Practicing Data Visualization with the North American Breeding Bird Survey

Data visualization is everywhere. Beautiful and coherent charts, graphs and fancy little graphics transform raw numbers into entire stories.
When it comes to data visualization, I could be more clueless, but I’m not exactly a master either. My go-to technique is to bludgeon data into submitting to my will, which usually requires a great deal of trial or error and compensating for my lack of skills with dogged manual labor as necessary.
This project proved no different. With an impressive “looking for data” to “actually manipulating data” ratio of 15:1, I bludgeoned, trialed, mostly erred and compensated my way through a faulty dataset and a couple of different data visualization platforms to arrive here and with — even more impressively, I think — something to show.
Selecting a Data Set
Every good data visualization starts with a dataset. I landed upon bird population data from the Migratory Bird Data Center, a partnership between the U.S. Fish and Wildlife Service, Division of Migratory Bird Management and U.S. Geological Survey, Patuxent Wildlife Research Center.
Next, I selected a raw data set from the North American Breeding Bird Survey, a long-term, large-scale avian monitoring program that is used to estimate population trends at various geographic scales. My customized data set described a species totals report for both breeders and migrant or non-breeder species reported at Fish Rock, CA between 1968 and 2014.

The Data Visualization Begins
In order to facilitate learning and experimentation, I decided to select just eleven common birds from the data set on which to practice.



The following slides show the distributions of the selected eleven avian breeders species counted at Fish Rock, CA in 1970, 1982, 1992, 2002 and 2012. Or, in other words, which birds are more common, and how did their numbers in respect to each other change over each decade? Created with Tableau.





The following charts show the population trends of two birds between 1968 and 2014 at Fish Rock, CA for two birds: the Brown Creeper and Brandt’s Cormorant. Created with Excel.


Conclusion:
The purpose of my project being to research, learn, and apply aesthetic techniques for presenting data relating to environmental topics, I established a couple of different goals:
1. Research and learn techniques of data visualization.
2. Apply methods of data visualization to a specific topic.
3. Produce a collection of infographics.
4. Accumulate new skills relevant to future projects.
I believe I met these four objectives. I began by researching different methods of data visualization and corresponding digital programs. Then I applied a variety of these methods to a selected data set. In the end, I only produced infographics using Excel and Tableau, but I labored away with Altair using my (limited but growing) skills as a Python programmer for many hours. Unfortunately, I did not manage to use Altair to produce anything coherent with this specific data set (although I did succeed with other data, which is something). While the data I organized into graphics here may not appear especially extraordinary, I believe that I made great use of this opportunity to learn and practice, and I look forward to using all of these skills in the future.
