Inspiration
Our project was inspired by one of our team members, who has been working on addressing food insecurity in Puerto Rico through her learning community. This sparked the idea of creating a global visual representation of food insecurity, showing how environmental factors can contribute to this pressing issue. To capture the complexity of the problem, we decided to expand beyond just food insecurity data and include four additional maps highlighting drought, flooding, wildfires, and infrastructure. This way, users can explore potential correlations and better understand the forces that influence food security around the world.
What it does
NourishMap is an interactive web platform that aims to raise awareness of the extensive influence of climate change on global food security. By visualizing correlations between food insecurity data and climate change indicators across countries, the platform allows users to explore trends, uncover insights, and gain a clearer understanding of how environmental factors affect food systems. The website uses color-coded maps, graphs, and AI-generated summaries to make complex relationships between climate and food insecurity accessible to a wide audience. Through this approach, we hope to educate the public, spark curiosity, and inspire collective action around sustainable food systems.
How we built it
To construct NourishMap, we leveraged publicly available datasets from organizations such as the FAO and World Bank. These datasets were imported into Supabase, which allowed us to organize and manage the data efficiently. For the interactive visualizations, we used Mapbox to create dynamic maps and Loveable to embed them into our website. Throughout the development process, ChatGPT and Gemini provided guidance, helping us structure our code, troubleshoot issues, and improve usability. By combining these tools, we were able to build a website that is both functional and engaging.
Challenges we ran into
One of our biggest challenges was dealing with datasets that were incompatible with Supabase or difficult for Loveable to read, which made it tricky to accurately display maps. To overcome this, we used Gemini to consolidate and clean multiple datasets into a single CSV file, ensuring smooth integration with Loveable and consistent map visualization.
Accomplishments that we're proud of
As beginners with no prior experience in coding or web development, we are proud of creating a finished product that looks clean, is usable, and delivers real insights. Learning to use Mapbox, Supabase, and Loveable from scratch was a significant accomplishment, and the final platform demonstrates both our technical growth and creative problem-solving.
What we learned
Through building NourishMap, we gained hands-on experience in web development, database management, and interactive data visualization. We learned the importance of data organization, normalization, and visualization design to make complex information understandable. Additionally, we developed skills in leveraging AI tools to accelerate learning, troubleshoot issues, and enhance user experience.
What's next for NourishMap
We are excited to continue improving the platform. Currently, data gaps in some countries limit the global impact, so our next goal is to find additional datasets to fill these gaps. We also plan to expand the AI insights, add more graphs, and create a richer educational experience to help users understand global food security challenges more deeply.
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