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Co-designing the 15-Minute City
Dr Alex Nurse
University of Liverpool
โ€˜Bikelashโ€™
Objectives
โ€ข To use real-time sensor data to understand patterns of
movement of residents at specific locations within a
neighbourhood and how they use active travel (i.e. walking
and cycling) to achieve their daily needs
โ€ข To work directly with local residents to understand the
motivations behind those movement patterns and, by
extension, planned improvements/changes which might be
required; and
โ€ข As a result, understand the role of data analytics in
community planning to co-design the interventions required
to make their neighbourhood a 15-minute city which reflects
residentโ€™s daily activities.
Our site
1
2
The right place the [right/wrong] time?
Traffic Patterns
Usage
Speedโ€ฆ
Turning
Co-Design Workshops
A Sequential Discussionโ€ฆ
Resident Perceptions
โ€ข Litter
โ€ข Resident instincts on traffic data was very
accurate
โ€ข [Beforehand] CGI was โ€˜too goodโ€™?
โ€ข โ€œThe picture on your leaflet looks like Utopia or maybe some affluent part
of Liverpool where an issue like this would be addressed asap.โ€œ
โ€ข [During] CGI put them in the space
โ€ข In favour of intervention
โ€ฆMeanwhile back on Kingsley
Road
โ€ข Sensor data turned resident views
โ€ข From 60:40 against (Meeting One)
โ€ข To 60:40 Pro (final meetings)
โ€ข Scheme retained
โ€ข But relationship with LCC wasnโ€™t especially
changedโ€ฆ
Core messages
The Good
โ€ข Sometimes itโ€™s about the simple things
โ€ข Trust residents to have good instincts about where they live. They
care.
โ€ข Sensor data turns heads.
โ€ข CGI turns heads.
The Bad
โ€ข Lead In
โ€ข External actorsโ€ฆ
โ€ข What can you learn from pilots?
The Complex
โ€ข Who is consultation for (whose street is it)?
โ€ข 150 houses on the street: 2400 through journeys per day
Thank You
Contact: a.nurse@liverpool.ac.uk

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Co-Designing the 15-Minute City

  • 1. Co-designing the 15-Minute City Dr Alex Nurse University of Liverpool
  • 3. Objectives โ€ข To use real-time sensor data to understand patterns of movement of residents at specific locations within a neighbourhood and how they use active travel (i.e. walking and cycling) to achieve their daily needs โ€ข To work directly with local residents to understand the motivations behind those movement patterns and, by extension, planned improvements/changes which might be required; and โ€ข As a result, understand the role of data analytics in community planning to co-design the interventions required to make their neighbourhood a 15-minute city which reflects residentโ€™s daily activities.
  • 5. The right place the [right/wrong] time?
  • 12. Resident Perceptions โ€ข Litter โ€ข Resident instincts on traffic data was very accurate โ€ข [Beforehand] CGI was โ€˜too goodโ€™? โ€ข โ€œThe picture on your leaflet looks like Utopia or maybe some affluent part of Liverpool where an issue like this would be addressed asap.โ€œ โ€ข [During] CGI put them in the space โ€ข In favour of intervention
  • 13. โ€ฆMeanwhile back on Kingsley Road โ€ข Sensor data turned resident views โ€ข From 60:40 against (Meeting One) โ€ข To 60:40 Pro (final meetings) โ€ข Scheme retained โ€ข But relationship with LCC wasnโ€™t especially changedโ€ฆ
  • 14. Core messages The Good โ€ข Sometimes itโ€™s about the simple things โ€ข Trust residents to have good instincts about where they live. They care. โ€ข Sensor data turns heads. โ€ข CGI turns heads. The Bad โ€ข Lead In โ€ข External actorsโ€ฆ โ€ข What can you learn from pilots? The Complex โ€ข Who is consultation for (whose street is it)? โ€ข 150 houses on the street: 2400 through journeys per day