Unlock Your City’s Hidden Solutions

An AI-Powered Approach to Urban Transformation

By Andreas Pawelke, Basma Albanna and Damiano Cerrone

Photo by CHUTTERSNAP on Unsplash

Cities around the world face urgent challenges — from climate change impacts to rapid urbanization and infrastructure strain. Municipal leaders struggle with limited budgets, competing priorities, and pressure to show quick results, making traditional approaches to urban transformation increasingly difficult to implement.

Every city, however, has hidden success stories — neighborhoods, initiatives, or communities that are achieving remarkable results despite facing similar challenges as their peers.

These “positive deviants” often remain unrecognized and underutilized, yet they contain the seeds of solutions that are already adapted to local contexts and constraints.

Data-Powered Positive Deviance (DPPD) combines urban data, advanced analytics, and community engagement to systematically uncover these bright spots and amplify their impact. This new approach offers a pathway to urban transformation that is not only evidence-based but also cost-effective and deeply rooted in local realities.

DPPD is particularly valuable in resource-constrained environments, where expensive external solutions often fail to take hold. By starting with what’s already working, cities can make strategic investments that build on existing strengths rather than starting from scratch. Leveraging AI tools that improve community engagement, the approach becomes even more powerful — enabling cities to envision potential futures, and engage citizens in meaningful co-creation.

Finding What Already Works: The Power of Positive Deviance

Traditional urban development approaches typically start by identifying problems and deficits — what’s missing, what’s broken, what needs fixing. In contrast, positive deviance begins with a different question: “What’s already working, and why?”

Positive deviance recognizes that within every challenging context, there are individuals or communities achieving better outcomes than their peers, despite having access to the same resources. These outliers have developed innovative practices and approaches that are inherently adapted to local constraints. By identifying and understanding these successes, cities can develop solutions that are both practical and contextually appropriate.

This approach is inherently resource-efficient. Rather than investing in expensive external interventions or untested innovations, cities can amplify what’s already working in their own neighborhoods. This reduces the risk of implementation failure and enables quicker wins — important considerations when budgets are tight and public expectations are high.

DPPD enhances this process by using data analysis to systematically identify these success stories. By analyzing various data sources — from traditional surveys to satellite imagery, mobile data, and social media — cities can spot neighborhoods that are outperforming statistical predictions.

A compelling example comes from Mexico City, where the DPPD method was used to identify safer public spaces for women. By analyzing crime data, official statistics, and administrative boundary data, a team of researchers identified areas with significantly fewer crime incidents than was to be expected based on socioeconomic, mobility, and infrastructure characteristics.

The next step involved visiting these neighborhoods to understand why they were outperforming expectations. Through participatory observation and exploratory walks with women residents, researchers discovered conditions that contributed to safety: open green spaces without fencing and with proper pruning, commercial activity creating natural surveillance, active police presence, continuity between public spaces, better lighting, and more security cameras.These insights directly informed an institutional action plan developed with city government agencies. Rather than starting from scratch with untested theories, officials could build on verified success factors. The Ministry of Women implemented an early recommendation to remove abandoned vehicles in parts of the city, demonstrating how quickly DPPD can lead to concrete action.

DPPD can also be instrumental in addressing pressing urban challenges such as air pollution, emergency response to disasters, urban heat, waste management and transportation riderships. By examining neighborhoods or cities that have experienced significant improvements in air quality, implemented pioneering recycling initiatives or those with effective urban-heat mitigation strategies, governments can uncover valuable insights. This analysis can inform the design of innovative policies and local initiatives and improved land use and infrastructure planning. By learning from these successful outliers, city planners can create tailored solutions that foster resilience and sustainability, ultimately leading to healthier and more vibrant urban environments.

The Power of AI + Collective Intelligence

While traditional data analysis can help identify outliers and pinpoint factors underlying their outperformance, advances in artificial intelligence now enable much more dynamic and interactive approaches to identifying and amplifying positive deviance. However, while technology can provide powerful insights, it alone lacks the contextual understanding and community engagement necessary for sustainable solutions. The most effective strategy merges AI capabilities with the collective intelligence of communities.

AI excels at processing vast amounts of urban data to detect patterns invisible to the human eye. Machine learning can help identify neighborhoods that are outperforming statistical predictions across various metrics — from safety to economic mobility, health outcomes to climate resilience. Generative AI takes this further by translating insights into visual possibilities that stakeholders can understand and engage with.

