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Active accessibility, or making it easy for people to access essential services and opportunities, is a key goal in today's sustainable urban development plans. The concept of the "15-minute city" has become a popular way to highlight the importance of having everything you need close to where you live. In this paper, we look at two ways to measure accessibility for pedestrians: cumulative opportunities (the total number of places a person can reach) and variety (how many different types of places a person can access), all within a 15-minute walk.

We studied a sample of European cities with populations of 100,000 or more to see how accessible they are on foot and how evenly this accessibility is distributed both within and between cities. To measure inequality, we used a calculation similar to the Gini coefficient, which is often used to measure income inequality.

Our results show that most European cities are not fully "15-minute cities" yet. There is also significant inequality in access to services within cities, although cities with a higher variety of services tend to have less inequality. When comparing different cities, we found that as cities grow denser, there are diminishing returns in both the number of accessible places and the variety of places people can reach.

Overall, our findings suggest that European cities can improve pedestrian access and reduce internal inequalities by increasing the variety of services and opportunities that are reachable on foot, alongside investing in better pedestrian infrastructure.

Methods
Data Sources
  • Administrative Boundaries: The boundaries of European cities were obtained from Eurostat’s GISCO dataset (Urban Audit 2020), focusing on "city" and "greater city" divisions.
  • Population Data: We used figures from Eurostat (2011–2020), focusing on cities with 100,000+ inhabitants, resulting in a final dataset of 585 cities across 31 countries.
  • Street Network: OpenStreetMap (OSM) data was used to create a pedestrian network through the Pandana Python library, excluding non-walkable streets. Origin-Destination (O-D) matrices were computed.
  • Points of Interest (POIs): POIs were collected using the Pyrosm Python library from OSM data (as of June 2021). POIs were filtered by type and access, with building centroids used for non-residential buildings.
  • Hexagon Grid: A grid was created using Uber's H3 spatial index (Level 10), resulting in 4,347,078 hexagons across all the cities.
Accessibility Measures
  • Place-Based Approach: Accessibility was calculated using a place-based methodology, focusing on access to opportunities (destinations) by walking, without considering individual characteristics or time-of-day variations.
  • Pedestrian Accessibility: Cumulative opportunities accessibility was calculated using three different walking speeds (0.7 m/s, 0.9 m/s, and 1.1 m/s), with a 15-minute walk distance. The final accessibility value for each location was the average across these speeds.
  • Indicators: Two indicators were used: the total number of accessible destinations and the variety of destination types (up to 10 types). Values were aggregated for each hexagon based on the nearest street network node.
Inequality Analysis
  • Gini Coefficients: Two pseudo-Gini coefficients were calculated to assess inequality:
  • Territorial Gini (T-Gini): Measures spatial inequality based on hexagons.
  • Population Gini (P-Gini): Adjusts for population distribution across hexagons using data from the Global Human Settlement Layer.
How to cite it

Vale, D., Lopes, A.S. (2023) Accessibility inequality across Europe: a comparison of 15-minute pedestrian accessibility in cities with 100,000 or more inhabitants. npj Urban Sustain 3, 55. https://doi.org/10.1038/s42949-023-00133-w

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Contacts.
For more information about the research and the dataset.
David Vale
david.vale@edu.ulisboa.pt
CIAUD, Research Centre for Architecture, Urbanism and Design, Lisbon School of Architecture, Universidade de Lisboa
André Lopes
soareslopes@gmail.com
CIAUD, Research Centre for Architecture, Urbanism and Design, Lisbon School of Architecture, Universidade de Lisboa
Curso de Arquitetura e Urbanismo, Centro de tecnologia, Universidade de Fortaleza, UNIFOR