Lessons in Cartography

Lessons in Cartography

Now in its third year, Azavea’s Summer of Maps Program has become an important resource for non-profits and student GIS analysts alike.  Non-profits receive pro bono spatial analysis work that can enhance their business decision-making processes and programmatic activities, while students benefit from Azavea mentors’ experience and expertise.   This year, three fellows worked on projects for six organizations that spanned a variety of topics and geographic regions.  This blog series documents some of their accomplishments and challenges during their fellowship.  Our 2014 sponsors, GoogleEsri and PennDesign helped make this program possible.  For more information about the program, please fill out the form on the Summer of Maps website.

 

Summer of Maps focuses on providing spatial analysis services to non-profits in the form of maps.

While I geocoded addresses, performed kernel densities, and converted between vector and raster, none of that means anything unless my maps effectively convey the content. That is, they need to make sense and look awesome. Depending on the ‘where’ and ‘what’ of my maps, I implemented various tips and tricks to make them both beautiful and understandable. One of my mentors, John Branigan, is quite the cartography guru and actually inspired this blog post so I will start with:

 

1. Lessons from John – When I would bring up a map for John to review, before I had even explained anything he would point out “it’s not projected” or “don’t use red.” His eye for detail is very acute. The following are small tips that make a big difference for the aesthetics of a map.

  • Color – Red can stand out and provide contrast but it also conveys feelings of negativity or danger. On the other hand, green gives off a positive connotation. When I used green for a layer of income I thought it was appropriate because it reminded me of money. As Cynthia Brewer says in Designing Better Maps, “darker colors are used to represent higher data values, and lighter colors represent lower values” (1161). Emphasizing poverty this way, however, does not make sense because the lighter green areas were the ones I wanted to focus on. Instead, I used more of an orange/red to emphasize impoverished census tracts and used a green gradient to emphasize wealth for a fundraising map.
  • Outlines – One of my projects was about New York City, a densely-populated place. My maps could have easily been overwhelmed by the sheer number of census tracts. I avoided this by removing the tract’s outline. This left just the colored polygons, free of distracting boundary lines.
  • Transparency – Another way I was able to convey a lot of information without it getting too “busy” was with transparency. Making a layer 50% transparent lessens the harshness from a strong color while also giving way for other layers to be seen.
  • Basemaps – Basemaps are nice because they give some geographical context. They can also add a lot more like streets, topography, and satellite images. Again, for a place like New York with countless streets, I chose a very simple basemap with minimal labels that would adjust to the scale.

 

2. Multiple Attribute Symbology – Most of the time a layer is symbolized either by color or size or shape. There are certain instances, however, when you need to show multiple attributes. I came across this for a couple of my maps. For example, I needed to map event locations by the type of event and number of participants. That is, showing a quantity and category simultaneously. I did this by using unique colors for the event type and graduated size for the participation.

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3. Labels – It can be important to identify specific features like streets and counties with labels. Unfortunately they can pose problems such as long names, overlapping and simply not fitting where you want them. I counteracted all these complications with the following tactics.

  • Truncate the Label – A layer of community districts was identified by the district code. The code was comprised of a number for the borough and then the district number. I removed the leading number so the label would just be the shortened version.
  • Convert to Annotation – This is a great trick that allows the labels to be manually edited and moved. I was able to place county names where they didn’t overlap other features and rotated them so they fit nicely.
  • Omitting – Using the “one label per feature” instead of “one label per feature part” drastically de-cluttered the Jamaica Bay islands.

 

4. Extent – Simplify a map by only showing what needs to be shown. I had specific study areas for my projects so any layers that spilled out like a highway were clipped away. Similarly, I removed parts of a density layer that overlapped water.

 

5. Multiple Data Frames – Including more than one data frame in a map can add detailed views and context.

  • Insets – Maybe there’s an area of the map that is very clustered or is of particular interest. Creating an inset map of the zoomed-in extent avoids squinting!
  • Context Map – A few of my layers were derived from analysis involving various datasets. Instead of writing out many sentences about the process, I made a layout with multiple data frames to visually explain how many layers were added together to produce the main one on display.