Dashboard week - completed!

Today's task was to look at Eastern Bering Sea Crab Distribution. I decided to do what I've done all week and plan my questions and get my ideas on paper.

• Who - Focus on specific crabs?
• When - When is fishing season? Does this impact where the crabs are located?
• What - What depth are they caught at?
• How does this depth look against temperature, is this the same for surface? What does this look like over time?
• Why - Is this a consequence of increased water temperatures?

In theory, I assumed that rising temperatures led to crabs moving deeper and therefore becoming harder to fish, so less catch? As soon as I started working with the data, I realised that this was not the case. I was inspired by a study that was conducted which almost supported this theory. I started off with that as my basis in my subheading.

I decided to focus on simply the King Crab. I knew that this broke down by the two kinds and therefore I coloured them as my legends and included a general overview of their averages.

Next, I wanted to explore the average catch per area and what trends I could see within this. I thought this would lead me to explore my further questions a bit better or invalidate them. Interestingly, catch per area has reduced. This led me to ask why. I also included the option to view a heatmap as to when the fishing season for the two King Crabs are including the day that results in the most catches.

The next 2 questions I wanted to answer followed from that first bit of analysis including the research paper. I wanted to explore depth to see whether they were being fished from greater depths. I realised that if anything, they moved closer towards the surface. After that argument was invalidated. I thought maybe a comparison against temperature may help. I created a scatter graph with row ID used to plot every single red and blue King Crab recorded. I thought that actually, this isn't the most useful chart to see change over time and so included that option for the user also with an animated year view.

The next part of my analysis looked at exploring why these trends (or not), are happening. I supported each argument with information that I read about across several resources. I found it interesting that the temperatures work in cycles and are impacted by the pacific decadal oscillation.

Here is the link to my final viz: https://public.tableau.com/profile/alisha7755#!/vizhome/DoKingCrabsgoDeeptoAvoidWarmWater-DashboardWeekDay5/Dashboard1

Overall, dashboard week has been an interesting experience to say the least. The datasets provided have been fascinating and I've learned so much. I think it's important that we teach others and to be able to have the opportunity to do this through data is incredible. I've definitely felt better going through the week and after starting with a bit of a disaster, can safely say that I've improved and got better at thinking about what the data is showing me, and then going off to explore why.