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Changes in the Representativeness of Strava Bicycling Data during COVID-19

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COVID-19 prompted a large global increase in Strava app use that could have implications for the representativeness of Strava bicycling data. We evaluated change in Strava representation of the general bicycling population from 2019 to 2020 by correlating Strava to counts of all bicyclists in Vancouver and Victoria, Canada. Strava data became more representative during COVID-19 lockdowns, likely because more bicycling activities were logged on the app. Age and gender bias in Strava data decreased and demographics of app users became more representative of the general population. For cities looking to increase Strava data reliability we suggest a benchmark Strava usage rate of ~7% of the total bicycle volumes.
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TRANSPORT FINDINGS
Changes in the Representativeness of Strava Bicycling Data during
COVID-19
Jaimy Fischer
1 a , Trisalyn Nelson
2 , Meghan Winters
1
1 Faculty of Health Sciences, Simon Fraser University, 2 Department of Geography, University of California Santa Barbara
Keywords: active transportation, bicycling, big data, crowdsourced, COVID-19, Strava
https://doi.org/10.32866/001c.33280
Findings
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Corresponding author: Corresponding author:
Jaimy Fischer
jaimyf@sfu.ca
778-676-7773
a
Fischer, Jaimy, Trisalyn Nelson, and Meghan Winters. 2022. “Changes in the
Representativeness of Strava Bicycling Data during COVID-19.”
Findings
, March.
https://doi.org/10.32866/001c.33280.
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2. Methods
Study area
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Findings 2
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Location Location PopulationPopulationa a Temperature and precipitationTemperature and precipitationb b Bicycle facilitiesBicycle facilitiesc c Bicycle mode shareBicycle mode shareaa (%) (%)
People People Density (ppl/Density (ppl/
kmkm22) )
Winter avg. Winter avg.
temp (temp (°C)
Summer avg. Summer avg.
temp (temp (°C)
Annual precipitation Annual precipitation
(mm) (mm)
Total distance Total distance
(km) (km)
Density (km/Density (km/
1000 ppl) 1000 ppl)
City City B.C. B.C. Canada Canada
City of
Vancouver
631,486 5,492.0 4.1 18.0 1189.0 495.6 0.8 6.1 2.5 1.4
Greater
Victoria
235,689 1,673.2 4.6 16.9 882.9 205.4 0.9 8.7 2.5 1.4
(*/%.!(''&(- ,.1,(.!',(&. B@AF'!''-/-Q..!-.!-'B@AFR
%!&..1,(.!',(&'0!,('&'.'Q'0!,('&'.'%!&. ''B@BAR
!3%!',-.,/./,1-(.!',(&(*'.(,'(/0,Q!.3('(/0,B@BBR'-.5,(&. *!.%!('%!-.,!.(,,.,!.(,!Q!'%/!'!.(,!K$3K'! K'-+/!&%.RL
 '-!'. *,-'..!0'--(.,0!3%!'./,!'UAI
Findings 3
%BL '!'.,0/-,.-'!.3U%0%(,,%.!('L
City City Year Year Strava app Strava app
use use
Trips logged to Trips logged to
Strava Strava
City-level Strava usage City-level Strava usage
rate rate
RR
, city-level (pooled , city-level (pooled
sample) sample)
Vancouver 2019 Reference Reference 5.7% 0.42
2020 +48 % +41 % 12.1% 0.73
Victoria 2019 Reference Reference 7.0% 0.85
2020 +26 % +27 % 15.8% 0.87
Analysis
((&*,.,'-.1'!.!-K1%/%.. *,(*(,.!('% '!'. 
'/&,(**/-,-'!3%!'.!0!.!-%(.(.,0/-!'.*,(8%-
,(& .  .,0 - (, Q.,0 .,( B@BBRL ( 0%/. (1 .,0
,!,- !*,*,-'. (/'.-( %% !3%!-.-K1&-/,.,0/- ,.-
'----(,,%.!('.1'.,0'(/'.-(%%!3%!-.-L .,0
/- ,. !- .  *,(*(,.!(' ( %% .,!*- (/'. . !.3 !$ (/'.,- . .
1,*./,!' .  .,0. Q& (U(,,,(- .%L B@BARK ' . 
(,,%.!('!'!.-1 . ,.,0!3%!-.-*,(0! ((**,(2!&.!('
( %% !3%!-.- Q ' ', B@BARL /-.- ( .,0 . &3  &(,
,*,-'..!0 ( .  ',% *(*/%.!(' ' ,!0 .,'- (-,0 !' . 
