Weekly Viz – STD Infection Rates (1996 – 2014)

#MakeoverMonday Week 31 2019

This weeks dataset is from CDC and be found here.

The original visualization is as follows:

What’s good?

• Provides an insight into all the information that the dataset packs within
• Legible labels

What can be improved?

• I view this as an information overload. Although very insightful – it is too much to process
• Multiple graphs can be combined into one

I happened to stumble upon multiple articles that outlined data visualization best practices. I realized that although exhaustive – many of the visualizations i had been creating violated many important points highlighted in these best practices.

As someone who creates visualizations, we often become well versed with the dataset; this often translates into non intuitive representations for the end users.

So, for this weeks visualization I embarked on the path of data minimalism – trying to convey as much as possible with as little as possible. Here’s the rendition I made –

Click on the image to view the interactive viz

This visualization shows the progression of the no. of cases over the years. Since the dashboard is quite big – below are two gifs that show the dashboard in action:

I would love suggestion on best practices and on this visualization in general.

Follow me on twitter @viraj155

Advertisement

Weekly Viz – International Protection in EU+ : Overview

#MakeoverMonday Week 28 2019

Week 28 saw an interesting dataset on #MakeoverMonday – International Protection in EU+ Countries. The data set can be found here.

With the turmoil and unrest in the middle east – the statistics as regards the refugees being granted an asylum are staggering.

This is the original visualization –

What’s good?

The visualization tells a story of the countries where these refugees come from – It gives a brief overview of the no. of applications, the applications that were accepted, rejected or are pending.

However, the dataset attached does not have the data regarding the country of origin. This visualization lacks coherence with the data given.

Here’s my slant –