ArticleHeliyon2023
Determining spatial disparities and similarities regarding heat exposure, green provision, and social structure of urban areas - A study on the city district level in the Ruhr area, Germany.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
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Who cites it
2 citing papers in PubMed.
- Spatial-temporal analysis of patient complaints in Shanghai from 2015 to 2022.BMC health services research · 2025Article
- Lower regional urbanicity and socioeconomic status attenuate associations of green spaces with hypertension and diabetes mellitus: a national representative cross-sectional study in China.Environmental health and preventive medicine · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heat islands and ongoing urbanization make cities places where the negative impacts of global climate change on society are becoming increasingly evident. Especially the interplay and potential multiplication of heat, low green provision, and the presence of socially deprived urban dwellers constitutes complex challenges. Emerging climate injustices and potential health issues require a powerful counter-reaction in form of adaptation action. For our study, we consider eight cities located in the densely populated and historically highly segregated Ruhr area in Western Germany, which is one of the largest metropolitan areas in Europe with a heterogeneous distribution of socio-spatial problems, economic potential, heat stress, and green infrastructures. We use land surface temperature (LST), data on green provision (normalized difference vegetation index (NDVI)), and social indicators to reveal the relationships between these indicators on the city district level (n = 275). Therefore, we first analyze the data regarding spatial autocorrelation (Moran's
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