Lectura, filtrado y visualización de datos vectoriales geoespaciales del departamento de Cauca en R

Introducción.

Este es un R Markdown Notebook en el que se manejan funciones basicas de las bibliotecas “simple features” (sf) y “tidyverse” (tidyverse). Tiene como proposito responder a la actividad de aprendizaje de las funcionalidades geoespaciales R en la clase de geomática básica de la Universidad Nacional de Colombia. Antes de empezar es necesario tener instalados las bibliotecas necesarias.

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

Una vez intaladas las bibliotecas es necesario cargarlas

library(tidyverse)

library(sf)

1. Lectura de datos vectoriales.

Primero leeremos un shapefile que representa a los departamentos colombianos que se que se puede descargar de DIVA-GIS en https://www.diva-gis.org/data

Cambiaremos el lugar de lectura de archivos a la carpeta donde puestos los archivos descargados, el nombre del archivo a leer es “COL_adm1.shp”

setwd("C:/Users/rojas/OneDrive/Escritorio/R estudio/Geomatica/Departamento/19_CAUCA")

The working directory was changed to C:/Users/rojas/OneDrive/Escritorio/R estudio/Geomatica/Departamento/19_CAUCA inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

deptos <- read_sf("COL_adm1.shp")

Para saber el contenido del objeto “deptos” pudemos hacer lo siguiente

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
geographic CRS: WGS 84

resulta útil observar las funciones básicas proporcionadas por la biblioteca sf. Por ejemplo, podemos usar la función “st_crs()” para saber cuál es el sistema de referencia de coordenadas de los datos vectoriales almacenados en el objeto “deptos”

st_crs(deptos)

Coordinate Reference System:
  User input: WGS 84 
  wkt:
GEOGCRS["WGS 84",
    DATUM["World Geodetic System 1984",
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["latitude",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["longitude",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    ID["EPSG",4326]]

2. Usando ggplot para visualizar datos geoespaciales:

Para comprender los datos resulta muy util trazarlos. Para lo cual odemos utilizar las funcionalidades de ggplot:

ggplot() + geom_sf(data = deptos)

Es posible utilizar cualquier sistema de referencia de coordenadas para trazar los datos, aunque se debe tener cuidado pues hay sistemás de referencias de coordenadas que estan definidos explicitamente para un país o una región pues esto puede crear deformaciones. Puede encontrar más información sobre los códigos EPSG en https://epsg.io/

# El CRS 3978 es el sistemás de referencias de coordenadas usado en Canadá
ggplot() + geom_sf(data = deptos) + coord_sf(crs=st_crs(3978))

as propiedades del CRS con el código EPSG 32618. Corresponde a UTM 18 N. Por lo cual, en caso de requerir usar dicho CRS, es necesario convertir el objeto espacial de EPSG4326 a EPSG: 32618.Para ello realizamos lo siguiente

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
projected CRS:  WGS 84 / UTM zone 18N

ggplot() + geom_sf(data = deptos_utm)

3. Filtrado de datos geoespaciales basados en atributos

Como solo nos interesa un departamento que es Cauca vamos a realizar un filtro

cauca <-  deptos %>%   filter(NAME_1 == "Cauca")

trazamos el nuevo objeto

ggplot()+geom_sf(data=cauca)

Repetiremos los pasos anteriores para cargar los municipios colombianos y filtrar los del departamento de cauca en el archivo de nombre “COL_adm2.shp” que descargamos anteriormente

setwd("C:/Users/rojas/OneDrive/Escritorio/R estudio/Geomatica/Departamento/19_CAUCA")

The working directory was changed to C:/Users/rojas/OneDrive/Escritorio/R estudio/Geomatica/Departamento/19_CAUCA inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

munic <-  read_sf("COL_adm2.shp")
mun_cauca <- munic %>% filter(NAME_1 == "Cauca")
ggplot() + geom_sf(data = mun_cauca) 

mun_cauca

Simple feature collection with 38 features and 11 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -78.02388 ymin: 0.953701 xmax: -75.8478 ymax: 3.316301
geographic CRS: WGS 84

Hora antes de hacer el plot le vamos a asignar un centroide acada poligono que representan a los municipios de Cauca para poder ponerles una etiqueta de ID a los municipios.

