A Geographically Weighted Regression Model to Examine the Spatial Variation of the Socioeconomic and Land-Use Factors Associated with Strava Bike Activity in Austin, Texas

Munira, Sirajum; Sener, Ipek N.; Texas A&M Transportation Institute

doi:10.1016/j.jtrangeo.2020.102865

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A Geographically Weighted Regression Model to Examine the Spatial Variation of the Socioeconomic and Land-Use Factors Associated with Strava Bike Activity in Austin, Texas

2020-10-01
By Munira, Sirajum ; Sener, Ipek N.

[PDF-1.51 MB]

English

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Creators:

Munira, Sirajum ; Sener, Ipek N.

Munira, Sirajum ; Sener, Ipek N. Less -
Corporate Creators:

Texas A&M Transportation Institute
Corporate Contributors:

Virginia Tech Transportation Institute ; Safety through Disruption (Safe-D) University Transportation Center (UTC)

Virginia Tech Transportation Institute ; Safety through Disruption (Safe-D) University Transportation Center (UTC) Less -
Subject/TRT Terms:

[+]

Bicycling Built Environment Geographically Weighted Poisson Regression Intersections Land Use Nonmotorized Transportation Poisson Distributions Regression Analysis Socioeconomic Factors Spatial Variations Strava Data Walking
DOI:

https://doi.org/10.1016/j.jtrangeo.2020.102865
Resource Type:

Manuscript
Geographical Coverage:

United States
Corporate Publisher:

Safety through Disruption (Safe-D) University Transportation Center (UTC)
Abstract:

Despite evidence showing the spatial nonstationarity of the determinants of bike activity, very few studies have addressed the phenomena, probably due to the limited sample size of the traditional count data. To address this gap, this study demonstrated the applicability of Strava bike activity data by developing a geographically weighted Poisson regression (GWPR) model that can reveal how the influence of socioeconomic and land-use factors vary across a region. The city of Austin was selected as a case study, and Strava bike volume was gleaned from 1494 intersections. The representativeness of the Strava data was first examined by comparing those data with the video-based actual bicycle volume data from 43 intersections in the study area. Despite the high deviation in several locations, Strava volume exhibited moderate linear relationships with actual volume. The GWPR model developed in this study outperformed the traditional global model and revealed significant spatial variability of nine variables related to age, income, education, transit stops, hub locations, offices, schools, trails, and sidewalk facilities. Notable spatial variations on bike activity were observed across the study area in terms of magnitude, direction, and significance of the impact for all model variables. The analysis and discussion offer guidance to practitioners and policymakers in tailoring policies and programs that consider the spatial context. The study also provides insights for understanding the potential use of crowdsourced data in examining bike activity, especially when resources are limited.
Format:

PDF
Funding:

69A3551747115
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:cfe09e70fdd0d342c22754e5df9a2ef86f083a51eb0ece1b8b5a18eb50463ede
Download URL:

https://rosap.ntl.bts.gov/view/dot/60242/dot_60242_DS1.pdf
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A Geographically Weighted Regression Model to Examine the Spatial Variation of the Socioeconomic and Land-Use Factors Associated with Strava Bike Activity in Austin, Texas

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