Show simple item record

dc.contributor.authorPerez, Kimberly C.
dc.date.accessioned2020-09-11T11:46:52Z
dc.date.created2020-03
dc.date.issued2020-05-06
dc.date.submitted2020
dc.identifier.citationPerez, Kimberly C. 2020. A Method for Quantifying Sidewalk Shade to Assess Pedestrian Access to Relief From Direct Sun. Master's thesis, Harvard Extension School.
dc.identifier.urihttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364871*
dc.description.abstractUrban heat is a public health risk and is increasing due to climate change, with heat waves more intense and longer-lasting than before. Meanwhile, many cities are designing for walkability, encouraging people to drive less and walk more. The convergence of these trends leaves people vulnerable when temperatures are dangerously high and there’s no escape from direct sun. Pedestrian access to shade offers relief from the heat. Quantifying shade can help citizens assess walkability and enable city planners to design shade-rich environments. My primary hypothesis was that shade can be accurately modeled and quantified using a Geographic Information System (GIS). My secondary hypotheses were that areas of higher heat-related risk will have less shade than areas of lower risk, and income will be the variable most closely associated with shade. I used GIS, building footprints, LiDAR-derived elevation and height data, and tree mapping to map, measure and analyze shade on 16 sample walking routes of 540 meters each in the increasingly hot City of Pasadena, CA. I created 3D models of walking routes with Esri’s ArcGIS Pro (v. 2.3.0), using LiDAR data to extract 3D trees and add height to building footprints. I manually moved, sized and shaped each building and tree, using Google Street View (GSV) as a source of information, and ground-truthed each model by comparing shade maps to field measurements. Modeled shade maps matched field tests with a mean match rate of 91.6% for total meters shaded and 70.4% for distribution of shade. I conclude that it is possible to quantify sidewalk shade using 3D modeling in ArcGIS Pro and GSV as a source of information, with some limitations. To see if access to shade is correlated with risk factors related to income, age, or access to a car, I mapped shade for two different times of day and calculated minutes walking unshaded and gaps in shade for each route. Employing statistical analysis using linear mixed effects models (LMM), and additional analysis and data visualization in Excel, I found that neither of my secondary hypotheses was supported by the data. Assessing access to shade is complex and site-specific. Having a method for quantifying shade can help policy-makers identify where people spend a dangerous amount of time in direct sun, and then use that insight to help people remain safe during heat waves, to consider shade when designing for walkability, and to plan for more equitable access to shade across a city.
dc.description.sponsorshipSustainability
dc.format.mimetypeapplication/pdf
dash.licenseLAA
dc.subjectUrban resilience, heat wave, shade mapping, quantifying sidewalk shade, pedestrian shade
dc.titleA Method for Quantifying Sidewalk Shade to Assess Pedestrian Access to Relief From Direct Sun
dc.typeThesis or Dissertation
dash.depositing.authorPerez, Kimberly C.
dc.date.available2020-09-11T11:46:52Z
thesis.degree.date2020
thesis.degree.grantorHarvard Extension School
thesis.degree.grantorHarvard Extension School
thesis.degree.levelMasters
thesis.degree.levelMasters
thesis.degree.nameALM
thesis.degree.nameALM
dc.contributor.committeeMemberLeighton, Mark
dc.contributor.committeeMemberBlossom, Jeff
dc.type.materialtext
thesis.degree.departmentSustainability
thesis.degree.departmentSustainability
dash.identifier.vireo
dash.author.emailkimperezwriter@gmail.com


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record