Análisis geomático del carbono orgánico del suelo en el Valle del Cauca mediante SoilGrids y R

1 Introducción

Este cuaderno elaborado en R muestra de manera práctica cómo obtener datos sobre las propiedades del suelo —a distintas profundidades— desde la plataforma SoilGrids, desarrollada por el International Soil Reference and Information Centre (ISRIC). Estos datos se encuentran en formato raster con una resolución espacial de 250 metros, lo que permite representar de forma continua las características del suelo a escala global.

Una de las principales ventajas de realizar esta descarga mediante R es la automatización y reproducibilidad del proceso. A diferencia del método manual a través de un navegador web, el uso de R permite:

Definir con precisión el área de interés (por ejemplo, un departamento o municipio).

Descargar únicamente las capas y variables requeridas, evitando archivos innecesarios.

Integrar los datos directamente en flujos de trabajo analíticos, sin pasos intermedios.

Repetir el procedimiento fácilmente para diferentes regiones o variables, garantizando consistencia entre análisis.

En el cuaderno de referencia del profesor Iván Lizarazo (2025), se describen tres alternativas principales para acceder a los datos de SoilGrids:

Web Mapping Service (WMS): orientado a la visualización rápida de mapas en línea.

Web Coverage Service (WCS): diseñado para descargar subconjuntos específicos de mapas y usarlos en modelos o análisis espaciales.

WebDAV: la opción más flexible para obtener archivos completos en formato VRT o GeoTIFF, ideal para procesamiento local en R.

En este documento se utiliza la opción WebDAV, por ser la más adecuada para descargar y procesar grandes volúmenes de información de manera programática y eficiente dentro del entorno de RStudio.

2 Configuración

En primer lugar vamos a limpiar nuestro espacio de trabajo.

rm(list=ls())

SoilGrids puede accederse desde R a través de los siguientes servicios proporcionados por el Centro Internacional de Referencia e Información de Suelos (ISRIC) [https://www.isric.org/explore/soilgrids]:

Web Mapping Service (WMS): permite el acceso para la visualización y exploración general de los mapas.

Web Coverage Service (WCS): es la mejor opción para obtener un subconjunto de un mapa y utilizar los datos de SoilGrids como entrada en otros modelos o análisis.

Web Distributed Authoring and Versioning (WebDAV): posibilita descargar los mapas globales completos en formato VRT para su uso local.

En este cuaderno utilizaremos el servicio WebDAV, por ser el más adecuado para descargar y procesar los datos directamente desde R. En caso de cualquier duda, se recomienda consultar la documentación oficial sobre el acceso a SoilGrids WebDAV desde R.

Luego instalamos las librerías desde la consola, no desde el cuaderno.

##it is assummed that you already installed terra, ggplot2, tidyerra, sf, and leaflet
##paquetes= c('XML','rgdal', 'dplyr','devtools', 'stars', 'mapview', 'RColorBrewer')
##install.packages(paquetes)
## in addition, use either 1 or 2 options below
##option1## 
#devtools::install_github("JoshOBrien/gdalUtilities")
##option2## 
#devtools::install_github('gearslaboratory/gdalUtils')
#The utilite from 

Vamos a llamar las librerías.

library(terra)

## Warning: package 'terra' was built under R version 4.5.2

## terra 1.8.80

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.5.2

library(tidyterra)

## Warning: package 'tidyterra' was built under R version 4.5.2

## 
## Adjuntando el paquete: 'tidyterra'

## The following object is masked from 'package:stats':
## 
##     filter

library(sf)

## Warning: package 'sf' was built under R version 4.5.2

## Linking to GEOS 3.13.1, GDAL 3.11.4, PROJ 9.7.0; sf_use_s2() is TRUE

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.5.2

library(RColorBrewer)

## Warning: package 'RColorBrewer' was built under R version 4.5.2

library(gdalUtilities)

## 
## Adjuntando el paquete: 'gdalUtilities'

## The following object is masked from 'package:sf':
## 
##     gdal_rasterize

library(XML)

## Warning: package 'XML' was built under R version 4.5.2

library(dplyr)

## 
## Adjuntando el paquete: 'dplyr'

## The following objects are masked from 'package:terra':
## 
##     intersect, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

2.1 ¿Qué es SoilGrids?

SoilGrids es una plataforma global de modelamiento digital de suelos desarrollada por el ISRIC – World Soil Information, que utiliza técnicas de aprendizaje automático (machine learning) para predecir propiedades y clases del suelo a diferentes profundidades. Su objetivo principal es ofrecer una base de datos mundial, estandarizada y accesible, que permita conocer la distribución espacial de variables edáficas como el carbono orgánico, la textura, el pH o la densidad aparente, entre otras.

El sistema se fundamenta en un conjunto de covariables globales (como topografía, clima, uso del suelo, vegetación y geología) y miles de perfiles de suelo provenientes de observaciones de campo, que son integrados mediante modelos predictivos ajustados a escala planetaria. De esta forma, cada celda del mapa representa un valor estimado del suelo en una resolución espacial de 250 metros, permitiendo visualizar patrones y tendencias a gran escala.

Es importante resaltar que, aunque SoilGrids ofrece información muy útil para estudios regionales y globales, los mapas nacionales suelen ser más detallados y precisos dentro de sus áreas de cobertura. En el caso de Colombia, el Instituto Geográfico Agustín Codazzi (IGAC) es la entidad encargada de generar cartografía de suelos con mayor resolución y validación local, por lo que se recomienda comparar los resultados de SoilGrids con los mapas del IGAC al realizar análisis de carácter local o aplicado.

Por último, SoilGrids permite trabajar tanto con el valor medio (mean) como con la mediana (0.5 quantile) de cada propiedad del suelo. El valor medio representa la predicción esperada, ofreciendo una estimación imparcial, mientras que la mediana tiende a ser más robusta ante valores atípicos y puede ser útil en estudios comparativos o regionales. Set variables of interest:

Primero, definimos la URL correspondiente al sitio webDAV del ISRIC, Es decir, la ruta de acceso a los datos de SoilGrids disponibles en línea.

url = "https://files.isric.org/soilgrids/latest/data/" 

2.2 Definir variables de interés

Luego, creamos algunos objetos que indican qué información necesitamos obtener del ISRIC:

— cadenas básicas — carbono orgánico del suelo

voi = "soc" # soil organic carbon 
depth = "15-30cm"
quantile = "mean"

Concatenamos las cadenas de texto.

(variable = paste(url, voi, sep=""))

## [1] "https://files.isric.org/soilgrids/latest/data/soc"

Ahora definimos la propiedad del suelo que deseamos descargar —es decir, la capa de interés—.

(layer = paste(variable,depth,quantile, sep="_"))

## [1] "https://files.isric.org/soilgrids/latest/data/soc_15-30cm_mean"

Con esto, la especificación del archivo VRT queda completa.

(vrt_layer = paste(layer, '.vrt', sep=""))

## [1] "https://files.isric.org/soilgrids/latest/data/soc_15-30cm_mean.vrt"

3 Descargar variable de interés

Vamos a leer un geopackage de Colombia y después seleccionamos nuestro departamento de interés.

Cambia la ruta y el nombre del archivo para que coincidan con los datos que estás utilizando.

(stder = st_read("ValleDelCaucaDANE.gpkg"))

## Multiple layers are present in data source C:\Users\Yoly\OneDrive\Desktop\RstudioProyecto\ValleDelCaucaDANE.gpkg, reading layer `mgn_anm_mpios'.
## Use `st_layers' to list all layer names and their type in a data source.
## Set the `layer' argument in `st_read' to read a particular layer.

## Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet, :
## automatically selected the first layer in a data source containing more than
## one.

