ArticlePloS one2026
Measurement of urban vitality and the influence mechanism of the built environment on it based on multi-source data: A case study of Yantai City.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Under a human-centered approach, accurately identifying the spatial patterns of urban vitality and revealing the mechanisms through which the built environment affects it can scientifically guide the organic cultivation of urban vitality. In light of this, the main urban area of Yantai City is taken as a case study, utilizing multi-source geographic big data to conduct both theoretical and empirical research. An index system for the urban built environment is established based on four dimensions: human perception, functional, accessibility, and building form. Advanced methods, including Deep Fully Convolutional Neural Networks (SegNet), Random Forest Regression (RFR), and Spatial Lag Regression (SLR), are employed to explore the impact of the built environment on urban vitality. The research findings indicate that: (1) Urban vitality presents a composite spatial structure that embodies both "multi-center" and "clustered" characteristics, exhibiting two primary types of local spatial autocorrelation: "high-high" clustering and "low-low" clustering. (2) The disparities in urban vitality reflect an imbalance in the distribution of functional, accessibility, building form, and human perception, with functional playing a more critical role in nighttime and daytime urban vitality than other dimensions. (3) The effects of the built environment on daytime and nighttime urban vitality show varying degrees of heterogeneity regarding significance and direction. Factors such as BPOI(Commercial Points of Interest), integration, accessibility, and vibrancy have a substantial positive impact on vitality clustering, while human perception becomes increasingly important for enhancing nighttime vitality. These results provide refined technical support for urban micro-renewal, enhancing the relevance and effectiveness of response strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.