cauca_points<- st_centroid(mun_cauca)

st_centroid assumes attributes are constant over geometries of xst_centroid does not give correct centroids for longitude/latitude data

cauca_points <- cbind(mun_cauca, st_coordinates(st_centroid(mun_cauca$geometry)))

st_centroid does not give correct centroids for longitude/latitude data

ggplot(cauca) +
    geom_sf(data=deptos) +
    geom_sf(data = cauca_points, fill = "antiquewhite",) + 
    geom_text(data = cauca_points, aes(x=X, y=Y,label = ID_2), size = 2, ) +
    coord_sf(xlim = c(-78.25, -75.65), ylim = c(0.75, 3.5), expand = FALSE)

library(scales)

Se puede hacer un mejor plot agregando “scale_fill_distiller” que nos permite ponerle un título y usar una paleta de colores para distinguir los ID por tamaño.

ggplot(cauca) +
  geom_sf(data=cauca_points, aes(x=X, y=Y, fill =ID_2), color = "black", size = 0.25) + geom_text(data = cauca_points, aes(x=X, y=Y,label = ID_2), size = 2) +
  theme(aspect.ratio=1)+
  scale_fill_distiller(name="ID_2", palette = "YlOrBr", breaks = pretty_breaks(n =4))+labs(title="Municipios del departamento de Cauca")

Ignoring unknown aesthetics: x, y

Para más información sobre las paletas de colores en https://www.datanovia.com/en/blog/top-r-color-palettes-to-know-for-great-data-visualization/

Aunque la visualización aun no es un mapa real podemos guardarlo como pdf Desmencional el código para guardar.

#ggsave("cauca_municipios.pdf")

Otra opción para guardarlo es como un .png

#ggsave("map_cauca.png", width = 6, height = 6, dpi = "screen")

4. Usando “leaflet” para visualizar datos

Para esto necesitamos instalar primero otra libreria y cargar la biblioteca:

#install.packages("leaflet")
library(leaflet)

Para utilizar esta biblioteca, necesitamos convertir de características simples a puntos espaciales de la siguiente manera.

cau_points <- as(cauca_points, 'Spatial')

ahora observaremos que hay dentro del objeto

head(cau_points)

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Para obtener el area de los municipios instalar la siguiente libreria y cargarla

#install.packages("lwgeom")
library(lwgeom)

calculamos el área de cada municipio (en metros cuadrados) de la siguiente manera

mun_cauca$area <- st_area(mun_cauca)

Creamos un campo para almacenar el área en kilómetros cuadrados y verificamos la salida

mun_cauca$km2 <- mun_cauca$area/(1000000)
mun_cauca$km2

Units: [m^2]
 [1]  274.72660  406.61021  425.32006  937.90081  559.65249  694.38763
 [7]  441.39622  427.19456  310.16505 3110.03278   28.42409 2290.54097
[13]  776.19762  288.18776  285.46309  511.56356 2618.86896  716.18639
[19]  150.93714  397.75317 1332.08401  112.85927  757.24137  143.80980
[25]  484.58346  100.17483  620.81551  167.99831  510.78564 4284.26094
[31]  519.01836  643.54302  471.71734  221.64733  302.87909 1817.89882
[37]  453.44198  567.48848

Nuevamente tenemos que hacer una conversión de características simples a polígonos espaciales y verificamos su contenido

cau_mun <- as(mun_cauca, 'Spatial')
head(cau_mun)

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[71,] -76.6084 2.945401
[72,] -76.6153 2.928100
[73,] -76.6234 2.915401
[74,] -76.6297 2.893501
[75,] -76.6372 2.872699



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -76.703115   2.993272

Slot "ID":
[1] "5"

