干旱区生态环境知识资源中心(Arid): Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression

Arid

DOI	10.1007/s10980-019-00794-y
	Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression
	Zhang, Yujia 1; Middel, Ariane 2,3; Turner, B. L., II 1,4
通讯作者	Zhang, Yujia
来源期刊	LANDSCAPE ECOLOGY
ISSN	0921-2973
EISSN	1572-9761
出版年	2019
卷号	34 期号:3 页码:681-697
英文摘要	Context Land surface temperature (LST) directly responds to incoming solar radiation and is strongly influenced by vertical urban structures, such as trees and buildings that provide shade. Conventional LST-planar land-cover assessments do not explicitly address the vertical dimension of the urbanscape and therefore do not capture the heterogeneity of solar radiation exposure of planar surfaces adequately. Objectives To fill this gap, this study compares and integrates novel spherical land-cover fractions derived from Google Street View (GSV) with the conventional planar land-cover fractions in estimating daytime and nighttime LST variations in the Phoenix metropolitan area, AZ. Methods The GSV spherical dataset was created using big data and machine learning techniques. The planar land cover was classified from 1 m NAIP imagery. Ordinal least square (OLS) and geographically weighted regression (GWR) were used to assess the relationship between LST and urban form (spherical and planar fractions) at the block group level. Social-demographic variables were also added provide the most comprehensive assessment of LST. Results The GSV spherical fractions provide better LST estimates than the planar land-cover fractions, because they capture the multi-layer tree crown and vertical wall influences that are missing from the bird-eye view imagery. The GWR regression further improves model fit versus the OLS regression (R-2 increased from 0.6 to 0.8). Conclusions GSV and spatial regression (GWR) approaches improve the specificity of LST identified by neighborhoods in Phoenix metro-area by accounting for shading. This place-specific information is critical for optimizing diverse cooling strategies to combat heat in desert cities.
英文关键词	Google Street View 3D urban form Geographically weighted regression Land surface temperature Urban heat island
类型	Article
语种	英语
国家	USA
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000463741600015
WOS关键词	HEAT-ISLAND ; AIR-TEMPERATURE ; METROPOLITAN PHOENIX ; LANDSCAPE ; IMPACTS ; COVER ; ARIZONA ; WATER ; VULNERABILITY ; VEGETATION
WOS类目	Ecology ; Geography, Physical ; Geosciences, Multidisciplinary
WOS研究方向	Environmental Sciences & Ecology ; Physical Geography ; Geology
来源机构	Arizona State University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/217493
作者单位	1.Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85287 USA; 2.Arizona State Univ, Sch Arts Media & Engn, Tempe, AZ 85287 USA; 3.Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85287 USA; 4.Arizona State Univ, Sch Sustainabil, Tempe, AZ 85287 USA
推荐引用方式 GB/T 7714	Zhang, Yujia,Middel, Ariane,Turner, B. L., II. Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression[J]. Arizona State University,2019,34(3):681-697.
APA	Zhang, Yujia,Middel, Ariane,&Turner, B. L., II.(2019).Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression.LANDSCAPE ECOLOGY,34(3),681-697.
MLA	Zhang, Yujia,et al."Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression".LANDSCAPE ECOLOGY 34.3(2019):681-697.