Lectura, filtrado y visualizacion de datos vectoriales geoespaciales en R

Ana María Montaño Hernández

23/03/2020

1. Introducción

Este cuaderno de R Markdown ilustra las funciones básicas proporcionadaas por las librerías simple features (sf) y tidyverse. Su objetivo es difundir las funcionalidades geoespaciales de R para los estudiantes de Geomática Básica en la Universidad Nacional de Colombia. A continuación, se mencionan los pasos a seguir:

Primero se deben instalar las librerías

#install.packages(c("tidyverse", "sf"))

Luego, se deben cargar ambas librerías

library(tidyverse)

library(sf)

2. Lectura de datos vectoriales

Ahora, podemos leer el shapefile que representa a los departamentos de Colombia, el cual fue descargado previamente de DIVA-GIS

library(sf)
deptos = read_sf("C:/Users/user/Documents/Norte de Santander/COL_adm/QGIS.N.Santander/COL_adm1.shp")

Se puede saber que contiene el objetos deptos

head(deptos)

Simple feature collection with 6 features and 9 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -77.149 ymin: -4.228429 xmax: -69.36835 ymax: 11.10792
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs

Es de utilidad revisar las funciones básicas que proporciona la librería sf¨. Por ejemplo:para saber cuál es el sistema de coordenadas de los datos vectoriales almacenados en el objeto deptos , se puede usar la función st_crs

st_crs(deptos)

Coordinate Reference System:
  EPSG: 4326 
  proj4string: "+proj=longlat +datum=WGS84 +no_defs"

3. Usar ggplot para visualizar datos geoespaciales

Una manera adecuada para comprender los datos inicialmente, es trazarlos. Para esto, se puede usar las funcionalidades de ggplot

ggplot()+ geom_sf(data = deptos)

Es posible utilizar cualquier sistema de referencia de coordenadas para trazar los datos. Sin embargo, no es correcto utilizar un sistema de referencia de coordenadas que se haya definido para otro país o región. Se puede encontrar más información sobre los códigos EPSG en epsg.io.

# El CRS 3978 es usado en Canadá
ggplot()+ geom_sf(data = deptos) + coord_sf(crs = st_crs(3978))

En seguida, se buscan las propiedades del CRS con el código EPSG 32618. Corresponde a UTM 18 N. En caso de que se necesite usar dicho CRS, es necesario convertir el objeto espacial de EPSG 4326 a EPSG 32618.

deptos_utm = st_transform(deptos, crs = st_crs(32618))

deptos_utm

Simple feature collection with 32 features and 9 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -245935.3 ymin: -469204.3 xmax: 1407491 ymax: 1763314
epsg (SRID):    32618
proj4string:    +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs

ggplot() + geom_sf(data = deptos_utm)

4. Filtrar datos geoespaciales basados en atributos

Se pueden filtrar los datos ya que, solo nos interesa un departamento; en este caso, Norte de Santander.

Norte.de.Santander = deptos %>% filter(NAME_1 == "Norte de Santander")

Ahora se traza el nuevo objeto:

ggplot() + geom_sf(data = Norte.de.Santander)

Se pueden repetir los pasos anteriores para cargar los municipios de Colombia, filtrando los que pertenecen al departamento de Norte de Santander.

munic = read_sf("C:/Users/user/Documents/Norte de Santander/COL_adm/QGIS.N.Santander/COL_adm2.shp")
mun_Norte.de.Santander = munic %>% filter(NAME_1 == "Norte de Santander")
ggplot() + geom_sf(data = mun_Norte.de.Santander)

mun_Norte.de.Santander

Simple feature collection with 40 features and 4 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -73.6528 ymin: 6.857401 xmax: -72.03216 ymax: 9.296101
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs

Norte.de.Santander_points = st_centroid(mun_Norte.de.Santander)

## Warning in st_centroid.sf(mun_Norte.de.Santander): st_centroid assumes attributes are 
## constant over geometries of x

