Note: This post was originally published by Luis Natera on his personal blog. It has been republished here as part of TYN Studio's content.
I'm excited to share that our research on quantifying urban liveability was published in the International Conference on Complex Networks and Their Applications. Working with Dávid Deritei, Anna Vancsó, and Orsolya Vásárhelyi, we developed "a data-driven, network-based method to quantify the liveability of a city, taking into account the walkability."
The Approach
The study analyzes Budapest's pedestrian infrastructure using three data sources: networks, points of interest, and city attributes. We used OSMnx to extract data from OpenStreetMap, giving us detailed information about the walking network and amenities throughout the city.
We categorized points of interest into six groups:
- Family friendliness: Parks, playgrounds, schools
- Healthcare and sports access: Hospitals, clinics, gyms
- Art and culture: Museums, theaters, galleries
- Nightlife: Bars, clubs, entertainment venues
- Environment: Green spaces, natural areas
- Public safety: Police stations, well-lit areas
Network-Based Calculation
Rather than measuring straight-line distances, we calculated actual walking distances through the pedestrian network. This gives a much more realistic picture of accessibility—a park that's 500 meters away in a straight line might be 800 meters via walkable streets, or it might not be accessible at all if there's a highway or river in between.
We used graph-Voronoi tessellation to divide the city into regions based on network distance to amenities. Each area of the city gets a life quality index (LQI) based on:
- Diversity of amenities available
- Walking distance to those amenities
- Number of options in each category
Key Finding
The research reveals that "category LQI-s are highly correlated, less liveable neighborhoods are constant regardless of the amenity category." In other words, neighborhoods that lack one type of amenity typically lack others as well.
Suburbs experience lower quality scores due to amenity scarcity and extended walking distances. This isn't surprising—suburban areas are typically designed around car access rather than walking—but now we can quantify exactly how this affects liveability.
Implications for Urban Planning
This methodology helps cities:
- Identify underserved areas: Precisely locate neighborhoods lacking amenities
- Prioritize interventions: Understand which areas would benefit most from new amenities
- Measure walkability: Quantify pedestrian accessibility, not just amenity presence
- Track improvement: Measure changes in liveability over time as infrastructure develops
The approach can be applied to any city with OpenStreetMap data, making it a powerful tool for urban planners worldwide.
Read the full article or free preprint