From our blog

Check out our blog post and discover how analytics and data vizualisation can be applied in multiple contexts.

About yield curve, summer 2019, part 1

By Datapleth.io on August 17, 2019

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 two posts is to replicate these visuals and to extend to other countries such as France. Inspiration As per NY Times, in A 3-D View of a Chart That Predicts The Economic Future: The Yield Curve :

Continue reading

Visualize Satellite images with Mapbox API

By Datapleth.io on August 12, 2019

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”.

Continue reading

China mainland population density

By Datapleth.io on July 14, 2016

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.

Continue reading

About pollution,particle matters, PM2.5, Beijing vs. Shanghai and other cities - Part 2

By Datapleth.io on June 24, 2016

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.

Continue reading