ArticlePloS one2021
Spatio-temporal evolution of urban thermal environment and its driving factors: Case study of Nanjing, China.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed, 19 citations in OpenAlex.
- Spatiotemporal evolution of the thermal environment and influencing factors in Kashgar City's Urban Expansion.PloS one · 2026Article
- An Optimized Instance Segmentation of Underlying Surface in Low-Altitude TIR Sensing Images for Enhancing the Calculation of LSTs.Sensors (Basel, Switzerland) · 2024Article
- Thermal remote sensing over heterogeneous urban and suburban landscapes using sensor-driven super-resolution.PloS one · 2022Article
- Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model.Computational intelligence and neuroscience · 2022Article
- Study of coordinated development of county urbanization in arid areas of China: The case of Xinjiang.PloS one · 2022Article
- Research on the Spatio-Temporal Impacts of Environmental Factors on the Fresh Agricultural Product Supply Chain and the Spatial Differentiation Issue-An Empirical Research on 31 Chinese Provinces.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, with rapid urbanization, the underlying urban surface has changed dramatically. Various urban eco-environmental problems have emerged globally, among which the urban heat island effect has become one of the most obvious urban eco-environmental problems. In this study, Nanjing, China, was chosen as the study area. Based on Landsat 8 remote sensing image data collected in Nanjing from 2014 to 2018, land surface temperatures were retrieved, the spatiotemporal variation track and characteristics of the thermal environment pattern were systematically depicted, and the driving factors of these variations were revealed. The results show that over the past five years, the spatial pattern of the heat field in Nanjing changed from a scattered distribution in the periphery of the city to a centralized distribution in the centre of the city, and the heat island intensity increased annually. Changes in administrative divisions, changes in the layout of the transportation trunk lines, transfer of industrial centres, and ecological construction projects are important driving factors for the evolution of the land surface thermal environment patterns of these regions. These research results will provide scientific and technological support for similar cities with typical heat island effects elsewhere in the world to formulate urban development plan, and to improve the urban ecological environment.
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.