, the basic conditions at the community level and the situation of public facilities contributed approximately 49% to the fitted social cohesion. Individual heritage participation and personal socioeconomic status respectively contributed around 25% .
Secondly, the non-linear relationships and threshold effects are evident in many factors such as plot ratio, greening rate, housing price, perception and evaluation of facilities, participation in heritage inheritance, the proportion of the elderly/ tenants/ the poor in the community, etc. Take the greening rate for example, when it increases from 30% to 35%, the improvement of community cohesion is more significant. But when it exceeds 35%, the marginal benefit decreases significantly (similar key scales exist for other factors, which will not be elaborated here). The existence of nonlinear relationships between these factors and social cohesion often implies the presence of a key scale. Planning actions within this scale can achieve the highest efficiency and the most significant results in enhancing social cohesion.
Additionally, through factor analysis we reduced the dependent variable of social cohesion to five dimensions: community dependence, neighborhood mutual assistance, community participation, cultural inclusiveness and neighborhood interaction. The importance of some indicators varies significantly across different dimensions, which can help us better understand the mechanisms of community cohesion and strategies for its enhancement.
Conclusion
This study mainly explored the key scales at which different factors influence the social cohesion of heritage communities. Planning actions within these key scales can lead to more optimal efficiency and results. To enhance social cohesion in heritage communities, strategies should be implemented in aspects like optimizing the physical environment (for instance, maintaining the greening rate at around 35%), ensuring appropriate community scale, taking care of key groups (the elderly and renters), and promoting heritage participation. These measures can help in the renewal, transformation, and sustainable development of heritage communities.
References
Cerreta, M. and di Girasole, E.G. (2020) Towards Heritage Community Assessment: Indicators Proposal for the Self - Evaluation in Faro Convention Network Process, Sustainability, 12(23), p. 9862.
Jennings, V. and Bamkole, O. (2019) The Relationship between Social Cohesion and Urban Green Space: An Avenue for Health Promotion, International Journal of Environmental Research and Public Health, 16(3), p. 452.
Lai, Y., Wang, P. and Wen, K. (2024) Exploring the Impact of Public Spaces on Social Cohesion in Resettlement Communities from the Perspective of Experiential Value: A Case Study of Fuzhou, China, Buildings, 14(10), p. 3141.
Li, H. et al. (2024) How heritage promotes social cohesion: An urban survey from nara city, japan, Cities, 149, p. 104985.
Luo, X., Zhang, W., Chai, Y. (2022) Research on threshold effects of built environment settings in 15 - minute life - circles, Geography Research, 41(08), 2155–2170.
Wang, S. et al. (2023) Research on the Conservation Methods of Qu Street’s Living Heritage from the Perspective of Life Continuity, Buildings, 13(6), p. 1562.
Zhang, W. et al. (2020) Nonlinear effect of accessibility on car ownership in Beijing: Pedestrian - scale neighborhood planning, Transportation Research Part D - Transport and Environment, 86, p. 102445.
| Keywords | Heritage Community; Social Cohesion; Non-linear Relationships |
|---|---|
| Best Congress Paper Award | No |
