What makes a city comfortable?
A new AI-powered index offers clues
A multidimensional AI-driven model uncovers what conventional liveability rankings often overlook.
In The Economist Intelligence Unit’s Global Liveability Index 2025, Copenhagen edged out Vienna to claim the top spot, ending the Austrian capital’s threeyear reign as the world’s most liveable city. With its picture-perfect harbours and famously cycle-friendly streets, the Scandinavian capital scored nearperfect marks across the board.
But what exactly makes a city liveable — and who gets to decide?
The answer is not as straightforward as it seems. Most global indices rely on fixed sets of criteria: housing, transport, safety, healthcare, education. While these metrics are important, they often miss what it actually feels like to live somewhere. The noise outside your bedroom window. The way a street makes you feel at night. Or how unbearable the heat gets in the middle of the day.
Assistant Professor Filip Biljecki led a group to develop a comprehensive urbancomfort index that combines hard data with subjective elements.
Urban comfort, in other words, extends way beyond cold infrastructure. It’s also about perception — the human experience of a place. It’s what Assistant Professor Filip Biljecki and his research group from the Department of Architecture, College of Design and Engineering, National University of Singapore, sought to capture with a new approach with data, design and AI as its core ingredients.
Published in the journal Sustainable Cities and Society, the study proposes a new way of measuring how people experience cities. Asst Prof Biljecki led a team to develop a comprehensive urban comfort index, tested in Amsterdam, that combines hard data (think air quality, access to public transport and how closely buildings are packed), with subjective elements like how beautiful or safe a street looks, based on widely available street-level imagery and human ratings.
Crucially, the team’s work also models how comfort shifts over time and space. “Cities are really like living systems,” says PhD student Ms Lei Binyu, also the paper’s first author. “A street that feels pleasant on a spring morning might feel stifling in summer. We wanted to reflect that complexity and show how comfort emerges from the dynamic interaction between people and their urban environments.”
To go about this, the team, in collaboration with the Department of Geography at the NUS Faculty of Arts & Social Sciences, assembled 44 different features across four broad dimensions: 3D urban morphology, socio-economic conditions, environmental factors and human perception. These were selected through extensive literature review and grounded in real-world understanding of how people engage with urban environments, from greenery and building density to noise, walkability and visual aesthetics.
Issue 06 | Aug 2025
The data was fed into a graph-based neural network model, a spatial form of deep learning that maps how characteristics in one neighbourhood ripple into the next. Layered on top of this was “explainable AI” (XAI), which breaks open the “black box” of machine learning to reveal which features drive the final comfort scores.
“Traditional models often assume simple, linear relationships,” says Asst Prof Biljecki. “XAI enables us to see how multiple factors interact and vary across different contexts, which is key to understanding the nuanced realities of city life.”
The team found that neighbourhoods with more greenery, better access to amenities and lower noise levels tended to rank higher on the comfort scale. Interestingly, some of Amsterdam’s central districts — long regarded as prime urban real estate — scored lower than their suburban counterparts. (Sorry, real estate agents!) Despite ticking the usual boxes for liveability, these areas were dragged down by visual monotony, crowding and poor street-level perceptions.
To validate the findings, the researchers pored over Google Places reviews, limiting them to Dutch-language entries to focus on local voices. The sentiment analysis confirmed the model’s results: while tourists might gush over the city centre, locals were more likely to groan about crowds, noise and poor upkeep. In contrast, less-celebrated suburbs drew fewer complaints and reflected a more cheery take on day-to-day living.
“XAI enables us to see how multiple factors interact and vary across different contexts, which is key to understanding the nuanced realities of city life.”
Apart from diagnosing issues, the urban comfort index also suggests solutions. In one case study, the researchers used a digital twin of Amsterdam (a 3D virtual model of the city) to simulate improvements in a neighbourhood just below the comfort baseline. By tweaking features like street design, air quality and facility access, they were able to model the impact of various interventions. Small changes, like planting vertical gardens or improving pedestrian infrastructure, produced outsized positive effects.