Datos de suelos para el departamento de Córdoba

1 1. Introducción

Este cuaderno de R ilustra cómo descargar datos de propiedades de suelos a cualquier profundidad desde la plataforma SoilGrids.

Dichos datos están en estructura Raster con resolución espacial de 250m.

1.1 Ventajas de descargar datos usando R

El uso de R para descargar datos de SoilGrids ofrece varias ventajas sobre el uso de un navegador web:

1. Automatización: Se puede replicar el proceso para múltiples regiones o variables sin intervención manual.
2. Reproducibilidad: El código documenta exactamente qué datos se descargaron y cómo se procesaron.
3. Precisión espacial: Se puede especificar exactamente el área de interés usando límites administrativos o coordenadas precisas.
4. Integración: Los datos se pueden procesar, analizar y visualizar directamente en el mismo entorno de trabajo.
5. Eficiencia: No es necesario descargar tiles completos de 2x2 grados; solo se descarga el área específica necesaria.

1.2 Opciones para descargar datos

SoilGrids ofrece tres servicios principales para acceder a los datos:

1. Web Mapping Service (WMS): Útil para visualización y exploración general de los datos.
2. Web Coverage Service (WCS): La mejor opción para obtener subconjuntos de mapas y usar SoilGrids como entrada para otros modelos.
3. Web Distributed Authoring and Versioning (WebDAV): Permite descargar mapas globales completos en formato VRT.

En este cuaderno utilizaremos el servicio WebDAV, que nos permite descargar datos específicos para nuestra región de interés de manera eficiente.

2 2. Configuración

Primero vamos a limpiar nuestro espacio de trabajo.

rm(list=ls())

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

##it is assummed that you already installed terra, ggplot2, tidyterra, 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')

Vamos a llamar las librerías.

library(terra)
library(ggplot2)
library(tidyterra)
library(sf)
library(leaflet)
library(RColorBrewer)
library(gdalUtilities)
library(XML)
library(dplyr)

2.1 2.1. ¿Qué es SoilGrids?

SoilGrids es un sistema de cartografía digital de suelos a escala global que utiliza información de perfiles de suelo de todo el mundo y datos de covariables para modelar la distribución espacial de las propiedades del suelo.

SoilGrids es una colección de mapas de propiedades del suelo producidos mediante aprendizaje automático (machine learning) con una resolución de 250 metros.

2.1.1 Características principales:

• Cobertura global: Proporciona datos para cualquier ubicación del planeta
• Alta resolución: 250 metros de resolución espacial
• Múltiples propiedades: Incluye carbono orgánico, pH, textura, densidad aparente, entre otros
• Múltiples profundidades: Datos disponibles para diferentes capas del suelo (0-5cm, 5-15cm, 15-30cm, etc.)
• Cuantiles de predicción: Incluye predicciones de media, mediana y intervalos de confianza

Nota importante: Para Colombia, es recomendable comparar los datos de SoilGrids con los mapas nacionales producidos por el Instituto Geográfico Agustín Codazzi (IGAC), ya que los mapas nacionales suelen tener mayor precisión al estar basados en información de suelos más detallada a nivel local.

2.2 2.2. Definir variables de interés

url = "https://files.isric.org/soilgrids/latest/data/" # Ruta al servidor webDAV

# Variables disponibles: soc, phh2o, clay, silt, sand, cec, bdod, cfvo, nitrogen, ocd
voi = "soc" # carbono orgánico del suelo
depth = "15-30cm" # profundidades disponibles: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm
quantile = "mean" # opciones: mean, Q0.05, Q0.5, Q0.95

# Concatenamos las cadenas de texto
(variable = paste(url, voi, sep=""))

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

(layer = paste(variable, depth, quantile, sep="_")) # capa de interés

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

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

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

Explicación del resultado: El código construye la URL completa del archivo VRT (Virtual Raster) que apunta a los datos de carbono orgánico del suelo (SOC) en la profundidad de 15-30 cm, usando el valor medio de las predicciones. Este formato VRT permite acceder eficientemente a subconjuntos de datos globales sin necesidad de descargar el archivo completo.

3 3. Descargar variable de interés para un departamento específico

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

(col = st_read("DeptoColombia.gpkg"))

## Reading layer `DeptoColombia' from data source 
##   `C:\Users\usuagro\Documents\GB\Proyect5\Proyect5\DeptoColombia.gpkg' 
##   using driver `GPKG'
## Simple feature collection with 32 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -81.84153 ymin: -4.228429 xmax: -66.87033 ymax: 15.91247
## Geodetic CRS:  WGS 84

## Simple feature collection with 32 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -81.84153 ymin: -4.228429 xmax: -66.87033 ymax: 15.91247
## Geodetic CRS:  WGS 84
## First 10 features:
##    ID_0 ISO   NAME_0 ID_1                   NAME_1 ID_2    NAME_2    TYPE_2
## 1    53 COL Colombia    5                  Bolívar  163      Achí Municipio
## 2    53 COL Colombia   18               La Guajira  653 Barrancas Municipio
## 3    53 COL Colombia   29                   Tolima  966 Alpujarra Municipio
## 4    53 COL Colombia   27                Santander  855    Aguada Municipio
## 5    53 COL Colombia    6                   Boyacá  199   Almeida Municipio
## 6    53 COL Colombia   26 San Andrés y Providencia  852  n.a. (1)      <NA>
## 7    53 COL Colombia   17                    Huila  616   Acevedo Municipio
## 8    53 COL Colombia    2                Antioquia    9 Abejorral Municipio
## 9    53 COL Colombia   19                Magdalena  663 Aracataca Municipio
## 10   53 COL Colombia   25                Risaralda  838      Apía Municipio
##       ENGTYPE_2 NL_NAME_2 VARNAME_2                           geom
## 1  Municipality      <NA>      <NA> MULTIPOLYGON (((-74.9564 10...
## 2  Municipality      <NA>      <NA> MULTIPOLYGON (((-72.9464 10...
## 3  Municipality      <NA>      <NA> MULTIPOLYGON (((-75.968 2.9...
## 4  Municipality      <NA>      <NA> MULTIPOLYGON (((-73.9762 5....
## 5  Municipality      <NA>      <NA> MULTIPOLYGON (((-73.556 4.8...
## 6          <NA>      <NA>      <NA> MULTIPOLYGON (((-81.83986 1...
## 7  Municipality      <NA>      <NA> MULTIPOLYGON (((-76.1585 1....
## 8  Municipality      <NA>      <NA> MULTIPOLYGON (((-75.9337 5....
## 9  Municipality      <NA>      <NA> MULTIPOLYGON (((-74.4607 9....
## 10 Municipality      <NA>      <NA> MULTIPOLYGON (((-76.0129 4....

Vamos a extraer nuestro departamento de interés.

cordoba <- col %>%
  filter(NAME_1 == "Córdoba")

cordoba

## Simple feature collection with 1 feature and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -76.5189 ymin: 7.3516 xmax: -74.7731 ymax: 9.448473
## Geodetic CRS:  WGS 84
##   ID_0 ISO   NAME_0 ID_1  NAME_1 ID_2 NAME_2    TYPE_2    ENGTYPE_2 NL_NAME_2
## 1   53 COL Colombia    7 Córdoba  322 Ayapel Municipio Municipality      <NA>
##   VARNAME_2                           geom
## 1      <NA> MULTIPOLYGON (((-76.5183 7....

Transformamos a la proyección Interrupted Goode Homolosine (IGH), que es la proyección utilizada por SoilGrids.

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

Obtenemos el bounding box (cuadro delimitador) de nuestra área de interés:

(bbox <- st_bbox(cordoba_igh))

##       xmin       ymin       xmax       ymax 
## -8541357.2   818376.4 -8352761.5  1051799.2

Definimos los límites en el formato requerido por GDAL (upper left y lower right).

ulx = bbox$xmin  # x mínima (esquina superior izquierda)
uly = bbox$ymax  # y máxima (esquina superior izquierda)
lrx = bbox$xmax  # x máxima (esquina inferior derecha)
lry = bbox$ymin  # y mínima (esquina inferior derecha)
(bb <- c(ulx, uly, lrx, lry))

##       xmin       ymax       xmax       ymin 
## -8541357.2  1051799.2 -8352761.5   818376.4

Definimos las rutas para la descarga de datos:

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

IMPORTANTE: Ejecuta este código solo la primera vez. Después coméntalo para evitar descargas repetidas innecesarias.

# DESCOMENTAR PARA PRIMER USO
# DESPUÉS, COMENTAR DE NUEVO
gdal_translate(paste0(sg_url,datos), file,
               tr=c(250,250),
               projwin=bb,
               projwin_srs=igh)

Leemos el archivo descargado y aplicamos el factor de conversión. Según la documentación de SoilGrids, los valores de SOC están en unidades mapeadas y deben dividirse por 10 para obtener gramos por kilogramo (g/kg).

