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winter 2024
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There had previously been a map-based tool in SI, which had been deprecated prior to me joining the team. However, clients clearly craved and made requests to our customer success team for a more visual way to explore their data. And, as time went on, the quantity and types of data Regrow had available continued to grow. With Regrow’s clients being some of the largest food and agriculture businesses in the world (ex. Cargill, General Mills), users were interested in analyzing and making management decisions for up to millions of acres.
A new mapping tool would need to be able to show insights for 12 key performance indicators (KPI's) that each fell into one of two categories: agricultural practices and outcomes. Clients needed to be able to draw insights from combinations of these KPI’s in order to understand the effects of transitioning to more sustainable land management.
Also crucial to this process would be designing a visualization that couldas Regrow expanded outside of the world of carbon into other realms of sustainability, such as water and yield.
After conducting an audit of the deprecated tool and interviewing Regrowers about their experiences with the product, it was clear that the tool needed to parse different geographic scales. For example, viewing data between states and also for counties or watersheds.
To get a sense for other ways that clients might want to manipulate data, I interviewed 8 Regrowers across the Product, GTM, Data Science, and CS teams. It became clear that users were interested in three sets of actions:
A map is an immensely complex tool; entire companies exist for the exclusive purpose of mapping! For the sake of keeping a neat scope for this project, I worked closely with product to create boundaries and aim to achieving the following goals:
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For the design of the map portion, I referenced other mapping tools created by government agencies or that used publicly-available climate and agriculture data sets for inspiration. Initially, I explored using a combination of choropleth coloring and symbols to represent different KPI’sI. For example: cover crop adoption could be represented by coloring on gradient scale, and water risk rates could be overlaid on top of those counties as proportionally-sized circles in those respective colors. However, upon a second round of internal user feedback, it seemed this was still quite visually intense.
To simplify this, I adopted a bivariate choropleth color scheme. In this model, agricultural practice KPI's are represented by one color gradient (yellow), and outcome KPI's are represented by another (blue). The two are combined the create a third color (green) where there is overlap. With this scale, I needed to prioritize accessibility. The color scale would need to have enough visually-distinct colors that someone could pick apart different regions easily, with enough contrast, and with clear boundaries. The coloring also could not conflict with any additional layers. Through a series of explorations, I landed on the final designs shown here:
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With the more aggregate-level view simplified to only the most crucial pieces of information, users still needed a way to view individual areas (in this example, counties) in greater detail. If the user clicks on an area in the side panel or on the map, or, if they manually zoom in on the map, they are able to see a more detailed view.
In this view, the user can see landscape features, such as city name and waterways, at a more granular level. In the side panel, they are able to see the specific number of farms, total land area, and crop types for that geography. They are also able to their selected KPI metrics broken down by those additional dimensions, as well as volume for each crop on that piece of land.
The final element to this visualization was allowing users to customize their map view by .
In my research into other tools, I found some common sets of features, including labels, roads, and boundary lines. These all were standard features out-of-the-box. The complexity began when we started to consider how to incorporate new data sets, such as information about water scarcity. Questions about water usage were top of mind for our clients, and for the agritech industry as a whole, but due to technical limitations Regrow couldn’t yet offer remotely-sensed insights.
So, the best way for us to incorporate this information was using publicly available data. To do this, we referenced the Aqueduct Water Risk Atlas, focusing on water quality indicators. I worked with our geospatial engineer to create a simple visual indicator in the waterways that would utilize this data and allow users to see contamination levels.
With these designs, I conducted a second round of internal interviews to validate the designs further. Overall, the qualitative results were resoundingly positive, with customer success managers eager to share the designs with their clients. Having entered into the project with a very specific scope in mind, we as a product team began to brainstorm the next features to explore, including:
• Allowing users to add custom points, shapes, and boundaries
• Adding the ability to take and share snapshots of the map
• Adding more public data sets for things like livestock and yield resilience
However, at this point in time I left Regrow as the first version of the map was at just the beginning of its development and unfortunately did not get to oversee the final implementation. Over the course of this project I was certainly challenged; I was new to designing for map-making, which is both an art and a science. In particular, I enjoyed the technicality of this design and the finesse of having to create simple and reusable patterns out of the chaos of data and geoscience!