The analysis revealed that both neighborhoods suffer from limited accessibility to essential services, with many residents forced to travel outside their communities for daily needs. The study identified centralized patterns of service distribution that contribute to exclusion and segregation within these communities. Network analysis further demonstrated that while there may be numerous amenities in the vicinity, their spatial distribution often extends beyond neighborhood boundaries, highlighting accessibility challenges for residents.

By visualizing these spatial relationships through overlay methods and network analysis, the research provides valuable insights for urban planners and policymakers. The findings emphasize the need for interventions that prioritize inclusive urban infrastructure to enhance residents' quality of life. The methodology also lays groundwork for future machine learning applications that could deepen understanding of urban dynamics in underserved communities.

Our work went beyond data collection to include direct engagement with residents, observing their daily movements and listening to their experiences of navigating these neighborhoods. We discovered that public spaces and essential services are often concentrated in ways that create inequity, requiring targeted interventions. This methodology offers practical tools for decision-makers to identify where and how to create healthier, more connected communities that bridge existing divisions rather than reinforce them.