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

## class       : SpatRaster 
## size        : 935, 755, 1  (nrow, ncol, nlyr)
## resolution  : 250, 250  (x, y)
## extent      : -8541500, -8352750, 818250, 1052000  (xmin, xmax, ymin, ymax)
## coord. ref. : Interrupted_Goode_Homolosine 
## source(s)   : memory
## varname     : soc_igh_15_30 
## name        : soc_igh_15_30 
## min value   :           7.8 
## max value   :         183.3

4 4. Exploración de datos

Primero generamos un histograma para visualizar la distribución de los valores de carbono orgánico del suelo.

terra::hist(cordoba_soc, 
            main="Distribución del Carbono Orgánico del Suelo",
            xlab="SOC (g/kg)", 
            ylab="Frecuencia",
            col="#8B7355")  # Color tierra

Un resumen estadístico de los datos:

summary(cordoba_soc)

##  soc_igh_15_30   
##  Min.   :  7.80  
##  1st Qu.: 18.90  
##  Median : 26.00  
##  Mean   : 30.53  
##  3rd Qu.: 36.80  
##  Max.   :178.10  
##  NA's   :10020

Cambiamos el nombre del atributo para que sea más claro:

(names(cordoba_soc) <- "soc")

## [1] "soc"

Extraemos los valores válidos (sin NA) para crear la paleta de colores:

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

Creamos una paleta de colores geomorfológica (tonos tierra a verde oscuro):

# Paleta geomorfológica: desde tonos claros (bajo SOC) a oscuros (alto SOC)
# Simula la transición de suelos pobres en materia orgánica a suelos ricos
orangecyan <- colorNumeric(
  palette = c("#F4E4C1", "#D4A574", "#8B7355", "#6B8E23", "#2F4F2F"), 
  domain = valores,
  na.color = "transparent"
)

Ahora vamos a visualizar el contenido de carbono orgánico en el departamento de Córdoba con un mapa interactivo:

# Para Córdoba: longitud ≈ -75.5, latitud ≈ 8, zoom ≈ 10
leaflet::leaflet(cordoba) %>% 
  addTiles() %>% 
  setView(-75.5, 8, 10) %>% 
  addPolygons(color = "#8B4513", weight = 2.0, smoothFactor = 0.5,
              opacity = 0.8, fillOpacity = 0.05) %>% 
  addRasterImage(cordoba_soc, 
                 colors = c("#F4E4C1", "#D4A574", "#8B7355", "#6B8E23", "#2F4F2F"), 
                 opacity = 0.7) %>%
  addLegend(pal = orangecyan, 
            values = valores, 
            title = "Carbono Orgánico<br>del Suelo (SOC)<br>[g/kg]",
            position = "bottomright")

5 5. Visualización alternativa con tidyterra

En esta sección utilizamos el paquete tidyterra para crear una visualización estática más profesional y personalizable.

# Convertir a formato sf para overlay
cordoba_sf <- st_as_sf(cordoba)

# Crear visualización con ggplot2 y tidyterra
ggplot() +
  geom_spatraster(data = cordoba_soc) +
  geom_sf(data = cordoba_sf, fill = NA, color = "#8B4513", linewidth = 1.2) +
  scale_fill_gradientn(
    colors = c("#F4E4C1", "#D4A574", "#8B7355", "#6B8E23", "#2F4F2F"),
    name = "SOC (g/kg)",
    na.value = "transparent"
  ) +
  labs(
    title = "Carbono Orgánico del Suelo - Departamento de Córdoba",
    subtitle = "Profundidad: 15-30 cm | Fuente: SoilGrids 250m",
    caption = "Elaborado por: Julian Santiago Gonzalez Malpica"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    plot.subtitle = element_text(size = 12, hjust = 0.5, color = "gray40"),
    plot.caption = element_text(size = 9, color = "gray50"),
    legend.position = "right",
    legend.key.height = unit(1.5, "cm"),
    panel.grid = element_line(color = "gray90", linewidth = 0.3),
    axis.text = element_text(size = 9)
  ) +
  coord_sf()

## <SpatRaster> resampled to 500532 cells.

6 6. Muestreo aleatorio del departamento

En esta sección generamos un conjunto de muestras aleatorias del dataset de SOC, lo cual es útil para: - Análisis estadísticos - Validación de modelos - Estudios de campo - Análisis de correlación con otras variables

# Establece una semilla para reproducibilidad
# CAMBIAR AQUÍ: Puedes usar cualquier número entero
set.seed(123456)

# Convertimos el raster a proyección geográfica (WGS84)
geog = "+proj=longlat +datum=WGS84"
(geog.soc = project(cordoba_soc, geog))

## class       : SpatRaster 
## size        : 960, 844, 1  (nrow, ncol, nlyr)
## resolution  : 0.002187792, 0.002187792  (x, y)
## extent      : -76.53678, -74.69029, 7.349996, 9.450277  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs 
## source(s)   : memory
## name        :        soc 
## min value   :   8.394538 
## max value   : 182.589554

# CAMBIAR AQUÍ: Ajusta el número de puntos de muestreo según tu necesidad
# Muestreo aleatorio de 2000 puntos
(samples <- terra::spatSample(geog.soc, 2000, "random", as.points=TRUE))

##  class       : SpatVector 
##  geometry    : points 
##  dimensions  : 2000, 1  (geometries, attributes)
##  extent      : -76.5335, -74.69138, 7.35109, 9.449183  (xmin, xmax, ymin, ymax)
##  coord. ref. : +proj=longlat +datum=WGS84 +no_defs 
##  names       :   soc
##  type        : <num>
##  values      : 29.71
##                37.94
##                17.74

Convertimos las muestras a formato sf para facilitar su manipulación:

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

## Simple feature collection with 2000 features and 1 field
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -76.5335 ymin: 7.35109 xmax: -74.69138 ymax: 9.449183
## 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  29.70628 POINT (-75.31271 9.320103)
## 2  37.94474 POINT (-75.81153 9.359483)
## 3  17.73541 POINT (-75.40241 9.175709)
## 4  43.51871 POINT (-75.71527 8.932864)
## 5  27.22719 POINT (-74.92547 8.952554)
## 6        NA POINT (-75.97561 9.289474)
## 7  39.88127 POINT (-76.14845 8.611258)
## 8  13.40279 POINT (-75.54681 8.685643)
## 9  16.54341 POINT (-75.94061 8.000864)
## 10 88.07214 POINT (-76.12438 7.790836)

Eliminamos los valores NA (muestras que cayeron fuera del área con datos):

nmuestras <- na.omit(muestras)

Extraemos las coordenadas y valores de SOC:

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

Resumen estadístico de las muestras:

summary(soc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.25   19.33   26.10   30.40   36.22  138.03

length(soc)

## [1] 1682

# Calculamos cuántas muestras se perdieron por caer en áreas sin datos
muestras_perdidas <- 2000 - length(soc)
cat("Muestras con datos válidos:", length(soc), "\n")

## Muestras con datos válidos: 1682

cat("Muestras sin datos (NA):", muestras_perdidas, "\n")

## Muestras sin datos (NA): 318

Creamos un data frame con las muestras:

