Introdução à Ciência de Dados no R

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Aula 10 - Visualização de Mapas

Aula 10

Antonio Vinícius Barbosa

02-05-2023

Visualização de Dados

Mapas em R

De todas as visualizações disponíveis, talvez o mapas sejam os mais fáceis de visualizar. O R possui vários recursos para a construção e manipulação de mapas.

Objetivo: Introduzir conceitos básicos de estatísticas espaciais usando mapas em R

Pacotes espaciais

Mapas básicos

Nesta aula, utilizaremos alguns pacotes úteis para plotar maps em R. Alguns deles já trazem as formas e elementos essenciais para uma visualização espacial básica. Para mapas básicos, podemos utilizar o pacote maps

# Carregar pacote
library(maps)
map("world","Brazil")

Mapas básicos

Para adicionar outros elementos básicos ao mapa através do pacote maps, fazemos:

map("world", "Brazil", fill = T, col = "grey95") # destacar a região
map(add = T) # adicionar regiões vizinhas
map.axes() # inserir eixos com coordenadas
map.scale(x = -48, ratio = F, cex = 0.5) # inserir escala
abline(h = 0, lty = 2) # linha horizontal
map.cities(country = "Brazil", col = "grey40", 
           pch = 19, cex = 0.8) # inserir municípios > 40 mil habitantes
points(-34.870154, -7.1299447, col = 2, pch = 18) # adicionar pontos

Mapas básicos

Tais mapas, no entanto, são bastante simples e não permitem uma análise espacial mais elaborada. Precisamos de ferramentas adicionais para gerar mapas mais interessantes.

Shapefiles

O que são shapefiles?

Um shapefile é um formato de armazenamento de dados geoespaciais, tais como pontos, linhas e polígonos.

A principal fonte de shapefiles para o Brasil é o IBGE. Para este exemplo, iremos explorar o shapefile dos municípios da Paraíba link.

O que são shapefiles?

Shapefiles são compostos pelos seguintes arquivos:

• .shp (shape format): geometria base do território
• .shx (shape index format): índice de posicionamento da geometria base
• .dbf (attribute format): atributos para cada elemento (shape)
• .prj (projection format): sistema de coordenadas e de informações de projeção
• .cpg (code page): usado em conjunto com o arquivo .dbf para identificar a codificação de caracteres a ser usada

Objetos espaciais

Para importar o shapefile para o R, lemos o arquivo .shp através da função st_read() do pacote sf.

# Ler shapefile
library(sf)
shape_PB <- st_read("Shapefiles/25MUE250GC_SIR.shp")
## Reading layer `25MUE250GC_SIR' from data source 
##   `/home/aviniciusbb/Dropbox/UFPB/Disciplinas/Graduacao/2022.2/icdr/Slides/10-Visualizacao-Mapas-R/Shapefiles/25MUE250GC_SIR.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 223 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -38.7656 ymin: -8.302955 xmax: -34.79309 ymax: -6.025912
## Geodetic CRS:  SIRGAS 2000
class(shape_PB)
## [1] "sf"         "data.frame"

Objetos espaciais

Como resultado, obtemos os dados referentes ao estado e municípios da Paraíba em um formato do tipo sf (simple features). Trata-se de um data frame com uma variável (geometry) contendo informações de latitude e longitude dos limites territoriais (polígonos).

# Visualizar shapefile
head(shape_PB)
## Simple feature collection with 6 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -38.45059 ymin: -7.739078 xmax: -35.66183 ymax: -6.509971
## Geodetic CRS:  SIRGAS 2000
##             NM_MUNICIP CD_GEOCMU                       geometry
## 1          ALAGOA NOVA   2500403 MULTIPOLYGON (((-35.76231 -...
## 2    AREIA DE BARAÚNAS   2501153 MULTIPOLYGON (((-36.99376 -...
## 3 SANTANA DE MANGUEIRA   2513505 MULTIPOLYGON (((-38.37213 -...
## 4             PAULISTA   2510907 MULTIPOLYGON (((-37.77414 -...
## 5            ESPERANÇA   2506004 MULTIPOLYGON (((-35.92728 -...
## 6               AGUIAR   2500205 MULTIPOLYGON (((-38.28421 -...

