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2019-02-19 | J++ Nyhetsbrev #69

A data viz is worth a thousand words

In A picture is worth more than a thousand words, Brian Romer the Data Visualization Lead at Thomson Reuters Labs explained how data visualisation has a crucial role to play for people to learn and retain information.
"The human brain has an automatic ability to detect small differences, for example, in size or color among similar objects. Data visualization takes advantage of this to deliver information that can be evaluated prior to conscious thinking."
This Reuters blog post dates back to 2015, but 2019 is still an exciting time for data visualisation! This newsletter has data visualisation as its focus, and shows examples of visualisations as well as tools for creating informative visualisations.

World Data Visualization Prize 2019
A good data visualisation can indeed be worth money, as in prize money.
You might know David McCandless from his first bestseller book Information is beautiful about infographics. His data visualisation studio organises data visualisation awards and prizes each year.
The winners for the World Data Visualization Prize 2019 were released earlier this month. They are the ones who best conveyed information on how governments influence people's lives.
The Grand Prize winner used a Machine Learning technique called t-SNE (t-Distributed Stochastic Neighbor Embedding) to discover clusters of nations that are related by happiness score, health expenditure, investment in education and other variables. The clusters are then visualised interactively and can be explored to get more insight on how particular nations are related.
Mapping US racial segregation
In this interactive visual story Alvin Chang, senior reporter at Vox, maps the finding of a research project on racial segregation in US cities that compares where people live and where they work hence painting two different pictures.
If you wish to know more about the historical context, I'd recommend you watch the video made about this project (which also displays the interactive visualisation). But this quote sums it up:
"Government policies forced people of color into poor, racially segregated neighborhoods, which were incredibly harmful to residents. And these neighborhoods ended up being the basis for how we form our social networks."
Meanwhile in Sweden :)
As Kalmar votes, Sweden votes
Hamid Sarve, an Algorithm engineer from Gothenburg, used Python to analyse the results of the last General Elections of 2018 looking at political ideologies and representing them on a two dimensions graph adding information on the city population (circle size) and its global location (northmost cities in blue and southmost cities in red). The economic and liberal/conservative beliefs axes intersect at the national ideological level. The city closest to the national level is Kalmar while this used to be the city of Karlstad.
Where the journalists lives
Our colleagues Leo Wallentin and Katarina Lind investigated where Swedish journalists are based throughout Sweden from their postcode registered at the Swedish Journalism Union four years after a first analysis for a the Swedish Institute of Media Studies. Their findings are published in a book (in Swedish) and the analysis is explained in this blog post (in Swedish). Overall most journalists live in the largest cities and the density is the highest in a particular area of Stockholm, Södermalm.
Data viz tools

Flourish launches visual storytelling with audio
Flourish is an online platform to create interactive and shareable data visualisations without writing any code.
Their users can now create "Talkies" which are data visualisations with a soundtrack that are animated when pushing a play button. In this blog post they explain their process of developing this product and how to use it to tell compelling stories.

Python data visualisation simplified with PyViz
The Python ecosystem is rich in libraries. This is very useful, but can also be confusing when there are so many libraries available to visualise data.
The PyViz project aims at simplifying the data visualisation landscape in Python. This blog post explains how to easily create interactive visualisations while still being able to integrate with other Python libraries.
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