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

##        id    longit    latit       soc
## 1       1 -75.31271 9.320103  29.70628
## 2       2 -75.81153 9.359483  37.94474
## 3       3 -75.40241 9.175709  17.73541
## 4       4 -75.71527 8.932864  43.51871
## 5       5 -74.92547 8.952554  27.22719
## 6       6 -76.14845 8.611258  39.88127
## 7       7 -75.54681 8.685643  13.40279
## 8       8 -75.94061 8.000864  16.54341
## 9       9 -76.12438 7.790836  88.07214
## 10     10 -75.12019 9.088197  15.43511
## 11     11 -75.01080 9.446995  14.47740
## 12     12 -75.21864 7.585184  17.18873
## 13     13 -75.80278 9.048817  45.14080
## 14     14 -76.15939 8.053371  17.03923
## 15     15 -76.47662 8.051184  37.10542
## 16     16 -75.03705 8.057747  15.64432
## 17     17 -76.35191 7.779897  26.23966
## 18     18 -75.87279 8.700958  35.94440
## 19     19 -76.05219 7.556742  42.43592
## 20     20 -75.05237 7.618001  28.25255
## 21     21 -75.99530 7.372968 114.50206
## 22     22 -75.62338 9.035690  36.65998
## 23     23 -75.17269 7.836780  19.28768
## 24     24 -76.06750 8.171512  14.04875
## 25     25 -75.52274 8.869418  20.13382
## 26     26 -74.99986 8.051184  16.40576
## 27     27 -75.68464 7.607062  56.19049
## 28     28 -75.81372 9.215089  46.44375
## 29     29 -75.99968 7.359841  99.99400
## 30     30 -75.12456 9.077258  18.18007
## 31     31 -74.85546 8.821286  61.60587
## 32     32 -75.26021 9.250094  30.08397
## 33     33 -76.30816 8.690019  36.59639
## 34     34 -75.23176 9.394488  17.33454
## 35     35 -76.31253 7.876160  36.54565
## 36     36 -76.04562 7.902414  31.78607
## 37     37 -75.29521 9.182272  35.72442
## 38     38 -75.63651 8.954742  57.69430
## 39     39 -76.47443 8.403418  37.71477
## 40     40 -75.17269 7.357653  28.73750
## 41     41 -75.44836 9.296037  18.34413
## 42     42 -75.07862 8.580629  31.24746
## 43     43 -75.34115 9.394488  19.86845
## 44     44 -75.14206 8.344348  30.20000
## 45     45 -74.79858 9.449183  22.70000
## 46     46 -75.31928 8.659390  26.57494
## 47     47 -76.42849 8.073061  25.68927
## 48     48 -75.59713 7.445165  33.31372
## 49     49 -75.79403 8.278714  14.24524
## 50     50 -76.30378 7.512987  84.00249
## 51     51 -74.81390 9.164770  18.56395
## 52     52 -75.98218 7.464855  54.95287
## 53     53 -75.74371 8.427484  29.47605
## 54     54 -76.38254 7.694573  46.77526
## 55     55 -75.94717 8.751277  43.25720
## 56     56 -76.39348 8.125568  26.71153
## 57     57 -74.87515 9.241343  12.95794
## 58     58 -76.41317 7.795212  25.28840
## 59     59 -75.81590 7.585184  33.30376
## 60     60 -75.50961 7.919916  15.20293
## 61     61 -76.32566 8.836601  34.37433
## 62     62 -75.55775 7.561118  25.29725
## 63     63 -74.82483 7.690198  18.95585
## 64     64 -75.59494 9.230404  23.56649
## 65     65 -75.27990 9.383549  14.57718
## 66     66 -76.25346 7.716451  32.30333
## 67     67 -75.41773 7.922104  21.25891
## 68     68 -75.71964 9.226028  41.03026
## 69     69 -74.92329 8.574066  43.34637
## 70     70 -75.70870 7.889287  16.13618
## 71     71 -76.50068 7.959296  27.82693
## 72     72 -75.35428 8.744714  24.42528
## 73     73 -75.50743 7.854282  16.84308
## 74     74 -76.37817 7.738329  49.89487
## 75     75 -74.82265 8.136507  46.13801
## 76     76 -75.08081 7.618001  22.57127
## 77     77 -76.28409 8.652826  26.05977
## 78     78 -76.37817 7.898038  36.61701
## 79     79 -76.06969 8.617822  31.53890
## 80     80 -75.50743 9.438244  24.03780
## 81     81 -74.89703 8.108066  20.94312
## 82     82 -75.81590 8.961305  47.93019
## 83     83 -74.79858 8.611258  27.70127
## 84     84 -74.86859 8.024930  40.00053
## 85     85 -74.83796 8.784094  56.50644
## 86     86 -74.80514 8.810347  83.07249
## 87     87 -74.82265 8.125568  46.55940
## 88     88 -76.44818 8.147446  34.42247
## 89     89 -75.56212 8.294028  17.37979
## 90     90 -74.96485 7.723015  20.93943
## 91     91 -75.05237 8.676892  78.30000
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## 602   602 -75.29302 7.541428  26.51969
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## 801   801 -75.66495 7.574245  59.09109
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## 825   825 -76.00406 9.072883  31.33019
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## 838   838 -75.13550 8.652826  23.80000
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## 840   840 -75.38053 7.572057  16.92114
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## 850   850 -76.36942 7.825841  32.74864
## 851   851 -75.67589 7.915540  17.12547
## 852   852 -75.44398 9.020376  22.38313
## 853   853 -75.84872 7.591747  44.77834
## 854   854 -75.82028 7.425475  63.66330
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## 859   859 -75.25802 9.267596  24.73022
## 860   860 -75.38053 9.123202  34.18081
## 861   861 -76.23377 8.167137  45.34909
## 862   862 -74.98673 8.873793  41.66769
## 863   863 -75.25364 8.112442  13.25027
## 864   864 -76.19877 8.149634  27.07802
## 865   865 -76.09594 8.469052  30.80940
## 866   866 -76.32347 8.501869  38.85441
## 867   867 -75.50961 8.434047  17.74782
## 868   868 -75.41773 8.495305  25.35350
## 869   869 -75.21426 8.162761  16.84739
## 870   870 -75.16832 7.725202  19.94981
## 871   871 -76.07406 8.294028  28.46635
## 872   872 -75.21864 8.405606  31.16596
## 873   873 -74.95829 7.924292  13.10287
## 874   874 -75.72839 8.884732  49.94963
## 875   875 -75.20989 8.615634  32.48585
## 876   876 -75.41335 7.642066  20.22253
## 877   877 -75.47242 8.267775  16.31703
## 878   878 -75.87060 7.782085  29.62812
## 879   879 -75.10050 8.657202  34.30692
## 880   880 -74.88828 8.217456  23.14027
## 881   881 -75.50961 8.821286  15.58993
## 882   882 -75.47461 8.490930  17.10524
## 883   883 -75.40460 9.051005  17.88661
## 884   884 -76.18783 9.002873  45.75496
## 885   885 -75.39804 9.180084  17.05508
## 886   886 -75.79621 7.733954  24.78238
## 887   887 -75.17707 8.464676  40.26644
## 888   888 -75.26896 8.276526  30.55158
## 889   889 -75.70652 7.593935  63.12630
## 890   890 -75.94061 7.539240  60.91172
## 891   891 -75.89685 8.000864  22.57769
## 892   892 -75.52274 8.103691  15.08880
## 893   893 -75.25364 9.180084  18.37489
## 894   894 -76.17689 9.123202  39.72609
## 895   895 -75.09831 9.309164  13.89421
## 896   896 -75.51837 7.694573  31.98104
## 897   897 -75.50086 8.827850  15.14525
## 898   898 -75.17051 9.138516  22.06160
## 899   899 -76.05656 8.812535  32.94693
## 900   900 -75.47680 9.116638  17.53671
## 901   901 -75.54024 8.392479  19.73673
## 902   902 -75.39366 7.475794  23.91556
## 903   903 -76.08938 7.447353  71.15376
## 904   904 -75.09393 9.228216  13.35454
## 905   905 -74.94079 7.410160  46.58970
## 906   906 -75.50961 8.963493  29.14844
## 907   907 -75.56212 8.444986  20.60600
## 908   908 -74.96048 8.943803  31.17779
## 909   909 -75.45273 8.486554  18.45807
## 910   910 -76.05000 9.333230  37.34833
## 911   911 -75.57087 8.709709  21.02025
## 912   912 -75.68245 8.013991  16.94035
## 913   913 -75.41773 9.051005  15.86771
## 914   914 -75.64307 8.215268  15.37632
## 915   915 -75.31271 7.714263  18.52473
## 916   916 -75.60150 9.177897  31.51003
## 917   917 -75.24270 8.357474  35.98312
## 918   918 -76.08938 8.626573  36.22956
## 919   919 -76.05656 8.932864  41.44108
## 920   920 -75.66714 9.317915  28.84446
## 921   921 -75.04361 8.409981  61.49945
## 922   922 -75.54024 7.591747  22.38066
## 923   923 -75.26458 8.368413  30.34333
## 924   924 -75.08299 9.035690  13.49635
## 925   925 -76.39786 7.755832  25.31122
## 926   926 -76.26878 8.477803  34.74492
## 927   927 -75.31271 7.642066  32.01402
## 928   928 -75.59275 8.523747  23.87437
## 929   929 -74.86422 8.311531  46.06808
## 930   930 -76.30378 8.458113  30.79923
## 931   931 -75.19676 8.906610  18.05258
## 932   932 -74.97798 8.532498  40.83230
## 933   933 -75.62119 9.040066  37.62578
## 