Introduction
Imagine if Indiana Jones ditched his whip for code and hunted mosquitoes instead of ancient relics—this is the adventure he’d be on. Welcome to the wild world of malaria risk mapping, powered by Google Earth Engine (GEE)—a geospatial cloud platform within the Google Cloud ecosystem purpose-built for planetary-scale geospatial analysis.
This project taps into GEE’s massive climate raster data stored in Google Earth Engine Data catalog and Google’s cloud computing muscles to map where mosquitoes are most likely to throw their next block party—driven by climate change, rainfall patterns, human activities-land cover shifts, and elevation data. Everything is done in the cloud—no need to spin up your own servers or fight with giant CSV files. The result? An interactive malaria risk mapping web-app designed to help scientists, policymakers, and C-suite decision-makers pinpoint areas at highest malaria risk, all with a few clicks. The app helps to channel resources where needed most.
Let’s get into the weeds (hopefully not mosquito-infested) of how we built an interactive web-app that tells you where malaria risk is hot and where it’s just lukewarm.
Maria Risk Earth Engine Web-App Architecture
To set the stage for the project architecture, it is developed on Google Cloud Platform using the GEE Code Editor. You would need to read about Creating an Earth Engine Cloud Project in my earlier article.
Although the application runs entirely on Google Earth Engine’s cloud-native platform, it adheres to GCP's Well-Architected Framework pillars and perspectives such as scalable infrastructure, data-driven decision support, and minimal operational overhead offering a secure, cost-effective way to monitor malaria risk at national scale. We loaded our data from the Google Earth Engine data catalog then processed it in Earth engine code editor, created our model and later designed an intuitive sleek interactive web map as sketched below..
Interactive Web App-Map Showing Malaria Risk Scores
This is the showstopper—the Beyoncé of the app.
• Once a county is selected, the system processes all the data and spits out a malaria breeding conditions map.
• Areas are color-coded using a 5-level scale:
Legend? You bet. We even added a custom swatch legend (hello, UX excellence) and printed dynamic values for average temperature, rainfall, elevation, and land cover directly from the study area.
And the real gem? A dynamic malaria score legend panel, which not only tells you your county’s risk but also helps make data feel like data with purpose.
And if you love graphs (who doesn’t?), you’ll get live time series charts showing rainfall and temperature trends—no need to squint at spreadsheets anymore.
We build the same project into two versions. The JavaScript version and Python Version. Our choropleth map built on Python version contains an extended module-it’s now possible to understand the population under risk of malaria infection if it strikes. Sounds cool? With the county population data, enables us and just like allows users to select a county and view its malaria risk score, population, and risk level. Samburu, for example, has a population of 553,419 and a moderate risk level with a score of 406. Time series charts of rainfall and temperature help contextualize the data over time.
Interactive Map Showing Malaria Risk Scores
Who needs mystery novels when you have maps like this? Our choropleth visualization isn’t just colorful—it’s smart. Using GEE’s layered rendering, we highlight Kenya’s counties with malaria risk scores derived from temperature, rainfall, elevation, and land cover data. It’s like giving your map a PhD in epidemiology.
Each county has a unique story: Let’s assess the malaria risk level for Samburu county, on our sleek, interactive dashboard that translates our malaria model into actionable insight at a glance. From the map, the county is a home to over 553,000 people and currently flagged at a Moderate Risk level (Score: 406). This risk score means that a population of about 553 thousand is at moderate risk of malaria attack. Hovering over any region unveils its malaria threat level and population.
Picture this: the CEO of a national health agency in a boardroom, with other executives deciding where to send malaria bullets first. The CEO clicks open the application and there’s no loading spinner drama—just an intuitive, zoomable map of Kenya, decked out in vibrant choropleth layers. Each county is color-coded by its malaria risk score, from cool green (no risk) to fiery pink (very high risk). It will be easy for them to make quick decisions.
For those who do want to peek under the hood, there are embedded time-series charts showing historical rainfall and temperature trends. This allows directors and planners to contextualize the risk levels and align them with seasonal patterns or climate anomalies. It’s a decision-support system that merges remote sensing, climate modeling, and good design to help leaders act quickly, allocate resources better, and plan public health interventions smarter.
In conclusion, the battle against malaria, knowing where it could strike next is half the fight. This GEE app turns mosquito mayhem into meaningful maps, showing how climate and terrain align with vector breeding conditions. We've shown how to blend geospatial smarts, climate science, scripting, and cloud computing to build something powerful and practical. This app empowers leaders to act quickly, allocate resources effectively, and implement data-driven interventions.
In short: we translated code into clarity—and gave C-suite folks a map they can actually read (and trust).
This is part of projects we work on here at GEE DEVS Nairobi Community which I would encourage you to join. This project was also presented at GEO Health Community of Practice-AfriGEO as a series of work being done in Africa sponsored by AfriGeo.