*((% -&*% Q%% (-,0.!('- (&!'R Q!- ,K %-('K ' !'.,-
B@B@N%-('K(3K.%LB@BARN-(K1%/%./-,.-'(,,%.!('
&-/,-(,.  *((%-&*% (.,0,!,- !* ' (,-/-.-( .,0
.3.,!**/,*(-'&(,* !-L*!8%%3K1/-%%-!'. .,0
..(2.,..-/-.-(,,,.!(''(&&/.,!,- !*K'(,. 
-&*%(,!,- !*',.31(&''3(%,/%.-QEEZRL
3. Findings
 '/&,(*(*%/-!'.,0.(.,$ !3%!'.!0!.!- '. .(.%
0(%/&(!3%!'.!0!.!-,(,('.,0!',-!'(. !.!-K1!. 
. ,.-.!',--!''(/0,Q%BRL''(/0,**/-,- !*',%3
(/% QDH_ !',-R ' .,0 !3%!' .!0!.!- !',- 3 DA_K
1 ,- !' !.(,! ** /- ' .!0!.!- !',- 3 BF ' BG_K
,-*.!0%3L
!.3U%0%.,0/-,.-'(,,%.!('-.1'.,0'(/'.-(%%
!3%!-.-**,.(%!'$K1!. /-,.-^GUA@_*(--!%3(,,-*('!'
.(-.,('!.3U%0%(,,%.!('QR\[@LGRL(!%%/-.,.K. .,0/-,.
,(- ,(& ELG .( ABLA_ !' '(/0, ' !.3U%0% (,,%.!(' !',-
-/-.'.!%%3K #/&*!' ,(& @LDB !' B@AI .( @LGC !' B@B@L ,U
(,,%.!('1-%,3-.,('!'!.(,!1 '. .,0/-,.1-G_
QB@AIR'!',-&,!'%%3!'B@B@Q@LHE.(@LHGR0'-. .,0/-
,.2AE_L
 '-!'. *,-'..!0'--(.,0!3%!'./,!'UAI
Findings 4
!/,AL '!'(,,%.!('QB@AIUB@B@R.1'.,0'(/'.-(%%!3%!-.-L - %$%!'&,$-. 
0%/(,-.,('(,,%.!('Q@LG(, ! ,RL,U('U3,(,,%.!('!',-!'(. !.!-K/.'(/0, . 
,.-.!'-K1!. (,,%.!('#/&*!',(&@LDB!'B@AI.(@LGC!'B@B@L,U(,,%.!('1-%,3-.,('!'
!.(,!!'B@AI'!',-&,!'%%3!'B@B@L -/-.(.,0.',.31(&'&('-.,.-.,('
(,,%.!('-!'(. !.!-!'B@B@K'(,,%.!('!'. -/-.',.3(%,/%.-!',--/-.'.!%%3!'
'(/0,L
!5,'-!'(,,%.!('3.,!**/,*(-'&(,* !-!&*%3. .-/-.-
( .,0 ,!,- !* &3  &(, ,*,-'..!0 ( .  ',% !3%!'
*(*/%.!('L''(/0,K%%.,0.-/-.-- (13,U('U3,!',-
!'(,,%.!('1!. (/'.-(%%!3%!'K'. -.,('-.(,,%.!('(0,%%
1-(,. -&*%(.,0,!,- !*',.31(&'Q@LGIRL'!.(,!
. -.,('-.(,,%.!('(/,,!'. -&*%(,!,- !*',.3(%,
/%.- Q@LHHRL ' (.  !.!-K %, !',-- !' (,,%.!(' QBHUAD@_R !' . 
,,.!('% ,!,- !* -&*% -/-.'.!. ,-,  &('-.,.!' . .
!3%!'(,,,.!('!',-,&.!%%31 '!.!-1'.!'.(%($(1'
/,!'. 8,-.10(. *'&!Q/ %,'/ ,B@BARL
!.  ! , !.3U%0%.,0 /- ,.-K 1%-( - -.,(', (,,%.!('-!'
.  . -/-.- (, 1(&' ' (%, /%.-K -/-.!' . - ! , /-
,.- &3  !'!.!0 ( .., ,*,-'..!(' ( 1(&' ' (%, /%.-
!'.  .L !-!' .,0. ,%..( 1 ( !- /-!'.  ** !&*.-.
