Data management is a popular topic and rightfully so; gains in volume and density show no signs of slowing, security is a huge concern, and storage capacity is a serious challenge. But managing data is more than a back-end matter. Sarah Bell highlighted this point during a Lightening Talk at Esri UC 2017 titled “Cartography: Bring Your Data to Life.”

Going from the collection and storage of raw datasets to an effective presentation can be overwhelming. Workflow design is important for success, but if you’re in search of a fixed template, you might want to reconsider. Bell, cartographer and data visualization specialist at Esri, says there is no magic workflow for mapmaking. Why? “All of you are working from different datasets and you all have a different purpose and a different audience,” she says. “Workflow design is as unique as your organization’s data.”

While I’m guilty of attending her presentation with the hope of gaining a standardized mapmaking formula to bring back to you, I must admit, she has a point. As Bell stated, custom is key and the best workflow design for mapmaking begins as open-ended. There is no one-size-fits-all way of managing data for presentation.

Standardization in workflow is inefficient for two reasons, Bell says:

  1. Your data may not lend itself to the maps and visualizations you want to make.
  2. It limits your ability to explore what your unique data story is.

“In those first stages when you’re beginning your workflow design, embracing the freedom that you have and the initial lack of workflow specificity will set a tone for your project that I have found to be extremely valuable. That is the capacity to eliminate, pivot and integrate,” Bell explains.

Entering into the mapmaking process with a sense of discovery helps you uncover data stories you may have never thought of. Bell offers these tips:

  1. Know your data. You can’t illustrate it if you don’t know it.
  2. Know your statistics. They reveal empirical evidence supported by your data that you may want to highlight.

Once you’ve analyzed the overall dataset and stats, it’s time to consider what visualization tools to present them with. A map is a flexible format and there are a number of ways to mark it up. Point data, for example, is a straightforward way to indicate location on a map, but making it as representative of reality as possible isn’t as easy as it sounds.

In her presentation, Bell pulled up a crime map of a city covered in points to indicate the location of crime reported in a given year. An important flaw she immediately noted was that it made the city look like it was completely covered in crime when it wasn’t. Lesson: Point size matters. When she shrunk the points, I could see all of the blank spaces where no crime had been recorded; it turned out the majority of the city had experienced no crime during that time.

Another lesson: Putting points on a map reveals spatial patterns that cannot be so easily recognized on a chart. The map format clearly illustrated high-crime areas by placing groups of dots together. I couldn’t have picked up on that by reading a list, at least not as quickly.

Last lesson: Context. With points placed on a bare white map of the city, one might think, “That’s a strange coincidence that the location of crime takes on a linear/grid pattern.” Including streets on the map quickly addresses this confusion by providing context, as cities are organized along streets. At the same time, knowing what context not to provide is crucial as well. Include too much — for instance vegetation and topography — and the points are hard to see. Plus, those characteristics may not be relevant to the story being told … or maybe they are!

This could go on and on, as there is so much to consider when managing the presentation of geospatial data. The key takeaway is that if you were looking for a catchall cartography workflow, Bell can’t help you and neither can I. Each mapmaking experience is one of a kind. So listen to what the data is telling you and frame the presentation in the way that most clearly highlights its story.

What’s your philosophy on geospatial data visualization? Do you have a set workflow for mapmaking? Let us know!