What does this research mean for the field?

In high-density historic districts, street-level visible greenery exhibits significant positive spatial clustering and is strongly associated with vegetation background, built-up intensity, and street-interface characteristics. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Quantify the street-level Green View Index in high-density historic districts and determine how spatial morphology and environmental covariates drive visible greenery patterns.

September 2, 2026LandOpen Access

Managing Street–Level Visible Greenery in High–Density Historic Districts: Spatial Differentiation and Associated Factors in Guangzhou, China

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Authors

YDYin DingSouth China Agricultural University CYChangdong YeSouth China Agricultural University LZLong Zhou

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Implication

Spatial analysis reveals built-up intensity and street interfaces limit visible greenery in high-density historic districts, highlighting targets for context-sensitive urban micro-renewal.

Key Points

Quantify the street-level Green View Index in high-density historic districts and determine how spatial morphology and environmental covariates drive visible greenery patterns.
Applied Mask2Former semantic segmentation to 45,380 Baidu Street View sampling points aggregated across 892 grid cells measuring 150 m × 150 m.
Assessed spatial clustering using Global and Local Moran’s I diagnostics and modeled associations using ordinary least squares, spatial lag models, and spatial error models.
Mean Green View Index was 16.74% at the point level and 16.70% at the grid level, exhibiting significant positive spatial autocorrelation (Global Moran’s I = 0.354, z = 19.585, permutation p = 0.001).
Visible greenery maintained persistent positive associations with NDVI and negative associations with NDBI, SVI, and SEI across model specifications.
The spatial error model yielded the best statistical fit among tested specifications, capturing remaining spatial dependence in the error structure.

Cite This Study

Ding et al. (2026) studied this question.

synapsesocial.com/papers/6a97e318c562ede874ec7a60 https://doi.org/10.3390/land15091611

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Implication

Spatial analysis reveals built-up intensity and street interfaces limit visible greenery in high-density historic districts, highlighting targets for context-sensitive urban micro-renewal.

Managing Street–Level Visible Greenery in High–Density Historic Districts: Spatial Differentiation and Associated Factors in Guangzhou, China

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Member takes

Implication

Key Points

Cite This Study

Managing Street–Level Visible Greenery in High–Density Historic Districts: Spatial Differentiation and Associated Factors in Guangzhou, China

Discussion

Member takes

Implication

Key Points

Cite This Study

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