TRABAJO GEOMATICA

Diego Arturo Gonzalez Delgadillo

23 de marzo de 2020

1.Introducción

Primero se debe instalar las librerias requeridas

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

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing packages into 㤼㸱C:/Users/ASUS/Documents/R/win-library/3.6㤼㸲
(as 㤼㸱lib㤼㸲 is unspecified)
also installing the dependencies 㤼㸱desc㤼㸲, 㤼㸱pkgbuild㤼㸲, 㤼㸱rprojroot㤼㸲, 㤼㸱pkgload㤼㸲, 㤼㸱praise㤼㸲, 㤼㸱colorspace㤼㸲, 㤼㸱sys㤼㸲, 㤼㸱ps㤼㸲, 㤼㸱plyr㤼㸲, 㤼㸱testthat㤼㸲, 㤼㸱farver㤼㸲, 㤼㸱labeling㤼㸲, 㤼㸱munsell㤼㸲, 㤼㸱RColorBrewer㤼㸲, 㤼㸱viridisLite㤼㸲, 㤼㸱askpass㤼㸲, 㤼㸱rematch㤼㸲, 㤼㸱prettyunits㤼㸲, 㤼㸱processx㤼㸲, 㤼㸱backports㤼㸲, 㤼㸱generics㤼㸲, 㤼㸱reshape2㤼㸲, 㤼㸱assertthat㤼㸲, 㤼㸱fansi㤼㸲, 㤼㸱R6㤼㸲, 㤼㸱tidyselect㤼㸲, 㤼㸱ellipsis㤼㸲, 㤼㸱pkgconfig㤼㸲, 㤼㸱BH㤼㸲, 㤼㸱plogr㤼㸲, 㤼㸱gtable㤼㸲, 㤼㸱isoband㤼㸲, 㤼㸱scales㤼㸲, 㤼㸱withr㤼㸲, 㤼㸱vctrs㤼㸲, 㤼㸱curl㤼㸲, 㤼㸱openssl㤼㸲, 㤼㸱utf8㤼㸲, 㤼㸱clipr㤼㸲, 㤼㸱cellranger㤼㸲, 㤼㸱progress㤼㸲, 㤼㸱callr㤼㸲, 㤼㸱fs㤼㸲, 㤼㸱whisker㤼㸲, 㤼㸱selectr㤼㸲, 㤼㸱lifecycle㤼㸲, 㤼㸱e1071㤼㸲, 㤼㸱broom㤼㸲, 㤼㸱cli㤼㸲, 㤼㸱crayon㤼㸲, 㤼㸱dbplyr㤼㸲, 㤼㸱dplyr㤼㸲, 㤼㸱forcats㤼㸲, 㤼㸱ggplot2㤼㸲, 㤼㸱haven㤼㸲, 㤼㸱hms㤼㸲, 㤼㸱httr㤼㸲, 㤼㸱lubridate㤼㸲, 㤼㸱modelr㤼㸲, 㤼㸱pillar㤼㸲, 㤼㸱purrr㤼㸲, 㤼㸱readr㤼㸲, 㤼㸱readxl㤼㸲, 㤼㸱reprex㤼㸲, 㤼㸱rstudioapi㤼㸲, 㤼㸱rvest㤼㸲, 㤼㸱tibble㤼㸲, 㤼㸱tidyr㤼㸲, 㤼㸱xml2㤼㸲, 㤼㸱classInt㤼㸲, 㤼㸱DBI㤼㸲, 㤼㸱units㤼㸲

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The downloaded binary packages are in
    C:\Users\ASUS\AppData\Local\Temp\RtmpEHe6Uf\downloaded_packages

Se debe cargar las 2 librerias

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.0     ✓ purrr   0.3.3
## ✓ tibble  2.1.3     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(sf)

## Linking to GEOS 3.5.1, GDAL 2.2.2, PROJ 4.9.2

2. Lectura datos vectoriales

Se leere el archivo que representa los departamentos de Colombia,descargado de DIVA-GIS

deptos <-  read_sf("C:/Users/ASUS/Documents/ARAUCA/COL_adm1.shp")

Se conoce el contenido de “deptos” con:

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

En la libreria “sf” se conoce los sistemas de referencia de los datos vectoriales usando la función “st_crs”

st_crs(deptos)

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

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

3. uso de “ggplot” para visualizar datos geoespaciales

Utilizar las funcionalidades de ggplot

ggplot() + geom_sf(data = deptos)

