Towards livable communities: Perceptual scale optimized urban human settlement evaluation at community levels

Abstract

Human settlement quality (HSQ) is a critical component of sustainable urban development, directly affecting residents’ health, well-being, and quality of life. However, most existing studies rely on expert-defined service radii and indicator weights at the city scale, overlooking residents’ perceptions and failing to capture fine-grained variations at the community level. This study proposes a Perceptual-Scale Optimized Random Forest (PSO-RF) to enable human-centered, community-scale HSQ evaluation by integrating subjective satisfaction data with objective environmental indicators. The framework captures multi-scale perceptual differences in environmental features and determines the optimal measurement scale for each indicator, leading to more accurate HSQ assessments. Five case cities in China and Germany -Beijing, Changsha, Shenzhen, Berlin, and Munich -were selected to reflect diverse regional, socio-economic, and developmental contexts, based on multi-source spatiotemporal data from 2010, 2015, and 2020. The findings reveal that: (1) Residents perceive HSQ across two dominant spatial scales: local (860 m) and accessible (2,050 m); (2) Chinese communities emphasize socio-economic conditions within close proximity, while German communities prioritize broader natural environmental factors; (3) The PSO-RF model reduces evaluation error by 9.6 % compared to fixed-radius approaches by identifying indicator-specific perceptual scales; (4) The generated HSQ and shortcoming maps uncover localized human settlement challenges and offer practical guidance for targeted urban planning. This study advances the methodological foundation for perception-driven livability research and provides actionable insights for precision urban governance.

Keywords

Human settlement / Community level / Urban sustainability / Perceptual scale / Interpretable machine learning

Ethical statement

Ethical approval was not required for this study since human participants were ensured following local legislation and institutional requirements. All proceeds of this research were carried out following the Helsinki Declaration principles of human subject investigation. Participation in this survey was anonymous and voluntary, assuring consent of prospective respondents before participation. Data accumulated for this research was treated confidentially.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Yichen Lei: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Xiuyuan Zhang: Writing – review & editing, Conceptualization. Shuping Xiong: Data curation. Ge Tan: Data curation. Haoyu Wang: Writing – review & editing. Shihong Du: Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

Acknowledgement

The work presented in this paper was funded by the National Natural Science Foundation of China (Grant No. 42330103), and by the Ningbo Science and Technology Bureau (Grant No. 2022Z081).

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