## Reading layer `mgn_anm_mpios' from data source 
##   `C:\Users\Yoly\OneDrive\Desktop\RstudioProyecto\ValleDelCaucaDANE.gpkg' 
##   using driver `GPKG'
## Simple feature collection with 42 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -77.54977 ymin: 3.091239 xmax: -75.70724 ymax: 5.047394
## Geodetic CRS:  MAGNA-SIRGAS

## Simple feature collection with 42 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -77.54977 ymin: 3.091239 xmax: -75.70724 ymax: 5.047394
## Geodetic CRS:  MAGNA-SIRGAS
## First 10 features:
##    DPTO_CCDGO MPIO_CCDGO          MPIO_CNMBR MPIO_CDPMP VERSION STCTNENCUE
## 1          76        001                CALI      76001    2018     749642
## 2          76        036           ANDALUCÍA      76036    2018       9439
## 3          76        041        ANSERMANUEVO      76041    2018      10533
## 4          76        054             ARGELIA      76054    2018       2900
## 5          76        111 GUADALAJARA DE BUGA      76111    2018      48459
## 6          76        113        BUGALAGRANDE      76113    2018      10610
## 7          76        122          CAICEDONIA      76122    2018      11841
## 8          76        126              CALIMA      76126    2018      11549
## 9          76        130          CANDELARIA      76130    2018      30849
## 10         76        147             CARTAGO      76147    2018      50848
##                              geom
## 1  MULTIPOLYGON (((-76.59175 3...
## 2  MULTIPOLYGON (((-76.22406 4...
## 3  MULTIPOLYGON (((-76.01558 4...
## 4  MULTIPOLYGON (((-76.14316 4...
## 5  MULTIPOLYGON (((-76.31608 3...
## 6  MULTIPOLYGON (((-76.15131 4...
## 7  MULTIPOLYGON (((-75.8539 4....
## 8  MULTIPOLYGON (((-76.51747 4...
## 9  MULTIPOLYGON (((-76.30455 3...
## 10 MULTIPOLYGON (((-75.94518 4...

Observa el CRS (sistema de referencia de coordenadas) del objeto stder.

Dado que las capas del ISRIC utilizan la proyección Homolosine, es necesario reproyectar nuestra capa empleando la librería sf

igh='+proj=igh +lat_0=0 +lon_0=0 +datum=WGS84 +units=m +no_defs'
stder_igh <- st_transform(stder, igh)

Obtengamos ahora el cuadro delimitador (bounding box) de nuestra área de interés

(bbox <- st_bbox(stder_igh))

##       xmin       ymin       xmax       ymax 
## -8636772.5   344115.1 -8436619.1   561873.3

ul significa upper left (esquina superior izquierda) lr significa lower right (esquina inferior derecha)

ulx = bbox$xmin
uly = bbox$ymax
lrx= bbox$xmax
lry = bbox$ymin
(bb <- c(ulx, uly, lrx, lry))

##       xmin       ymax       xmax       ymin 
## -8636772.5   561873.3 -8436619.1   344115.1

Ahora podemos usar la función gdal_translate para descargar la capa en formato VRT. Primero, definamos la ubicación donde se guardarán los datos.

sg_url="/vsicurl/https://files.isric.org/soilgrids/latest/data/"
datos = 'soc/soc_15-30cm_mean.vrt'
file = "soc_igh_15_30.tif"

#Lo siguiente va sin comentarlo la primera vez que se use, luego comentarlo de vuelta

library(gdalUtilities)

gdal_translate(paste0(sg_url,datos), file ,
               tr=c(250,250),
              projwin=bb,
              projwin_srs =igh)

Se divide por 10 porque las unidades estaban en la tabla.

(stder_soc <- terra::rast(file)/10)

## class       : SpatRaster 
## size        : 872, 802, 1  (nrow, ncol, nlyr)
## resolution  : 250, 250  (x, y)
## extent      : -8637000, -8436500, 344000, 562000  (xmin, xmax, ymin, ymax)
## coord. ref. : Interrupted_Goode_Homolosine 
## source(s)   : memory
## varname     : soc_igh_15_30 
## name        : soc_igh_15_30 
## min value   :          13.2 
## max value   :         313.4

4 Exploración de datos

Enseguida vamos a tener un histograma para ver la distribución de los datos.

terra::hist(stder_soc)

Un resumen es muy util:

summary(stder_soc)

## Warning: [summary] used a sample

##  soc_igh_15_30   
##  Min.   : 13.30  
##  1st Qu.: 36.20  
##  Median : 62.40  
##  Mean   : 93.94  
##  3rd Qu.:164.10  
##  Max.   :306.10  
##  NA's   :10946

Vamos a cambiar el nombre del atributo SOC:

(names(stder_soc) <-  "soc")

## [1] "soc"

5 Visualización del mapa

Es momento de plotear

Ponemos los valores de SOC en una variable:

valores <- values(stder_soc, na.rm=TRUE)

Creamos una paleta de color:

orangecyan <- colorNumeric(c("orange","yellow2", "darkseagreen", "cyan" ), valores,
  na.color = "transparent")

Por ultimo ploteamos el mapa

leaflet::leaflet(stder) %>% 
  addTiles() %>% 
  setView(-76.5, 3.8, 8,1) %>% 
   addPolygons(color = "gray", weight = 1.0, smoothFactor = 0.5,
    opacity = 0.5, fillOpacity = 0.10,
    popup = paste("Municipio: ", stder$MPIO_CNMBR)) %>%
  addRasterImage(stder_soc, colors ="Spectral", opacity = 0.8)  %>%
  addLegend(pal = orangecyan, values = valores, title = "Soil Organic Carbon (SOC) [g/kg]")

## Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs).
## Need '+proj=longlat +datum=WGS84'

6 Visualización alternativa

Paleta continua azul–verde

pal_soc_cont <- leaflet::colorNumeric(
  palette = c("#08306B","#2171B5","#41B6C4","#A1DAB4","#FFFFCC"),
  domain  = valores,
  na.color = "transparent"
)

leaflet(stder) %>%
  addProviderTiles(providers$Esri.WorldImagery) %>%
  addProviderTiles(providers$CartoDB.PositronOnlyLabels) %>%  
  setView(lng = -76.5, lat = 3.8, zoom = 8) %>%
  addRasterImage(stder_soc, colors = pal_soc_cont, opacity = 0.85) %>%
  addPolygons(color = "white", weight = 1, fill = FALSE,
              label = ~paste0("Municipio: ", MPIO_CNMBR)) %>%
  addLegend(pal = pal_soc_cont, values = valores,
            title = "SOC (g/kg)", opacity = 1)

## Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs).
## Need '+proj=longlat +datum=WGS84'

Otra visualización:

Corta el continuo en 7 clases por cuantiles

qs <- quantile(valores, probs = seq(0, 1, length.out = 8), na.rm = TRUE)
pal_soc_cls <- leaflet::colorBin(
  palette = c("#00441B","#1B7837","#5AAE61","#A6DBA0",
              "#D9F0D3","#FEE08B","#F46D43"),
  domain  = valores,
  bins    = qs,
  na.color = "transparent"
)

leaflet(stder) %>%
  addProviderTiles(providers$Esri.WorldTopoMap) %>%
  setView(lng = -76.5, lat = 3.8, zoom = 8) %>%
  addRasterImage(stder_soc, colors = pal_soc_cls, opacity = 0.85) %>%
  addPolygons(color = "gray30", weight = 1, fill = FALSE,
              label = ~paste0("Municipio: ", MPIO_CNMBR)) %>%
  addLegend(pal = pal_soc_cls, values = valores,
            title = "SOC (g/kg) — cuantiles", opacity = 1)

## Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs).
## Need '+proj=longlat +datum=WGS84'

Una visualización más

Si no lo tienes:

if (!requireNamespace("exactextractr", quietly = TRUE)) install.packages("exactextractr")
library(exactextractr)

## Warning: package 'exactextractr' was built under R version 4.5.2

stder_wgs <- tryCatch(sf::st_transform(stder, 4326), error = function(e) stder)
soc_wgs   <- tryCatch(terra::project(stder_soc, "EPSG:4326"), error = function(e) stder_soc)
ctr <- sf::st_coordinates(sf::st_centroid(sf::st_union(stder_wgs)))

Media por municipio

stder_wgs$SOC_mean <- exactextractr::exact_extract(soc_wgs, stder_wgs, 'mean')

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Paleta por cuantiles (7 clases)

pal_muni <- colorBin(
  palette = c("#00441B","#1B7837","#5AAE61","#A6DBA0","#D9F0D3","#FEE08B","#F46D43"),
  domain  = stder_wgs$SOC_mean,
  bins    = quantile(stder_wgs$SOC_mean, probs = seq(0,1,length.out = 8), na.rm = TRUE),
  na.color = "transparent"
)

leaflet(stder_wgs) %>%
  addProviderTiles(providers$Esri.WorldTopoMap) %>%
  setView(lng = ctr[1], lat = ctr[2], zoom = 8) %>%
  addPolygons(
    fillColor = ~pal_muni(SOC_mean),
    color = "gray30", weight = 1, opacity = .8, fillOpacity = .8,
    label = ~paste0("Municipio: ", MPIO_CNMBR, " , ",
                    "SOC medio: ", round(SOC_mean, 2), " g/kg")
  ) %>%
  addLegend(pal = pal_muni, values = stder_wgs$SOC_mean,
            title = "SOC promedio (g/kg)", opacity = 1)

7 Muestreo de puntos

Debemos hacer lo siguiente si estamos interesados en obtener una muestra de datos del SOC. Usaremos la libreria terra para este proposito.