UrbanistAI exemplifies this transformative potential in public engagement within urban design. Traditional consultation methods often rely on technical drawings and jargon-heavy documents, which can alienate many residents from meaningful participation. In contrast, UrbanistAI uses generative AI to create real-time visualizations of potential changes to urban environments, inviting active involvement from the community.

In Helsinki, for example, residents were able to visually modify urban spaces in real-time using an intuitive interface, transforming them from passive commentators to active place-makers. This approach was particularly beneficial for involving communities traditionally excluded from planning processes due to technical barriers. Other examples include designing future parks, planning more feminist urban spaces and enhancing public understanding around climate risks. The AI-generated visualizations not only informed urban projects but also fostered collaborative decision-making among diverse stakeholders. This aligns closely with a key principle of the Positive Deviance approach: “Seeing trumps hearing and doing trumps seeing.” When people can visualize and co-create potential solutions, they are far more likely to embrace them than if they merely hear about them.

Importantly, AI here serves as an enabler of human collective intelligence rather than replacing it. The technology amplifies the distributed knowledge of residents, workers, and other stakeholders who understand their neighborhoods at a granular level that no algorithm can match. This combination of machine processing power and human contextual intelligence creates solutions that are both data-informed and locally appropriate.

For resource-constrained municipalities, this approach offers particular benefits. The visualization capabilities of AI tools can reduce the need for expensive physical models or elaborate presentation materials. The improved community engagement can decrease resistance to changes, reducing costly delays. And the ability to test multiple scenarios virtually allows for more thorough assessment before committing limited implementation resources.

Bringing It All Together: Our Integrated Approach

The true power emerges when these complementary approaches work in concert. By combining positive deviance, urban data, AI capabilities, and collective intelligence, cities can develop solutions that are simultaneously evidence-based, contextually appropriate, and publicly supported.

This integrated approach addresses key challenges that often derail urban transformation efforts:

1. It focuses resources on high-impact interventions by identifying what’s already working rather than starting from scratch.
2. It bridges the typical divide between technical expertise and community knowledge. Data and AI provide the analytical power to spot patterns and generate possibilities, while community engagement supplies the contextual understanding and social capital needed for successful implementation.
3. It creates a pathway for scaling successful initiatives. By understanding the underlying factors that make neighborhood-level solutions work, cities can adapt and apply these insights to similar contexts. The visualization capabilities of AI tools help communicate these possibilities to stakeholders in other areas, accelerating the diffusion of effective practices.

In practical terms, this approach follows a systematic process:

1. Identification: Using data analysis to identify neighborhoods, initiatives, or strategies that are outperforming expectations based on their context
2. Understanding: Engaging directly with these communities to understand the underlying factors behind their success, combining technical assessment with local knowledge
3. Visualization: Using AI tools to create accessible representations of these success factors and how they might be adapted to other contexts
4. Co-design: Develop implementation strategies that account for local variations and resource constraints
5. Implementation and Diffusion: Supporting the adaptation and scaling of successful initiatives, with continuous monitoring and refinement

This process offers benefits beyond immediate outcomes by building trust and social connections between municipalities and communities. It strengthens local problem-solving abilities, highlights existing successes that can draw additional resources and political support, and establishes a sustainable approach for city administrators. By leveraging current assets, this method maximizes the impact of investments, lowers the likelihood of implementation failures, and fosters community ownership, paving the way for long-term success.

Take Part in the Program

Urban transformation doesn’t have to start from scratch. Every city contains hidden solutions waiting to be discovered, understood, and scaled. The combination of positive deviance, data analytics, and AI-powered community engagement provides a powerful framework for uncovering and amplifying these existing successes.

We’re designing a program to help cities implement this integrated approach. Through this initiative, participating cities will receive:

• Technical support in identifying positive deviants through data analysis
• Training in community engagement methods that uncover the factors behind successful outcomes
• Access to AI visualization tools that make complex urban possibilities accessible to diverse stakeholders
• Guidance on designing implementation strategies that build on existing strengths

The program will be designed to accommodate different resource levels and contexts. Whether you’re a large metropolitan area with sophisticated data capabilities or a smaller municipality with limited technical resources, the methodology will be adapted to your specific situation.

We are inviting city officials, urban developers, and funders interested in being part of this program to reach out to us at mail@datapoweredpd.org. We’d be happy to schedule a meeting to discuss your priorities and how this approach might complement your existing efforts.

Every city has hidden solutions. Together, we can find and help them flourish.