The format of this feedback could be: “which 2 things to keep, which 2 things to change”. Also, if you have additional ideas for the other project, definitely let them know in that feedback.
Feedback by Laura Knüppel on Blog 7
Hey guys! You picked a really interesting and very complex topic to vizialize, I’m excited to see the outcomes!
I really like the sunburst-piechart diagram as a way to give an overview over which types & subtypes of conflicts are happening in different geographical regions. I think it could be very well combined with the visual from your “Are the types of conflicts changing over time?” post! That could be a very simple but effective way to show how conflicts in different regions are changing over time.
The d3 visual of “Who is on conflict with whom?” is really impressive, good job guys!
When I first saw the sketch, I was worried that using different colours for different countries could get crowded and muddy really fast with loads of different actors, but you solved it well! Maybe the individual dots for the countries could be a bit further apart, to make it easier to spot differences on first sight?
If you want to see how the number of different kinds of conflicts relates to GDP (per capita or growth), I’d suggest using a type of visual with an X and Y axis instead of stacked bars — like you suggested in your flag visual. That would make it easier to see trends/correlations.
The overall concept for the visual based on Governance Indicators is really nice, I like the idea because it should make it quite easy to detect patterns. I am a bit confused on how the color scale codes for country though, like how the countries are represented by the different parts of the colour gradient. That would need some explanation or legend of some sort.
Just a quick note on the side: The structure of your blog made it kind of hard for me to get to know your project. It’s missing some kind of summary/intro to the project post, where you present your dataset concisely and summarize the kind of questions you’ll want to answer/explore. Ii had a bit of a hard time giving feedback on your visuals, because I had to go through all the individual blog post first to get a better picture of the things you are interested in doing/exploring.
Feedback by Pavel Novotny on Blog 13
Hey guys! Nice topic you’ve picked for your project, given our group is also visualizing football data, that is quite inspiring for us as well!
I’d say the the preliminary visuals in your first blog post of players value and salaries are already quite insightful, I think it might be interesting to take it further and maybe try:
- to to combine it with the players’ performance
- to visualize the players value or salary evolution over time
- to visualize the general growth of players’ value over time — for example what were the average and highest transfer prices 10, 15, 20 years ago compared to nowadays (if it is interesting enough to look for such data).
I personally quite like the insights from the second blog posts about the transfers, however reading it from those maps isn’t really easy, also some states have no values and just take up space on the map, maybe it might be better to look for another way of visualising that?
Good luck and have fun visualizing!
Feedback by Arabella D’Havé on Blog 19
Hello everybody at team EnergyDB, congratulations on the many work that has been done and the results achieved!
I took a closer look at your fist sketch, which you also kept for the converge phase (converge 1), displaying the average energy use intensity (EUI) measures. I really like the radial layout. As you mentioned, a line is drawn connecting the values for all spokes. this makes it easy to compare values of adjacent types of buildings such as lodging and parking, but a bit more difficult to visually compare values of non adjacent building types such as lodging with warehouse, for example. My suggestion would be to keep the radial layout but reshape the layout like this (mind you, I did not add all the types of buildings; the thick line represents the site EUI and the dashes represent the source EUI). This way, it would be possible to compare the values of all building types from one place on the graph:
This would make it easier to visually compare the EUI from all types of buildings, even if they are not adjacent in the graph.
Also with Converge 1, you state that you try to integrate more variables into fewer graphics in order to create more straightforward understanding. When you click on a type of building, it should give you the line chart of the E-star values per year and per geographical area. I think it could be interesting to also add a chart which gives you the overview with all buidling types included by replacing the lines by layers. The thickness of each layer could represent the E-star value, and the color could present the building type. This suggestion is based on the fact that it would be difficult from a cognitive point of view for the user to imagine this from the individual graphs.
Overall, I really like the idea of converge 1. It gives me a clear idea of what visualizations to expect and how they coordinate with each other. Visualizations accessed by the “clicks” seem efficient and provide more detailed information, without going to deep or presenting too much variables. It seems like a kind of “drill-down” or “slice and dice”, where it is easy to remember the navigation choices you have made. Meaning that I don’t have the feeling that someone could end up at a point not knowing were he or she came from.
Personally, I find converge 2 a bit less intuitive. I imagine myself getting presented with the first chart, which gives me an idea of the evolution of the average source EUI per year. Clicking the “mini”-graph provides me with the detail of that year. Per building type there is an indication of the EUI using a color gradient, but what is the rest of the space (indicated by “same”) within the diagram for?
Rather than limiting data to functionality, the information is presented using a colorful, minimalist design palette. The data visualizations result, in my opinion, in accessible subjects and a sleek, simple design. To me, this is indicative of the symbiotic relationship between data and design: colors, shapes and formats convey a huge amount of information without taking up much space or overwhelming the reader.
I hope this feedback was useful to you and I’m interested to see the final results!
Feedback by Elizabeth North on Blog 25
Hey-o, Team Robin!
I’m Elizabeth, nice to meet you. Thank you for putting together a nice blog, I see your clock-work orange reference!
Wow, it’s quite an ambitious project. From your first blog post I totally get a vibe — it’s a bit like you want to visualize who is migrating where, and why, right? You can feel some passion there, and I wonder what inspired this for you. What’s your “why?” I hope you remember it, and come back to it.
I say this because it feels like with each passing post, your goal comes a bit closer, but also smaller. Not in like a focused sort of way (though it is focused, nice work) moreso in like a being realistic sort of way. I get downsizing for feasibility’s sake, of course, but I wonder if your original goal is getting lost a bit? How can you downsize while still sticking to your og goal? That’s the vibey stuff, now let me put on my scientist hat — ahem.
I like the design on the bottom here, and the variance of widths based on sex. I agree it makes sense to include sex as a variable since it’s possible people that identify as women are more likely to migrate. I like singling out by country because it makes it easier for my to visualize. When it’s a lot of countries at once I would say the effect of the sex variable could get lost.
I really like the idea of visualizing the emigration (hey thanks for teaching me the difference between emigration and immigration) and immigration with the mirror image map thing.
Overall I would say it seems like you could do nicely to spend some time thinking about the very core of what your vibe is, and figuring out how to capture that. To what extent do you need to integrate GDP and global food security indices in order to accomplish your goals? I am not seeing them too much at the moment.
Also, on a personal note, I wonder if you have checked out the Human Development Index? I have studied human rights and sustainability, and in those discourses GDP is commonly referred to as a flawed metric, and it’s so commonly used anymore. Here’s a cool link I found (but you could really read any link you like) on the topic. The gist I’ll quote from the article here: “For example, countries with the same GDPs can have vastly different HDIs. If two countries have similar GDPs but their HDIs are out of sync, it can help policy makers identify the fundamental issues in their countries that need to be addressed, such as education or health.”
Hey, I wish you luck, and really, if you want a bit more feedback feel free to reach out. Onward!
Warmly,
Elizabeth