## Warning in st_centroid.sfc(st_geometry(x), of_largest_polygon = 
## of_largest_polygon): st_centroid does not give correct centroids for longitude/
## latitude data

Norte.de.Santader_points = cbind(mun_Norte.de.Santander, st_coordinates(st_centroid(mun_Norte.de.Santander$geometry)))

## Warning in st_centroid.sfc(mun_Norte.de.Santander$geometry): st_centroid does not give
## correct centroids for longitude/latitude data 

Un mejor gráfico se puede producir utilizando el siguiente chunk:

ggplot(Norte.de.Santander) + geom_sf() + geom_sf(data = Norte.de.Santader_points, fill = "antiquewhite") + geom_text(data = Norte.de.Santader_points, aes(x=X, y=Y, label = ID_2), size = 2) + coord_sf(xlim = c(-74, -72),ylim = c(6.7, 9.5), expand = FALSE)

library(scales)

Attaching package: 㤼㸱scales㤼㸲

The following object is masked from 㤼㸱package:purrr㤼㸲:

    discard

The following object is masked from 㤼㸱package:readr㤼㸲:

    col_factor

ggplot(Norte.de.Santander) + geom_sf(data = Norte.de.Santader_points, aes(x=X, y=Y, fill = ID_2), color = "black", size = 0.25) + geom_text(data = Norte.de.Santader_points, aes(x=X, y=Y, label = ID_2), size = 2) + theme(aspect.ratio = 1) + scale_fill_distiller(name = "ID_2", palette = "YlGn", breaks = pretty_breaks(n = 5)) + labs (title = "Otro mapa de Norte de Santander")

Ignoring unknown aesthetics: x, y

Sin embargo, esta visualización no es un mapa real. De todos modos, la salida se puede guardar como un pdf o como un png

ggsave("Norte.de.Santander_municipios.pdf")

Saving 7 x 7 in image

ggsave("map_Norte.de.Santander.png", width = 6, height = 6, dpi = "screen")

5. Usar leaflet para visualizar datos

Primero, se debe instalar la librería requerida:

#install.packages("leaflet")

Ahora, se carga la librería:

library(leaflet)

Para usar la librería, se necesita convertir de características simples a puntos espaciales.

ant_points = as(Norte.de.Santader_points, "Spatial")

Luego, se descomenta el siguiente comando para ver qué hay en el objeto

#head(ant_points)

head(ant_points)

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Slot "plotOrder":
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Slot "proj4string":
CRS arguments:
 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 

Después, se instala la librería lwgeom para obtener el área de los municipios:

#install.packages("lwgeom")

Cargar la librería

library(lwgeom)

## Linking to liblwgeom 3.0.0beta1 r16016, GEOS 3.6.1, PROJ 4.9.3

En seguida, se va a calcular el área de cada municipio (en metros cuadrados):

mun_Norte.de.Santander$area = st_area(mun_Norte.de.Santander)  #Tener cuidado con las unidades

Ahora, se crea un nuevo campo para almacenar el área en kilómetros cuadrados:

mun_Norte.de.Santander$km2 = mun_Norte.de.Santander$area/ (1000000)

Verificar la salida

mun_Norte.de.Santander$km2

Units: [m^2]
 [1] 1529.56138  505.00325  183.40764  234.37008  715.82753  132.40233
 [7]  181.44132 1152.25765  998.06953  392.94656  182.75656 1838.12016
[13]  638.79501  507.15371  163.12553  356.89687  153.46219  534.58078
[19]  266.56178  249.51168   75.71771  106.44038  141.16264  381.48340
[25]  284.98224  170.41673  161.41362  101.84699  472.28135  441.52525
[31]  153.91790 1182.10259  150.94312  413.04877 1341.46524  871.15429
[37] 3162.85634 1682.44050  380.99595  213.21860

Nuevamente, se necesita una conversión de características simples a polígonos espaciales:

ant_mun = as(mun_Norte.de.Santander, "Spatial")

Luego, se descomenta el siguiente código para ver que hay dentro del objeto espacial:

