In the rapidly evolving landscape of urban development, the question “Whose city is it?” reverberates with growing urgency. A pioneering study published in the prestigious journal npj Urban Sustainability shines a new light on this query, leveraging citizen-guided artificial intelligence (AI) to map urban livability from the perspective of the very people who inhabit these spaces. This cutting-edge research, conducted by Campomanes, Abascal, Oliveira, and colleagues, heralds a transformative approach in urban planning, where technology and public perception intertwine to redefine how cities are understood and shaped.
Traditional models of urban livability often rely on quantitative metrics alone, such as air quality indices, infrastructure statistics, or economic indicators. While crucial, these metrics frequently overlook the nuanced, subjective experiences of residents—their sense of safety, community, accessibility, and overall well-being. The novel framework introduced by this team uses an AI system guided directly by citizen input, capturing the lived realities and perceptions that standard datasets tend to miss. This participatory methodology ensures the technology is not detached or abstract but intimately connected to human experience.
Central to the study is the concept of “perceived urban livability,” which acknowledges that the quality of life in a city cannot be fully comprehended without understanding residents’ emotions and opinions about their environment. By integrating citizen-generated data, the AI analyzes patterns in subjective assessments across different neighborhoods, discovering areas where official measures of livability diverge from public sentiment. These findings have profound implications for urban policy, highlighting zones where intervention may be urgently needed or where grassroots strengths already prevail.
The research employs advanced machine learning algorithms to process and synthesize large volumes of qualitative feedback from a diverse cross-section of urban dwellers. This approach empowers the AI to identify latent themes and correlations that human analysts might struggle to discern at scale. Importantly, the model remains transparent and adaptive, allowing citizens to continuously shape its parameters and outputs. This ongoing dialogue between the AI and the populace fosters democratic engagement, counterbalancing the risk of technocratic decision-making.
One of the technical breakthroughs of the study lies in its hybrid analysis framework, combining natural language processing (NLP) techniques with geospatial data visualization. Residents provide open-ended responses describing their neighborhood experiences, which NLP algorithms parse to extract sentiments and key factors affecting livability. These textual insights are then mapped geographically, producing dynamic, interactive urban livability atlases accessible to policymakers, planners, and the public alike. This marriage of subjective narratives and geographical precision is unprecedented in urban research.
Moreover, the citizen-guided AI platform addresses the challenge of data inclusivity. Urban environments are heterogeneous, with inhabitants differing widely in socioeconomic status, ethnicity, and mobility. Previous methods often suffered from sampling biases or resulted in oversights of marginalized communities. The participatory nature of this AI model ensures that diverse voices contribute meaningfully, enabling a more equitable representation of urban realities. This inclusivity is a critical step toward social justice within city governance.
The study also explores temporal dynamics, analyzing how perceptions of urban livability evolve in response to policy changes, environmental shifts, or unforeseen events such as pandemics. By maintaining ongoing citizen engagement, the platform captures real-time feedback, providing a living document of urban sentiment. Planners can thus respond agilely to emerging issues, measuring the efficacy of interventions not just through hard numbers but through the lens of public experience.
Technically, the AI framework draws on state-of-the-art transformer models, known for their prowess in handling complex, context-rich data inputs. These models decode subtle linguistic cues and recognize regional dialects or slang, enhancing the fidelity of sentiment extraction. Additionally, sophisticated clustering algorithms segment neighborhoods into perceptual zones, revealing micro-scale variations within seemingly homogenous districts. This granularity allows for targeted, localized strategies that respect the unique character of each community.
Importantly, the research underscores the ethical considerations inherent in deploying AI within urban contexts. Privacy safeguards and data anonymization protocols are rigorously implemented, ensuring citizen contributions are protected. The platform also incorporates mechanisms for transparency, allowing users to understand how their inputs influence the AI outputs. This trust-building element is crucial in fostering widespread adoption and preventing skepticism toward AI-driven urban governance.
This groundbreaking investigation further engages with questions of power and agency. By inviting residents to co-create knowledge about their cities, the AI repositions citizens from passive subjects to active collaborators. This democratization challenges conventional top-down urban planning paradigms, empowering communities to articulate their needs and aspirations directly. It opens pathways for new forms of civic participation, where technology amplifies rather than diminishes human voices.
The practical applications of this citizen-guided AI extend beyond academia. Urban planners and local governments can harness these rich data streams to craft policies that resonate authentically with inhabitants’ experiences. Public health initiatives, transportation systems, green space allocations, and housing developments may all be better calibrated to human needs when informed by these perceptual maps. The result promises more responsive, inclusive, and ultimately livable cities.
Looking ahead, the study’s authors envision a future where citizen-guided AI becomes an integral fixture in urban management globally. As cities grapple with challenges from climate change to demographic shifts, flexible and human-centered tools are indispensable. This research sets a powerful precedent, illustrating how technology, when deployed ethically and collaboratively, can bridge the gap between technical expertise and grassroots wisdom.
In sum, the npj Urban Sustainability publication “Whose city is it? Mapping perceived urban livability with citizen-guided AI” presents a forward-thinking synthesis of AI innovation and community engagement. It reimagines the city as a co-created space, dynamically shaped by both data-driven insights and human experience. By charting where official statistics and lived realities converge or diverge, it equips stakeholders with the knowledge needed to craft cities for all.
This work arrives at a pivotal moment in urban science, underscoring the necessity of integrating technological advancements with participatory governance. As cities continue to grow and evolve amidst complex social and environmental pressures, methodologies like those introduced in this study will be vital to fostering sustainable, equitable urban futures. The dialogue between citizens and AI is not just a conceptual novelty — it is an imperative for realizing truly livable cities.
The success of this research hinges on continuous refinement and broad-based citizen involvement, implying that urban sustainability is a collective journey rather than a fixed destination. With its combination of sophisticated AI architecture and democratic principles, it sets a standard for future explorations at the intersection of technology, society, and place. “Whose city is it?” — thanks to this remarkable work — we now have a more profound and actionable answer.
Subject of Research: Mapping perceived urban livability through citizen-guided artificial intelligence, emphasizing participatory approaches to urban planning and quality of life measurement.
Article Title: Whose city is it? Mapping perceived urban livability with citizen-guided AI.
Article References:
Campomanes V, F., Abascal, A., Oliveira, L.T. et al. Whose city is it? Mapping perceived urban livability with citizen-guided AI. npj Urban Sustain (2026). https://doi.org/10.1038/s42949-025-00320-x
Image Credits: AI Generated
