Investigating urban heat island through spatial analysis of New York City streetscapes
Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial patter...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shaker, Richard R. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Umfang: |
21 |
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| Übergeordnetes Werk: |
Enthalten in: Self-assembled 3D hierarchical MnCO - Rajendiran, Rajmohan ELSEVIER, 2020, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:233 ; year:2019 ; day:1 ; month:10 ; pages:972-992 ; extent:21 |
| Links: |
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| DOI / URN: |
10.1016/j.jclepro.2019.05.389 |
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ELV047343443 |
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| 520 | |a Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. | ||
| 520 | |a Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. | ||
| 650 | 7 | |a Sustainable urbanization |2 Elsevier | |
| 650 | 7 | |a Urban landscape |2 Elsevier | |
| 650 | 7 | |a Streetscapes |2 Elsevier | |
| 650 | 7 | |a Landscape configuration |2 Elsevier | |
| 650 | 7 | |a Geographically weighted regression |2 Elsevier | |
| 650 | 7 | |a Urban heat island |2 Elsevier | |
| 700 | 1 | |a Altman, Yaron |4 oth | |
| 700 | 1 | |a Deng, Chengbin |4 oth | |
| 700 | 1 | |a Vaz, Eric |4 oth | |
| 700 | 1 | |a Forsythe, K.Wayne |4 oth | |
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10.1016/j.jclepro.2019.05.389 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000961.pica (DE-627)ELV047343443 (ELSEVIER)S0959-6526(19)31932-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Shaker, Richard R. verfasserin aut Investigating urban heat island through spatial analysis of New York City streetscapes 2019transfer abstract 21 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Sustainable urbanization Elsevier Urban landscape Elsevier Streetscapes Elsevier Landscape configuration Elsevier Geographically weighted regression Elsevier Urban heat island Elsevier Altman, Yaron oth Deng, Chengbin oth Vaz, Eric oth Forsythe, K.Wayne oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:233 year:2019 day:1 month:10 pages:972-992 extent:21 https://doi.org/10.1016/j.jclepro.2019.05.389 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 233 2019 1 1001 972-992 21 |
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10.1016/j.jclepro.2019.05.389 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000961.pica (DE-627)ELV047343443 (ELSEVIER)S0959-6526(19)31932-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Shaker, Richard R. verfasserin aut Investigating urban heat island through spatial analysis of New York City streetscapes 2019transfer abstract 21 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Sustainable urbanization Elsevier Urban landscape Elsevier Streetscapes Elsevier Landscape configuration Elsevier Geographically weighted regression Elsevier Urban heat island Elsevier Altman, Yaron oth Deng, Chengbin oth Vaz, Eric oth Forsythe, K.Wayne oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:233 year:2019 day:1 month:10 pages:972-992 extent:21 https://doi.org/10.1016/j.jclepro.2019.05.389 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 233 2019 1 1001 972-992 21 |
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10.1016/j.jclepro.2019.05.389 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000961.pica (DE-627)ELV047343443 (ELSEVIER)S0959-6526(19)31932-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Shaker, Richard R. verfasserin aut Investigating urban heat island through spatial analysis of New York City streetscapes 2019transfer abstract 21 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Sustainable urbanization Elsevier Urban landscape Elsevier Streetscapes Elsevier Landscape configuration Elsevier Geographically weighted regression Elsevier Urban heat island Elsevier Altman, Yaron oth Deng, Chengbin oth Vaz, Eric oth Forsythe, K.Wayne oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:233 year:2019 day:1 month:10 pages:972-992 extent:21 https://doi.org/10.1016/j.jclepro.2019.05.389 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 233 2019 1 1001 972-992 21 |
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10.1016/j.jclepro.2019.05.389 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000961.pica (DE-627)ELV047343443 (ELSEVIER)S0959-6526(19)31932-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Shaker, Richard R. verfasserin aut Investigating urban heat island through spatial analysis of New York City streetscapes 2019transfer abstract 21 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Sustainable urbanization Elsevier Urban landscape Elsevier Streetscapes Elsevier Landscape configuration Elsevier Geographically weighted regression Elsevier Urban heat island Elsevier Altman, Yaron oth Deng, Chengbin oth Vaz, Eric oth Forsythe, K.Wayne oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:233 year:2019 day:1 month:10 pages:972-992 extent:21 https://doi.org/10.1016/j.jclepro.2019.05.389 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 233 2019 1 1001 972-992 21 |
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10.1016/j.jclepro.2019.05.389 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000961.pica (DE-627)ELV047343443 (ELSEVIER)S0959-6526(19)31932-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Shaker, Richard R. verfasserin aut Investigating urban heat island through spatial analysis of New York City streetscapes 2019transfer abstract 21 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. Sustainable urbanization Elsevier Urban landscape Elsevier Streetscapes Elsevier Landscape configuration Elsevier Geographically weighted regression Elsevier Urban heat island Elsevier Altman, Yaron oth Deng, Chengbin oth Vaz, Eric oth Forsythe, K.Wayne oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:233 year:2019 day:1 month:10 pages:972-992 extent:21 https://doi.org/10.1016/j.jclepro.2019.05.389 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 233 2019 1 1001 972-992 21 |
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Shaker, Richard R. |
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Investigating urban heat island through spatial analysis of New York City streetscapes |
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Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. |
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Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. |
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Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R 2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research. |
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