A partial archive of https://score.community/ as of Monday March 04, 2024.

Liveability - Partner Meeting April 2020


Summary of the Working table – Liveability
Attendance: Bradford, Bradford University, Foundation for Public Code, Gothenburg

We started the session with Inske giving an update on where the project stands at the moment. She introduced the PanorAMS framework, which we developed to be able to train algorithms to recognize and localize common urban objects in panoramic street view images. She also briefly went over the steps ahead on how from this starting point we want to start analysing those urban objects in the context of liveability.

During the first part of the discussion we discussed how this solution could be implemented in different cities. The following points will need to be considered when adapting the solution for a different city:

  • The objects for which GIS information is available – OpenStreetMap, which is available across cities, is one source from which information on different objects could be extracted;
  • The error margin on the GPS location of the images – in case there is a large error, it will be difficult to implement the PanorAMS solution as is
  • Object measurement information – the solution will need to be adapted to incorporate knowledge on the average measurements of types of objects (e.g. a trash can in Amsterdam may have different measurements than a trash can in Bradford)

During the rest of the discussion, we looked ahead at the different aspects of liveability that cities are interested in and for which data is available. We got some great input on:

  • Most cities and countries (including SCORE partners) measure liveability on a regular basis (on e.g. neighbourhood level);
  • The way in which liveability is measured often differs between different cities/countries;
    • e.g. in the Netherlands, liveability indicators are published at a national and regional level;
  • The aspects of liveability that are of interest:
    • What is the influence of the design of the public space (e.g. parks in the area, range of services available in an area) on citizen well-being (e.g. mental health, loneliness, physical well-being)?
    • What factors influence that citizens feel connected and engaged with an area?
    • How do different aspects affect different age groups? A teenager may have a very different view on an area than an elderly person.
    • Citizen employment and opportunities – what is the relation between citizen employment and career opportunities and the liveability in the area in which they live?
  • Different liveability indicators that are available:
  • Data sources available in different cities:

An important question for us to tackle is how to combine the various open data sources to accurately measure and evaluate urban liveability for different age groups in a fast, reliable and accurate way – and, of course, this must be in such a way that our solution can be relatively easily employed in different partner cities.