Objetos espaciais

Para plotar os município da Paraíba, fazemos:

# Carregar pacote
library(tidyverse)

# Plotar mapa
ggplot(shape_PB) +
  geom_sf(fill = "gray20", color = "gray80", 
          size = .1, show.legend = FALSE) +
  theme_minimal()

Objetos espaciais

Objetos espaciais

Para plotar apenas o município de João Pessoa, filtramos os dados antes

# Plotar mapa
shape_PB |> 
  filter(
    NM_MUNICIP == "JOÃO PESSOA"
  ) |> 
  ggplot() +
  geom_sf(fill = "gray20", color = "gray80", 
          size = .1, show.legend = FALSE) +
  theme_minimal()

Objetos espaciais

Objetos espaciais

Podemos adicionar um ponto (ou conjunto de pontos) através da imputação direta com geom_point()

# Adicionar pontos
locais <- data.frame(
  id = c("Lagoa", "UFPB"),
  lat = c(-7.1260272, -7.1368997),
  long = c(-34.8883438, -34.847522)
  )

shape_PB |> 
  filter(
    NM_MUNICIP == "JOÃO PESSOA"
  ) |> 
ggplot() + 
  geom_sf(fill = "white", col = "black") +
  geom_point(data = locais, aes(x = long, y = lat),
             col = "blue") +
  theme_minimal()

Objetos espaciais

O pacote geobr

O pacote geobr é um pacote que contem informações oficiais do Brasil, inclusive shapefiles, desenvolvido pelo IPEA (https://github.com/ipeaGIT/geobr).

O pacote possui dados em diferentes escalas geográficas e diferentes anos:

# Instalar pacote 
install.packages("geobr")
# Disponível através do Github
devtools::install_github("ipeaGIT/geobr", subdir = "r-package")
library(geobr)

geobr: dados disponíveis

geobr: dados disponíveis

geobr: dados disponíveis

O pacote geobr

A sintaxe da funções do pacote geobr é fácil e direta. Como exemplo, suponha que queiramos baixar informações do estado da Paraíba. Fazemos:

# Estado do Paraíba
estado_pb <- read_state(code_state = "PB", year = 2020)

# Municípios da Paraíba
munic_pb <- read_municipality(code_muni = "PB", year = 2020)

# Município de João Pessoa
munic_jp <- read_municipality(code_muni = 2507507, year = 2020)

Como resultado, obtemos os dados referentes ao estado e municípios da Paraíba em um formato do tipo sf (simple features).

O pacote geobr

Uma vez baixados os dados, podemos visualizar com ggplot2 atavés da geometria geom_sf()

# Carregar pacote
library(geobr)

# Municipios da Paraiba
munic_pb <- read_municipality(code_muni = "PB", year = 2020)
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O pacote geobr

Uma vez baixados os dados, podemos visualizar com ggplot2 atavés da geometria geom_sf()

# Carregar pacote
library(geobr)

# Municipios da Paraiba
munic_pb <- read_municipality(code_muni = "PB", year = 2020)

# Plotar
munic_pb %>% 
  ggplot() +
  geom_sf(fill = "#2D3E50", color = "#FEBF57", 
          size = .1, show.legend = FALSE) +
  theme_void()

O pacote geobr

O pacote geobr

Ok! Vamos adicionar alguns elementos no mapa. Se quisermos colorir o gráfico a partir da expectativa de vida dos município, fazemos:

# Municípios da Paraíba
library(abjData)

# Dados de expectativa de vida
data_munic <- abjData::pnud_min %>% 
  filter(ano == 2010,
         uf_sigla == "PB") %>% 
  select(code_muni = muni_id, espvida)

# Merge e plot
munic_pb %>% 
  mutate(code_muni = as.character(code_muni)) %>% 
  left_join(data_munic) %>% 
  ggplot() +
  geom_sf(aes(fill = espvida), color = "white", 
          size = .1) +
  theme_minimal()

O pacote geobr

O pacote geobr

Podemos mudar o padrão de cores dos mapas:

