Promote healthy walking habits for everyone in Scotland and ensuring paths remain safe and welcoming.

  

The Approach

The first step was to review the attitudinal survey, and the documentation associated with it. The initial impressions were that the survey was too long and the formatting of the spreadsheet that capture the results was inadequate to apply data visualization and advanced analytical techniques. As a result, it was agreed with the client to focus the analysis on the questions that best captured different attitudes towards walking and treat the analysis as a proof of concept for further iterations.

By applying Factor Analysis, we were able to classify all attitudinal questions into themes which allows the ability to condense information. Moreover, the reliability of the survey was assessed by calculating the Cronbach Alpha to ensure the consistency of responses.  Finally, a k-means algorithm was implemented to group respondents into segments that share similar traits.

 

The Client

Paths for All is a Scottish charity dedicated to promoting everyday walking as a means to create a happier, healthier, and greener Scotland. Their mission focuses on encouraging people to walk more frequently, improving the environments where they walk, and supporting communities to integrate walking into daily life.

The Client's Problem

The client had previously conducted a survey to gauge attitudes to walking in Scotland and was looking for a way to summarise the data and extract key insights. 

The Solution

• Powerpoint Report comprising the different analytical outputs and key recommendations.
• R Markdown technical documentation covering in depth all analytical techniques in the analysis.
• Two prototype Shiny apps that summarise the outputs of the cluster analysis and illustrate how the process of deriving them works.

The Benefits

• Awareness of how survey response needs to be captured for future iterations to allow effective data visualisation and analysis.
• Capability to streamline future surveys by using less questions but capturing the same attitudinal traits.
• Insights about attitudes to walking and ability to group respondents into cohorts of similar attitudes.
• Understanding of R capabilities to undertake statistical analysis, create technical documentation and interactive dashboards.

 

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