Slot "area":
[1] 0.04552716


[[6]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -76.751864   2.648973

Slot "area":
[1] 0.05647143

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
          [,1]     [,2]
 [1,] -76.8180 2.580601
 [2,] -76.8163 2.588701
 [3,] -76.8157 2.601999
 [4,] -76.8325 2.601500
 [5,] -76.8480 2.600399
 [6,] -76.8618 2.600500
 [7,] -76.8734 2.610899
 [8,] -76.9062 2.611100
 [9,] -76.9091 2.616301
[10,] -76.9178 2.625000
[11,] -76.9282 2.634801
[12,] -76.9334 2.651001
[13,] -76.9351 2.671201
[14,] -76.9351 2.701901
[15,] -76.9282 2.741100
[16,] -76.9288 2.758400
[17,] -76.9213 2.754900
[18,] -76.9104 2.761300
[19,] -76.8890 2.768100
[20,] -76.8706 2.773201
[21,] -76.8556 2.774899
[22,] -76.8504 2.768499
[23,] -76.8377 2.758601
[24,] -76.8175 2.751600
[25,] -76.7818 2.750300
[26,] -76.7611 2.743901
[27,] -76.7449 2.735700
[28,] -76.7265 2.735101
[29,] -76.7103 2.723999
[30,] -76.7040 2.711300
[31,] -76.6855 2.711801
[32,] -76.6757 2.702500
[33,] -76.6682 2.689201
[34,] -76.6596 2.689201
[35,] -76.6642 2.683401
[36,] -76.6572 2.670100
[37,] -76.6498 2.664901
[38,] -76.6457 2.659600
[39,] -76.6301 2.653200
[40,] -76.6198 2.652601
[41,] -76.6002 2.647299
[42,] -76.5898 2.640900
[43,] -76.5771 2.633401
[44,] -76.5621 2.626400
[45,] -76.5483 2.619401
[46,] -76.5448 2.617600
[47,] -76.5344 2.618701
[48,] -76.5316 2.619300
[49,] -76.5241 2.622701
[50,] -76.5200 2.626199
[51,] -76.5120 2.627899
[52,] -76.5045 2.627300
[53,] -76.4987 2.623200
[54,] -76.4975 2.618001
[55,] -76.5091 2.616900
[56,] -76.5194 2.611699
[57,] -76.5194 2.605400
[58,] -76.5148 2.592700
[59,] -76.5096 2.578201
[60,] -76.5108 2.567801
[61,] -76.5258 2.562700
[62,] -76.5477 2.573200
[63,] -76.5650 2.588801
[64,] -76.5725 2.579600
[65,] -76.5765 2.558801
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[68,] -76.5990 2.537600
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[70,] -76.6549 2.545301
[71,] -76.6785 2.554101
[72,] -76.7073 2.561700
[73,] -76.7408 2.563600
[74,] -76.7644 2.564799
[75,] -76.7909 2.570700
[76,] -76.8082 2.570800
[77,] -76.8174 2.570800
[78,] -76.8180 2.580601



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -76.751864   2.648973

Slot "ID":
[1] "6"

Slot "area":
[1] 0.05647143



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

Slot "bbox":
         min        max
x -77.409088 -76.497498
y   1.596499   3.115201

Slot "proj4string":
CRS arguments: +proj=longlat +datum=WGS84 +no_defs 

Ahora preparamos el plot

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


labels <- mun_cauca$NAME_2

labels

 [1] "Almaguer"               "Argelia"               
 [3] "Balboa"                 "Bolívar"               
 [5] "Buenos Aires"           "Cajibío"               
 [7] "Caldonó"                "Caloto"                
 [9] "Corinto"                "El Tambo"              
[11] "Florencia"              "Guapí"                 
[13] "Inzá"                   "Jambaló"               
[15] "La Sierra"              "La Vega"               
[17] "López de Micay"         "Mercaderes"            
[19] "Miranda"                "Morales"               
[21] "Páez"                   "Padilla"               
[23] "Patía"                  "Piendamó"              
[25] "Popayán"                "Puerto Tejada"         
[27] "Puracé"                 "Rosas"                 
[29] "San Sebastián"          "Santa Rosa"            
[31] "Santander de Quilichao" "Silvia"                
[33] "Sotará"                 "Suárez"                
[35] "Timbío"                 "Timbiquí"              
[37] "Toribío"                "Totoró"                

es hora de crear el plot

m <- leaflet(cau_mun) %>% 
  
  addProviderTiles(providers$CartoDB.PositronNoLabels) %>%
   
  setView(-77, 2, 7.4)  %>% addPolygons(
  fillColor = ~pal(km2),
  weight = 2,
  opacity = 1,
  color = "black",
  dashArray = "3",
  fillOpacity = 0.7,
  highlight = highlightOptions(
    weight = 5,
    color = "#800",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label = labels) %>%
  addLegend(pal = pal, values = ~km2, opacity = 0.7, title = NULL,
    position = "bottomright")

