Well, this was a pretty epic fail. No, it was a really big fail. Those that tuned in to Watch Me Viz will be well aware of my struggles with this data set. I don't know why. Maybe it was a concentration issue. I hope it was still useful to everyone, and I hope it was good to see someone with so much experience still struggle.

Unlike traditional maps, tile grid maps allow you to allocate equal space to each geographical area. We've probably all seen hex maps of the United States. Tile Grid Maps are similar, except they are squares with each block being the same shape and size.

In this video, I show you first how to create the tile grid map, then how to overlay time series data. I then show you three different visualization types for the time series. You could easily create bar charts as well.

Enjoy!

I have three teens, so this week's dataset had me particularly curious. While my daughter prefers Snapchat, my boys prefer Twitter. One big missing piece with this dataset is the lack of demographic information. I'm curious as to how my teens compare to those surveyed.

The original viz comes to us from Business Insider:

What works well?

What could be improved?

My Ideas

This tells the story simply, but also doesn't show enough of the change over time. Next, I took the original and turned it into a line chart, labeling only the start and the end and also changing the colors to match the official colors of each app.


I think the line charts help make the change and trends much more obvious than the bar charts in the original. From there, I decided to look at the change since the starting period (spring 2015) to make the growth or decline of each app easier to understand. And with that, here's my Makeover Monday week 47.

Tough data set for me this week as I know nothing about cycling. In addition, I raced this clock this week and completed Makeover Monday in an hour with the Data School. We then all presented our work back to Eva.

If you're particularly bored, here's a recording of my screen for the whole hour.



Ok, still awake? Hopefully you enjoyed my Spotify playlist at least. This week's viz that we're reviewing is knomea.

What works well?

What could be improved?

This is a really exciting week for Makeover Monday. It marks the one year anniversary of the first Makeover Monday Live that Andy Cotgreave and I ran last year at TCOT London. This also means that Makeover Monday is live again at this year's TCOT London. These events are so much fun and an amazing way to introduce people to the project.

For week 23, we are looking at the popularity of America's National Parks. As an American, I've learned to cherish the amazing, free natural wonders sprinkled around the country. In particular, when we lived in California, we made it a point to visit Yosemite, Joshua Tree and the Grand Canyon amongst other places. They truly are worth a visit.

The viz that we are making over this week comes from FiveThirtyEight and it really quite fantastic, like most of their vizzes.

What works well?

The topic for Makeover Monday week 19 has come about because I'm headed to Amsterdam this week to work with our team there and to run two Makeover Monday events for customers. I need to give Martijn Verstrepen from our Dutch team a shout out. He came up with the idea for this week, found the article, created the data set, and even translated it to English for us. Thanks Martijn!!!

The viz to makeover this week is merely a table:

What works?

Sasha Pasulka has been a good friend of mine for many years and is moving to London from the US soon in her new role with Tableau. Like anyone else that’s moving to a new country, she finds the entire process is ridiculously overwhelming. Trying to find apartments from thousands of miles away, not knowing anything about neighbourhoods, can be an incredibly daunting task. To help her, I decide to build a viz in Tableau.

I started by using the London Crimes web data connector from Tableau Junkie, only to realise that it’s somehow not returning all of the data. It looks like it returns the data via a radius versus for an entire postcode.

No worries though. Instead I went to data.police.uk and downloaded all crimes from the Metropolitan Police Service. This returned a separate CSV for each month and it also wasn’t limited to just the London area. On the London Datastore I was able to find a list of all LSOAs in the London area. Great! All I needed to do was union all of the CSVs then join them to the London LSOAs. I love that I can do all of this straight inside Tableau now.

From there, it was a matter of building a simple visualisation that allows Sasha to pick boroughs and see the crimes in those areas. Note that I set the map to only display when there are 3 or fewer boroughs selected. did this because the map was simply too slow to draw the dots. Hopefully she likes it and makes it easier for her to settle in.