Se puede usar otro sistema de referencia para el comando de los datos

# The CRS 3978 is used in Canada
ggplot() + geom_sf(data = deptos) + coord_sf(crs=st_crs(3978))

Convertir de EPSG4326 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

Filtar el codigo para el departamento elegido

arauca <-  deptos %>%   filter(NAME_1 == "Arauca")

ggplot() + geom_sf(data = arauca)

munic <-  read_sf("C:/Users/ASUS/Documents/ARAUCA/COL_adm2.shp")
mun_arauca <- munic %>% filter(NAME_1 == "Arauca")
ggplot() + geom_sf(data = mun_arauca)

mun_arauca

Simple feature collection with 7 features and 11 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -72.42239 ymin: 5.988501 xmax: -69.43657 ymax: 7.092551
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs

arauca_points<- st_centroid(mun_arauca)

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

arauca_points <- cbind(mun_arauca, st_coordinates(st_centroid(mun_arauca$geometry)))

Otra alternativa de producir el comando es:

ggplot(arauca) +
    geom_sf() +
    geom_sf(data = arauca_points, fill = "antiquewhite") + 
    geom_text(data = arauca_points, aes(x=X, y=Y,label = ID_2), size = 2) +
    coord_sf(xlim = c(-72.42239 ymin: 5.988501 xmax: -69.43657 ymax: 7.092551), ylim = c(5.3, 9), expand = FALSE)

Error: unexpected symbol in:
"    geom_text(data = arauca_points, aes(x=X, y=Y,label = ID_2), size = 2) +
    coord_sf(xlim = c(-72.42239 ymin"

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

ggplot(arauca) + 
  geom_sf(data=arauca_points, aes(x=X, y=Y, fill =
                                       ID_2), color = "black", size = 0.25) +
  geom_text(data = arauca_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="Another  Map of Arauca")

ggsave("arauca_municipios.pdf")

## Saving 7 x 5 in image

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

5.Usar “leaflet” para visualizar los datos

s Se instala la libreria requerida

install.packages("leaflet")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into 㤼㸱C:/Users/ASUS/Documents/R/win-library/3.6㤼㸲
(as 㤼㸱lib㤼㸲 is unspecified)
probando la URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/leaflet_2.0.3.zip'
Content type 'application/zip' length 2335279 bytes (2.2 MB)
downloaded 2.2 MB

package ‘leaflet’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\ASUS\AppData\Local\Temp\RtmpEHe6Uf\downloaded_packages

Cargar la libreria

library(leaflet)

Se debe convertir a puntos espaciales para usar la libreria

ant_points <- as(arauca_points, 'Spatial')

Se debe ejecutar el siguiente comando

#head(ant_points)

Para tener el area de los municipios se debe instalar:

install.packages("lwgeom")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into 㤼㸱C:/Users/ASUS/Documents/R/win-library/3.6㤼㸲
(as 㤼㸱lib㤼㸲 is unspecified)
probando la URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/lwgeom_0.2-1.zip'
Content type 'application/zip' length 5355998 bytes (5.1 MB)
downloaded 5.1 MB

package ‘lwgeom’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\ASUS\AppData\Local\Temp\RtmpEHe6Uf\downloaded_packages

## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/3.6'
## (as 'lib' is unspecified

Cargar la libreria

library(lwgeom)

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

Se debe calcular el area del minucipio en metros

mun_arauca$area <- st_area(mun_arauca) #Take care of units

Se debe crear un nuevo campo para el area en kilometros

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

Revisar

mun_arauca$km2

Units: [m^2]
[1] 5697.7959 1087.4702 5989.6469 1702.2164 4580.5997  898.6505 5381.4623

Se debe convertir a poligonos espaciales

ant_mun <- as(mun_arauca, 'Spatial')

Se debe ejecutar el siguiente codigo

#head(ant_mun)

Se prepara el comando

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


labels <- mun_arauca$NAME_2

labels

[1] "Arauca"        "Arauquita"     "Cravo Norte"   "Fortul"       
[5] "Puerto Rondón" "Saravena"      "Tame"         

Crear el comando

m <- leaflet(ant_mun) %>%
  setView(-75.5, 7, 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")

Observar

m

Otra forma de realizarlo es:

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)

Para mejorar el mapa:

#leaflet() %>%
#  addProviderTiles(providers$

plot(cars)

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