In a previous post, we published some data vizualisations (heat map and maps) inspired by an article of The Wall Street Journal about the impact of measles vaccine in the USA. We are now going to present an alternative visualization based on 3D plots. Such plots are not always the best for data vizualisation but they are still interesting, especially when they are annimated.
The Wall Street Journal has published in 2015 an set a of data visualization to illustrate the impact of vaccines on the USA population (DeBold & Freedman - 2015). This article presents heat maps across all 50 USA states for various conditions. For most conditions, such as measles, polio, rubella, the effect of the vaccine is very visible. In this post we are going to replicate the analysis and attempt to generate similar heat maps.
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 article we are going to plot a map of China urbanization rate per provinces together with Chinese cities with at least 2 millions population. In a nutshell, we’ll get first rural and urban population data from official China statistic bureau, then clean the data, we’ll repeat the same two steps for Chinese largest cities. Secondly, we’ll prepare a map of China with provinces. Then we will add the main Chinese cities and their population and a choropleth of urbanization rate, add main cities
In this article we are going to plot a simple map of China with different levels of subdivisions using both base and ggplot2 systems. In a nutshell, we will have first to get shape files with different subdivision levels, then a bit of data cleaning will be necessary in order to get proper provinces Chinese names. Finally we will plot China base map with subdivisions and add subdivisions names on the map.