🗺️ Mapping Portugal - Geospatial data with R 🗺️

This repository provides standardized geospatial files for Portugal's 3.092 parishes and 308 municipalities and was designed to bridge the gap between raw data and geographic visualization.

These files provide a reliable baseline for mapping administrative trends in RStudio. They enable you to visualize how variables - including census results, election returns, and socioeconomic indicators - translate into visual geographic insights."

Project context

This repository was developed during my work with the PÚBLICO Data Unit to solve a recurring problem: the lack of accessible, ready-to-use shapefiles for Portuguese administrative divisions.

Although the files are based on the 2014 CAOP (Official Administrative Map of Portugal) standards, it remains a structural reference for longitudinal studies and mapping trends.

Usage

Once you have cloned this repository, you can import the shapefiles (available for both municipalities and parishes) into R for geospatial analysis and visualization.

1. Environment setup

To handle spatial data, we rely on the sf package. For data manipulation and visualization, we use the tidyverse.

The following block ensures these dependencies are installed and present.

# Installing packages

install.packages("tidyverse")
install.packages("sf")

# Loading packages

library(tidyverse)
library(sf)

2. Importing the shapefiles

After loading the libraries, import the shapefiles using the st_read() function.

To ensure your code is reproducible, it's recommended to use relative paths based on your project’s root directory.

# Importing the parish (freguesia) shapefiles

map_parishes_ <- st_read("~/shapefiles-portugal/maps/freguesias-files/freguesias_pt.shp")  

# For municipalities, use:

map_municipalities <- st_read("~/shapefiles-portugal/maps/municipios-files/municipios_pt.shp")

3. Visualizing the maps

The quickest way to verify your data is to plot in R is by using ggplot2. The geom_sf() function is designed to handle "Simple Features" objects automatically, maintaining the correct aspect ratio of the map.

# Generate a basic plot of all 3,092 parishes

ggplot(data = map_parishes) +
  geom_sf() +
  labs(
    title = "Map of portuguese parishes",
    caption = "Source: CAOP 2014",
  )

Data dictionary & Provenance

The primary source of these shapefiles is the Carta Administrativa Oficial de Portugal (CAOP)- 2014, from Direcção-Geral do Território. While the original 2014 distribution is no longer hosted at its initial URL, the shapefiles remain a baseline for longitudinal spatial analysis in Portugal.

The Direcção-Geral do Território has a section dedicated to open data, where you can consult other databases.

Variable Definitions

Header	Description	Data Type
dicofre or dico	The unique number assigned to an individual parish or municipality.	number
freguesia	Name of the corresponding parish.	character
municipio	Name of the corresponding municipality, which, typically, it's a set of parishes.	character
distrito	Equivalent to the states, because it's a set of municipalities.	character
nuts_i	NUTS is the acronym for "Nomenclature of Territorial Units for Statistics", a hierarchical system for dividing up the territory into regions. nuts_i divides Portugal in 3 groups: Continente, Região Autónoma dos Açores, Região Autónoma da Madeira.	character
nuts_ii	Divides Portugal in 7 groups: Norte, Centro, Área Metropolitana de Lisboa, Alentejo, Algarve, Região Autónoma dos Açores, Região Autónoma da Madeira.	character
nuts_iii	Divides Portugal in 25 groups: Alto Minho, Cávado, Ave, Área Metropolitana do Porto, Alto Tâmega, Tâmega e Sousa, Douro, Terras de Trás-os-Montes, Oeste, Região de Aveiro, Região de Coimbra, Região de Leiria, Viseu Dão Lafões, Beira Baixa, Médio Tejo, Beiras e Serra da Estrela, Área Metropolitana de Lisboa, Alentejo Litoral, Baixo Alentejo, Lezíria do Tejo, Alto Alentejo, Alentejo Central, Algarve, Região Autónoma dos Açores, Região Autónoma da Madeira.	character
nuts1_id	Corresponding number to identify the groups of nuts_i .	number
nuts2_id	Corresponding number to identify the groups of nuts_ii .	number
nuts3_id	Corresponding number to identify the groups of nuts_iii .	number
geometry	These are the primitive geometric shapes like points, lines, and polygons. These shapes, together with data attributes that are linked to each shape, create the representation of the geographic data.