R

Climate circles

The climate of a place is usually presented through climographs that combine monthly precipitation and temperature in a single chart. However, it is also interesting to visualize the climate on a daily scale showing the thermal amplitude and the daily average temperature. To do this, the averages for each day of the year of the daily minimums, maximums and averages are calculated. The annual climate cycle presents a good opportunity to use a radial or polar which allows us to clearly visualize seasonal patterns.

Firefly cartography

*Firefly* maps are promoted and described by [John Nelson](https://twitter.com/John_M_Nelson) who published a [post](https://adventuresinmapping.com/2016/10/17/firefly-cartography/) in 2016 about its characteristics. However, these types of maps are linked to ArcGIS, which has led me to try to recreate them in R.

Bivariate dasymetric map

A disadvantage of choropleth maps is that they tend to distort the relationship between the true underlying geography and the represented variable. It is because the administrative divisions do not usually coincide with the geographical reality where people live. Besides, large areas appear to have a weight that they do not really have because of sparsely populated regions. To better reflect reality, more realistic population distributions are used, such as land use. With Geographic Information Systems techniques, it is possible to redistribute the variable of interest as a function of a variable with a smaller spatial unit.

A heatmap as calendar

Recently I was looking for a visual representation to show the daily changes of temperature, precipitation and wind in an application [xeo81.shinyapps.io/MeteoExtremosGalicia](https://xeo81.shinyapps.io/MeteoExtremosGalicia/) (in Spanish), which led me to use a heatmap in the form of a calendar. The [shiny](https://shiny.rstudio.com/) application is updated every four hours with new data showing calendars for each weather station.

Climate animation of maximum temperatures

In the field of data visualization, the animation of spatial data in its temporal dimension can show fascinating changes and patterns. As a result of one of the last publications in the social networks that I have made, I was asked to make a post about how I created it. Well, here we go to start with an example of data from mainland Spain.