Tableau, Travel and Triathlons
#data19 has arrived and with it a live #MakeoverMonday session with A LOT of people. We want to use that opportunity of getting everyone in a room with their ideas and skills to tackle a topic that is very relevant today. Literacy and how literacy rates differ across countries, ages and gender.
For week 45, Andy selected a dataset about Las Vegas. It contains a number of metrics, including visitor numbers, gaming revenue, occupancy rates, etc.
There are quite a few stories in the data.
Before winter really takes hold, let’s look at some sunshine hours in cities around the world.
Content warning: This post and the data visualisations within it focus on the topic of suicide. If you are not comfortable with the topic, please don’t feel obliged to continue.
Being a triathlete and a big Ironman Hawaii fan myself, I thought, why not challenge our Makeover Monday community to visualise the medal table for the men’s and the women’s race.
For week 41, Andy chose a dataset about donations accepted by political parties, published by the electoral commission. The visualisation is an interactive Tableau dashboard, but it’s far from best practices and screams for a makeover
For week 40 I chose a rich dataset with a simple viz showing population predictions for London.
For week 39 we are looking at eviction notices for San Francisco, leading to tenants having to vacate their homes. The original article investigates possible reasons for these evictions and finds strong indications that evictions – for whatever reason – may be linked to the tech boom and the desire by landlords to cash in on the influx of high-paid tech professionals.
This week #MakeoverMonday is collaborating once again with the team of the SDG Action Campaign ahead of the Global Goals week during the UN General Assembly.
There is a dataset we’ve had in our backlog for months and I’m excited that Andy decided to use it for this week. It’s about book checkouts at the Seattle Public Library, particularly those of work by James Patterson.