# Municípios da Paraíba
munic_pb %>% 
  mutate(code_muni = as.character(code_muni)) %>% 
  left_join(data_munic) %>% 
  ggplot() +
  geom_sf(aes(fill = espvida), color = "white", 
          size = .1, show.legend = TRUE) +
  theme_void() +  
  scale_fill_gradient2(low = "#d73027", 
                       mid = "yellow", 
                       high = "#1a9850",
                       midpoint = median(data_munic$espvida))

O pacote geobr

O pacote geobr

Vamos observar agora a localização dos estabelecimentos de saúde no estado. Utilizando a função do geobr, encontramos:

estabel_saude <- geobr::read_health_facilities() |> 
  filter(
    abbrev_state == "PB"
    ) |> 
  mutate(
    code_muni = as.character(code_muni)
    )
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O pacote geobr

head(estabel_saude)
## Simple feature collection with 6 features and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -38.08404 ymin: -7.074198 xmax: -35.3754 ymax: -6.782212
## Geodetic CRS:  SIRGAS 2000
##   code_cnes code_muni code_state abbrev_state date_update year_update
## 1   2357313    250077         25           PB  2015-05-27        2015
## 2   2357690    250077         25           PB  2015-05-27        2015
## 3   2606119    250077         25           PB  2015-05-27        2015
## 4   4015029    250077         25           PB  2015-05-27        2015
## 5   2608006    250080         25           PB  2015-05-27        2015
## 6   2321262    250115         25           PB  2015-05-27        2015
##   data_source                        geom
## 1        PMAQ POINT (-38.08334 -6.783695)
## 2        PMAQ POINT (-38.08334 -6.783695)
## 3        PMAQ POINT (-38.08404 -6.784766)
## 4        PMAQ POINT (-38.08334 -6.783695)
## 5        PMAQ  POINT (-35.3754 -6.782212)
## 6        PMAQ POINT (-36.97367 -7.074198)

Estabelecimentos de saúde na PB

# Plotar
ggplot(data = munic_pb) +
  geom_sf() +
  geom_sf(data = estabel_saude, col = "red") +
  theme_void() 

Estabelecimentos de saúde na PB

Pacotes adicionais

Pacotes cartográficos adicionais

• leaflet - Mapas interativos
• osmdata - Open Street Maps
• ggmap - Google Maps

Exemplo do pacote leaflet

Suponha um mapa com uma localização específica:

library(leaflet)  
  
leaflet() %>%
  addTiles() %>%  
  addMarkers(lng = -34.8472693, 
             lat = -7.1356015, 
             popup = "UFPB")

Exemplo do pacote leaflet

Exemplo do pacote osmdata

Com o pacote osmdata, podemos obter dados específicos dos mapas. Mais parâmetros em osmdata::available_features().

# Carregar pacote
library(osmdata)

# Obter dados cartograficos
jp_estradas <- getbb("João Pessoa") |> 
  opq() |> 
  add_osm_feature(key = "highway") |> 
  osmdata_sf()

# Plot
ggplot() +
  geom_sf(data = jp_estradas$osm_lines,
          color = "gray60", size = .1) +
  theme_void()

Exemplo do pacote osmdata

Exemplo do pacote osmdata

# Carregar pacote
library(osmdata)

# Obter dados cartograficos
jp_estradas <- getbb("João Pessoa") |> 
  opq() |> 
  add_osm_feature(key = "highway") |> 
  osmdata_sf()

jp_rios <- getbb("João Pessoa") |> 
  opq() |> 
  add_osm_feature(key = "water") |> 
  osmdata_sf()

# Plot
ggplot() +
  geom_sf(data = jp_estradas$osm_lines,
          color = "gray60", size = .1) +
  geom_sf(data = jp_rios$osm_lines,
          color = "cyan", size = .1) +
  theme_void()

Exemplo do pacote osmdata

(Twitter) - R City Views

Quizz #1

• Este quizz consiste em recriar o mapa do IDHM Educação para os municípios do NE.
• Utilizar os dados do abjData::pnud_min para obter os dados
• Criar as faixas de IDHM conforme o exemplo

20:00