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Spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in Beijing city

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Published in:Computational Urban Science
Format: Online Article RSS Article
Published: 2026
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container_title Computational Urban Science
description
discipline_display Geography
discipline_facet Geography
format Online Article
RSS Article
genre Journal Article
id rss_article:91817
institution FRELIP
journal_source_facet Computational Urban Science
last_indexed 2026-06-16T02:03:04.397Z
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in Beijing city
Geography
General
Geography
sub_discipline_display General
sub_discipline_facet General
subject_display Geography
General
Geography
Geography
General
Geography
subject_facet Geography
General
Geography
title Spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in Beijing city
title_auth Spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in Beijing city
title_full Spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in Beijing city
title_fullStr Spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in Beijing city
title_full_unstemmed Spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in Beijing city
title_short Spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in Beijing city
title_sort spatial heterogeneities of heat waves’ impact on emotions and their causal factors based on multi-source geographic big data-a case study in beijing city
topic Geography
General
Geography
url https://link.springer.com/article/10.1007/s43762-026-00272-7