From personally gathered datasets to publicly available resources, the options for collecting data are seemingly endless, so let’s start by taking a moment to explore the diverse range of data sources at your disposal. That way, we understand the full scope of information you may be using to craft your visualization. With that in mind, here’s a breakdown of common data sources and the unique value each one brings to the table:

• Your Own Data: This is often the backbone of your project. It may be contamination data, elevation measurements, well data, drillhole information, GPS or GPR data, or any other details you’ve meticulously collected yourself. Since you’re familiar with its origin and format, this data might be the easiest to work with and tailor to your needs.
• Country Data: Many countries provide publicly available data. The USGS, for instance, has a treasure trove of resources for the United States, including digital elevation models (DEMs) for visualizing terrain, lidar data for accurate 3D modeling, and even hyper-local drone imagery. These insights can deliver the precise U.S.-related details that your visualization needs. The Geological Survey of Canada is another example. It provides access to credible databases with information useful for developing maps for mineral exploration, infrastructure management, and land use in Canada. 
• State/Provincial Data: Some states maintain robust data repositories with lidar data, drone images, and more that you can access. From infrastructure details to environmental datasets, state-level data is a fantastic resource for regional projects.
• City Data: Some cities take data collection even further, offering their own granular data like zoning maps, traffic patterns, or even tree inventories. These city datasets can provide the hyper-local insights you need to make your project truly shine.
• County Data: Need to zoom in a little closer? Counties sometimes have their own datasets available for download, which can include everything from parcel boundaries to utility networks.
• Surveyors: If you want to spend the money, you can get shapefiles from surveyors. These files can outline features like roads, buildings, or property boundaries, making them a versatile tool in your visualization toolkit.

Working with data from at least three different sources is a common scenario—and you might be in that very situation right now. Maybe you’ve already pinpointed a few sources from the list above, or perhaps you’re juggling even more. Either way, don’t sweat it. Combining multiple datasets into a clear, cohesive visualization is absolutely doable. With the right strategies, you can seamlessly integrate your data—no matter how many sources are in play—and craft a map that truly comes to life.

To demonstrate how easy it is to combine data from multiple sources into one map, let’s walk through an example. Say you’re using Surfer and have an aerial photo from Google, a DEM from the USGS, and drillhole data you’ve collected yourself. In this situation, you’d import all of your data into Surfer. Then, you’d build your visualization by ensuring each dataset plays its role in bringing your overall story to life.

For instance, your aerial photo would serve as the base layer, providing geographical context for your project site. Your DEM would act as the contour layer, showcasing elevation, and your drillhole data would form the drillhole layer, pinpointing the locations of drillholes in your field area. Together, these datasets from different sources would work together to show a comprehensive picture of your project site in a single map.

However, simply combining various datasets to create a single visualization isn’t the end of the design process. Once the technical steps are complete, it’s important to implement creative strategies to make your map stand out and impress stakeholders.

When you’re working with diverse datasets, various creative strategies are key to developing a map that’s not only clear and cohesive but also compelling. You can check out our article on making cool data visualizations for a complete list of design best practices, but below are four big ones that will set you up for success.

A well-designed map does more than just display data—it guides your stakeholders through a narrative. That’s why your map should aim to tell a clear story that engages stakeholders. You can do this by framing your visualization within a narrative structure that not only captures attention but also makes your insights more memorable. Practical steps you can take include providing the right amount of context, emphasizing key takeaways, and ensuring a logical flow that connects each insight to the next. 

But remember one key point: this isn’t about adding fluff or unnecessary details. It’s about giving your data meaning and purpose. When you frame your visualization as an engaging story, you’re not just presenting numbers; you’re taking stakeholders on a journey that helps them see the bigger picture and truly understand the value of your work.

When it comes to color in data visualization, less truly is more. A cluttered palette can overwhelm your audience and dilute your message, so it’s essential to use colors intentionally and strategically. Keeping that in mind, stick to a limited color scheme to highlight key data points and choose hues that align with the story you’re telling. Thoughtful color choices will not only enhance clarity but also tap into the emotional and intuitive associations your audience naturally makes.

For example, using red for a contamination plume can immediately convey urgency or danger to a general audience, while green can signal safety or positive outcomes. These subtle yet impactful choices make your visualizations more memorable, guiding stakeholders to quickly understand and connect with your data’s narrative. Just make sure the colors you choose resonate with your audience. If green, for instance, doesn’t symbolize safety to viewers, choose the color that actually has that connotation.

Combining visuals with clear text can enhance memory and comprehension, especially when dealing with complex information. This is where thoughtful labeling can make all the difference. When creating your map, clearly label your axes and specify any units of measurement. If your map includes multiple datasets, a straightforward legend can also guide your audience without leaving them scratching their heads. For an extra touch of clarity, consider placing labels directly on data points or along lines to eliminate the need for unnecessary guesswork. These small but impactful steps will ensure your visualization is not only visually appealing but also easy to interpret and act on.

Data is rarely static. In fact, it’s common to receive new information regularly—sometimes even daily. That’s why it’s crucial to continuously update and refine your map. Incorporate new insights as they become available, and don’t be afraid to tweak your visualization to reflect evolving information. Refinement will help your map stay current and ensure it remains clear and impactful over time. Think of it as an ongoing process of making your map the best it can be.