,%!!%!.3Q'',B@BARL/,8'!'-!&*%3. .'',!-!'
.,0**/-&3 0,-!'. -!.!-'. ..,0.&
&(,,*,-'..!01 '**/--/,!'. 8,-.&('. -(. *'&!L
 -8'!'-%-(,9. (1!3%!'K-*!%%3(,,,.!('K!',--
!.!-1'.!'.(*'&!%($(1'-!'B@B@L!,./%(''.!('K1(,$,(&
(&K'*(%!3. .-/**(,.-.!0.,'-*(,..!(',%!$%3.(('.!'/Q0'
' !.%(2B@BARK /.. %('U.,& 5.-(UAI('!3%!'
 '-!'. *,-'..!0'--(.,0!3%!'./,!'UAI
Findings 5
'.,0/-,-.!%%/'$'(1'L2*..%-.-(&(. &(,* !
- !.- !' .,0 . .( *,-!-.K /. /./, -./!- 1(/% %* %,!3 . -
/',.!'.!-L
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,-, ,- &! . /- . - -/-.- &(, */,*(-/%%3 .( 0' .!0
.,'-*(,..!(' +/!.3 (%- - . - , *,!(,!.3 *(*/%.!('- (, !',-!'
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.,0. 3&3-/-&*%(.,0.. .-.,*,-'.*(*/%.!('U
%0%!3%!'*..,'-L'(. !.!-K!',--!'.,0**/-(,,-*('
1!.  .., ,*,-'..!(' Q!LLK ! , (,,%.!('R ( .  ',% !3%!'
*(*/%.!('K /. /,. , !'- &3  &,!'% 1 ' .  *,(*(,.!(' ( %%
!3%!'.,!*- %(3.,0, - -(&. ,- (%L(,!.!- %(($!'.(
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,.(]^G_L !-,(&&'.!('(/%(%-.,3!.!('%-./!-
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/-,.-K&*!'-. .'(/,!3%!-.-.(&*. !,,!-('.,0'
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Acknowledgements
  /. (,- 1(/% %!$ .( $'(1% .,0 (, *,(0!!' .  . '
.!&%3,0!1( (/,1(,$' -.5..  *!.%!('% !-.,!.' . 
!.3('(/0,(,*,(0!!'/-1!. (6!%!3%(/'..L
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Findings 6
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... It is widely use in the runner's community who openly shares GPS recorded workouts. Strava popularity increased particularly during the COVID-19 pandemic as it offers, especially during lockdown periods, opportunities for people to be followed up, to participate in virtual challenges or competitions and to be connected to friends and peers' trough virtual community while social distancing and restrictions prevent group training and events (Couture, 2021;Fischer et al., 2022). One of the most innovating features of Strava is the concept of segments, which are portions of road or trail created by members where athletes can compare times. ...
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... Strava is utilized by those with access to the technology, which excludes people without smart devices. Strava users are often younger men who tend to have higher SES, and individuals with low SES are likely underrepresented [47,79,85]. Reaching underrepresented populations would allow for a more accurate account of the routes selected by runners from all backgrounds. ...
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Running can improve physical health and psychological wellbeing. However, the characteristics of conducive running environments are relatively unknown. This study determines neighborhood factors that attract running and explores how age and gender mediate built environment preferences. Spatial patterns of runners in Metro Vancouver were identified using crowdsourced fitness data from Strava, a popular application for tracking physical activities. The influence of socio-economic status (SES), green and/or blue space, and urbanicity on route popularity was assessed using a Generalized Linear Model (GLM). The influence of these neighborhood variables was also calculated for runners by age and gender. The results show high neighborhood SES, the presence of green and/or blue space, and high population density are associated with increased running activities in all age and gender groups. This study contributes a novel approach to understanding conducive running environments by demonstrating the utility of crowdsourced data in combination with data about urban environments. The patterns of this large group of runners can be used to inform planning for cities that promote running, as well as seek to encourage equal participation among different ages and genders.
... Similarly, in the Netherlands a diligent advertising campaign for the Bike PRINT app was able to reached representative samples (Garber, Watkins, and Kramer 2019). During the COVID-19 lockdowns, the bias in Strava representativeness (age-wise and gender-wise) decreased in Vancouver and Victoria, Canada (Fischer, Nelson, and Winters 2022). This is might be the case in other locations as well as the pandemic encouraged more AT as a non-pharmaceutical intervention. ...
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