Cambios que necesitamos:

set.seed(123456)

Conversión del sistema de coordenadas de referencias del SOC

geog ="+proj=longlat +datum=WGS84"
(geog.soc = project(stder_soc, geog))

## class       : SpatRaster 
## size        : 884, 840, 1  (nrow, ncol, nlyr)
## resolution  : 0.002216296, 0.002216296  (x, y)
## extent      : -77.55485, -75.69317, 3.089326, 5.048532  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs 
## source(s)   : memory
## name        :       soc 
## min value   :  13.37023 
## max value   : 309.09845

Obtención de 2000 puntos de muestra al azar:

(samples <- terra::spatSample(geog.soc, 2000, "random", as.points=TRUE))

##  class       : SpatVector 
##  geometry    : points 
##  dimensions  : 2000, 1  (geometries, attributes)
##  extent      : -77.55153, -75.69427, 3.090434, 5.047424  (xmin, xmax, ymin, ymax)
##  coord. ref. : +proj=longlat +datum=WGS84 +no_defs 
##  names       :   soc
##  type        : <num>
##  values      : 191.5
##                   NA
##                207.6

Observa dos cosas:

1. la clase del objeto que genera la salida, y
2. el sistema de referencia de coordenadas (CRS) del conjunto de datos de muestra.

Por lo tanto, necesitamos realizar varios procesos de tratamiento de datos. A continuación, se explica cada bloque de código que sigue.

(muestras <- sf::st_as_sf(samples))

## Simple feature collection with 2000 features and 1 field
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -77.55153 ymin: 3.090434 xmax: -75.69427 ymax: 5.047424
## Geodetic CRS:  GEOGCRS["unknown",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ID["EPSG",6326]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433],
##         ID["EPSG",8901]],
##     CS[ellipsoidal,2],
##         AXIS["longitude",east,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]],
##         AXIS["latitude",north,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]]]
## First 10 features:
##          soc                   geometry
## 1  191.45763 POINT (-76.77804 4.916662)
## 2         NA POINT (-77.44736 4.956556)
## 3  207.56955 POINT (-77.09054 4.770387)
## 4         NA  POINT (-77.3986 4.524378)
## 5  190.73111 POINT (-76.81794 4.544325)
## 6   70.45535 POINT (-76.32813 4.885634)
## 7   29.53420 POINT (-75.97574 4.198582)
## 8  205.42062 POINT (-76.75588 4.273936)
## 9  135.55244 POINT (-77.21465 3.580236)
## 10        NA POINT (-77.42742 3.367471)

Vamos a omitir los valores NA

nmuestras <- na.omit(muestras)

Ahora veremos las muestras limpias

longit <- st_coordinates(nmuestras)[,1]
latit <- st_coordinates(nmuestras)[,2]
soc <- nmuestras$soc

¿Qué obtenemos?

summary(soc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   14.73   36.48   61.50   92.53  161.57  242.38

length(soc)

## [1] 1748

¿Cuántas muestras no contenian unos datos validos?

id <- seq(1,1748,1)
(sitios <- data.frame(id, longit, latit, soc))

