Evidence map›Paper›PMID 42371907›Full record

ArticlePloS one2026

Evaluation of urban community digital landscape and runoff regulation effect by GIS and InVEST model.

Lina Yan, Xin Gu

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lina YanCollege of Artificial Intelligence Xi'an Siyuan University, Xi'an, China.ORCID 0009-0009-7828-1838
Xin GuCollege of Artificial Intelligence Xi'an Siyuan University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In high-density urban communities, the expansion of impervious surfaces and the fragmentation of green spaces increase surface runoff. Accurate identification of runoff regulation effects at the community scale has therefore become an important issue in landscape optimization. This study examined a typical high-density urban community. A Geographic Information System (GIS) was used to process high-resolution remote sensing images, terrain data, and meteorological records. An eight-class digital landscape classification system was then established. The Integrated Valuation of Ecosystem Services and Trade-offs-Sediment Delivery Ratio (InVEST-SDR) module was combined with landscape pattern metrics to construct a community-scale runoff regulation assessment framework. Model parameters were calibrated using observed runoff data. Runoff regulation results under current conditions and optimized scenarios were subsequently compared. The results showed that the model achieved a coefficient of determination of 0.89 and an average relative error of 1.3%, indicating reliable performance at the community scale. When impervious surfaces accounted for 37.2% of the area, the mean annual runoff depth reached 41.8 mm and the runoff coefficient was 0.52. After increasing green-space coverage by 15% and patch aggregation by 23%, the mean annual runoff depth decreased to 27.3 mm. Runoff regulation efficiency increased by 34.7%. Landscape fragmentation showed a significant positive correlation with runoff volume. Patch aggregation showed a significant positive correlation with runoff reduction rate. A dispersed green-space layout improved regulation efficiency by 18.6% compared with a centralized layout. These results indicate that the spatial structure of digital landscapes influences runoff regulation at the community scale. The GIS-InVEST coupling framework provides quantitative support for landscape optimization and spatial planning of sponge communities.

Indexed as

Conservation of Natural ResourcesGeographic Information SystemsModels, TheoreticalCitiesEcosystemEnvironmental Monitoring

Identifiers

PMID42371907
PMCPMC13313334

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.