934   934 -75.02174 8.164949  19.09641
## 935   935 -75.36741 8.991934  21.40711
## 936   936 -75.34772 7.642066  21.63668
## 937   937 -76.34754 7.451728  37.77755
## 938   938 -75.82028 7.486733  50.92726
## 939   939 -76.27534 8.770967  18.08842
## 940   940 -76.42849 7.471418 138.03250
## 941   941 -75.82247 9.186648  33.40129
## 942   942 -75.89029 8.696582  30.79379
## 943   943 -76.09157 8.768779  42.37355
## 944   944 -75.01299 9.107887  13.88073
## 945   945 -75.25364 8.210892  13.35229
## 946   946 -76.18345 8.449362  24.10767
## 947   947 -76.17908 7.817090  51.97223
## 948   948 -76.26221 7.390470  67.20956
## 949   949 -75.39585 8.110254  15.91073
## 950   950 -74.95610 7.946169  14.47388
## 951   951 -75.46367 7.788648  19.32167
## 952   952 -76.10907 7.611437  34.36462
## 953   953 -76.02156 8.042432  16.84802
## 954   954 -74.91672 7.672695  17.36109
## 955   955 -75.72839 7.954921  19.21695
## 956   956 -75.85529 9.134141  66.54368
## 957   957 -74.96923 7.445165  40.76360
## 958   958 -75.84653 7.666132  35.54352
## 959   959 -75.66932 8.265587  13.78458
## 960   960 -75.88810 7.786461  22.63736
## 961   961 -76.30159 8.486554  35.81098
## 962   962 -75.87279 7.530489  60.95716
## 963   963 -75.47242 7.427663  61.97232
## 964   964 -74.80296 9.387925  41.16862
## 965   965 -75.55993 7.528301  38.87437
## 966   966 -75.99312 7.793024  28.38739
## 967   967 -75.79184 7.456104  79.22942
## 968   968 -76.02375 9.278535  46.00330
## 969   969 -75.64963 8.768779  18.80131
## 970   970 -75.71964 8.125568  26.50038
## 971   971 -76.29066 8.497493  26.38518
## 972   972 -76.14845 9.029127  38.50859
## 973   973 -75.97561 9.352920  41.12701
## 974   974 -74.83796 8.298404  56.61275
## 975   975 -74.85546 7.528301  33.04683
## 976   976 -75.93623 8.053371  16.27352
## 977   977 -76.32785 8.208705  27.24314
## 978   978 -75.90560 8.585005  20.14849
## 979   979 -75.98874 7.895850  26.08396
## 980   980 -75.13331 8.486554  31.93549
## 981   981 -74.77670 8.313719  34.81502
## 982   982 -74.81171 8.436235  24.60382
## 983   983 -76.17033 8.954742  23.88338
## 984   984 -75.35647 7.832404  14.61511
## 985   985 -75.13988 8.945991  15.81304
## 986   986 -75.90123 8.333409  30.15732
## 987   987 -74.97798 7.488921  42.43579
## 988   988 -75.07424 8.512808  27.95882
## 989   989 -76.44161 7.832404  34.01843
## 990   990 -75.22520 7.854282  14.43548
## 991   991 -75.13113 8.103691  20.45538
## 992   992 -76.20533 8.272151  20.34375
## 993   993 -75.60369 8.447174  22.46824
## 994   994 -74.82483 9.442620  23.34878
## 995   995 -75.18363 7.974611  19.66827
## 996   996 -76.40005 8.458113  19.75743
## 997   997 -74.95829 7.412348  46.18487
## 998   998 -75.42429 9.048817  23.43327
## 999   999 -76.12438 7.954921  21.74113
## 1000 1000 -76.22065 7.729578  36.31503
## 1001 1001 -74.81390 8.237146  31.87206
## 1002 1002 -76.04781 7.655193  40.95618
## 1003 1003 -75.13331 8.265587  16.54504
## 1004 1004 -75.54899 7.786461  15.46916
## 1005 1005 -74.88828 8.335597  25.81197
## 1006 1006 -75.87498 8.309343  21.31498
## 1007 1007 -76.19658 7.355465  99.25343
## 1008 1008 -75.22083 8.869418  35.49560
## 1009 1009 -75.00423 7.379531  32.61233
## 1010 1010 -76.44380 8.059935  30.24286
## 1011 1011 -75.27333 7.410160  37.13396
## 1012 1012 -76.21408 7.390470  87.68735
## 1013 1013 -75.94061 7.397034  85.25726
## 1014 1014 -75.79184 8.536873  16.36449
## 1015 1015 -74.94079 9.320103  22.45759
## 1016 1016 -75.01299 7.462667  44.74491
## 1017 1017 -76.32566 8.663765  18.89649
## 1018 1018 -75.67589 8.760028  18.57121
## 1019 1019 -75.50305 8.059935  14.72785
## 1020 1020 -75.11581 8.965681  21.06819
## 1021 1021 -76.19658 8.335597  33.48870
## 1022 1022 -76.09594 9.278535  26.56308
## 1023 1023 -74.89703 7.484545  37.62207
## 1024 1024 -75.69995 9.366047  20.03942
## 1025 1025 -75.71745 7.834592  22.03269
## 1026 1026 -75.99530 7.674883  34.93811
## 1027 1027 -75.85966 9.302601  26.74128
## 1028 1028 -75.55775 7.537052  30.70842
## 1029 1029 -75.51618 8.700958  20.98976
## 1030 1030 -75.00205 7.383907  33.72773
## 1031 1031 -76.35629 7.379531  60.65879
## 1032 1032 -75.82247 8.013991  16.42451
## 1033 1033 -74.92329 9.335418  26.84778
## 1034 1034 -76.05656 8.289653  18.87939
## 1035 1035 -75.96249 8.263399  16.62427
## 1036 1036 -76.19658 9.016000  45.69299
## 1037 1037 -76.17470 8.377165  23.06229
## 1038 1038 -75.74590 8.342160  25.69683
## 1039 1039 -75.69995 8.825662  40.10125
## 1040 1040 -75.59931 8.361850  26.72877
## 1041 1041 -74.81608 9.083822  21.26543
## 1042 1042 -75.47023 8.278714  19.46693
## 1043 1043 -75.90342 7.801775  28.00784
## 1044 1044 -75.57087 8.825662  14.56766
## 1045 1045 -74.80952 8.479991  30.09767
## 1046 1046 -76.38254 7.491109  60.63597
## 1047 1047 -75.54899 7.532677  54.89117
## 1048 1048 -75.89248 9.140704  77.95279
## 1049 1049 -75.40898 7.561118  21.14771
## 1050 1050 -75.05018 8.751277  38.65660
## 1051 1051 -75.72183 8.221831  13.97571
## 1052 1052 -75.39147 7.585184  30.06821
## 1053 1053 -75.34772 8.372789  35.79988
## 1054 1054 -75.57962 8.105878  23.03632
## 1055 1055 -76.24034 8.228395  31.66798
## 1056 1056 -75.97343 7.981174  27.63120
## 1057 1057 -75.71308 8.221831  15.40122
## 1058 1058 -75.46148 9.361671  30.24371
## 1059 1059 -75.13769 7.558930  26.95961
## 1060 1060 -75.56650 9.206338  18.99504
## 1061 1061 -75.74152 7.729578  20.72012
## 1062 1062 -75.74152 7.793024  25.57063
## 1063 1063 -75.84872 8.241521  17.65266
## 1064 1064 -74.90578 8.576254  37.69237
## 1065 1065 -74.84453 7.506423  60.79795
## 1066 1066 -75.49868 8.250273  34.64444
## 1067 1067 -75.65620 9.317915  22.19436
## 1068 1068 -75.73933 7.357653  78.22575
## 1069 1069 -75.55556 8.862855  10.67857
## 1070 1070 -75.50524 7.648630  22.90534
## 1071 1071 -74.82921 8.554376  37.84660
## 1072 1072 -76.22502 8.694394  29.60337
## 1073 1073 -76.12220 8.832225  29.43189
## 1074 1074 -75.25802 8.322470  32.55151
## 1075 1075 -75.26239 8.777531  34.96756
## 1076 1076 -75.26458 9.101324  25.25603
## 1077 1077 -75.70652 8.792845  30.14904
## 1078 1078 -75.41116 8.858479  33.98648
## 1079 1079 -75.44398 7.873972  18.02133
## 1080 1080 -76.15064 9.112263  30.08873
## 1081 1081 -75.31271 7.497672  17.42356
## 1082 1082 -74.92985 9.352920  24.07016
## 1083 1083 -75.01299 7.753644  27.28232
## 1084 1084 -75.36303 9.169145  26.33121
## 1085 1085 -75.17707 9.241343  14.05338
## 1086 1086 -74.85546 7.607062  17.42180
## 1087 1087 -75.76996 7.650817  65.19297
## 1088 1088 -75.24270 9.195399  16.42969
## 1089 1089 -76.02812 8.814723  28.22983
## 1090 1090 -74.94079 9.315728  22.47965
## 1091 1091 -76.45912 8.571878  38.68979
## 1092 1092 -75.96905 8.576254  16.71910
## 1093 1093 -75.68245 8.495305  16.19130
## 1094 1094 -75.83122 9.005061  45.13445
## 1095 1095 -76.14407 8.182451  21.57467
## 1096 1096 -76.49850 7.589559  64.73574
## 1097 1097 -76.41974 7.598310  72.39017
## 1098 1098 -75.36084 7.808339  13.92381
## 1099 1099 -74.88172 8.149634  17.02110
## 1100 1100 -75.29740 7.578620  23.32813
## 1101 1101 -75.92529 9.217277  71.24690
## 1102 1102 -74.78764 8.617822  35.14270
## 1103 1103 -76.11782 8.639700  29.62918
## 1104 1104 -75.77652 8.374977  16.53347
## 1105 1105 -76.23377 8.256836  31.56776
## 1106 1106 -76.12657 9.236967  42.67487
## 1107 1107 -75.38929 7.716451  17.91168
## 1108 1108 -75.79403 7.843343  27.48067
## 1109 1109 -75.16394 8.751277  21.02300
## 1110 1110 -75.93405 8.003052  20.08908
## 1111 1111 -76.13313 9.182272  32.76191
## 1112 1112 -75.61244 7.587371  48.00023
## 1113 1113 -75.84872 7.924292  19.48026
## 1114 1114 -75.13550 8.827850  29.28609
## 1115 1115 -74.84453 9.138516  13.87275
## 1116 1116 -75.45492 8.324658  23.26517
## 1117 1117 -76.09594 8.582817  32.17333
## 1118 1118 -75.04143 9.208526  27.54056
## 1119 1119 -75.46805 9.199775  18.26040
## 1120 1120 -75.17488 7.642066  24.24971
## 1121 1121 -75.63869 8.337784  22.78170
## 1122 1122 -76.40005 7.639879  53.84393
## 1123 1123 -74.81390 7.871785  33.90000
## 1124 1124 -76.22283 7.939606  58.36282
## 1125 1125 -76.41755 8.836601  46.56200
## 1126 1126 -76.22502 8.092752  28.49686
## 1127 1127 -75.63869 7.517362  80.84524
## 1128 1128 -75.87716 7.587371  72.28958
## 1129 1129 -74.96048 7.526113  30.26068
## 1130 1130 -75.84653 7.834592  25.98020
## 1131 1131 -75.59494 8.042432  17.56333
## 1132 1132 -74.88391 9.173521  14.76367
## 1133 1133 -75.76340 7.637691  85.98421
## 1134 1134 -75.96905 8.860667  36.06340
## 1135 1135 -75.85747 8.547812  24.56441
## 1136 1136 -75.48336 9.123202  17.05854
## 1137 1137 -75.29083 8.101503  13.21512
## 1138 1138 -75.90123 9.317915  36.85718
## 1139 1139 -74.95610 7.729578  37.47295
## 1140 1140 -76.34098 8.722836  33.58381
## 1141 1141 -75.36084 7.884911  12.09836
## 1142 1142 -74.86640 7.736141  20.77940
## 1143 1143 -76.40880 8.318094  40.96564
## 1144 1144 -76.13751 8.687831  31.64289
## 1145 1145 -76.17689 8.670329  41.37494
## 1146 1146 -75.30834 7.362029  29.26368
## 1147 1147 -75.78746 9.256657  34.06647
## 1148 1148 -75.68683 7.786461  19.07673
## 1149 1149 -76.00843 8.808160  32.41613
## 1150 1150 -74.98454 8.250273  19.75903
## 1151 1151 -75.52274 8.646263  21.31819
## 1152 1152 -75.05455 8.213080  15.10847
## 1153 1153 -75.63651 8.140883  15.89920
## 1154 1154 -75.14863 8.156198  14.37640
## 1155 1155 -74.75264 8.847540  72.47778
## 1156 1156 -74.87953 8.073061  18.36209
## 1157 1157 -76.10688 9.197587  45.41451
## 1158 1158 -75.99530 8.641887  28.03526
## 1159 1159 -75.52274 8.593756  17.80690
## 1160 1160 -75.38710 8.646263  29.53828
## 1161 1161 -75.22301 7.891475  19.57010
## 1162 1162 -75.60806 8.311531  11.20418
## 1163 1163 -75.58400 8.033681  15.24518
## 1164 1164 -75.04361 9.350732  20.26902
## 1165 1165 -75.62775 7.731766  14.96422
## 1166 1166 -75.68464 7.723015  16.77913
## 1167 1167 -75.45273 8.893484  21.44551
## 1168 1168 -75.76777 9.374798  37.56506
## 1169 1169 -75.19676 8.158385  14.85487
## 1170 1170 -74.80077 8.464676  33.65074
## 1171 1171 -75.47898 7.893662  12.61651
## 1172 1172 -75.36522 7.423287  33.15825
## 1173 1173 -75.10268 8.788470  28.93061
## 1174 1174 -75.02830 8.429672  74.19197
## 1175 1175 -75.53149 8.615634  20.80810
## 1176 1176 -75.50305 7.771146  21.66469
## 1177 1177 -75.93842 8.630949  22.71518
## 1178 1178 -75.06987 9.226028  20.51358
## 1179 1179 -75.34115 9.228216  28.31785
## 1180 1180 -74.99767 8.230582  19.78901
## 1181 1181 -75.82028 9.042253  46.32408
## 1182 1182 -76.03031 8.867230  34.52888
## 1183 1183 -76.07406 7.954921  38.28168
## 1184 1184 -75.55337 8.094939  19.44548
## 1185 1185 -74.82702 8.998498  24.43308
## 1186 1186 -75.90560 7.928667  21.83529
## 1187 1187 -75.13988 9.031315  19.61955
## 1188 1188 -76.17252 8.094939  28.90912
## 1189 1189 -76.17689 7.637691  32.44364
## 1190 1190 -74.84890 7.412348  46.78194
## 1191 1191 -75.08299 7.593935  24.98462
## 1192 1192 -75.65620 9.256657  28.15974
## 1193 1193 -75.78965 7.390470  63.11734
## 1194 1194 -75.19238 7.587371  20.14668
## 1195 1195 -75.20332 7.797400  24.10000
## 1196 1196 -76.08500 7.712076  34.40908
## 1197 1197 -75.41773 7.917728  16.43126
## 1198 1198 -74.78545 8.396855  40.16665
## 1199 1199 -75.18145 9.337605  10.25122
## 1200 1200 -75.98874 8.501869  32.63424
## 1201 1201 -75.96905 8.322470  14.69519
## 1202 1202 -75.30177 8.486554  25.38338
## 1203 1203 -75.76996 9.396676  63.33838
## 1204 1204 -75.10268 7.473606  35.95013
## 1205 1205 -75.04143 7.591747  26.14547
## 1206 1206 -75.78746 9.322291  39.69701
## 1207 1207 -76.38692 8.626573  40.91625
## 1208 1208 -76.34754 8.665953  19.93557
## 1209 1209 -76.47662 8.234958  19.56361
## 1210 1210 -75.28427 7.639879  24.10449
## 1211 1211 -74.87734 7.458292  36.84495
## 1212 1212 -75.82466 9.359483  58.18667
## 1213 1213 -75.66932 7.407973  38.10924
## 1214 1214 -74.82046 8.167137  32.35516
## 1215 1215 -75.29740 7.865221  15.64083
## 1216 1216 -74.90797 9.230404  13.53438
## 1217 1217 -75.58181 8.628761  17.25048
## 1218 1218 -76.29503 8.412169  36.21975
## 1219 1219 -76.18127 7.913353  29.01003
## 1220 1220 -76.44818 7.405785  30.91367
## 1221 1221 -76.04125 8.348723  17.60000
## 1222 1222 -75.85747 7.832404  20.27722
## 1223 1223 -76.18783 7.628940  36.20961
## 1224 1224 -74.74826 9.381361  42.52728
## 1225 1225 -75.90779 7.834592  17.82155
## 1226 1226 -75.72839 7.423287  61.67617
## 1227 1227 -74.88172 9.449183  22.62557
## 1228 1228 -76.22065 8.740338  15.62817
## 1229 1229 -75.40898 8.560939  38.16990
## 1230 1230 -76.12876 7.622376  35.77271
## 1231 1231 -76.19221 8.690019  24.75639
## 1232 1232 -75.59713 9.068507  17.13345
## 1233 1233 -74.84671 9.374798  21.89719
## 1234 1234 -74.89484 9.029127  24.91126
## 1235 1235 -74.97798 7.655193  22.34053
## 1236 1236 -74.90578 8.521559  36.48755
## 1237 1237 -75.49211 7.884911  13.12893
## 1238 1238 -75.78528 8.528122  21.12897
## 1239 1239 -75.72839 8.138695  21.72324
## 1240 1240 -75.27333 8.665953  14.18790
## 1241 1241 -76.46787 7.655193 133.05750
## 1242 1242 -75.59713 8.952554  49.24029
## 1243 1243 -75.13769 7.499860  41.51832
## 1244 1244 -75.15082 9.064131  20.64192
## 1245 1245 -74.80296 8.256836  49.94634
## 1246 1246 -75.25583 7.366404  26.03350
## 1247 1247 -76.05656 7.460480  66.54750
## 1248 1248 -75.54024 7.440789  66.16447
## 1249 1249 -74.89047 9.361671  16.10123
## 1250 1250 -75.34115 7.849907  16.65007
## 1251 1251 -76.23377 8.714085  19.33092
## 1252 1252 -75.04361 8.805972  39.26598
## 1253 1253 -75.15519 9.418554  17.08595
## 1254 1254 -75.74590 7.828029  16.46835
## 1255 1255 -76.47443 7.381719  35.09389
## 1256 1256 -75.38929 8.595944  24.67417
## 1257 1257 -75.77215 9.271972  33.46922
## 1258 1258 -75.07424 7.817090  17.13588
## 1259 1259 -75.42867 8.412169  13.55366
## 1260 1260 -75.79184 9.208526  82.74667
## 1261 1261 -75.23176 7.410160  43.26044
## 1262 1262 -76.36504 8.530310  20.06520
## 1263 1263 -75.17051 9.261033  11.88640
## 1264 1264 -74.80952 9.304789  33.14876
## 1265 1265 -75.64307 7.458292  36.96115
## 1266 1266 -75.58619 8.602507  15.70172
## 1267 1267 -75.26239 8.900047  15.91173
## 1268 1268 -75.92311 8.287465  19.09776
## 1269 1269 -75.81153 8.048996  19.88416
## 1270 1270 -75.46367 7.582996  21.69326
## 1271 1271 -74.95829 8.510620  41.08432
## 1272 1272 -75.42867 8.121193  23.23595
## 1273 1273 -75.44836 9.247906  16.27979
## 1274 1274 -75.53149 7.480170  36.71832
## 1275 1275 -75.45273 7.696761  19.84388
## 1276 1276 -75.39147 9.188836  23.94105
## 1277 1277 -74.83577 8.013991  22.59053
## 1278 1278 -75.67370 7.744893  20.92607
## 1279 1279 -75.01955 8.259024  24.27122
## 1280 1280 -75.64963 9.370422  40.22321
## 1281 1281 -75.92967 8.749089  49.37669
## 1282 1282 -76.00624 8.086188  27.41932
## 1283 1283 -75.10925 8.000864  37.39289
## 1284 1284 -74.93422 8.329033  27.52984
## 1285 1285 -75.43742 7.974611  30.28390
## 1286 1286 -74.87297 8.075249  21.32703
## 1287 1287 -75.36522 8.781906  17.68769
## 1288 1288 -75.30177 8.046808  12.18770
## 1289 1289 -75.96249 8.191202  14.13260
## 1290 1290 -75.59275 9.245718  19.20054
## 1291 1291 -76.41098 7.843343  22.94906
## 1292 1292 -75.57087 9.016000  28.41992
## 1293 1293 -74.80514 8.267775  36.15821
## 1294 1294 -75.33678 9.298225  13.47978
## 1295 1295 -74.89922 9.247906  23.56413
## 1296 1296 -75.04580 7.935231  15.76148
## 1297 1297 -74.78983 8.950366  55.89396
## 1298 1298 -75.36084 7.871785  13.29808
## 1299 1299 -75.78309 8.510620  19.93726
## 1300 1300 -75.79403 9.204150  91.70965
## 1301 1301 -75.39147 7.582996  28.48048
## 1302 1302 -75.93842 8.884732  31.10103
## 1303 1303 -74.92766 9.252282  19.74701
## 1304 1304 -75.66495 9.083822  30.87532
## 1305 1305 -76.17033 7.628940  24.75811
## 1306 1306 -75.57087 8.164949  31.12089
## 1307 1307 -75.84216 8.230582  20.32197
## 1308 1308 -75.74808 