##        id    longit    latit       soc
## 1       1 -76.77804 4.916662 191.45763
## 2       2 -77.09054 4.770387 207.56955
## 3       3 -76.81794 4.544325 190.73111
## 4       4 -76.32813 4.885634  70.45535
## 5       5 -75.97574 4.198582  29.53420
## 6       6 -76.75588 4.273936 205.42062
## 7       7 -77.21465 3.580236 135.55244
## 8       8 -76.35473 4.681735  77.91371
## 9       9 -76.32148 3.159140  17.74600
## 10     10 -76.53425 4.641842 182.51727
## 11     11 -77.03070 3.633427 177.21919
## 12     12 -76.92875 3.631211 177.12788
## 13     13 -77.29666 4.734926 106.60297
## 14     14 -75.95358 4.289451  33.99530
## 15     15 -76.35916 3.130328  24.79914
## 16     16 -76.76031 3.192384  44.74848
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## 804   804 -76.97308 4.103282 164.33264
## 805   805 -77.21022 4.630760 171.14456
## 806   806 -77.18806 4.247341 152.66360
## 807   807 -76.75366 3.480503  38.11129
## 808   808 -75.74746 4.043442  65.58820
## 809   809 -76.70712 3.790784  31.39376
## 810   810 -76.60517 4.721628 195.72504
## 811   811 -75.81174 3.682185  68.64558
## 812   812 -76.47441 5.003098 183.96169
## 813   813 -76.91324 4.814713 242.38382
## 814   814 -76.57636 3.148058  27.07156
## 815   815 -75.80509 3.937060  59.22770
## 816   816 -76.55420 4.553190 189.90363
## 817   817 -77.21244 4.535459 176.07320
## 818   818 -76.90215 3.624562  45.71042
## 819   819 -76.41235 3.589101  17.36136
## 820   820 -76.04888 3.861705  72.38399
## 821   821 -76.72928 4.429077 178.28026
## 822   822 -76.39462 3.857273  30.95070
## 823   823 -76.12423 3.354174  58.51859
## 824   824 -77.15038 5.047424 185.64983
## 825   825 -76.35030 4.251774  58.18201
## 826   826 -76.58522 4.138742  63.19795
## 827   827 -75.75190 3.451691  58.85822
## 828   828 -77.25455 3.307631 145.98502
## 829   829 -76.98416 3.179086  42.35112
## 830   830 -76.72042 4.526594 191.24818
## 831   831 -76.46554 3.664455  30.64300
## 832   832 -76.13753 4.608597  39.47217
## 833   833 -76.57193 4.092200  58.87195
## 834   834 -76.74923 4.136526  79.22907
## 835   835 -76.74923 3.950357 182.01765
## 836   836 -76.85783 4.380319 177.74939
## 837   837 -75.90261 4.160905  42.36477
## 838   838 -75.84055 4.238476  43.03549
## 839   839 -76.89551 4.943258 227.49411
## 840   840 -76.50322 4.666221  75.74952
## 841   841 -76.66279 4.925527 224.12520
## 842   842 -76.74701 3.387418  50.45637
## 843   843 -76.56084 3.737593  26.80086
## 844   844 -76.12202 4.967637  82.73714
## 845   845 -76.23948 3.615697  21.09064
## 846   846 -76.50987 4.925527 161.18762
## 847   847 -75.82503 3.750891  68.71880
## 848   848 -77.23903 3.305415 141.63727
## 849   849 -77.13487 4.258422 174.05135
## 850   850 -76.25943 3.272171  20.66496
## 851   851 -76.26386 3.673320  19.58984
## 852   852 -76.26608 4.910013  67.06650
## 853   853 -76.71377 3.832894  56.52465
## 854   854 -76.41457 3.937060  35.45692
## 855   855 -76.44781 4.240692  85.46769
## 856   856 -77.16368 4.287234 195.72702
## 857   857 -76.31484 3.145842  21.00083
## 858   858 -75.92698 4.127661  47.43319
## 859   859 -76.01120 3.655590  59.18201
## 860   860 -76.36581 3.367471  19.38195
## 861   861 -76.17743 4.466754  46.40459
## 862   862 -75.85163 3.657806  44.14805
## 863   863 -76.77804 3.509314  29.46845
## 864   864 -76.24391 3.318713  25.41512
## 865   865 -77.03956 4.347074 183.82381
## 866   866 -75.93807 3.715430  70.12364
## 867   867 -76.97972 4.021279 146.25983
## 868   868 -76.68939 3.753107  33.19421
## 869   869 -76.84010 4.932176 226.20619
## 870   870 -77.15703 3.937060 188.61804
## 871   871 -77.43628 4.273936 127.76015
## 872   872 -76.14196 4.477836  51.88884
## 873   873 -76.36359 4.863471  32.61682
## 874   874 -75.87823 4.717196  27.78446
## 875   875 -76.29932 3.748674  20.96815
## 876   876 -76.93540 4.464538 182.88481
## 877   877 -75.74082 3.693267  53.69862
## 878   878 -75.78293 3.730944  58.99075
## 879   879 -77.13043 4.054523 152.39507
## 880   880 -76.78469 4.087768 128.10616
## 881   881 -77.13930 4.081119 151.69453
## 882   882 -76.39241 3.974737  34.30663
## 883   883 -76.80242 3.400716  34.26619
## 884   884 -76.16413 3.212331  49.45187
## 885   885 -76.94426 4.081119 166.36830
## 886   886 -77.14595 3.502666 143.61176
## 887   887 -76.61847 4.475619 212.27884
## 888   888 -76.85783 4.203015 182.69569
## 889   889 -77.41634 3.216763 149.69452
## 890   890 -76.13532 3.850624  59.53920
## 891   891 -77.14816 3.358606  81.45389
## 892   892 -75.84055 4.245125  45.52176
## 893   893 -76.45890 3.799649  30.99602
## 894   894 -77.10827 4.411347 164.66582
## 895   895 -75.92034 4.595299  28.00668
## 896   896 -76.04002 4.774819  38.94470
## 897   897 -76.47219 3.309848  22.61734
## 898   898 -76.63398 4.050091  75.39086
## 899   899 -76.17521 3.859489  46.45251
## 900   900 -76.64506 3.168005  27.95307
## 901   901 -75.80952 3.112597  60.48175
## 902   902 -77.23903 4.409131 175.30634
## 903   903 -75.74303 4.774819  31.90362
## 904   904 -76.64506 4.717196 185.77631
## 905   905 -76.88664 4.905581 222.16718
## 906   906 -75.95801 3.269954  63.63010
## 907   907 -76.85561 4.417996 177.69354
## 908   908 -76.60074 4.067821  52.69486
## 909   909 -75.88044 4.732710  25.09189
## 910   910 -76.77139 4.054523 176.06444
## 911   911 -76.97972 4.402482 210.66206
## 912   912 -76.48771 3.976953  46.99942
## 913   913 -76.29267 4.067821  21.19676
## 914   914 -76.91102 4.823578 229.65524
## 915   915 -75.95358 4.499999  23.08739
## 916   916 -76.36359 4.604165  78.52150
## 917   917 -75.86271 4.681735  27.96127
## 918   918 -77.15038 4.298316 182.18983
## 919   919 -77.19027 3.593534 112.14040
## 920   920 -76.31040 4.644058  43.14389
## 921   921 -76.79577 3.797433  52.34900
## 922   922 -76.69382 3.289901  76.50442
## 923   923 -77.01962 4.772603 204.69313
## 924   924 -75.98239 3.941492  61.47963
## 925   925 -76.52760 4.214096  51.58466
## 926   926 -76.05331 4.921095  43.91668
## 927   927 -76.64285 4.524378 186.76244
## 928   928 -76.14861 4.914446  56.10828
## 929   929 -75.74746 3.994683  73.73946
## 930   930 -75.87601 3.165789  56.67101
## 931   931 -75.77849 3.952574  62.76696
## 932   932 -77.11935 4.628544 190.71872
## 933   933 -77.00632 3.332011  38.15084
## 934   934 -76.52760 4.063388  94.34996
## 935   935 -76.39019 4.723845  95.89396
## 936   936 -76.22397 3.216763  23.07729
## 937   937 -76.61404 4.109931  50.53913
## 938   938 -76.58966 5.023044 229.67467
## 939   939 -76.04223 3.894950  56.37081
## 940   940 -76.63398 4.043442  70.52096
## 941   941 -76.69161 4.497782 165.12883
## 942   942 -76.45668 4.118796  62.99043
## 943   943 -76.77583 4.632976 190.58990
## 944   944 -76.19737 3.746458  38.47778
## 945   945 -75.79622 4.584218  23.87990
## 946   946 -76.48327 3.216763  29.99386
## 947   947 -76.36803 3.442826  17.48351
## 948   948 -76.10207 4.464538  24.11856
## 949   949 -76.02672 3.784135  75.65672
## 950   950 -76.53647 3.983602  50.95091
## 951   951 -76.50765 4.495566  62.42426
## 952   952 -77.12822 4.285018 179.19264
## 953   953 -76.94870 4.358156 191.41557
## 954   954 -76.06883 4.701682  40.42658
## 955   955 -76.34365 3.793000  23.40408
## 956   956 -75.99569 4.034577  56.30268
## 957   957 -76.82902 3.394067  34.77643
## 958   958 -77.08611 3.835110 138.72461
## 959   959 -76.55863 4.251774  85.09911
## 960   960 -75.82282 4.721628  20.54504
## 961   961 -76.91545 3.185735  42.54154
## 962   962 -76.13975 3.622345  45.47478
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## 965   965 -75.84498 3.247791  58.80474
## 966   966 -76.08213 3.533694  55.87187
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## 971   971 -76.20402 4.036793  21.91187
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## 975   975 -75.78071 3.101516  62.96891
## 976   976 -76.18186 3.369688  34.03838
## 977   977 -76.81572 3.380769  58.43588
## 978   978 -76.00899 4.542108  24.42191
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## 981   981 -76.30375 3.957006  20.62739
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## 983   983 -76.10207 3.092651  86.16557
