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Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression

  • Yujia ZhangEmail author
  • Ariane Middel
  • B. L. TurnerII
Research Article
  • 86 Downloads

Abstract

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 (R2 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.

Keywords

Google Street View 3D urban form Geographically weighted regression Land surface temperature Urban heat island 

Notes

Acknowledgements

This research was supported by Technische Universität Kaiserslautern, Grant “Microclimate Data Collection, Analysis, and Visualization”, the Gilbert F. White Fellowship, the Graduate. School Completion Fellowship, the Central Arizona-Phoenix Long-Term Ecological Research program (NSF Grant No. BCS-1026865), the National Science Foundation (NSF) under Grant No. SES-0951366, NSF IMEE Grant No. 1635490, NSF DMS Grant No. 1419593 and USDA NIFA Grant No. 2015-67003-23508. The research was undertaken in the Environmental Remote Sensing and Geoinformatics Lab, Arizona State University, AZ. We thank for the valuable inputs from our reviewers.

Supplementary material

10980_2019_794_MOESM1_ESM.docx (317 kb)
Supplementary material 1 (DOCX 316 kb)

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Geographical Science and Urban PlanningArizona State UniversityTempeUSA
  2. 2.School of Arts, Media and EngineeringArizona State UniversityTempeUSA
  3. 3.School of Computing, Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA
  4. 4.School of SustainabilityArizona State UniversityTempeUSA

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