Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic

From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the di...
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Autor*in:	Yecheng Zhang [verfasserIn] Qimin Zhang [verfasserIn] Yuxuan Zhao [verfasserIn] Yunjie Deng [verfasserIn] Hao Zheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Coronavirus disease Spatial risk factors Deep learning Incidence prediction Design improvement

Übergeordnetes Werk:	In: International Journal of Applied Earth Observations and Geoinformation - Elsevier, 2022, 112(2022), Seite 102942-
Übergeordnetes Werk:	volume:112 ; year:2022 ; pages:102942-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jag.2022.102942

Katalog-ID:	DOAJ030516447

Internformat


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allfields	10.1016/j.jag.2022.102942 doi (DE-627)DOAJ030516447 (DE-599)DOAJd22b6813f7df406f85fd37492dbbcf30 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Yecheng Zhang verfasserin aut Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further. Coronavirus disease Spatial risk factors Deep learning Incidence prediction Design improvement Physical geography Environmental sciences Qimin Zhang verfasserin aut Yuxuan Zhao verfasserin aut Yunjie Deng verfasserin aut Hao Zheng verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 112(2022), Seite 102942- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:112 year:2022 pages:102942- https://doi.org/10.1016/j.jag.2022.102942 kostenfrei https://doaj.org/article/d22b6813f7df406f85fd37492dbbcf30 kostenfrei http://www.sciencedirect.com/science/article/pii/S156984322200139X kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4700 AR 112 2022 102942-
spelling	10.1016/j.jag.2022.102942 doi (DE-627)DOAJ030516447 (DE-599)DOAJd22b6813f7df406f85fd37492dbbcf30 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Yecheng Zhang verfasserin aut Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further. Coronavirus disease Spatial risk factors Deep learning Incidence prediction Design improvement Physical geography Environmental sciences Qimin Zhang verfasserin aut Yuxuan Zhao verfasserin aut Yunjie Deng verfasserin aut Hao Zheng verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 112(2022), Seite 102942- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:112 year:2022 pages:102942- https://doi.org/10.1016/j.jag.2022.102942 kostenfrei https://doaj.org/article/d22b6813f7df406f85fd37492dbbcf30 kostenfrei http://www.sciencedirect.com/science/article/pii/S156984322200139X kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4700 AR 112 2022 102942-
allfields_unstemmed	10.1016/j.jag.2022.102942 doi (DE-627)DOAJ030516447 (DE-599)DOAJd22b6813f7df406f85fd37492dbbcf30 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Yecheng Zhang verfasserin aut Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further. Coronavirus disease Spatial risk factors Deep learning Incidence prediction Design improvement Physical geography Environmental sciences Qimin Zhang verfasserin aut Yuxuan Zhao verfasserin aut Yunjie Deng verfasserin aut Hao Zheng verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 112(2022), Seite 102942- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:112 year:2022 pages:102942- https://doi.org/10.1016/j.jag.2022.102942 kostenfrei https://doaj.org/article/d22b6813f7df406f85fd37492dbbcf30 kostenfrei http://www.sciencedirect.com/science/article/pii/S156984322200139X kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4700 AR 112 2022 102942-
allfieldsGer	10.1016/j.jag.2022.102942 doi (DE-627)DOAJ030516447 (DE-599)DOAJd22b6813f7df406f85fd37492dbbcf30 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Yecheng Zhang verfasserin aut Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further. Coronavirus disease Spatial risk factors Deep learning Incidence prediction Design improvement Physical geography Environmental sciences Qimin Zhang verfasserin aut Yuxuan Zhao verfasserin aut Yunjie Deng verfasserin aut Hao Zheng verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 112(2022), Seite 102942- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:112 year:2022 pages:102942- https://doi.org/10.1016/j.jag.2022.102942 kostenfrei https://doaj.org/article/d22b6813f7df406f85fd37492dbbcf30 kostenfrei http://www.sciencedirect.com/science/article/pii/S156984322200139X kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4700 AR 112 2022 102942-
allfieldsSound	10.1016/j.jag.2022.102942 doi (DE-627)DOAJ030516447 (DE-599)DOAJd22b6813f7df406f85fd37492dbbcf30 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Yecheng Zhang verfasserin aut Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further. Coronavirus disease Spatial risk factors Deep learning Incidence prediction Design improvement Physical geography Environmental sciences Qimin Zhang verfasserin aut Yuxuan Zhao verfasserin aut Yunjie Deng verfasserin aut Hao Zheng verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 112(2022), Seite 102942- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:112 year:2022 pages:102942- https://doi.org/10.1016/j.jag.2022.102942 kostenfrei https://doaj.org/article/d22b6813f7df406f85fd37492dbbcf30 kostenfrei http://www.sciencedirect.com/science/article/pii/S156984322200139X kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4700 AR 112 2022 102942-
language	English
source	In International Journal of Applied Earth Observations and Geoinformation 112(2022), Seite 102942- volume:112 year:2022 pages:102942-
sourceStr	In International Journal of Applied Earth Observations and Geoinformation 112(2022), Seite 102942- volume:112 year:2022 pages:102942-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Coronavirus disease Spatial risk factors Deep learning Incidence prediction Design improvement Physical geography Environmental sciences
isfreeaccess_bool	true
container_title	International Journal of Applied Earth Observations and Geoinformation
authorswithroles_txt_mv	Yecheng Zhang @@aut@@ Qimin Zhang @@aut@@ Yuxuan Zhao @@aut@@ Yunjie Deng @@aut@@ Hao Zheng @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	359784119
id	DOAJ030516447
language_de	englisch
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callnumber-first	G - Geography, Anthropology, Recreation
author	Yecheng Zhang
spellingShingle	Yecheng Zhang misc GB3-5030 misc GE1-350 misc Coronavirus disease misc Spatial risk factors misc Deep learning misc Incidence prediction misc Design improvement misc Physical geography misc Environmental sciences Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic
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topic_title	GB3-5030 GE1-350 Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic Coronavirus disease Spatial risk factors Deep learning Incidence prediction Design improvement
topic	misc GB3-5030 misc GE1-350 misc Coronavirus disease misc Spatial risk factors misc Deep learning misc Incidence prediction misc Design improvement misc Physical geography misc Environmental sciences
topic_unstemmed	misc GB3-5030 misc GE1-350 misc Coronavirus disease misc Spatial risk factors misc Deep learning misc Incidence prediction misc Design improvement misc Physical geography misc Environmental sciences
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title	Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic
ctrlnum	(DE-627)DOAJ030516447 (DE-599)DOAJd22b6813f7df406f85fd37492dbbcf30
title_full	Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic
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title_sort	urban spatial risk prediction and optimization analysis of poi based on deep learning from the perspective of an epidemic
callnumber	GB3-5030
title_auth	Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic
abstract	From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.
abstractGer	From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.
abstract_unstemmed	From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.
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title_short	Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic
url	https://doi.org/10.1016/j.jag.2022.102942 https://doaj.org/article/d22b6813f7df406f85fd37492dbbcf30 http://www.sciencedirect.com/science/article/pii/S156984322200139X https://doaj.org/toc/1569-8432
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up_date	2024-07-03T15:27:52.098Z
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