Group_by(COD_ESTADO, estado, CODIBGE) %>% # SUM THE DIFFERENT SUB-GROUPS OF NATURAL FOREST Select(COD_ESTADO, estado, CODIBGE, nivel2, nivel3, "data/Dados_Cobertura_MapBiomas_3.1_Biomas-UF-Municipios_%20SITE_UNPROTECTED.xlsx",ĭplyr::mutate(CODIBGE = as.numeric(CD_GCMUN)) %>% To plot Brazil’s map divided by city using the function brazilmaps::get_brmap. I’m also creating the data set brazil.map with the shapefiles The variables in this worksheet are: Variable IBGE’s xlsx file has the following worksheet.
I’ll use the LAND COVER - MUN_UF worksheet. MapBiomas’ xlsx file has the following worksheet:
#Gifski r code#
Xlsx files from the official sites by clicking here for MapBiomas’ data set and here for IBGE’s data set.įor the sake of maintaining my code, I’m also uploading the original databases toĭata I’ll use in my code below.
#Gifski r download#
The code below imports the data sets to R, but if you want you can download the No cities with over 100% of their area covered by native vegetation were observed when using 2016 data. Clearly, these cities were divided but this wasn’t accounted for in MapBiomas’ data set. I was getting some incorrect results using data from 2017 or 2018, like cities with over 100% of their area covered by native vegetation. Due to changes in the size of the cities over the years, I’ve decided to use data from 2016. I’m also using data on the area of the cities in Brazil from IBGE (the Brazilian Institute of Geography and Statistics). The complete description of the project can be found at “ “MapBiomas Project - is a multi-institutional initiative to generate annual land cover and use maps using automatic classification processes applied to satellite images. “Project MapBiomas - Collection 3.1 of Brazilian Land Cover & Use Map Series, accessed on through the link:
#Gifski r free#
The MapBiomas data are public, open and free through the simple reference of the source: Section 3: The Data Credit Where Credit is Due # "Lato Black" "Lato" "Lato Hairline" "Lato Light" If it’s your first time using these fonts you’ll have to run the font_import() command (this might take a while depending on the number of installed fonts you have, the good thing is that you’ll have to run this command only once). The first thing I do is make sure that R can use this font, so I’ll use the extrafont package for that.
#Gifski r install#
So, make sure you downloadĪnd install the Lato font before we start or you can just let R use the default system font - you won’t have to change the code, you will get some warning messages, but R will still plot the map. I’m using Lato, an open-source font family. I use Windows, so I don’t know if the same steps apply to other systems (sorry Mac and Unix users).
#Gifski r how to#
I’ll begin by going over the fonts I used and how to install them. R file with the complete code from my GitHub. Section 4 gives you the code for the final map. I also go over steps of importing the data set and doing some data manipulation to obtain the information that I want. In Section 3, I go over the data set and talk about its variables and their definitions.
The second section just goes over the packages and fonts that I used and how to download these fonts. This tutorial has 3 sections, besides this introduction. I wanted to focus on Natural Forest Formation data only and see how its total area evolved since 1985. I’m also using the brazilmaps package to plot Brazil’s map. Of a city area that is covered by native vegetation. Water bodies - detailed information about these and their subdivisions can beĪnd even though their website provides tools for anyone to create their own maps, I’ve decided to create my own animated map using R and the ggplot2 and gifski packages because I wanted to see the percentage This data set has information about the total area (in hectares) of forestįormations, non-forest natural formations, farming, non-vegetated areas and
Set on land use and land cover for the period of 1985 to 2017. ( MapBiomas) launched the Mapbiomas v.3.1 data In March 2019, the Brazilian Annual Land Use and Land Cover Mapping Project
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