MM CW 10 – What policymaker know about girls and women issues


Policymakers in 5 different countries (Colombia, India, Indonesia, Kenya, and Senegal) were asked to give their estimate on indicators related to girls and woman’s issues. Examples are share of female labour force, early marriage rate for women, etc.

Original Viz:

How the viz could be improved:

  • It is not explained what the diamonds stand for (they stand for one answer of a policymaker).
  • A summary of the answers which shows how far off the different policymakers were for each topic would facilitate the interpretation of each chart.
  • The gridlines are not necessary. They compete with the most recent available data line for attention.

My makeover:

This time I created two versions of the makeover. My first viz did receive some critical feedback on which I will focus later on. First my thoughts on my first version:

MMCW10_Dash (1)

The key message I want to convey:

  • I wanted to highlight the topics which the biggest discrepancy between estimate and actual measure. I achieved by calculating the average of all answers of all policymakers and country and comparing this to the correct answer right next to it.

The design choices I made:

  • I opted for a diverging bar chart. This is normally a good choice when comparing 2 measures over multiple topics.
  • I also added an additional overview box to the dashboard. The arrows show if the policymakers on average have overestimated or underestimated an issue. The colors signal if the average of the policymaker’s answers lies within the 20% range of the correct answer.

The feedback I got for this viz during the viz review live meeting was quite mixed. Major criticisms were:

  • Too many colors
  • The arrows are confusing since they display two measures at the same time: Overestimation/Underestimation with the direction of the arrows and within 20%range / not within 20% range with the color palette. This might be confusing because either preattentive attribute (color and shape) could represent either measure.
  • The comparison between the correct answer and estimate could be facilitated by using the correct answer as a target rather than showing it as an individual bar.

Taking this feedback into account, I created a new version of the viz:

MMCW10_Dash (2)

Now, the correct answers are not separate bar charts, but target lines in the estimates. Also, I got rid of the arrows and replaced them with bar charts which show the difference between guess and estimate. The bar charts are colored depending on if they fall within the 20% range of the correct answer or not.

I got rid of some colors as well. The background color now is in a more subtle eggshell tone and the countries, which are used as filters, are colored in dark grey rather than in pink.



MM CW 9 – World Economic Freedom

Hello folks,

the topic of this weeks’ makeover is the Economic Freedom Index. This index measures the economic freedom of almost every country in the world. Since its creation in 1970 almost every year, new countries have been added. The latest data of this data source is from 2015.

What I dislike about the viz – In this case not much.

  • Overall this is a clean and straightforward visualization. The index rating is split into 4 groups: “Most free”, “second quartile”, third quartile” and “least free”. The countries on the world map are colored according to this color legend.
  • This is an explorative visualization, meaning insights have to be discovered on your own. However, it would have been nice to have some key findings pointed out. E.g. which country has improved the most, lost the most index points, etc. The additional table on the left only provides information about the ranking and the increase/decrease in points relative to the last measured index.
  • The countries in the table are not colored according to their group (most free, etc.). To find out about the group you would have to cross-check on the map.

My makeover:


The key message I want to convey

  • I want to show which countries have gained and lost the most index points since the first measurement (1970). This one took me way longer than expected and I changed the message multiple times. My first table calculation which showed the difference between the Index value measured in 2015 and the first year of measurement didn’t return meaningful results. Georgia and Mauritius were the countries with the largest improvement. This is because some countries were not included in 1970 and were added in the following years. This would result in a very high improvement for some countries since their respective value in 2015 – 0 (value in 1970) obviously shows a larger gain in index points in comparison to countries which a have already been included in the index in 1970. Considering this, I created a set of countries which comprised only countries which already were included in the 1970 index. This shows a completely different picture. Two South American countries took very different turns. Chile, once far below Venezuela in economic freedom, has taken a big upsurge and overtaken Venezuela. Venezuela, on the other hand, was well above average in ECF as of 1970, but as of 2015, it lags far behind all other countries.

    The design choices I made

  • I opted for a jitter plot. Using the random function I located the data points for the countries along the x-axis between a range from  0 to 1 for each year. The y-axis shows the Summary index of the ECF
  • I highlighted the countries Venezuela and Chile with bright colors and increased the bubble size.
  • I added an average line for each year.

MM CW 8 – Where do our Drugs and Medicine come from?

