Leveraging Data Communication Systems to Empower Local Innovations in Marginalized Slum Communities through Behavioral Profiling

This paper examines Community Data Empowerment (CDE) as a framework for supporting marginalized slum communities to use data in ways that drive locally led problem-solving and innovation. While open data initiatives often fail to reach or empower low-income neighborhoods due to resource barriers and technical complexity, many marginalized slum communities are in fact digitally connected and eager for relevant, actionable information. Focusing on the Campana-Altamira community in northern Mexico, this study uses workshops and school-based activities with teenagers to explore motivational patterns through participatory behavioral profiling and speculative mapping. Students’ short essays about role models were analyzed using thematic coding and automated tools such as Apply Magic Sauce and Large Language Models Artificial Intelligence (ChatGPT), revealing diverse motivational archetypes including Achievers, Competitors, Socializers, and Explorers. Rather than treating these profiles as fixed categories, the study uses them as design heuristics for tailoring data communication strategies that align with local values and preferences. This includes developing narrative, visual, and interactive formats that make data more engaging, relatable, and actionable. The findings suggest that effective CDE requires more than attractive visuals or technical dashboards. It demands participatory, context-sensitive design that respects local motivations, provides clear pathways to act, and offers incentives for collective problem-solving. By centering community voices and creating tools that invite meaningful engagement, CDE can help seed grassroots innovation, strengthen local governance, and empower residents as agents of change.