## 984   984 -76.04002 3.881652  59.87176
## 985   985 -76.79577 3.101516  52.18586
## 986   986 -76.28602 4.171987  22.67177
## 987   987 -75.87379 3.473854  55.56315
## 988   988 -76.83345 3.897166 171.66379
## 989   989 -77.35650 4.021279 144.87375
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## 991   991 -75.81617 4.546541  26.49754
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## 993   993 -75.90482 4.537676  41.80297
## 994   994 -76.58744 3.917113  33.45612
## 995   995 -75.73195 3.843975  67.06799
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## 998   998 -76.86669 4.661788 220.69136
## 999   999 -77.20135 4.103282 176.68814
## 1000 1000 -75.74303 4.287234  38.99258
## 1001 1001 -76.91767 4.043442 163.65404
## 1002 1002 -76.73593 4.641842 190.46083
## 1003 1003 -75.87823 3.533694  65.39639
## 1004 1004 -77.23460 3.305415 139.95673
## 1005 1005 -76.41235 3.819596  43.02121
## 1006 1006 -77.14151 3.363039  77.13309
## 1007 1007 -77.20579 3.919329 161.66568
## 1008 1008 -76.60295 3.892734  37.01601
## 1009 1009 -76.38354 4.573136  57.57359
## 1010 1010 -75.84277 4.460105  28.34731
## 1011 1011 -75.89152 4.952123  35.05818
## 1012 1012 -75.97574 3.640076  60.98776
## 1013 1013 -75.90261 3.187951  51.50850
## 1014 1014 -76.49657 3.168005  30.99903
## 1015 1015 -76.79356 4.974286 155.69499
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## 1018 1018 -77.48504 3.223412 104.37109
## 1019 1019 -76.60295 4.426861 187.94539
## 1020 1020 -76.13753 4.349291  43.21474
## 1021 1021 -76.15083 3.640076  44.39165
## 1022 1022 -75.79844 4.557622  27.24135
## 1023 1023 -76.25721 3.919329  25.12054
## 1024 1024 -77.06838 4.874553 196.81804
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## 1026 1026 -75.88709 4.646274  39.28791
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## 1029 1029 -77.26563 3.250008 151.37920
## 1030 1030 -76.04223 4.898932  47.14351
## 1031 1031 -76.15526 3.110381  51.42384
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## 1033 1033 -76.39241 3.622345  20.78507
## 1034 1034 -76.78026 3.201249  49.28410
## 1035 1035 -76.07326 4.932176  86.71581
## 1036 1036 -76.43230 3.872787  40.71330
## 1037 1037 -76.80020 3.846191  90.88989
## 1038 1038 -76.23283 4.608597  38.08915
## 1039 1039 -76.17078 3.925978  55.79535
## 1040 1040 -75.86936 3.972520  65.06240
## 1041 1041 -75.80509 4.415779  28.15877
## 1042 1042 -76.64950 3.945925  33.55584
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## 1045 1045 -76.88442 3.378553  53.08199
## 1046 1046 -76.93318 4.415779 192.89299
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## 1049 1049 -75.95580 4.772603  40.61293
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## 1052 1052 -76.45225 3.134760  23.81989
## 1053 1053 -76.53647 4.340425  77.78336
## 1054 1054 -76.45668 3.804082  24.37038
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## 1056 1056 -75.83168 4.196366  42.65870
## 1057 1057 -76.68052 3.642292  28.83820
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## 1059 1059 -76.65615 3.498233  37.17800
## 1060 1060 -76.93983 3.810731 160.96161
## 1061 1061 -77.01740 3.560289 106.24591
## 1062 1062 -76.10872 3.804082  64.09809
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## 1064 1064 -75.93142 3.132544  61.52530
## 1065 1065 -76.44116 4.801415  57.10860
## 1066 1066 -75.86936 3.305415  51.11992
## 1067 1067 -76.36803 3.369688  18.18738
## 1068 1068 -76.30375 3.824028  21.72899
## 1069 1069 -76.97308 4.163122 139.86052
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## 1072 1072 -76.07769 4.340425  19.96403
## 1073 1073 -75.80066 3.292117  34.59095
## 1074 1074 -76.33922 4.941042 174.80782
## 1075 1075 -75.89596 3.105949  59.02617
## 1076 1076 -76.04445 4.987584  53.50146
## 1077 1077 -75.78514 4.391400  28.15177
## 1078 1078 -76.36359 4.422428  48.55284
## 1079 1079 -76.79134 3.906031 159.55983
## 1080 1080 -76.80242 4.367021 197.23711
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## 1082 1082 -76.94870 4.382535 192.14572
## 1083 1083 -75.92477 3.165789  61.88306
## 1084 1084 -76.67166 4.449024 192.15182
## 1085 1085 -77.18362 3.451691 128.52019
## 1086 1086 -76.57636 4.464538 190.61148
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## 1088 1088 -76.75588 3.400716  45.97694
## 1089 1089 -77.17698 4.763738 200.37502
## 1090 1090 -77.29001 4.836876 214.31688
## 1091 1091 -77.04400 3.181303 162.70221
## 1092 1092 -77.29001 3.225629 145.50092
## 1093 1093 -76.10429 4.790333  45.70738
## 1094 1094 -76.35030 4.404698  50.39275
## 1095 1095 -75.83168 4.912230  29.18367
## 1096 1096 -76.37246 4.158689  39.95785
## 1097 1097 -76.84675 4.163122 169.76727
## 1098 1098 -75.83833 4.081119  61.73335
## 1099 1099 -77.27671 4.597516 135.67078
## 1100 1100 -75.93142 3.764188  75.87709
## 1101 1101 -75.81839 3.163572  56.04260
## 1102 1102 -75.86271 3.172437  54.33210
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## 1104 1104 -76.71155 3.385202  61.38246
## 1105 1105 -76.11315 3.730944  87.00273
## 1106 1106 -75.75411 3.152491  55.51636
## 1107 1107 -75.86050 4.812496  23.08051
## 1108 1108 -76.99967 4.819145 222.81006
## 1109 1109 -76.82458 4.205231 195.64786
## 1110 1110 -76.58522 4.227394 201.76816
## 1111 1111 -75.89374 3.959222  72.00139
## 1112 1112 -77.22130 4.832443 190.24033
## 1113 1113 -76.06439 3.292117  71.51599
## 1114 1114 -76.58079 3.420663  34.65446
## 1115 1115 -76.10650 4.340425  25.09004
## 1116 1116 -76.64506 3.582452  26.31425
## 1117 1117 -77.28336 3.161356 138.77446
## 1118 1118 -76.02007 3.502666  77.95392
## 1119 1119 -76.89329 4.916662 215.41348
## 1120 1120 -76.89772 4.417996 201.80077
## 1121 1121 -77.22352 5.040775 183.58533
## 1122 1122 -76.14196 4.732710  45.04676
## 1123 1123 -76.60295 3.908248  31.82361
## 1124 1124 -76.93318 4.169771 165.13763
## 1125 1125 -76.74036 4.007981 213.41739
## 1126 1126 -76.35473 4.872336  69.43579
## 1127 1127 -76.61847 4.502215 204.02286
## 1128 1128 -76.53425 4.803631  78.62790
## 1129 1129 -76.77583 4.794766 209.23933
## 1130 1130 -77.11935 5.016396 176.65996
## 1131 1131 -77.30995 3.216763 145.95789
## 1132 1132 -76.26164 3.921545  22.79194
## 1133 1133 -76.34365 3.214547  18.26742
## 1134 1134 -76.92432 3.449474  46.92191
## 1135 1135 -77.21244 3.518180 135.37067
## 1136 1136 -77.13930 4.426861 177.82452
## 1137 1137 -77.08832 3.673320 118.89805
## 1138 1138 -76.13088 4.777036  57.71829
## 1139 1139 -76.87778 3.090434  41.03513
## 1140 1140 -76.86448 3.161356  44.28620
## 1141 1141 -76.76031 3.099300  43.85008
## 1142 1142 -75.87158 3.411797  57.00348
## 1143 1143 -76.45003 3.622345  19.63950
## 1144 1144 -76.13753 4.768170  52.29483
## 1145 1145 -76.83123 3.212331  46.85202
## 1146 1146 -77.07281 4.451240 175.70863
## 1147 1147 -76.21510 4.134310  22.58021
## 1148 1148 -76.61182 3.868354  49.82751
## 1149 1149 -76.00012 3.394067  67.92110
## 1150 1150 -76.74258 3.274387  69.33772
## 1151 1151 -76.92875 3.221196  45.91851
## 1152 1152 -77.39417 3.312064 119.18986
## 1153 1153 -76.03115 3.901599  66.56503
## 1154 1154 -77.20800 4.276153 186.08803
## 1155 1155 -77.25011 4.258422 166.39125
## 1156 1156 -77.01297 3.385202  65.27734
## 1157 1157 -75.84277 3.801865  71.32051
## 1158 1158 -77.12822 3.294334 156.57053
## 1159 1159 -77.03956 3.345309  48.71790
## 1160 1160 -75.91369 4.570920  30.40044
## 1161 1161 -76.23283 3.185735  19.49940
## 1162 1162 -75.95136 4.969853  38.28213
## 1163 1163 -76.51874 3.597966  36.32127
## 1164 1164 -75.75411 4.728277  26.49774
## 1165 1165 -76.79577 4.171987 193.77481
## 1166 1166 -76.98416 3.451691  27.90106
## 1167 1167 -76.99967 3.460556 167.08714
## 1168 1168 -77.46066 4.251774 124.44624
## 1169 1169 -76.20624 4.754873  50.27273
## 1170 1170 -76.00012 4.298316  21.11626
## 1171 1171 -75.97574 3.119246  72.30196
## 1172 1172 -77.07059 3.666671 154.72191
## 1173 1173 -77.05730 4.947690 200.29523
## 1174 1174 -76.02007 3.307631  61.98272
## 1175 1175 -75.75190 3.298766  32.19315
## 1176 1176 -76.61182 4.484485 188.20076
## 1177 1177 -77.09054 4.972070 155.33298
## 1178 1178 -76.25500 3.739809  21.51424
## 1179 1179 -76.93097 4.050091 158.87796
## 1180 1180 -75.74968 3.471637  41.62827
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## 1254 1254 -76.94426 3.148058  38.56966