8.075249  20.08752
## 1309 1309 -76.16376 7.860846  27.14684
## 1310 1310 -76.27534 8.530310  32.62588
## 1311 1311 -76.03250 8.792845  37.45014
## 1312 1312 -76.27534 7.668320  35.39489
## 1313 1313 -75.10487 7.471418  35.27531
## 1314 1314 -75.34772 8.801596  22.27680
## 1315 1315 -74.83577 7.484545  44.35849
## 1316 1316 -75.36960 8.081813  14.86853
## 1317 1317 -75.83778 8.429672  16.95317
## 1318 1318 -74.84234 9.206338  11.81213
## 1319 1319 -76.26221 7.499860  60.44104
## 1320 1320 -74.93422 7.351090  50.34283
## 1321 1321 -74.82265 9.118826  27.54672
## 1322 1322 -74.86640 9.160394  27.51375
## 1323 1323 -75.74590 8.414357  31.03980
## 1324 1324 -75.70433 8.613446  23.26095
## 1325 1325 -75.11362 7.438602  47.54364
## 1326 1326 -74.75920 8.665953  39.77082
## 1327 1327 -74.86422 8.814723  63.70906
## 1328 1328 -75.98874 8.917549  28.32937
## 1329 1329 -75.81590 7.989925  19.93452
## 1330 1330 -75.57962 7.602686  23.68794
## 1331 1331 -74.89703 7.983362  26.18526
## 1332 1332 -76.26221 7.642066  32.23613
## 1333 1333 -75.06549 9.304789  15.17432
## 1334 1334 -75.57525 8.051184  15.77590
## 1335 1335 -75.10706 8.963493  24.95195
## 1336 1336 -75.63869 9.180084  31.96742
## 1337 1337 -76.26878 7.458292  63.13272
## 1338 1338 -75.02174 7.972423  20.91967
## 1339 1339 -75.88373 8.889108  37.71611
## 1340 1340 -75.88810 8.908798  42.10119
## 1341 1341 -76.20971 8.280902  20.87910
## 1342 1342 -75.14206 8.823474  26.15012
## 1343 1343 -75.59713 7.432038  40.01612
## 1344 1344 -75.73277 7.401409  52.21712
## 1345 1345 -75.35209 8.394667  18.61202
## 1346 1346 -75.61244 7.832404  11.61203
## 1347 1347 -75.52930 9.346357  29.88388
## 1348 1348 -75.76777 8.453737  31.42373
## 1349 1349 -76.29503 7.578620  39.03262
## 1350 1350 -75.26021 7.954921  14.49747
## 1351 1351 -75.34115 7.731766  22.04163
## 1352 1352 -75.91436 8.171512  14.76547
## 1353 1353 -76.19877 8.679080  27.41980
## 1354 1354 -76.12657 8.954742  30.16979
## 1355 1355 -75.13113 8.458113  47.84012
## 1356 1356 -75.86841 7.694573  25.41580
## 1357 1357 -75.85529 9.219465  22.59795
## 1358 1358 -75.75246 8.412169  28.83646
## 1359 1359 -74.87515 9.212901  11.05155
## 1360 1360 -75.44617 9.068507  38.90335
## 1361 1361 -75.13769 9.414178  11.02089
## 1362 1362 -75.82247 7.939606  26.80714
## 1363 1363 -76.06094 8.764404  32.27716
## 1364 1364 -75.21426 8.585005  32.57945
## 1365 1365 -75.60588 8.414357  14.49584
## 1366 1366 -75.00205 9.282911  17.62914
## 1367 1367 -75.73496 8.304967  43.91496
## 1368 1368 -75.91873 7.502048  66.62612
## 1369 1369 -76.38911 7.922104  40.70179
## 1370 1370 -76.37817 8.444986  20.80789
## 1371 1371 -75.03268 9.094761  22.42647
## 1372 1372 -76.48537 8.112442  38.13420
## 1373 1373 -76.19439 7.633315  38.50124
## 1374 1374 -76.23596 7.381719  73.61278
## 1375 1375 -76.25784 7.672695  43.01486
## 1376 1376 -76.00187 8.574066  13.80826
## 1377 1377 -74.84671 7.841155  21.79527
## 1378 1378 -76.11563 8.683456  32.77913
## 1379 1379 -76.02812 8.337784  33.34098
## 1380 1380 -74.91672 9.228216  14.44672
## 1381 1381 -75.23176 8.900047  21.89047
## 1382 1382 -74.95391 9.269784  11.55218
## 1383 1383 -75.51399 9.346357  32.94565
## 1384 1384 -75.78746 8.178075  25.54170
## 1385 1385 -75.97343 7.970235  24.58046
## 1386 1386 -75.98874 8.786282  40.81556
## 1387 1387 -75.20551 9.429493  16.20045
## 1388 1388 -75.00205 9.131953  16.67575
## 1389 1389 -74.97798 8.956930  30.19187
## 1390 1390 -76.05875 8.016179  35.79942
## 1391 1391 -76.04562 8.985371  21.95944
## 1392 1392 -75.60588 8.156198  22.84827
## 1393 1393 -76.07844 7.550179  39.92783
## 1394 1394 -75.25364 7.600498  20.99296
## 1395 1395 -75.92092 8.679080  21.38305
## 1396 1396 -76.02156 8.436235  15.72619
## 1397 1397 -75.48336 7.427663  99.51079
## 1398 1398 -76.15720 8.479991  21.33901
## 1399 1399 -76.04781 8.508432  32.66926
## 1400 1400 -75.04143 8.132132  15.79567
## 1401 1401 -76.20533 8.700958  26.69409
## 1402 1402 -75.72183 8.897859  44.99873
## 1403 1403 -74.97142 8.731587  35.74893
## 1404 1404 -76.41755 7.442977  88.54195
## 1405 1405 -74.87953 9.129765  22.95032
## 1406 1406 -75.81372 7.515174  53.37408
## 1407 1407 -75.40460 9.366047  19.70784
## 1408 1408 -75.07862 9.083822  16.64317
## 1409 1409 -76.04562 7.375156  80.87976
## 1410 1410 -75.38710 9.226028  17.02043
## 1411 1411 -74.98017 8.455925  51.20990
## 1412 1412 -75.24927 7.863033  15.10333
## 1413 1413 -75.56650 7.994301  14.11324
## 1414 1414 -75.18801 7.607062  21.66958
## 1415 1415 -75.79403 7.442977  66.38489
## 1416 1416 -75.61682 7.928667  19.00876
## 1417 1417 -75.50743 8.777531  16.85728
## 1418 1418 -75.54243 8.385916  24.10618
## 1419 1419 -75.61244 9.236967  12.35199
## 1420 1420 -75.94280 7.451728  57.96884
## 1421 1421 -75.97343 8.040245  15.22271
## 1422 1422 -75.30615 8.930676  30.39014
## 1423 1423 -75.59056 9.051005  27.26371
## 1424 1424 -75.03049 9.324479  16.82191
## 1425 1425 -75.70652 9.096948  30.62736
## 1426 1426 -75.89467 8.342160  16.02185
## 1427 1427 -75.18801 9.151643  14.74537
## 1428 1428 -75.27990 7.863033  16.23662
## 1429 1429 -75.58181 9.429493  47.45340
## 1430 1430 -75.34772 8.263399  20.82862
## 1431 1431 -76.34535 8.385916  32.88490
## 1432 1432 -75.40241 7.845531  23.07383
## 1433 1433 -75.68245 8.550000  18.35473
## 1434 1434 -76.41974 7.692386  33.09286
## 1435 1435 -76.31910 8.760028  27.34533
## 1436 1436 -75.26896 7.913353  14.88126
## 1437 1437 -75.32146 8.714085  28.81537
## 1438 1438 -75.45273 7.508611  36.76760
## 1439 1439 -75.66932 7.876160  16.71815
## 1440 1440 -76.29066 8.501869  23.63803
## 1441 1441 -74.91453 8.320282  36.41061
## 1442 1442 -75.48555 7.917728  24.37606
## 1443 1443 -76.27315 7.720827  33.36785
## 1444 1444 -75.29740 8.423108  15.56963
## 1445 1445 -75.65620 8.514996  16.78400
## 1446 1446 -75.77652 7.578620  61.39908
## 1447 1447 -75.38272 8.388104  12.23072
## 1448 1448 -75.19676 7.915540  18.56326
## 1449 1449 -75.27771 9.022563  28.05712
## 1450 1450 -75.90779 7.928667  19.37428
## 1451 1451 -75.90560 7.981174  20.62349
## 1452 1452 -75.30396 9.204150  21.21938
## 1453 1453 -76.06094 8.475615  42.53946
## 1454 1454 -76.28409 8.819099  43.19995
## 1455 1455 -75.90998 7.895850  19.61756
## 1456 1456 -75.57744 7.451728  31.47749
## 1457 1457 -75.20770 9.180084  14.72918
## 1458 1458 -75.96467 7.801775  52.68446
## 1459 1459 -75.36522 7.655193  19.76126
## 1460 1460 -75.01955 7.580808  20.61437
## 1461 1461 -75.42867 7.974611  32.15144
## 1462 1462 -75.20332 7.515174  30.66682
## 1463 1463 -74.85984 7.569869  27.77168
## 1464 1464 -75.01517 8.718460  44.35695
## 1465 1465 -75.14425 7.523926  35.53299
## 1466 1466 -76.07406 7.830216  27.13801
## 1467 1467 -75.61244 9.186648  40.28582
## 1468 1468 -75.77215 7.668320  26.99679
## 1469 1469 -75.59056 9.370422  40.34944
## 1470 1470 -75.93405 8.744714  51.20526
## 1471 1471 -75.45711 7.952733  30.95660
## 1472 1472 -76.37598 8.799409  41.80017
## 1473 1473 -75.98874 8.825662  37.42739
## 1474 1474 -75.29959 9.206338  22.64710
## 1475 1475 -75.06549 8.609071  35.84632
## 1476 1476 -75.91654 9.368235  27.68876
## 1477 1477 -75.06112 8.991934  34.08025
## 1478 1478 -75.66932 7.353278  45.49478
## 1479 1479 -75.90560 7.607062  23.42189
## 1480 1480 -75.24927 9.381361  21.06710
## 1481 1481 -74.89922 7.567681  22.56794
## 1482 1482 -74.91891 7.504235  39.75277
## 1483 1483 -75.53587 9.011624  27.92397
## 1484 1484 -75.42210 7.771146  25.92608
## 1485 1485 -75.50086 9.101324  37.14891
## 1486 1486 -75.94936 8.311531  25.33663
## 1487 1487 -75.41991 7.447353  32.82365
## 1488 1488 -75.11581 9.112263  20.61677
## 1489 1489 -75.71964 7.948357  23.28113
## 1490 1490 -75.05237 7.760207  17.97928
## 1491 1491 -76.44161 8.615634  35.89068
## 1492 1492 -75.56212 7.812714  14.81879
## 1493 1493 -75.55556 9.164770  16.67428
## 1494 1494 -75.75902 9.348544  28.06573
## 1495 1495 -75.81590 9.013812  