Tell us about yourself.
I’m a carto geek and have been making varying degrees of visual junk for almost 3 decades. I’m interested in showing data in a way that makes people curious to learn more. I’ve been at Esri for 23 years and I’m the Director of Solutions (which means 2 things: 1. my team builds industry specific maps and apps to make it easier to use and 2. problems tend to find me). Visualizing space and time in static printed maps has limited how we tell stories about data for hundreds of years, and the move to fluid digital data means some long-standing cartographic rules may need to be bent…
Tell us the story behind your map (what inspired you to make it, what did you learn while making it, or any other aspects of the map or its creation you would like people to know).
I grew up in the Midwest and tornadoes were a fact of life. But while I knew I lived in Tornado Alley, I never had a good sense of what was the extent of that alley. And the maps that tried to define it seemed based on ideas and thoughts and not data. So I used data aggregation along with 3D to visualize the historical frequency of where tornados occurred. The raw data is a spaghetti mess of lines, but when aggregated into hexagons it becomes clear there is no narrow ‘Alley’, rather a large neighborhood. Looking at the data more you could see a pattern on when and where tornadoes were more likely. And using interactive time sliders you can also explore the general direction of tornado travel (which varies widely by region).
Tell us about the tools, data, etc., you used to make the map.
I used the historical tornado data (1950-2015) from NOAA. I used ArcGIS to aggregate the data into hexagons and do the 2D and 3D visualizations. The aggregations were based on count of major tornadoes (above a F3 on the Fujita-Pearson scale) inside the hexagons. I used the same color scale in 2D and 3D to allow for easier comparisons. But I felt both 2D and 3D added to the understanding of the pattern.
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
Q: Tell us about yourself.
A: I like maps and playing with data (aka analysis). I’m a failed Biologist who became Geographer (CU Boulder, MSU Bozeman) and have been working with GIS for around 25 years now (The last 22 of them at Esri). Over the years I’ve been fortunate to be able to take on a number of ad-hoc mapping, analysis, and visualization projects. These have allowed me to explore creative ideas, some fairly “out there” analysis, and “what if” scenarios of data combinations.
Q: Tell us the story behind your map (what inspired you to make it, what did you learn while making it, or any other aspects of the map or its creation you would like people to know).
A: I experimented with hexagon mapping many years ago, and while it was well established (for 100s of years), I never really saw the attraction of it. I was into modeling and needed to do weighted surfaces and interpolation.
But a few years back I started working on more of the visualization and comprehension aspects of information. Aggregating data (or binning) into well-known shapes is a great approach for providing a higher level view of data. I was CERTAIN that squares (maybe rectangles) were the correct way to do this, but after some experimentation I found that hexagons in many ways worked better, as they don’t impose rigid linear sightlines. And the tessellation of nested hexagons is fascinating in multi-scale maps. (It still pains me to praise hexagons!)
So when the call for maps came out, I was working with hexagons and combined that with my fascination for map projections and showed the nested hexagons across a Goode Homolosine projection. (I also sent one in for another projection (Stereographic) that I thought was better — but the aspect ratio didn’t work for the calendar).
What did I learn? Other than there is a “cult” for Hexagon mapping out there? The nesting hexagons worked great, except that some of the bigger shapes got distorted and didn’t line up quite right. I realized that the hexagon sides with 2 point lines, and when projected they needed to be densified to make the smaller hexagons inside.
Q: Tell us about the tools, data, etc., you used to make the map.
A: Making the map was pretty straightforward. In ArcMap I used a sample script to produce the hexagons at several sizes in *un-projected* WGS 84 coordinate system, and then just played with changing the projection till I had a couple maps I liked.
The 2015 GeoHipster Wall Calendar makes a great holiday gift for the geogeek on your list, so pick up a few. The proceeds from the calendar sales will help GeoHipster offset our operational costs, stay ad-free, and maintain independence.
The 2015 GeoHipster Calendar is available for purchase from CafePress. All calendars are made to order (you need to specify January 2015 as Starting Month (as opposed to the default setting — the current month)).
The calendar features maps from the following map artists (screenshots below):
John Van Hoesen
IMPORTANT! The screenshot below is intended ONLY to give an overview of the overall layout — which map goes on which page, etc. When you order the 2015 calendar, you will get the 2015 calendar. You can verify this by reviewing each individual page online before you order.