#head(ant_mun)

head(ant_mun)

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Slot "data":

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Slot "area":
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Slot "area":
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Slot "plotOrder":
[1] 1

Slot "labpt":
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Slot "ID":
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Slot "area":
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Slot "Polygons":
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An object of class "Polygon"
Slot "labpt":
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Slot "area":
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Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
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Slot "plotOrder":
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Slot "labpt":
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Slot "ID":
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Slot "area":
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An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -72.67831   7.25046

Slot "area":
[1] 0.01084088

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
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Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -72.67831   7.25046

Slot "ID":
[1] "6"

Slot "area":
[1] 0.01084088



Slot "plotOrder":
[1] 1 5 2 4 3 6

Slot "bbox":
         min      max
x -73.423363 -72.5988
y   7.187201   8.2797

Slot "proj4string":
CRS arguments:
 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 

Ahora, se prepara el gráfico:

bins <- c(0, 50, 100, 200, 300, 500, 1000, 2000, Inf)
pal <- colorBin("YlOrRd", domain = ant_mun$km2, bins = bins)

labels <- mun_Norte.de.Santander$NAME_2
labels 

 [1] "Abrego"                 "Arboledas"             
 [3] "Bochalema"              "Bucarasica"            
 [5] "Cáchira"                "Cácota"                
 [7] "Chinácota"              "Chitagá"               
 [9] "Convención"             "Cucutilla"             
[11] "Durania"                "El Carmen"             
[13] "El Tarra"               "El Zulia"              
[15] "Gramalote"              "Hacarí"                
[17] "Herrán"                 "La Esperanza"          
[19] "La Playa de Belén"      "Labateca"              
[21] "Los Patios"             "Lourdes"               
[23] "Mutiscua"               "Ocaña"                 
[25] "Pamplona"               "Pamplonita"            
[27] "Puerto Santander"       "Ragonvalia"            
[29] "Salazar de las Palmas"  "San Calixto"           
[31] "San Cayetano"           "San José de Cúcuta"    
[33] "Santiago"               "Santo Domingo de Silos"
[35] "Sardinata"              "Teorama"               
[37] "Tibú"                   "Toledo"                
[39] "Villa Caro"             "Villa del Rosario"     

Posteriormente, se crea el gráfico:

m <- leaflet(ant_mun) %>% setView(-73,8,8) %>% addPolygons(fillColor = ~pal(km2), weight = 2, opacity = 1, color = "white", dashArray = "3", fillOpacity = 0.7, highlight = highlightOptions(weight = 5, color = "#666",  dashArray = "", fillOpacity = 0.7, bringToFront = TRUE), 
label = labels) %>%
  addLegend(pal = pal, values = ~km2, opacity = 0.7, title = NULL, position = "bottomright")

Ver el gráfico

m

A continuación, se muestra otra forma de graficar, que puede resultar más simple:

leaflet() %>%
  addProviderTiles(providers$Esri.WorldImagery, options = providerTileOptions(opacity = 0.99)) %>%
  addPolygons(data = ant_mun, popup = ant_mun$NAME_2, 
              stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25)

Finalmente, se pueden probar diferentes proveedores para mejorar el mapa. ¡Aproveche la función de completar pestañas para seleccionar el mapa base preferido con solo desplazarse por la lista de 110 proveedores! Para ello, se descomenta el siguiente chunk, se completa el código y se crea una visualización para la capital del departamento utilizado. En caso de cualquier problema, use la ayuda de R.

# leaflet()
# addProviderTiles(providers$

leaflet()  %>%
  addProviderTiles(providers$Esri.WorldImagery, options = providerTileOptions(opacity = 0.88)) %>% 
  addPolygons(data = ant_mun, popup = ant_mun$NAME_2, stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25) %>% 
  setView(lng = -72.5078201, lat = 7.8939099, zoom = 20) %>%
  addMarkers(lng = -72.5078201, lat = 7.8939099, popup = "Cúcuta")