I started this blog 624 days ago on August 17, 2009 with the goal of helping others (and me) learn best practices for data visualization.  There have been many twists and turns and the backlog of blog posts has gotten quite long.  The breadth of the audience of this blog worldwide has both fascinated and humbled me.  145 blog posts later, here are the stats, visualized of course.

The bubbles are sized based on the rank of the country for the stat & time frame chosen.  The color is based on the stat chosen.

Thanks for the visits and comments.  I learn a lot from your feedback.

I received my cell phone bill from AT&T today and noticed that there are usage reports on the site. I typically don't look at these because I really don't care about my usage, but for some reason I decided to look at them.

Here is how AT&T presents the data:



Icky, icky! Where are the dates? I can't tell the difference between some of the bars. Why are long distance and roaming included? You'd never be able to see them anyway. Why not use a simple line graph?



Come on AT&T, get your act together. Although I suspect these were created by a developer that only knows how to use the default graphs in Excel and thought "Oh, I can make these so pretty with the 3D bar charts."

Why the big spike in September? Conference calls...boooooo!

I was building out the pitching stats of baseball database I downloaded from Baseball Databank and happened to look at a trend of balks. I noticed this HUGE spike in the number of balks in 1988. I figured there had to be something wrong with the data, so I turned to Google. I found this terrific blog post that explains exactly what happened in 1988.

THE RULES WERE CHANGED!

So what happened? In 1988, the Oakland Athletics led the majors with 76 balks or just over 8% of the total. 1988 accounted for 37% of the A's balks from 1985-2008...that's ridiculous!

Dave Stewart set a record with 16 balks. Order was restored in 1989 and Dave Stewart had zero balks. I do remember Dave Stewart going to the plate quickly, so maybe it was the change in the rule that says "the pitcher must come to a single complete and discernible stop" that got him.

I read an article by Chris Pereira the other day that made an attempt to link the recession to video game usage. In fact, the subtitle is: "With economy troubles, gamers are buying a larger percentage of used games." While there is a link between video game usage and the current recession, this a pretty bold statement given the analysis presented (which Chris built on from a Time magazine article). I'll show you my evidence that backs up Chris in a bit.

The first chart presented by Chris (via Nielsen) analyzes video game usage trends over the last four years. I don't think this chart proves anything more than the fact that video game usage has increased year over year for the last four years...that's it. You simply can't say that the upswing in time spent playing video games in 2009 is due to the recession.



The second chart tries to make the same correlation, but again, I see the same type of trend from 2007-2009 that you see in the first chart. People are just playing more variety of games...that's all. There is absolutely no way from this chart to draw a conclusion that used video game sales are in any way related to the recession.



Ok, so how can I say that it's just a matter of fact observation? Look at this chart (created with Tableau). If you look at the four year trend of hours played, there is a continuous increase is hours played. You might say "I don't see this in 2009." But in each year in May, you see a big decrease in hours played. As Chris says in his evaluation: "May has traditionally seen a drop-off each year (blame the improving weather)." I can see the reasoning there.

You can also see that from 2007-2009, a similar trend can be derived from used game sales.



I tried to find a correlation between hours played and used game sales (since the article says both are related to the recession), but the facts don't support this.



I decided to look at other factors that could correlate the recession to video game hours played to help support Chris' argument. We have been hearing quite a bit regarding the housing crisis and it's impact on the economy. I gathered economic data from FRED and was able to demonstrate that a prolonged decrease in housing starts does indeed indicate that a recession is coming.



My next step was to identify correlations between housing starts and the increase in video game hours played. Based on this analysis, I believe that a much stronger argument can be made the an increase in hours played is a possible indication that a recession is taking place. I set the "hours played" scale to match the Nielsen Data.



I find the color-coding of the years very useful and, to me, the relationship can be clearly seen. As housing starts decrease, video game hours player per week decrease. Since the years are color-coded, you can tie them back to the housing starts/recession trend chart above.

What's the bottom line? I agree with Chris that there is a relationship between video game hours played and the recession, but the way to get there is much more conclusive if you use economic indicators to support the theory.