Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing

Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (U...
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Autor*in:	Zhu, Yanrong [verfasserIn] Wang, Juan Yuan, Yuting Meng, Bin Luo, Ming Shi, Changsheng Ji, Huimin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Geographical big data Sentiment analysis Urban functional area Association rules analysis Beijing

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Computational urban science - Springer Nature Singapore, 2021, 4(2024), 1 vom: 11. März
Übergeordnetes Werk:	volume:4 ; year:2024 ; number:1 ; day:11 ; month:03

Links:	Volltext

DOI / URN:	10.1007/s43762-024-00119-z

Katalog-ID:	SPR055094724

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allfields	10.1007/s43762-024-00119-z doi (DE-627)SPR055094724 (SPR)s43762-024-00119-z-e DE-627 ger DE-627 rakwb eng Zhu, Yanrong verfasserin aut Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. Geographical big data (dpeaa)DE-He213 Sentiment analysis (dpeaa)DE-He213 Urban functional area (dpeaa)DE-He213 Association rules analysis (dpeaa)DE-He213 Beijing (dpeaa)DE-He213 Wang, Juan aut Yuan, Yuting aut Meng, Bin aut Luo, Ming aut Shi, Changsheng aut Ji, Huimin aut Enthalten in Computational urban science Springer Nature Singapore, 2021 4(2024), 1 vom: 11. März (DE-627)1755847114 (DE-600)3061590-2 2730-6852 nnns volume:4 year:2024 number:1 day:11 month:03 https://dx.doi.org/10.1007/s43762-024-00119-z kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 4 2024 1 11 03
spelling	10.1007/s43762-024-00119-z doi (DE-627)SPR055094724 (SPR)s43762-024-00119-z-e DE-627 ger DE-627 rakwb eng Zhu, Yanrong verfasserin aut Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. Geographical big data (dpeaa)DE-He213 Sentiment analysis (dpeaa)DE-He213 Urban functional area (dpeaa)DE-He213 Association rules analysis (dpeaa)DE-He213 Beijing (dpeaa)DE-He213 Wang, Juan aut Yuan, Yuting aut Meng, Bin aut Luo, Ming aut Shi, Changsheng aut Ji, Huimin aut Enthalten in Computational urban science Springer Nature Singapore, 2021 4(2024), 1 vom: 11. März (DE-627)1755847114 (DE-600)3061590-2 2730-6852 nnns volume:4 year:2024 number:1 day:11 month:03 https://dx.doi.org/10.1007/s43762-024-00119-z kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 4 2024 1 11 03
allfields_unstemmed	10.1007/s43762-024-00119-z doi (DE-627)SPR055094724 (SPR)s43762-024-00119-z-e DE-627 ger DE-627 rakwb eng Zhu, Yanrong verfasserin aut Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. Geographical big data (dpeaa)DE-He213 Sentiment analysis (dpeaa)DE-He213 Urban functional area (dpeaa)DE-He213 Association rules analysis (dpeaa)DE-He213 Beijing (dpeaa)DE-He213 Wang, Juan aut Yuan, Yuting aut Meng, Bin aut Luo, Ming aut Shi, Changsheng aut Ji, Huimin aut Enthalten in Computational urban science Springer Nature Singapore, 2021 4(2024), 1 vom: 11. März (DE-627)1755847114 (DE-600)3061590-2 2730-6852 nnns volume:4 year:2024 number:1 day:11 month:03 https://dx.doi.org/10.1007/s43762-024-00119-z kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 4 2024 1 11 03
allfieldsGer	10.1007/s43762-024-00119-z doi (DE-627)SPR055094724 (SPR)s43762-024-00119-z-e DE-627 ger DE-627 rakwb eng Zhu, Yanrong verfasserin aut Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. Geographical big data (dpeaa)DE-He213 Sentiment analysis (dpeaa)DE-He213 Urban functional area (dpeaa)DE-He213 Association rules analysis (dpeaa)DE-He213 Beijing (dpeaa)DE-He213 Wang, Juan aut Yuan, Yuting aut Meng, Bin aut Luo, Ming aut Shi, Changsheng aut Ji, Huimin aut Enthalten in Computational urban science Springer Nature Singapore, 2021 4(2024), 1 vom: 11. März (DE-627)1755847114 (DE-600)3061590-2 2730-6852 nnns volume:4 year:2024 number:1 day:11 month:03 https://dx.doi.org/10.1007/s43762-024-00119-z kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 4 2024 1 11 03
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language	English
source	Enthalten in Computational urban science 4(2024), 1 vom: 11. März volume:4 year:2024 number:1 day:11 month:03
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topic_facet	Geographical big data Sentiment analysis Urban functional area Association rules analysis Beijing
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authorswithroles_txt_mv	Zhu, Yanrong @@aut@@ Wang, Juan @@aut@@ Yuan, Yuting @@aut@@ Meng, Bin @@aut@@ Luo, Ming @@aut@@ Shi, Changsheng @@aut@@ Ji, Huimin @@aut@@
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author	Zhu, Yanrong
spellingShingle	Zhu, Yanrong misc Geographical big data misc Sentiment analysis misc Urban functional area misc Association rules analysis misc Beijing Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing
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topic_title	Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing Geographical big data (dpeaa)DE-He213 Sentiment analysis (dpeaa)DE-He213 Urban functional area (dpeaa)DE-He213 Association rules analysis (dpeaa)DE-He213 Beijing (dpeaa)DE-He213
topic	misc Geographical big data misc Sentiment analysis misc Urban functional area misc Association rules analysis misc Beijing
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title	Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing
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title_full	Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing
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title_sort	spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in beijing
title_auth	Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing
abstract	Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. © The Author(s) 2024
abstractGer	Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. © The Author(s) 2024
abstract_unstemmed	Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. © The Author(s) 2024
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title_short	Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing
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Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing

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