47.41912
## 1496 1496 -75.66057 8.932864  53.41960
## 1497 1497 -76.47005 8.490930  21.38989
## 1498 1498 -76.27972 7.611437  25.97965
## 1499 1499 -75.97343 7.779897  27.42372
## 1500 1500 -75.49430 9.278535  16.49611
## 1501 1501 -75.45492 7.976799  30.91677
## 1502 1502 -75.35428 8.635324  36.54804
## 1503 1503 -74.89703 8.600319  45.53068
## 1504 1504 -75.28646 8.418733  24.98732
## 1505 1505 -76.48099 7.753644  24.62711
## 1506 1506 -75.13769 9.072883  19.64466
## 1507 1507 -75.19238 7.806151  44.67292
## 1508 1508 -75.43523 9.158206  15.57116
## 1509 1509 -76.14407 8.309343  22.89381
## 1510 1510 -75.12019 7.366404  36.65660
## 1511 1511 -75.15519 9.158206  23.04481
## 1512 1512 -75.01517 8.954742  26.51771
## 1513 1513 -75.39366 7.379531  33.62188
## 1514 1514 -74.94954 7.834592  14.83457
## 1515 1515 -76.06313 7.442977  66.60061
## 1516 1516 -76.09157 7.950545  65.64711
## 1517 1517 -75.34115 8.479991  15.11097
## 1518 1518 -75.83778 8.412169  23.02009
## 1519 1519 -76.25128 8.482179  38.43941
## 1520 1520 -74.92110 7.696761  19.96815
## 1521 1521 -74.92985 8.335597  27.74280
## 1522 1522 -75.27552 9.182272  26.84275
## 1523 1523 -75.48117 8.276526  16.03973
## 1524 1524 -74.98236 8.460301  47.63713
## 1525 1525 -74.95829 7.620188  16.26665
## 1526 1526 -75.85529 8.560939  26.46340
## 1527 1527 -75.33897 9.160394  13.13918
## 1528 1528 -75.13550 7.738329  14.36683
## 1529 1529 -74.91453 9.250094  18.84212
## 1530 1530 -75.71308 7.793024  25.02224
## 1531 1531 -75.99968 9.145080  36.66647
## 1532 1532 -75.46367 8.679080  23.82583
## 1533 1533 -75.86622 8.602507  23.16379
## 1534 1534 -75.15082 8.884732  24.55332
## 1535 1535 -75.03049 9.341981  18.16883
## 1536 1536 -76.22065 8.379352  35.51750
## 1537 1537 -76.32785 8.812535  32.97531
## 1538 1538 -76.26440 8.112442  38.73800
## 1539 1539 -75.40022 8.267775  11.87062
## 1540 1540 -75.16613 9.405427  14.58766
## 1541 1541 -75.68901 7.838968  19.85151
## 1542 1542 -75.40460 8.073061  24.71277
## 1543 1543 -75.20989 9.293850  26.15186
## 1544 1544 -76.17689 8.528122  33.41683
## 1545 1545 -75.73058 8.948178  49.11057
## 1546 1546 -76.46568 8.543437  23.91713
## 1547 1547 -74.94735 8.068686  22.72158
## 1548 1548 -75.48336 7.392658  85.75941
## 1549 1549 -75.01736 8.051184  16.35220
## 1550 1550 -76.31691 8.254648  37.89213
## 1551 1551 -75.03924 8.199953  16.00379
## 1552 1552 -76.31472 7.554555  73.33015
## 1553 1553 -75.29521 9.053192  18.67991
## 1554 1554 -76.04781 8.512808  33.14134
## 1555 1555 -75.36960 7.939606  17.38439
## 1556 1556 -75.08518 9.175709  13.70553
## 1557 1557 -74.88391 8.311531  25.52898
## 1558 1558 -75.41991 9.344169  28.46518
## 1559 1559 -75.26021 7.508611  27.15524
## 1560 1560 -74.93860 8.755653  76.87488
## 1561 1561 -75.39585 8.696582  21.45784
## 1562 1562 -75.95592 9.247906  45.66822
## 1563 1563 -74.90360 8.980995  51.35890
## 1564 1564 -76.44599 8.046808  38.96456
## 1565 1565 -75.85310 8.849728  22.17683
## 1566 1566 -75.49430 8.576254  16.82937
## 1567 1567 -75.43742 8.755653  33.16401
## 1568 1568 -76.50506 7.867409  41.38834
## 1569 1569 -75.19238 7.639879  23.48929
## 1570 1570 -75.13769 9.061944  25.97303
## 1571 1571 -76.18564 7.425475  72.52258
## 1572 1572 -74.75483 8.869418  50.66947
## 1573 1573 -76.21846 8.683456  16.61765
## 1574 1574 -76.00406 7.504235  53.15662
## 1575 1575 -74.92329 7.558930  43.61558
## 1576 1576 -74.93422 8.197766  13.37321
## 1577 1577 -75.62994 8.178075  14.64268
## 1578 1578 -75.43085 9.156019  20.56091
## 1579 1579 -76.43067 8.370601  31.08283
## 1580 1580 -76.11126 8.108066  14.18861
## 1581 1581 -75.50524 9.315728  30.19745
## 1582 1582 -75.86622 9.068507  53.27569
## 1583 1583 -75.43742 8.420920  16.11209
## 1584 1584 -75.84216 7.701137  38.75747
## 1585 1585 -75.97561 8.403418  21.72030
## 1586 1586 -75.67370 7.895850  12.78211
## 1587 1587 -74.94735 7.488921  27.54790
## 1588 1588 -75.42867 9.405427  29.33595
## 1589 1589 -75.36741 8.690019  24.69741
## 1590 1590 -76.02812 8.143071  14.67793
## 1591 1591 -76.10251 9.201962  38.24831
## 1592 1592 -75.70214 8.541249  17.03362
## 1593 1593 -76.32785 7.692386  37.23574
## 1594 1594 -75.96686 8.928488  23.83062
## 1595 1595 -75.76340 8.690019  48.90523
## 1596 1596 -74.82046 8.237146  37.50281
## 1597 1597 -76.20533 8.698770  19.96984
## 1598 1598 -75.21645 7.683634  23.20924
## 1599 1599 -76.43943 8.545625  20.63773
## 1600 1600 -75.25145 8.991934  30.46904
## 1601 1601 -76.50068 7.421099  77.89492
## 1602 1602 -76.19439 7.900226  28.30267
## 1603 1603 -75.66932 9.398864  29.22232
## 1604 1604 -75.61682 8.007428  16.57249
## 1605 1605 -75.50305 7.591747  23.46616
## 1606 1606 -75.68901 8.552188  17.56372
## 1607 1607 -75.71089 7.554555  31.32004
## 1608 1608 -76.38254 8.576254  24.29790
## 1609 1609 -75.82028 7.987738  20.06705
## 1610 1610 -75.43523 9.072883  34.22544
## 1611 1611 -75.22958 9.026939  29.02472
## 1612 1612 -75.81372 9.131953  53.77287
## 1613 1613 -75.86185 7.869597  27.70122
## 1614 1614 -76.51819 7.672695  94.58137
## 1615 1615 -75.96249 7.830216  29.69020
## 1616 1616 -75.60150 7.908977  20.51739
## 1617 1617 -74.91235 9.147268  10.57047
## 1618 1618 -76.23377 8.690019  49.79582
## 1619 1619 -76.28847 7.618001  29.14223
## 1620 1620 -75.11362 7.712076  19.32926
## 1621 1621 -76.37817 7.392658  86.22446
## 1622 1622 -76.17908 8.418733  24.57798
## 1623 1623 -75.54243 9.037878  20.16363
## 1624 1624 -75.33459 8.357474  16.36431
## 1625 1625 -75.27552 8.488742  19.41858
## 1626 1626 -74.99548 7.416724  47.75975
## 1627 1627 -75.66276 8.285277  27.79326
## 1628 1628 -75.31928 7.992113  20.13199
## 1629 1629 -76.27097 8.733775  30.15373
## 1630 1630 -75.58837 7.981174  20.21615
## 1631 1631 -76.40223 8.574066  24.74866
## 1632 1632 -74.78327 9.425117  35.82970
## 1633 1633 -76.36067 7.539240  73.32922
## 1634 1634 -76.27315 8.580629  28.09310
## 1635 1635 -74.99330 8.197766  14.08192
## 1636 1636 -75.11800 8.795033  25.35253
## 1637 1637 -74.86422 8.770967  42.49812
## 1638 1638 -74.97360 7.587371  22.88523
## 1639 1639 -75.37397 9.005061  22.47174
## 1640 1640 -75.60150 7.657381  26.35739
## 1641 1641 -74.80952 8.348723  28.89093
## 1642 1642 -76.06531 7.797400  36.21469
## 1643 1643 -75.51399 7.462667  38.66615
## 1644 1644 -75.64526 8.670329  14.11207
## 1645 1645 -75.62557 7.904601  15.17042
## 1646 1646 -75.76340 8.615634  18.17611
## 1647 1647 -75.19020 8.031493  15.31400
## 1648 1648 -75.12456 8.186827  11.06841
## 1649 1649 -75.19676 8.840977  31.42736
## 1650 1650 -75.19020 8.027118  16.65754
## 1651 1651 -75.97561 9.110075  27.56363
## 1652 1652 -76.43286 8.541249  14.87028
## 1653 1653 -75.23176 8.882545  24.82857
## 1654 1654 -75.60369 7.526113  40.37266
## 1655 1655 -74.77452 8.261212  30.50645
## 1656 1656 -74.88828 7.928667  19.41807
## 1657 1657 -74.86422 8.547812  32.19085
## 1658 1658 -76.09157 7.788648  41.78369
## 1659 1659 -75.51618 9.044441  19.65776
## 1660 1660 -75.53587 8.399043  13.42074
## 1661 1661 -75.59931 9.206338  24.87223
## 1662 1662 -75.60806 8.158385  28.33183
## 1663 1663 -76.13970 8.088376  23.96947
## 1664 1664 -76.13970 9.153831  52.79066
## 1665 1665 -75.95374 9.341981  43.91915
## 1666 1666 -76.35410 8.016179  34.12669
## 1667 1667 -75.05893 7.530489  28.73641
## 1668 1668 -75.01299 7.644254  22.25146
## 1669 1669 -76.05000 9.158206  37.93252
## 1670 1670 -76.37160 8.412169  34.60609
## 1671 1671 -74.99111 8.460301  46.39294
## 1672 1672 -75.43304 8.241521  12.25632
## 1673 1673 -74.92329 8.357474  23.28105
## 1674 1674 -75.11144 7.653005  30.78077
## 1675 1675 -75.78090 7.661756  32.68599
## 1676 1676 -75.15300 9.379174  13.74164
## 1677 1677 -76.01281 7.519550  92.45084
## 1678 1678 -76.30816 8.952554  51.54703
## 1679 1679 -75.97780 7.775522  25.25772
## 1680 1680 -75.90342 8.464676  18.84040
## 1681 1681 -75.03486 8.812535  61.39713
## 1682 1682 -76.02812 9.013812  26.27451

