The data I obtained is the admittance database for injured and sick wild birds at the Wild Bird Care Centre. You can download the data here:  Wild Bird Care Centre Data. They have a pretty detailed admittance form which they input into excel. Some of the key fields I found gave interesting results were the “reasons for admittance”, whether the bird was protected under an “wildlife protection act”, and the “status” of the bird at admittance and its “final status”.

I had all of 2013, 2014 and up to mid November for 2015. I  removed all of the calculations and validation work going on in each cell by copying the table and pasting it in a new tab and keeping only the data and number format. I then cleaned up the data and kept only the columns that might be of interest.

All of my analysis was done using pivot tables and filtering. I was able to do a lot of interesting combinations, that showed that over 290 birds that are protected under the Migratory Bird Convention Act and the Species at Risk Act were the result of human interference, with cases such as poisoning, removing them from their environment, destroying the nest, feeding it an incorrect diet and keeping it as a pet.

There was a specific case that stood out where an individual had removed a species at risk — chimney swifts, a threatened species — from a site, illegally. The staff was able to comment on that, and the procedures for dealing with species at risk when someone violates a law. The Ministry of Natural Resources will investigate any cases where someone may have interfered with a species under one of these acts.

I found that approximately 10 per cent of the birds (721) that were brought in were a direct result of human interference, and out of that 40 per cent of them (291) were protected under on of the acts.

I used Plotly to make the data visualizations. The visualizations are pretty basic, but I like that plotly makes them interactive and visually clean and appealing.

I had no location data to map it out (I suggested to the staff to add a location feature to their data because it would be interesting to map the locations of found injured/sick birds).

I was also able to look at the sensitivity of different protected species, to see if their were some that were easier to rehabilitate than others, which ended up providing some color to the story, and to give people a perspective on the challenges of rehabilitating species that are highly sensitive when out of their regular environment.