## 1255 1255 -76.46111 3.759756  28.65639
## 1256 1256 -76.99967 4.302748 169.90028
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## 1292 1292 -76.43895 4.841308  25.52040
## 1293 1293 -77.15703 3.420663  86.63546
## 1294 1294 -76.11980 4.608597  38.98667
## 1295 1295 -76.15526 4.007981  32.58981
## 1296 1296 -76.01342 3.850624  73.57230
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## 1298 1298 -76.99967 4.843525 196.50923
## 1299 1299 -75.94693 3.513747  63.16513
## 1300 1300 -77.07503 4.542108 167.10938
## 1301 1301 -77.10384 3.449474  47.76582
## 1302 1302 -76.34365 4.096633  33.07373
## 1303 1303 -75.84498 4.799199  32.87791
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## 1308 1308 -76.27716 3.203466  23.00799
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## 1310 1310 -75.98018 3.746458  61.74648
## 1311 1311 -75.95580 3.812947  58.52708
## 1312 1312 -76.86448 3.957006 168.79216
## 1313 1313 -76.34143 3.655590  17.37762
## 1314 1314 -77.31882 3.438393 106.81321
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## 1317 1317 -76.72042 3.243359  58.81072
## 1318 1318 -77.01075 3.852840 102.90450
## 1319 1319 -76.46776 3.573587  19.76447
## 1320 1320 -76.87556 4.003548 173.16190
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## 1322 1322 -76.02893 4.635193  16.95353
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## 1324 1324 -77.27892 4.462322 155.69910
## 1325 1325 -76.40349 4.754873 103.57730
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## 1329 1329 -76.40792 3.617913  18.79422
## 1330 1330 -76.21067 3.626778  22.80961
## 1331 1331 -77.19027 3.387418 156.62981
## 1332 1332 -76.73815 3.637860  37.94324
## 1333 1333 -75.94471 3.906031  64.14788
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## 1396 1396 -77.27449 3.223412 165.09781
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## 1399 1399 -75.90925 4.655139  23.08732
## 1400 1400 -77.24790 3.130328 197.70885
## 1401 1401 -76.11315 3.230061  59.28955
## 1402 1402 -77.39417 3.347525 126.80534
## 1403 1403 -77.19249 4.599732 185.15500
## 1404 1404 -75.76519 3.218980  70.17084
## 1405 1405 -76.54755 3.584668  43.05400
## 1406 1406 -76.33700 5.003098  75.40246
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## 1408 1408 -75.99347 3.699916  71.68795
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## 1411 1411 -76.32148 3.181303  20.05318
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## 1413 1413 -75.77849 3.748674  50.98845
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## 1419 1419 -75.97131 4.644058  20.98346
## 1420 1420 -76.37689 4.921095  93.28304
## 1421 1421 -76.60739 4.690600 208.23573
## 1422 1422 -75.80509 3.925978  52.74604
## 1423 1423 -77.23238 4.746008 148.59587
## 1424 1424 -77.07724 3.440609  48.19263
## 1425 1425 -76.51874 5.027477 217.13608
## 1426 1426 -76.65171 3.846191  65.11930
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## 1429 1429 -76.19072 4.136526  27.01420
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## 1431 1431 -76.19737 4.349291  29.35242
## 1432 1432 -76.19072 3.491584  32.86789
## 1433 1433 -76.98637 4.302748 168.55278
## 1434 1434 -77.44958 3.238926 103.98506
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## 1436 1436 -76.60960 4.311614 191.92218
## 1437 1437 -75.76963 4.087768  80.41204
## 1438 1438 -77.24346 3.903815 152.42755
## 1439 1439 -75.88487 3.496017  64.35846
## 1440 1440 -77.34541 3.296550 150.53879
## 1441 1441 -75.86050 4.007981  47.88081
## 1442 1442 -77.10384 4.101065 165.74489
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## 1444 1444 -76.54090 3.152491  25.94873
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## 1446 1446 -76.60517 3.759756  24.95078
## 1447 1447 -76.61404 3.493800  34.03888
## 1448 1448 -77.15260 4.615246 164.75569
## 1449 1449 -77.15260 3.507098 168.77275
## 1450 1450 -76.96421 3.560289  82.51413
## 1451 1451 -77.04400 4.799199 205.90065
## 1452 1452 -77.36979 4.061172 141.84959
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## 1454 1454 -76.01120 3.473854  77.58095
## 1455 1455 -77.06173 3.932627 130.72813
## 1456 1456 -75.98904 4.087768  42.30409
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## 1458 1458 -75.92034 3.230061  59.61453
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## 1461 1461 -76.15305 3.097083  52.88100
## 1462 1462 -77.02405 4.781468 204.65018
## 1463 1463 -77.32325 3.243359 139.53879
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## 1469 1469 -76.97529 4.801415 210.72536
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## 1471 1471 -76.76696 4.965421 210.22772
## 1472 1472 -76.82458 4.584218 203.17715
## 1473 1473 -75.96466 4.819145  25.99167
## 1474 1474 -77.33433 4.978719 204.14644
## 1475 1475 -76.87999 3.141409  44.79200
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## 1477 1477 -77.00854 3.241143  84.91908
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## 1479 1479 -75.86050 3.976953  52.25997
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## 1483 1483 -76.62290 3.553640  30.62351
## 1484 1484 -76.83567 3.336443  61.95407
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## 1489 1489 -76.02007 4.759305  27.93525
## 1490 1490 -76.70490 4.945474 192.56589
## 1491 1491 -76.00899 4.606381  32.73178
## 1492 1492 -77.04400 4.076686 145.32152
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## 1494 1494 -76.13532 4.688384  37.08624
## 1495 1495 -76.03780 4.754873  35.93631
## 1496 1496 -77.23238 3.185735 168.94362
## 1497 1497 -76.16856 3.356390  45.61197
## 1498 1498 -76.73815 4.874553 179.77615
## 1499 1499 -76.56971 3.555857  43.01748
## 1500 1500 -75.94693 4.222962  34.76273
## 1501 1501 -75.72752 4.187501  49.26937
## 1502 1502 -75.75633 4.003548  70.49745
## 1503 1503 -76.41457 3.329794  22.71875
## 1504 1504 -77.24790 4.752656 145.12427
## 1505 1505 -76.11315 3.892734  53.11564
## 1506 1506 -77.23903 5.031910 202.33566
## 1507 1507 -76.32592 4.546541  31.84894
## 1508 1508 -77.18584 4.783685 193.45825
## 1509 1509 -76.14861 5.029693  93.40835
## 1510 1510 -76.87556 3.283252  45.45449
## 1511 1511 -76.03337 4.065605  53.78588
## 1512 1512 -75.82282 4.477836  26.95209
## 1513 1513 -75.79622 3.996899  51.64433
## 1514 1514 -77.02405 4.067821 164.50641
## 1515 1515 -76.51430 3.272171  21.40075
## 1516 1516 -76.84010 3.919329 177.66562
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## 1518 1518 -75.85163 3.859489  65.80766
## 1519 1519 -76.43008 4.045658  41.30980
## 1520 1520 -76.20402 3.194600  31.28476
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## 1522 1522 -76.85783 4.754873 102.90000
## 1523 1523 -75.83390 4.763738  27.25376
## 1524 1524 -76.02007 3.314280  67.59564
## 1525 1525 -76.96864 4.845741 234.62822
## 1526 1526 -76.15970 3.369688  53.66520
## 1527 1527 -77.24568 4.739359 156.41846
## 1528 1528 -76.11759 4.267288  22.42252
## 1529 1529 -76.13310 4.189717  23.48024
## 1530 1530 -77.02848 4.475619 161.10374
## 1531 1531 -76.07104 4.938825  82.20901
## 1532 1532 -76.58966 3.963655  79.44055
## 1533 1533 -76.23283 4.402482  52.33321
## 1534 1534 -76.36581 3.693267  19.68710
## 1535 1535 -76.81794 3.850624  62.55151
## 1536 1536 -76.15970 5.003098  69.30100
## 1537 1537 -76.87334 3.143626  39.40308
## 1538 1538 -75.96688 3.653374  59.97458
## 1539 1539 -77.31660 4.890067 196.80002
## 1540 1540 -75.77628 4.114363  63.13062
## 1541 1541 -76.36138 4.539892  46.61900
## 1542 1542 -76.47219 4.129877  49.38313
## 1543 1543 -76.40349 3.648941  16.57396
## 1544 1544 -76.24170 4.885634  53.76058
## 1545 1545 -75.84498 4.123228  58.17385
## 1546 1546 -76.64728 4.121012 180.91684
## 1547 1547 -75.75190 3.199033  78.22055
## 1548 1548 -77.13265 3.972520 153.06519
## 1549 1549 -76.90880 3.735377  46.12214
## 1550 1550 -76.53425 4.537676  72.54921
## 1551 1551 -76.51430 3.910464  36.68642
## 1552 1552 -76.04666 3.850624  66.35525
## 1553 1553 -76.41679 3.906031  28.15901
## 1554 1554 -77.19471 3.303199 155.24179
## 1555 1555 -76.13088 