understanding data visualisation

Map making is, almost by definition, data visualisation. Base maps aren’t just readily present, they are created off of data collected from urban planning departments, remote sensing companies, and physical geographers. Base maps are often overlaid with even more information: what buildings and business are we showing?, what neighbourhoods are people being directed to?, what borders are erected? The cartographer makes these decisions based off of their relevant importance to the map’s purpose and their own harboured bias. This post is meant to help clarify some basic and introductory concepts for understanding and communicating the data in maps.

  • Types of Data Used in Maps
  • Visualising Data
  • Legends, Charts & Supplemental Features

Types of Data in Maps

Maps use geodata, meaning that this data has a spatial aspect to it. Some data might tell you how many evictions there are in a given time period, geospatial data will tell you where these evictions are happening. This spatial trend can help you discover targeted neighbourhoods. This could be addresses to places and latitude/longitude points, depending on how the data was collected and what was being recorded. Some examples of these file names and purposes are the following:

  • Raster – These files are used outside of geospatial technology and maps, but their relevance is worth bringing up. These pixel files, just like pictures taken off of your phone. You may notice that if you zoom in too much, you can see the squares materialise. The positive part of this is that raster files load much faster than vector files.
  • Vector – These files are based off of mathematical formulas to maintain the points, lines and polygons shape. Fonts and PDF files are examples of vector files. Although these files take more time to load, they can often be considered more beneficial if your zoom levels are large scaled.
  • Shapefiles – These are vector files–created by points, lines and polygons– are used in GIS software. These files usually have attributes to them as well.
  • MXD – These files have a map layout, description and other objects that can be saved on maps. These files can be used in GIS software.
  • KML – Keyhole Markup Language displays geographic data in an Earth browser, such as Google Earth. It has properties for place-markers, paths, polygons, styles for icons and overlays and much more.
  • GeoJSON – It’s the same JavaScript empowered JSON file that preserves its interactive features with a geographical component. This could mean that the file contains coordinate points, lines and/or polygons.
  • TIGER – Topologically Integrated Geographic Encoding and Referencing describes land attributes. The US Census uses these files for the geospatial map

There are a lot more files that you may come across while making maps, but understanding these should give you a good place to start understanding what it is you’re working with.

Visualising Data

Data visualisation is basically taking a whole bunch of numbers into a visual format that can be understood simpler, faster and by a wider community.  I think the most important part of data visualisation is audience comprehension– what’s the point of you putting in time and effort if your efforts are being read as ambitious? Data visualisation on maps can be represented through a number of different ways. Here are just a few examples:

  • Choropleth maps use colour to show densities and scarcities in places. These maps are valuable when looking for concentrations and scarcities of a phenomena. When choropleth mapping strategically, the deconstruction of borders becomes apparent as well– more on this later, however. Common choropleth maps are population density maps.
  • Flow maps show movement of phenomena. This map is good for showing how much of what is going where. Examples of flows may be migration patterns and agricultural distribution.
  • Cartograms distort space by the phenomena density in a place. This map is good for giving truth about a place more so than a space. Some great examples of cartograms can be found here.

Depending on what data you have and what spatial relationships you want to show will determine which type of map to use. Think first about what you to show and what you want your data to argue before you decide what kind of map you want to make. Don’t limit yourself! Think about combining these ideas too. Maybe you’re making a flow map that has a phenomena being sent at different scales, consider either changing the colours of the flows or graduating their size.

Legends, Charts & Supplemental Features

Equally as important to the map are the components that tell the reader just what they’re looking at. Legends are like cheat sheets that translate the map’s colours and symbols into an a linguistic format. Because legends tell people what your map is about, you should spend just as much time cleaning your map as you do constructing your map. The following image show an example of a poorly formatted (often default settings) of a legend and a manicured version.

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Chart legibility is also critical in giving meaning to your map and data. When editing your charts, try to give the greatest amount of ideas with the least amount of ink in the smallest space. Creating a good, legible chart requires balancing substance, statistics and design. When constructing your chart, consider these key concepts for legibility and clarity:

  • Show the data – This sounds simple, but these can be easily filled with extraneous information you didn’t mean to make as important as the chart implies. Just tell the reader what they need to know about your map and let them move forward.
  • Tell the truth about the data – Maybe the data actually disproves your hypothesis– and that’s totally okay. It’s more transparent for you to bring forth all  the information. Don’t make bias design decisions when communicating the data, you and your audience deserves the truth. If the data didn’t go the way you thought it did, maybe explore why?
  • Maximise dark ink – This is supplemental to your map, it isn’t the big show. Make it legible, but not to the point that it’s covering important parts of your map. Size is an important thing to note, try to keep a reasonable ratio between your map’s size and any supplemental aspect.
  • Minimise chart clutter – Sometimes it’s aesthetic, other times it’s distracting. Recall that the whole point of this is to supplement your map. If you have something that is too distracting or irrelevant, consider redesigning it or deleting it altogether.
  • Have a message – Your map has a purpose, and this supplemental piece should too. What is the purpose? What else do you want to say about your data? Perhaps there was a more niche set of information you delved into, or maybe there was blanket information that applied to the entire map extent. Be clear and precise.

Understanding proper data visualisation is a crucial step in cartography and communication in general. A legible and succinct design that gets the message across is the goal when translating data into a visual. Good luck!