Arizona Energy Map

Inspiration

Our team cares about the future's safety, and the first step Arizona can do to combat climate change is for its people to be aware how much energy they consume and how they do it in order to improve their habits.

What it does

The Arizona Energy map turns state-wide and county‑level energy and carbon data into an inviting, hands‑on experience: an interactive map and simple personal calculators help people see where emissions come from, test “what if” scenarios for renewables and efficiency, and discover practical steps they can take. The Arizona Energy Map also includes datacases for renewable initiatives so the user can see what direct actions they can take today. Cactina is a friendly AI chat assistant that answers questions in plain language, guides users through the data, and suggests actionable local measures—making learning feel like a conversation instead of a homework assignment.

By translating complex datasets into clear visuals, local comparisons, and bite‑sized recommendations, the site makes it easy for residents, planners, and educators to explore renewable opportunities, prioritize high‑impact projects, and build community momentum toward cleaner energy.

How we built it

We began by using Base44 to establish the initial UI layout and styling. This provided a functional foundation, but the codebase required refinement. From there, we transitioned to Claude Code, which helped expand the feature set - most notably by integrating carbon reduction metrics - while also improving usability and cleaning up inconsistencies left behind by Base44’s output.

The next step involved connecting external data. Since no model was able to properly interpret the Energy Information Administration (EIA) API, we incorporated it manually to ensure reliable integration. Finally, we leveraged GitHub Copilot to assist with building our Gemini-powered chatbot. This component gives users a more personalized, conversational experience and ties the technical foundation together with an approachable interface.

Challenges we ran into

One of the biggest technical hurdles was working with GeoJSON data. While it seemed like the natural choice for mapping, in practice it proved difficult to parse, align, and render cleanly across different counties and scales. Getting the boundaries to display consistently, and ensuring performance didn’t collapse under the weight of large datasets, took far more time than expected.

We also had to contend with the limits of AI-generated code. While models like Base44 and Claude Code accelerated our development, they often produced hallucinations - functions that didn’t exist, mismatched parameters, or “confidently wrong” implementations. This forced us to carefully debug, rewrite sections by hand, and validate every step instead of assuming the outputs were correct.

Together, these challenges slowed us down but also pushed us to refine our approach: building more robust data handling, testing workflows, and finding the right balance between AI assistance and human oversight.

Accomplishments that we're proud of

Our team is proud of the many things during this development journey. Our UI is easy to navigate and visually pleasing. The mascot we created is a cute prickly pear, named, Cactina. She pops up when the user needs help and communicates through our API integration of Gemini. Of course, we wouldn't have been able to be proud of those that we have produced if it weren't for our intensive communication where we were able to delegate responsibilities and help each other when we faced challenges. Last but not least, we are proud of the impact on Arizona's energy future that this project will have.

What we learned

Building The Arizona Energy Map taught us a lot - about data, design, and how to turn information into action. Working with county-level energy and carbon datasets made it clear that data quality and context matter more than raw numbers: timestamps, units, and metadata shape whether a visualization is trustworthy or misleading. We learned to prioritize clear provenance and simple explanations so users can trust what they see and dig deeper when they want to.

We learned how to use AI effectively to help expediate the coding process and also learned how to not overdo it. We learned API integration, teamwork, tech stacks, delegating, and more.

What's next for Arizona Energy Map

The next steps for The Arizona Energy Map are simple: continue to educate and inform people in terms they can understand and enjoy. New features can be implemented where users can see the data in terms they can understand: such as converting kWh into more friendly units. In addition, the initiatives database can be dynamic, where the latest initiatives can be added to the list. Perhaps a good expansion would be to allow users to add their own initiatives to the database to help consolidate people.

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