3.704348  70.89468
## 1556 1556 -76.78026 4.843525 162.54608
## 1557 1557 -76.56306 4.573136 213.06963
## 1558 1558 -77.02183 3.626778 129.73381
## 1559 1559 -76.75366 4.440159 207.62122
## 1560 1560 -76.25500 4.163122  21.26369
## 1561 1561 -76.46333 4.344858  35.33222
## 1562 1562 -76.24170 3.445042  20.39161
## 1563 1563 -76.52095 3.214547  19.60646
## 1564 1564 -75.80952 4.655139  25.04122
## 1565 1565 -75.85385 4.351507  27.10251
## 1566 1566 -76.00234 4.754873  29.34813
## 1567 1567 -75.85828 3.218980  71.85995
## 1568 1568 -76.27273 4.280585  33.94470
## 1569 1569 -76.60517 4.679519 194.18248
## 1570 1570 -76.92210 4.271720 169.91577
## 1571 1571 -77.00632 3.527045  42.12113
## 1572 1572 -77.10162 3.515963 182.27466
## 1573 1573 -76.14418 3.287685  56.64009
## 1574 1574 -77.34985 4.298316 124.57542
## 1575 1575 -75.88487 3.954790  61.64587
## 1576 1576 -76.79356 3.688834  36.76326
## 1577 1577 -77.26563 4.912230 173.76326
## 1578 1578 -76.77139 4.661788 202.51906
## 1579 1579 -76.54533 3.190168  25.14629
## 1580 1580 -77.38974 4.005765 125.71940
## 1581 1581 -76.01564 3.276603  63.32016
## 1582 1582 -76.58301 4.010197  48.55687
## 1583 1583 -76.19959 3.988034  22.39956
## 1584 1584 -76.17299 3.473854  48.42622
## 1585 1585 -75.72973 4.548757  24.56484
## 1586 1586 -76.82680 4.956556 228.94135
## 1587 1587 -77.14151 3.334227  68.32051
## 1588 1588 -75.95801 4.342642  30.78073
## 1589 1589 -75.90261 4.070037  62.19358
## 1590 1590 -76.22618 3.267738  19.78212
## 1591 1591 -76.67831 4.065605 208.98021
## 1592 1592 -76.40792 4.278369  69.36736
## 1593 1593 -77.05286 3.819596 123.59300
## 1594 1594 -76.18629 4.287234  24.11238
## 1595 1595 -77.42742 3.258873  96.68759
## 1596 1596 -77.45180 4.132094 165.31531
## 1597 1597 -76.02672 4.584218  30.63236
## 1598 1598 -75.84498 4.757089  41.42761
## 1599 1599 -77.18362 4.996449 187.72302
## 1600 1600 -76.09321 3.165789  58.14753
## 1601 1601 -77.52272 4.138742  96.84297
## 1602 1602 -77.04621 3.128112  70.73117
## 1603 1603 -76.12423 4.163122  25.73600
## 1604 1604 -76.99524 3.566938 146.92738
## 1605 1605 -76.26164 4.666221  52.96614
## 1606 1606 -77.13487 4.726061 205.13492
## 1607 1607 -76.07548 3.447258  61.22968
## 1608 1608 -76.47663 4.836876 225.53200
## 1609 1609 -77.45844 3.247791 113.65842
## 1610 1610 -76.78247 3.407365  36.76254
## 1611 1611 -77.06838 3.487151  52.24067
## 1612 1612 -76.40570 4.741575  83.44025
## 1613 1613 -76.18407 4.278369  25.24920
## 1614 1614 -75.90482 3.192384  50.28202
## 1615 1615 -76.25500 3.287685  17.49773
## 1616 1616 -77.17919 4.562055 159.42708
## 1617 1617 -77.15703 3.119246 213.60333
## 1618 1618 -76.23948 4.737142  36.81647
## 1619 1619 -76.18186 4.630760  48.59246
## 1620 1620 -75.75411 3.941492  55.39495
## 1621 1621 -77.22573 4.074470  72.61803
## 1622 1622 -76.08434 4.803631  40.80395
## 1623 1623 -76.72928 3.487151  52.40709
## 1624 1624 -75.85385 3.482719  44.53431
## 1625 1625 -76.98637 4.728277 204.14529
## 1626 1626 -76.68496 3.303199  66.95326
## 1627 1627 -75.84498 3.558073  70.94228
## 1628 1628 -77.00854 4.471187 136.67799
## 1629 1629 -75.93142 4.160905  52.76233
## 1630 1630 -76.50322 5.023044 222.96460
## 1631 1631 -75.91812 3.112597  60.66090
## 1632 1632 -76.44781 4.167554  75.69273
## 1633 1633 -76.39906 3.779702  23.35841
## 1634 1634 -77.17476 4.384751 159.92970
## 1635 1635 -76.37246 4.360372  41.25663
## 1636 1636 -76.87556 3.161356  35.89322
## 1637 1637 -77.25676 4.597516 121.94578
## 1638 1638 -76.72707 3.232277  51.67800
## 1639 1639 -76.23062 3.877220  36.46235
## 1640 1640 -76.05996 4.462322  18.87939
## 1641 1641 -76.05553 3.374120  64.07526
## 1642 1642 -76.95978 3.376337  62.19299
## 1643 1643 -77.06394 3.695483 118.89298
## 1644 1644 -76.62512 3.445042  48.01538
## 1645 1645 -75.98018 3.892734  59.00153
## 1646 1646 -76.90659 3.256657  41.42551
## 1647 1647 -76.90659 3.110381  28.05548
## 1648 1648 -76.62955 3.637860  29.79593
## 1649 1649 -76.78026 4.801415 194.92262
## 1650 1650 -76.39684 4.630760 105.50174
## 1651 1651 -75.93807 4.353723  27.65042
## 1652 1652 -76.80242 4.710547 172.94336
## 1653 1653 -75.80066 4.457889  26.16889
## 1654 1654 -76.20402 4.056739  20.64127
## 1655 1655 -76.97086 3.294334  42.39005
## 1656 1656 -76.74923 4.712763 191.96114
## 1657 1657 -76.46998 3.983602  37.83456
## 1658 1658 -76.52760 5.005314 180.32910
## 1659 1659 -75.78957 3.739809  64.77818
## 1660 1660 -77.05065 3.668888 150.77299
## 1661 1661 -76.86669 4.748224 205.09778
## 1662 1662 -76.82680 4.938825 224.65997
## 1663 1663 -77.35871 5.011963 186.55991
## 1664 1664 -77.46731 3.103732 143.26465
## 1665 1665 -76.21289 3.218980  26.83351
## 1666 1666 -77.02848 4.752656 232.96480
## 1667 1667 -76.95756 4.045658 170.44273
## 1668 1668 -75.81174 3.941492  60.65985
## 1669 1669 -77.13930 3.227845 198.58286
## 1670 1670 -76.44560 3.236710  24.50496
## 1671 1671 -77.31660 3.132544 164.16528
## 1672 1672 -76.15748 3.092651  50.32657
## 1673 1673 -76.87999 4.976502 223.29901
## 1674 1674 -77.13708 3.092651  68.05831
## 1675 1675 -76.90659 4.544325 202.57526
## 1676 1676 -76.26386 3.351957  19.04411
## 1677 1677 -77.05951 4.050091 116.58587
## 1678 1678 -76.20845 4.402482  49.76635
## 1679 1679 -76.59409 4.606381 197.12260
## 1680 1680 -76.70269 4.657356 180.96986
## 1681 1681 -76.08877 4.125445  28.92029
## 1682 1682 -76.06661 3.394067  59.23813
## 1683 1683 -75.98461 3.644508  64.05120
## 1684 1684 -76.76918 3.693267  54.45438
## 1685 1685 -76.52095 3.562505  33.27818
## 1686 1686 -76.64063 4.994233 196.36789
## 1687 1687 -75.90039 5.040775  39.98794
## 1688 1688 -76.11759 4.901148  67.35924
## 1689 1689 -76.49879 3.365255  19.42959
## 1690 1690 -77.00410 3.387418  30.71922
## 1691 1691 -76.32592 4.610813  61.38544
## 1692 1692 -76.12423 4.590867  48.26079
## 1693 1693 -76.36581 4.785901  75.51566
## 1694 1694 -76.98637 3.928194 156.87357
## 1695 1695 -77.07503 4.763738 197.89328
## 1696 1696 -77.45180 3.125895 145.66885
## 1697 1697 -77.20135 4.169771  92.59660
## 1698 1698 -77.45180 4.191934 180.04161
## 1699 1699 -76.80464 3.702132  44.86449
## 1700 1700 -75.96466 4.145391  43.44236
## 1701 1701 -76.52760 3.218980  21.00341
## 1702 1702 -77.35428 3.305415 145.82671
## 1703 1703 -76.94426 4.276153 175.35638
## 1704 1704 -77.16368 4.706114 172.73512
## 1705 1705 -76.39684 3.491584  22.24933
## 1706 1706 -76.91324 3.533694  48.91808
## 1707 1707 -76.73593 3.657806  36.03261
## 1708 1708 -76.11094 4.515513  30.28234
## 1709 1709 -76.59409 4.300532 190.99368
## 1710 1710 -76.43895 4.134310  54.01231
## 1711 1711 -76.04445 3.777486  83.01643
## 1712 1712 -76.87113 3.850624 104.59799
## 1713 1713 -77.19914 3.571371 139.58018
## 1714 1714 -77.25898 4.531027 135.79312
## 1715 1715 -76.92653 3.595750  67.19160
## 1716 1716 -75.83833 3.267738  52.41676
## 1717 1717 -76.24613 4.209664  28.01715
## 1718 1718 -77.51607 3.094867 134.11867
## 1719 1719 -76.32148 4.473403  36.31524
## 1720 1720 -76.00899 4.586434  27.22951
## 1721 1721 -76.93983 4.943258 208.46503
## 1722 1722 -76.50322 4.655139 118.49464
## 1723 1723 -76.32148 3.693267  23.81113
## 1724 1724 -76.79577 5.042991 203.87561
## 1725 1725 -77.32768 4.081119 151.12668
## 1726 1726 -77.09940 4.701682 189.95255
## 1727 1727 -76.75145 3.110381  53.92775
## 1728 1728 -75.82503 3.183519  57.68266
## 1729 1729 -77.39417 4.258422 137.59312
## 1730 1730 -76.29932 4.442375  57.99891
## 1731 1731 -76.54090 4.601948 222.36685
## 1732 1732 -77.26119 3.533694 140.17128
## 1733 1733 -76.92875 3.972520 131.85774
## 1734 1734 -76.90437 4.533243 203.07596
## 1735 1735 -76.50100 4.788117  70.06249
## 1736 1736 -76.90437 5.007530 204.15898
## 1737 1737 -76.72042 3.622345  31.54759
## 1738 1738 -76.42787 3.473854  16.07617
## 1739 1739 -76.54090 3.917113  49.69585
## 1740 1740 -77.08389 4.511080 172.03865
## 1741 1741 -75.73417 5.025261  45.46551
## 1742 1742 -76.35695 4.513296  30.21714
## 1743 1743 -76.83567 4.796982 227.89445
## 1744 1744 -76.91324 3.883868 125.53410
## 1745 1745 -76.46998 4.960988 212.85262
## 1746 1746 -77.22130 4.901148 226.45757
## 1747 1747 -76.64063 4.094417  63.89802
## 1748 1748 -76.39684 4.406914  47.48383

