Evidence map›Paper›PMID 39820149›Full record

ArticlePloS one2025

Spatial-temporal dynamics and driving factors of urban construction land in mega cross-river mountain cities: A case study of Chongqing's central urban area, China.

Fu-Hai Wang, Wei Zeng, Dan Chen, Chang-Hua He, Hui Li

Abstract read
In one paragraph

Article in PloS one, 2025. 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
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

5 authors.

Fu-Hai WangCollege of Architecture and Urban Planning, Chongqing University, Chongqing, China.
Wei ZengCollege of Architecture and Urban Planning, Chongqing University, Chongqing, China.ORCID 0009-0001-4754-510X
Dan ChenCollege of Architecture and Urban Planning, Chongqing University, Chongqing, China.
Chang-Hua HeChongQing Finance and Economice College, Chongqing, China.
Hui LiChongQing Finance and Economice College, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The evolutionary model of construction land serves as a fundamental pillar in national spatial development and planning research. However, previous studies have overlooked the "climbing" mode of construction land on three-dimensional terrains. To address this issue, utilizing elevation data and land use data from 2010 to 2020, this study employs slope analysis, intensity analysis, spatio-temporal transformation, and PLUS model to elucidate the spatial expansion process and driving forces of urban construction land in Chongqing from both two-dimensional and three-dimensional perspectives. The findings indicate that: (1) From a three-dimensional topographical standpoint, between 2010 and 2012, construction land gradually expanded towards low-slope areas, whereas between 2012 and 2020, it progressively extended into high-slope regions. (2) Regarding land type conversion patterns, the shift from arable land to construction land demonstrates a systematic inclination, while other transformations exhibit absolute or relative tendencies. Conversely, the conversion from construction land to arable land also displays a systematic pattern. (3) Since 2010, the growth process of construction land has transitioned from slow-equilibrium to rapid-disequilibrium with an expanding spatial disparity. (4) Most areas maintain relatively stable spatial conditions without significant jumps or transitions observed. (5) The expansion of construction land in Chongqing is primarily influenced by terrain, river, tunnel, rail transit, and other factors. The outcomes of this study can provide scientific foundations and decision-making references for rational planning in similar cities characterized by mountainous landscapes intersected by rivers.

Indexed as

City PlanningUrbanizationChinaCitiesHumansRiversSpatio-Temporal Analysis

Identifiers

PMID39820149
PMCPMC11737798

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LicenceCC BY
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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.