So apparently there’s a competition brewing between the two of us. I said it on Twitter, but I’ll say it again: the only victory here (for me) is the learning and growth opportunity I receive from developing the makeover and the subsequent review of peer work. It’s also the victory of having a portfolio of visualizations and something I’ve found to be very interesting: gauging the reactions of others to my vizzes. In the world of being a data communicator/visualizer, resonating with your consumers (audience) is critical. That’s probably what I’ve appreciated the most – seeing what gets noticed and what doesn’t.
Okay – now that the whole competition bit is out of the way, it’s time to dig in. Christopher’s viz this week is very functional, so from an audience perspective I’m not enticed or immediately captured by the data. I do think the data elements that are captioned out are eye-catching and I like the shape they’re creating. The map does a good job of orienting me to the Matamata-Piako region via the annotation. Nice that there are particulars about the geography. The picture doesn’t add to the overall viz for me, but maybe I’m missing something regarding New Zealand. There’s no interactivity with the data, it is very WYSIWYG – a static snapshot of data.
Moving to the mechanics and overall analysis. I would be doing a small injustice to a lot of the chatter on Twitter this week if I didn’t mention that there’s a little bit of concern on how the RTI is being used as a measure. The box and whisker plots are showing the RTI for domestic or international visitors for each region by year. The RTI for each region is summed for the year and this makes me uncomfortable. What’s kind of interesting is theoretically it “doesn’t matter” because if we were to average and divide everything by 12, the data shape would be the same. BUT (and this is the big “but”) 2016 doesn’t have all the months. So unfortunately the districts looks like they’ve been awesome except in 2016. From a visual perspective this can be seen by comparing the companion line chart (RTI by month) below. This shows a steady increase for international travelers perpetuating year over year.
I feel the same way about the caption stating that it’s RTI is 37,401 in the map. And I am slightly bummed that it seems like the regions are colored by name, a potential missed opportunity! Here – if all data points were used, then the coloration of RTI (when summed) would accurately represent which regions are hotter spots overall.
I’ve noticed an overall trend in Christopher’s style: he likes minimal chart labeling and leverages annotations. The minimal labeling helps by freeing up canvas space between the box plot and the line chart. There’s now space for the color legend and it couples the two charts together more seamlessly. I also appreciate the care taken to make an information button explaining what the RTI is. And as always – I think Christopher has demonstrated multiple times that he likes to help the end user zero in on what’s critical. Here the critical elements are clearly: 1) Matmata-Piako district is a huge tourist attraction, one that by box plot standards is considered an outlier. 2) Seems like this insanity started in 2011 and keeps getting more prominent. 3) It’s not even the biggest or greatest place, so again “what’s the deal?” 4) It’s in the northern part of New Zealand, and we now have a nugget of geography that will be retained.
I would go so far as to say if asked this trivia question in the future you’d be able to answer it given multiple choice options: “Which northern district in New Zealand boasts the highest amount of tourism although it has a population of only 34k?”
Finishing up here, I’ll end by saying this: each week of data comes with a new set of challenges and obstacles – this one seems to have had a few new landmines that tripped up most of the community. I’ll be interested to see if Christopher finds anything within my makeover.