This week’s makeover is based on the following viz:


What I do not like about the viz:

  • The bubbles block the view. The map in the background is hardly visible and seems redundant.
  • The points do not correspond to the countries they represent on the map. E.g. The African countries are scattered somewhere in the ocean and it looks like South Africa is above Egypt and like they are neighboring countries which is clearly not the case.
  • The bubble sizes make comparisons difficult. Is the bubble size of Belgium really bigger than the bubble size of France? It is really hard to tell, even though the numbers state that Belgium exports $4 Billion more than France.
  • A lot of bubbles are the same size. There are a lot of countries displayed which do not seem to play a major part in the market of Drugs and Medicine. These countries clutter the visualization and do not add additional insights.
  • The color scale is hard to distinguish. The colors difference for the >10% and the 5%-10% bins are barely noticeable. The same goes for the color for the lowest 2 bins.

My Makeover

MM8Dash (2)

The key message I want to convey

  • The Drugs and Medicine exports are dominated by very few countries. 
  • 5 countries export more Drugs and Medicine than the rest of the world.

The design choices I made

  • I opted for a simple stacked bar chart. This does the trick to show exports volumes by countries in relative comparison to each other.
  • I split the countries into two groups. By grouping the countries, a second layer of comparison is enabled. Now the group of top 5 exporting countries and the rest of the world can be compared along the Y-axis. Visualizing these 2 groups next to each other amplifies the huge difference in market share by country.
  • I chose a discrete color scale for the top 5 countries and colored all other countries uniform. This puts an additional emphasis on the biggest exporting countries.
  •  I added a reference line. The reference line at the end of the top 5 groups makes it easier the compare the two groups. The white space which expands from the end of the bar chart till the reference line shows the additional export volume of the top 5 group.
  •  The two big bubbles right to the stacked balk charts give perspective. To get a feeling for the total amount exported by the 2 groups, I added the bubbles which display the total value of Drugs and Medicine export in Billion $. They are not size mapped.

MM CW 7 – Winter Olympics Fever

For the start of the Winter Olympics in South Korea, a data viz which shows all medals earned since the first Winter Olympics in 1924 by Country and Medal(Bronze, Silver, Gold) was picked.

Overall, I find this to be an appealing viz. It looks orderly and clean. The message is clear and it is easy to find out how many and which medals a country won. The small KPI summary in each box also gives a nice overview of the total medals won and the split of medal ranks.

Points which could be improved

  • The subheaders are difficult to read. The subheader’s font size should be increased.
  • Comparisons are difficult. The countries are sorted by total medals won so it is easy to compare by this measure but besides this, a comparison between countries, for example by bronze medals, is difficult.
  • The viz’s stretches out very far. It is easy to get lost in the viz since you do have to scroll down quite far to reach the bottom. It would be an advantage to have an overview which does not require scrolling.
  • Looking for a specific country is difficult. The high amount of displayed countries makes it tedious to look for a specific country. A filter or highlighter could solve this issue.

My Makeover


The key message I want to convey:

  • Since this is an explorative viz, there is not one conclusion to draw but multiple insights which can be explored. However, the focus lies on the medal count split by medal rank.

The design choices I made:

  • I chose a scatter plot to show the count of medals won by all countries over the years. I took advantage of the random function to distribute the scatter plot dots randomly within the year column.
  • The rows are split by medal rank. Each row shows the medals won by rank(bronze, silver, gold). The color pallette is explained in the title, so no additional color legend is needed.
  • A country can be highlighted via the drop-down box. This facilitates the comparison between the countries. The selection of a country in the drop down menu results in an increase of the circle size for this country.
  • An overall summary of medals won is displayed on top of the scatter plot.

MM CW6 – Digging deeper into single data points of a data set

The target of this week’s makeover is the following viz:

What I dislike about the viz:

  • The choice for a stacked bar chart. The stacked bar charts create a white space between the years. This elicits the impression that there might be missing data in between the years which is not true. An area chart would have been the default design choice to show the relative change of the percentages over time. This would have fixed the white space issue.
  • The color legend is center of the viz and blocks the view. No information is lost by locating the color legend on the red bars for the white ethnicity, but the design feels off.
  • The color choices are counterintuitive. The black and white ethnicities are represented by the colors green and red. This could lead to increased cognitive load.

My Makeover:


The key message I want to convey

  • Only as late as in the mid-1960s, did the first Asian baseball player debut in the MLB. I had to think for some time, what to do with this viz. The intuitive thing would have been to just convert the stacked bar chart into an area chart and show the increased diversity in the MLB or highlight the increase of Latino players. However, I became curious about Asian proportion of player since the line for the Asian ethnicity starts long after the other 3 ethnicities. I did some Wikipedia research and discovered some fascinating facts about the first Asian player in the MLB who is responsible for the first bump in the prior flat lane of Asian players. Originally, I also wanted to annotate and enhance the backstory of the second bump in 1994. But unfortunately, I was not able to find additional information about the player who caused this bump.

