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Using mental mapping to unpack perceived cycling risk

Overview of attention for article published in Accident Analysis & Prevention, January 2016
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  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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9 X users

Citations

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63 Dimensions

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273 Mendeley
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Title
Using mental mapping to unpack perceived cycling risk
Published in
Accident Analysis & Prevention, January 2016
DOI 10.1016/j.aap.2015.12.017
Pubmed ID
Authors

Richard Manton, Henrike Rau, Frances Fahy, Jerome Sheahan, Eoghan Clifford

Abstract

Cycling is the most energy-efficient mode of transport and can bring extensive environmental, social and economic benefits. Research has highlighted negative perceptions of safety as a major barrier to the growth of cycling. Understanding these perceptions through the application of novel place-sensitive methodological tools such as mental mapping could inform measures to increase cyclist numbers and consequently improve cyclist safety. Key steps to achieving this include: (a) the design of infrastructure to reduce actual risks and (b) targeted work on improving safety perceptions among current and future cyclists. This study combines mental mapping, a stated-preference survey and a transport infrastructure inventory to unpack perceptions of cycling risk and to reveal both overlaps and discrepancies between perceived and actual characteristics of the physical environment. Participants translate mentally mapped cycle routes onto hard-copy base-maps, colour-coding road sections according to risk, while a transport infrastructure inventory captures the objective cycling environment. These qualitative and quantitative data are matched using Geographic Information Systems and exported to statistical analysis software to model the individual and (infra)structural determinants of perceived cycling risk. This method was applied to cycling conditions in Galway City (Ireland). Participants' (n=104) mental maps delivered data-rich perceived safety observations (n=484) and initial comparison with locations of cycling collisions suggests some alignment between perception and reality, particularly relating to danger at roundabouts. Attributing individual and (infra)structural characteristics to each observation, a Generalised Linear Mixed Model statistical analysis identified segregated infrastructure, road width, the number of vehicles as well as gender and cycling experience as significant, and interactions were found between individual and infrastructural variables. The paper concludes that mental mapping is a highly useful tool for assessing perceptions of cycling risk with a strong visual aspect and significant potential for public participation. This distinguishes it from more traditional cycling safety assessment tools that focus solely on the technical assessment of cycling infrastructure. Further development of online mapping tools is recommended as part of bicycle suitability measures to engage cyclists and the general public and to inform 'soft' and 'hard' cycling policy responses.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 273 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
France 1 <1%
Norway 1 <1%
Indonesia 1 <1%
Spain 1 <1%
Brazil 1 <1%
Japan 1 <1%
Croatia 1 <1%
Unknown 264 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 18%
Student > Master 48 18%
Researcher 26 10%
Student > Bachelor 24 9%
Other 13 5%
Other 45 16%
Unknown 69 25%
Readers by discipline Count As %
Engineering 48 18%
Social Sciences 45 16%
Psychology 16 6%
Medicine and Dentistry 12 4%
Design 10 4%
Other 62 23%
Unknown 80 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 August 2017.
All research outputs
#6,332,191
of 25,371,288 outputs
Outputs from Accident Analysis & Prevention
#1,247
of 4,178 outputs
Outputs of similar age
#91,676
of 399,778 outputs
Outputs of similar age from Accident Analysis & Prevention
#16
of 81 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 4,178 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 399,778 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 81 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.