Visualización interactiva de los puntos de muestreo con agrupamiento (clustering):

# Creamos paleta de colores para los marcadores según valor de SOC
pal_markers <- colorNumeric(
  palette = c("#F4E4C1", "#D4A574", "#8B7355", "#6B8E23", "#2F4F2F"),
  domain = sitios$soc
)

m <- leaflet() %>%
  addTiles() %>%  
  addCircleMarkers(
    data = sitios,  # 👈 importante
    lng = ~longit,
    lat = ~latit,
    radius = 5,
    fillColor = ~pal_markers(soc),  # 👈 sin "sitios$"
    color = "#8B4513",
    weight = 1,
    opacity = 0.8,
    fillOpacity = 0.7,
    popup = paste(
      "<b>ID:</b>", sitios$id, "<br>",
      "<b>SOC:</b>", round(sitios$soc, 2), "g/kg<br>",
      "<b>Longitud:</b>", round(sitios$longit, 4), "<br>",
      "<b>Latitud:</b>", round(sitios$latit, 4)
    ),
    clusterOptions = markerClusterOptions()
  ) %>%
  addLegend(
    pal = pal_markers,
    values = sitios$soc,
    title = "SOC (g/kg)",
    position = "bottomright"
  )

m

El parámetro clusterOptions = markerClusterOptions() agrupa los marcadores cercanos cuando el zoom está alejado, mejorando la legibilidad del mapa. Al hacer clic en un cluster, el mapa hace zoom para mostrar los puntos individuales.

7 7. Guardar las muestras

La función sf::st_write() guarda los datos espaciales en diversos formatos. En este caso usamos GeoPackage (GPKG), que es: - Un formato abierto y estándar - Eficiente para datos vectoriales - Compatible con múltiples software GIS - Puede almacenar múltiples capas en un solo archivo

# Asegúrate de que la carpeta "data" exista o cambia la ruta
sf::st_write(nmuestras, "data/soc_cordoba.gpkg", driver = "GPKG",
             append = FALSE)

## Deleting layer `soc_cordoba' using driver `GPKG'
## Writing layer `soc_cordoba' to data source `data/soc_cordoba.gpkg' using driver `GPKG'
## Writing 1682 features with 1 fields and geometry type Point.

¿Qué hace append = FALSE?

El parámetro append = FALSE indica que si el archivo ya existe, debe ser sobrescrito completamente. Si usáramos append = TRUE, se agregarían los datos al archivo existente sin eliminar el contenido previo. Usar FALSE asegura que siempre tenemos la versión más reciente de nuestros datos.

Importante: Recuerda llevar el archivo soc_cordoba.gpkg a clase o compartirlo con tu equipo de trabajo.

8 8. Referencia

Este trabajo está basado en:

Lizarazo, I. 2025. How to download soil data from SoilGrids. Disponible en: https://rpubs.com/ials2un/downloadSoilGrids

Adaptación para el departamento de Córdoba: - Autor: Julian Santiago Gonzalez Malpica - Fecha: Noviembre 6, 2025 - Variables analizadas: Carbono Orgánico del Suelo (SOC), profundidad 15-30 cm - Área de estudio: Departamento de Córdoba, Colombia

Fuente de datos: - SoilGrids 250m v2.0. ISRIC - World Soil Information. https://www.isric.org/explore/soilgrids

Cita sugerida: Gonzalez Malpica, J.S. 2025. Análisis de carbono orgánico del suelo en el departamento de Córdoba usando datos de SoilGrids. Trabajo de curso.

---

# Información de la sesión de R (útil para reproducibilidad)
sessionInfo()

## R version 4.5.1 (2025-06-13 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 10 x64 (build 19045)
## 
## 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] dplyr_1.1.4         XML_3.99-0.19       gdalUtilities_1.2.5
## [4] RColorBrewer_1.1-3  leaflet_2.2.3       sf_1.0-21          
## [7] tidyterra_0.7.2     ggplot2_4.0.0       terra_1.8-80       
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.10        generics_0.1.4     tidyr_1.3.1        class_7.3-23      
##  [5] KernSmooth_2.23-26 digest_0.6.37      magrittr_2.0.4     evaluate_1.0.5    
##  [9] grid_4.5.1         fastmap_1.2.0      jsonlite_2.0.0     e1071_1.7-16      
## [13] DBI_1.2.3          purrr_1.2.0        crosstalk_1.2.2    scales_1.4.0      
## [17] codetools_0.2-20   jquerylib_0.1.4    cli_3.6.5          rlang_1.1.6       
## [21] units_1.0-0        withr_3.0.2        cachem_1.1.0       yaml_2.3.10       
## [25] tools_4.5.1        vctrs_0.6.5        R6_2.6.1           png_0.1-8         
## [29] proxy_0.4-27       lifecycle_1.0.4    classInt_0.4-11    htmlwidgets_1.6.4 
## [33] pkgconfig_2.0.3    pillar_1.11.1      bslib_0.9.0        gtable_0.3.6      
## [37] glue_1.8.0         data.table_1.17.8  Rcpp_1.1.0         xfun_0.54         
## [41] tibble_3.3.0       tidyselect_1.2.1   rstudioapi_0.17.1  knitr_1.50        
## [45] farver_2.1.2       htmltools_0.5.8.1  rmarkdown_2.30     labeling_0.4.3    
## [49] compiler_4.5.1     S7_0.2.0

---

8.1 Notas finales

Colores utilizados: - Se empleó una paleta geomorfológica que simula la transición natural de suelos: - #F4E4C1: Suelos arenosos claros (bajo contenido orgánico) - #D4A574: Suelos arcillosos - #8B7355: Suelos con contenido medio de materia orgánica - #6B8E23: Suelos fértiles con alto contenido orgánico - #2F4F2F: Suelos muy ricos en materia orgánica