The design choices I made:

  • I opted for a simple line graph and filtered out the other ethnicities to magnify the small increase in the Asian Player pool. 
  • I chose the blue of the MLB Logo as a background color. For the color choice, I am still not 100% sure if this is the way to go since I do not really like this color scheme. But I did want to create some resemblance to the MLB Logo, so I stuck with it.

MM CW5 – Time for my first radial bar chart

The target of this week’s makeover is the following viz:


First thought, wow there is a lot going on. Where should I bring my attention to?

Which brings me to my next rubric:

What I dislike about the viz:

  • There is too much clutter. There are 5 layers of information. Company logo, company name, fortune rank, net income and profit per second. Company logo and company name are redundant – they convey the same information. The fortune rank and the net income are very small and get lost in the overall viz.
  • There are too many colors. The different colors represent the respective industries as dimensions. They are not used to make a point which, would, for example, highlight the highest profit per second. Using multiple colors for a dimension can be fine, but in this case, it just adds additional complexity.
  • The viz title is formulated as a question but the viz does not guide you to the answer. The big bar chart for Apple get lost in all the information and the immediate answer to the posed question remains uncertain.
  • The viz title only refers to one part of the viz. The title only mentions profit/second all the other information, given in the viz given as additional input, but do no help to answer the questions posed in the title.

My Makeover:

ProfitPerSecond (2)

The key message I want to convey:

  • Apple makes an unbelievable $1.444 profit per second and is the most profitable company in the world. Both of these findings are reflected in the title and the viz by the red color and size of the outer ring.

The design choices I made:

  • I chose a radial bar chart. Mostly, because I just watched a youtube tutorial the other day on how to do it. 🙂 I know that it has a little bit of a bad rep since bar carts would work in most cases just as good or even better because they make the comparison between the different bars easier. Radial bar charts often have distorted proportions and the ration comparison of the different rings are hard. For example, the second ring (JP Morgan Chase) of my viz should be about half of the size of the first ring (Apple). But is this really the case? This is difficult to grasp for the human eye. Even though the radial bar chart holds these downfalls, it is optical more appealing and more likely to grab the attention of the reader.
  • Reduction of colors. Again, I decided to reduce the color palette of the viz and stuck to only grey and red. I could have chosen to color the rings by the industry dimension, just as in the original viz, but I felt that I could convey this additional context just as good in the text box in the middle of the radial chart. Also, additional colors would have drawn away the attention from the most striking finding, namely that Apple makes the most profit/second.

My contribution to MakeOverMonday and my first published MakeOver

Since End of 2017 I take part in the MakeOverMonday Challenge. The essential concept is, to create a makeover of improvable visualizations which have been published on diverse internet mediums. The author of the homepage, Andy Kriebel, provides a new makeover datavisualization beginning of each with week, along with the corresponding dataset.

It is a great opportunity to put various design concept into practice and discover what works for the respective dataset and what does not.My datavisualization tool is tableau. I discovered this program while taking the cousera Data Visualization with Tableau specialization.

From now on I will share my weekly makeovers on my Homepage together with my thoughts on:

  • what I dislike about the original viz
  • the key message I want to convey with my viz
  • the design choices I made to convey this information

I will begin with my makeover of CW2 2018. The original viz is the following:

British both gender rankings-01 (1).png

What I dislike about the viz:

  • There are too many colors. Looking at the viz, no color in particular stands out to me. Red, for example, as a key signal color, is not used at all in the color palette.
  • Size as well, does little to guide the reader to the most important part of the viz. The biggest bar charts are purple and stand for the question: “They have/make a decent amount of money“. On first sight, the size of the balk chart could signal that this attribute is the most important. Only on second sight, do we see that it is ranked sixth. Meaning that in the lowest ranking option it has the highest value.
  • It is tough to compare the values of women and men next to each other. If I want to compare the values of men and women for the respective questions,I always have to move up and down between the 2 separate bar charts and find the corresponding color.
  • The key finding, called out in the title, is not highlighted in the viz. The title mentiones as the most important finding of the data – “Personality is the most important characteristic in a romantic partner, say half of the Brits.” However, the viz does not support this claim visually. Both bars, for men and for women, are just another color in the viz.

My Makeover:


The key message I want to convey:

  • I focused on the difference by gender for each question. Since the dataset also provided survey data for multiple countries therefore it was also feasible to focus on the visualization of cultural differences. However, in my experience it can be overwhelming for the audience when the viz tries to answer too many questions at once. I settled for a comprise with the filter option which makes it hard to compare between countries, but allows to switch between them.

The design choices I made:

  • I chose a butterfly chart to facilitate the comparison by gender. For each question, the percentage value by gender are shown along the y-axis.
  • There are only two colors now in the viz. This results in a reduction of the cognitive overload caused by the original viz.