The last European elections were on June 26th, 2019. One of the major observation was the increase of extreme right votes, and more particularily the shift toward extreme right of areas which used to be left voters. In this post we are going to visualize the results of this election designing a new shape for France. The objective is to create a choropleth map of the six main lists of candidates showing the percentage of votes per list for each french communes.
Following our previous post on the USA yield curve inversion, we are going to evaluate the situation in other countries, starting with France. We can observe similar pattern on the recent interest rates for which there is an inversion, the long term rates are lower than the short term rates. However, we were not (yet) able to get historical data for all horizon.
It’s summer 2019, the so called “yield curve” inversion is on the news, a great (as usual) nytimes inforgraphics is explaining what is at stake, to make it short, it’s about prediction of economic future. The objective of these posts is to propose alternatives to these visuals and to extend to other countries such as France. As per NY Times, in A 3-D View of a Chart That Predicts The Economic Future: The Yield Curve :
Accessing to satellite images based on geolocalization has many applications in data visualization and data sciences. There are several alternative of services which provide API interfaces which can be integrated in notebooks or articles, for instance : Google Maps, Bings Maps, OpenStreetMap, … You can find free, freemium or premium services. In this post we are going to illustrate a short demo of mapbox satellite API. Mapbox present itself as the “location data platform for mobile and web applications”.
In an earlier post we mapped the urbanization rate of China at province level. In this post we will go futher by visualizing where Chinese people are living using a gridded population map. We will use the NASA dataset (Population Density Grid, v3 (1990, 1995, 2000)) which consists of estimates of human population by 2.5 arc-minute grid cells. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells.
In this second part we are going to compare pollutions levels (pm2.5) between the cities of Beijing, Shanghai and Paris. We reused the data extracted in the previous post and we build simple visualisation to compare for each day, which city is the worse. Surprisingly (for french), Paris is not always the bet to live and there are some days, pollution is worse there than Shanghai or Beijing. Overall Process : Get data : from US embassy PM2.
We have a project aiming at Air Quality Predictions (see other posts on the same topic) from past air quality data and weather conditions. In a previous post we have downloaded air quality data for Shanghai, Beijing, Chengdu, Guangzhou, Shenyang and Paris. In this post we will get the associated weather data using web scrapping. There are several options for weather report website providing historical hourly data for weather station :