Ahora generemos las muestras

m <- leaflet() %>%
  addTiles() %>%  
  addMarkers(lng=sitios$longit,lat=sitios$latit, popup=sitios$soc, clusterOptions = markerClusterOptions())
m  

8 Guardar las muestras

La función sf::st_write() permite exportar un objeto espacial de R (como un conjunto de puntos, polígonos o líneas) a un archivo geoespacial estándar. En este caso, se utiliza para guardar las muestras de suelo generadas en formato GeoPackage (.gpkg), que es un tipo de archivo moderno, compacto y compatible con la mayoría de los programas SIG (como QGIS o ArcGIS).

Cambiamos la ruta y nombre para que se enlace a tus directorios, tambien el nombre de tu departamento

sf::st_write(nmuestras, "ValleDelCaucaDANE.gpkg", driver = "GPKG",
             append=FALSE)

## Deleting layer `ValleDelCaucaDANE' using driver `GPKG'
## Writing layer `ValleDelCaucaDANE' to data source 
##   `ValleDelCaucaDANE.gpkg' using driver `GPKG'
## Writing 1748 features with 1 fields and geometry type Point.

9 Referencia

Lizarazo, I. (2025). Cómo descargar datos de suelos desde SoilGrids. Disponible en: https://rpubs.com/ials2un/downloadSoilGrids

sessionInfo()

## R version 4.5.1 (2025-06-13 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=Spanish_Colombia.utf8  LC_CTYPE=Spanish_Colombia.utf8   
## [3] LC_MONETARY=Spanish_Colombia.utf8 LC_NUMERIC=C                     
## [5] LC_TIME=Spanish_Colombia.utf8    
## 
## time zone: America/Bogota
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] exactextractr_0.10.0 dplyr_1.1.4          XML_3.99-0.19       
##  [4] gdalUtilities_1.2.5  RColorBrewer_1.1-3   leaflet_2.2.3       
##  [7] sf_1.0-21            tidyterra_0.7.2      ggplot2_4.0.0       
## [10] terra_1.8-80        
## 
## loaded via a namespace (and not attached):
##  [1] s2_1.1.9                sass_0.4.10             generics_0.1.4         
##  [4] tidyr_1.3.1             class_7.3-23            KernSmooth_2.23-26     
##  [7] lattice_0.22-7          digest_0.6.37           magrittr_2.0.4         
## [10] evaluate_1.0.5          grid_4.5.1              fastmap_1.2.0          
## [13] jsonlite_2.0.0          e1071_1.7-16            DBI_1.2.3              
## [16] purrr_1.1.0             crosstalk_1.2.2         scales_1.4.0           
## [19] codetools_0.2-20        jquerylib_0.1.4         cli_3.6.5              
## [22] rlang_1.1.6             units_1.0-0             withr_3.0.2            
## [25] cachem_1.1.0            yaml_2.3.10             raster_3.6-32          
## [28] tools_4.5.1             vctrs_0.6.5             R6_2.6.1               
## [31] png_0.1-8               proxy_0.4-27            lifecycle_1.0.4        
## [34] classInt_0.4-11         leaflet.providers_2.0.0 htmlwidgets_1.6.4      
## [37] pkgconfig_2.0.3         pillar_1.11.1           bslib_0.9.0            
## [40] gtable_0.3.6            glue_1.8.0              Rcpp_1.1.0             
## [43] xfun_0.53               tibble_3.3.0            tidyselect_1.2.1       
## [46] rstudioapi_0.17.1       knitr_1.50              farver_2.1.2           
## [49] htmltools_0.5.8.1       rmarkdown_2.30          wk_0.9.4               
## [52] compiler_4.5.1          S7_0.2.0                sp_2.2-0