Challenge background
This project aims to leverage AI and participatory design to address urban food security and green space challenges in Mexico City. By creating a tool that optimizes the planning and management of urban food forests, it has the potential to significantly impact community well-being, urban biodiversity, and climate resilience.
Mexico City, one of the world's largest metropolitan areas, faces significant challenges in food security and urban green spaces. With over 21 million inhabitants in its metropolitan area, the city struggles with issues of food access, particularly in lower-income neighborhoods.
The problem
Many areas in Mexico City lack access to fresh, nutritious food, and the city has limited green spaces. Urban food forests could address both issues, but planning and maintaining them in a complex urban environment requires specialized knowledge and tools.
Goal of the project
1. Tool Adoption:
- Implement the planning tool in at least 20 neighborhoods across Mexico City within the first year.
- Achieve a 75% satisfaction rate among users (community leaders, urban planners, etc.).
2. Food Forest Creation:
- Facilitate the planning and implementation of at least 50 urban food forests in the first two years.
- Ensure that 30% of implemented food forests are in low-income, food-insecure neighborhoods.
3. Biodiversity Enhancement:
- Increase plant species diversity in food forest areas by 40% compared to traditional urban green spaces.
- Successfully incorporate at least 50 native and traditional Mexican plant species into food forest designs.
4. Community Impact:
- Engage at least 5,000 community members in food forest planning and maintenance.
- Increase access to fresh produce for 100,000 residents in participating neighborhoods.
5. Climate Resilience:
- Contribute to a measurable increase in urban tree canopy and reduction in urban heat island effect in food forest areas.
- Design food forests to be drought-resistant and water-efficient, reducing water usage by 30% compared to traditional gardens.
Project timeline
- 1
Week 1
Data collection and literature review
- 2
Week 2
Data preprocessing
- 3
Week 3
Data analysis
- 4
Week 4
Model development
- 5
Week 5
Research formulation
What you'll learn
1. Urban Ecology and Permaculture:
- Gain in-depth knowledge of urban ecosystem dynamics and permaculture principles.
- Learn to model complex ecological interactions in urban environments.
2. Machine Learning for Environmental Planning:
- Develop skills in creating predictive models for plant growth and ecosystem health in urban settings.
- Learn to integrate diverse data types (soil, climate, socioeconomic) into cohesive planning recommendations.
3. Geospatial Analysis and GIS:
- Enhance proficiency in using GIS tools for urban land use planning and optimization.
- Learn to create dynamic, interactive maps for community engagement and planning purposes.
4. Climate-Resilient Urban Design:
- Understand principles of designing urban spaces for climate change adaptation.
- Learn to model the impact of green spaces on urban microclimates.
5. Participatory Design and Citizen Science:
- Gain experience in facilitating community-led design processes.
- Learn to develop effective citizen science protocols for ongoing data collection and monitoring.
6. Traditional Ecological Knowledge Integration:
- Develop skills in ethically incorporating traditional and indigenous knowledge into modern planning tools.
- Learn methodologies for preserving and promoting local plant varieties and cultivation practices.
7. Urban Food Systems and Security:
- Gain comprehensive understanding of urban food production challenges and opportunities.
- Learn to assess and model the impact of urban agriculture on local food security.
8. Cross-Disciplinary Collaboration:
- Develop skills in facilitating collaboration between technologists, ecologists, urban planners, and community members.
- Learn to communicate complex technical and ecological concepts to diverse stakeholders.