ahora visualizaremos el plot

m

NA

Puede probar diferentes proveedores para mejorar su mapa. ¡Aproveche la función de completar con pestañas para seleccionar el mapa base preferido simplemente desplazándose por la lista de proveedores con addProviderTiles()

cap_cauca <- munic %>% filter(NAME_2 == "Popayán")
cap_cauca$area <- st_area(cap_cauca)
cap_cauca$km2 <-cap_cauca$area/(1000000)
cap_cauca$km2

484.5835 [m^2]

cau_cap <- as(cap_cauca, 'Spatial')
 

pal <- colorBin("OrRd", domain = cau_cap$km2, bins = bins)
Pop1 <- leaflet(cau_cap) %>% 
  
  addProviderTiles( providers$Stamen.Toner) %>%
   
  setView(-76.6, 2.445, 14)  %>%
addPolygons(data = cau_cap, popup= cau_cap$NAME_2
  ,
    stroke = TRUE, fillOpacity = 0.1, smoothFactor = 0.1, fillColor = "Set1")

Pop1

pal <- colorBin("OrRd", domain = cau_cap$km2, bins = bins)
Pop2 <- leaflet(cau_cap) %>% 
  
  addProviderTiles(providers$OpenStreetMap.Mapnik) %>%
   
  setView(-76.6, 2.445, 14)  %>%
addPolygons(data = cau_cap, popup= cau_cap$NAME_2
  ,
    stroke = TRUE, fillOpacity = 0.1, smoothFactor = 0.1, fillColor = "Set1")

Pop2

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=Spanish_Mexico.1252  LC_CTYPE=Spanish_Mexico.1252   
[3] LC_MONETARY=Spanish_Mexico.1252 LC_NUMERIC=C                   
[5] LC_TIME=Spanish_Mexico.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lwgeom_0.2-5    leaflet_2.0.3   scales_1.1.1    sf_0.9-5       
 [5] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
 [9] readr_1.3.1     tidyr_1.1.2     tibble_3.0.3    ggplot2_3.3.2  
[13] tidyverse_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5              lubridate_1.7.9        
 [3] lattice_0.20-41         leaflet.providers_1.9.0
 [5] class_7.3-17            assertthat_0.2.1       
 [7] digest_0.6.25           R6_2.4.1               
 [9] cellranger_1.1.0        backports_1.1.7        
[11] reprex_0.3.0            evaluate_0.14          
[13] e1071_1.7-3             httr_1.4.2             
[15] pillar_1.4.6            rlang_0.4.7            
[17] readxl_1.3.1            rstudioapi_0.11        
[19] blob_1.2.1              rmarkdown_2.3          
[21] htmlwidgets_1.5.1       munsell_0.5.0          
[23] broom_0.7.0             compiler_4.0.2         
[25] modelr_0.1.8            xfun_0.16              
[27] base64enc_0.1-3         pkgconfig_2.0.3        
[29] htmltools_0.5.0         tidyselect_1.1.0       
[31] fansi_0.4.1             crayon_1.3.4           
[33] dbplyr_1.4.4            withr_2.2.0            
[35] grid_4.0.2              jsonlite_1.7.0         
[37] gtable_0.3.0            lifecycle_0.2.0        
[39] DBI_1.1.0               magrittr_1.5           
[41] units_0.6-7             KernSmooth_2.23-17     
[43] cli_2.0.2               stringi_1.4.6          
[45] farver_2.0.3            fs_1.5.0               
[47] sp_1.4-2                xml2_1.3.2             
[49] ellipsis_0.3.1          generics_0.0.2         
[51] vctrs_0.3.3             RColorBrewer_1.1-2     
[53] tools_4.0.2             glue_1.4.2             
[55] hms_0.5.3               crosstalk_1.1.0.1      
[57] yaml_2.2.1              colorspace_1.4-1       
[59] classInt_0.4-3          rvest_0.3.6            
[61] knitr_1.29              haven_2.3.1