Last updated: 2021-05-19

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Knit directory: booksn_ppm/

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La idea es seleccionar un conjunto de parcelas en torno a Sierra Nevada de la forma menos subjetiva posible.

  • Leer datos de SN y crear buffer
sn <- st_read("data/data_raw/geoinfo/sn_enp.shp") 
Reading layer `sn_enp' from data source `/Users/ajpelu/Google Drive/MS/books/2021_SN/booksn_ppm/data/data_raw/geoinfo/sn_enp.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 10 fields
geometry type:  POLYGON
dimension:      XY
bbox:           xmin: 442911.5 ymin: 4085480 xmax: 536730.7 ymax: 4123200
projected CRS:  ED50 / UTM zone 30N
rodales <- st_as_sf(mapa.rodales) %>% 
  st_transform(crs = st_crs(sn)) %>% 

rodal2019 <- st_read(here::here("data/data_raw/geoinfo/coberturas_procesionaria/COB270616_ETRS89.shp")) %>% 
  st_transform(crs = st_crs(sn)) %>% 
Reading layer `COB270616_ETRS89' from data source `/Users/ajpelu/Google Drive/MS/books/2021_SN/booksn_ppm/data/data_raw/geoinfo/coberturas_procesionaria/COB270616_ETRS89.shp' using driver `ESRI Shapefile'
Simple feature collection with 4550 features and 11 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: 103172.9 ymin: 3991740 xmax: 599551.6 ymax: 4276615
projected CRS:  ETRS89 / UTM zone 30N

¿Cuantos rodales hay en cada buffer?

  • Generamos varios buffers (10, 15, 20, 25 km) del límite de SN

  • Calculamos la cantidad de parcelas que caen en cada buffer, clasificadas por los niveles de elevación que hemos definido previamente.

  • ojo parece que había un problema con la seleccion que hacía de las parcelas (la capa que me paso Luis no se hacia bien la interseccion). Por ello hemos realizado esta operación con dos conjuntos de datos espaciales (es indiferente porque lo que queremos es ver que buffer elegir)

bufferiza <- function(parcelas, enp, d){ 

  buffer <- st_buffer(enp, units::set_units(d, "km"))
  namebuffer <- paste0('buffer_',d) 
  df <- st_intersection(parcelas, buffer) %>% 
    st_drop_geometry() %>% 
    # dplyr::select(code = `N.rodal`) %>% 
    mutate({{namebuffer}} := 1) 

i25 <- bufferiza(rodales, sn, 25) %>% dplyr::select(code = `N.rodal`, buffer_25)
i25b <- bufferiza(rodal2019, sn, 25) %>% dplyr::select(code = `N_CODIGO`, buffer_25)

i20 <- bufferiza(rodales, sn, 20) %>% dplyr::select(code = `N.rodal`, buffer_20)
i20b <- bufferiza(rodal2019, sn, 20) %>% dplyr::select(code = `N_CODIGO`, buffer_20)

i15 <- bufferiza(rodales, sn, 15) %>% dplyr::select(code = `N.rodal`, buffer_15)
i15b <- bufferiza(rodal2019, sn, 15) %>% dplyr::select(code = `N_CODIGO`, buffer_15)

i10 <- bufferiza(rodales, sn, 10) %>% dplyr::select(code = `N.rodal`, buffer_10)
i10b <- bufferiza(rodal2019, sn, 10) %>% dplyr::select(code = `N_CODIGO`, buffer_10)

df <- i25 %>% 
  full_join(i20) %>% 
  full_join(i15) %>% 
dfb <- i25b %>% 
  full_join(i20b) %>% 
  full_join(i15b) %>% 
coplas2019 <- read_csv(here::here("data/coplas2019.csv"))
df_elev <- df %>% 
  left_join(coplas2019) %>% 
  dplyr::select(code, buffer_25:buffer_10, sp, elevF,  sp_abrev, especie)

df_elevb <- dfb %>% 
  left_join(coplas2019) %>% 
  dplyr::select(code, buffer_25:buffer_10, sp, elevF,  sp_abrev, especie)

n_parcelas <- df_elev %>% 
  group_by(elevF) %>%
  summarise(across(starts_with('buf'), sum, na.rm = TRUE)) %>% 

n_parcelasb <- df_elevb %>% 
  group_by(elevF) %>%
  summarise(across(starts_with('buf'), sum, na.rm = TRUE)) %>% 

¿Qué parcelas seleccionamos?

  • Seleccionamos buffer 20 km.
  • Incluimos el piquito de Motril, es decir, las parcelas GR140011 y GR140013.
  • Excluimos Sierra Alhamilla, i.e. algunas de las siguientes parcelas: AL088001, AL088002, AL088003, AL088004, AL088005, AL088006, AL078001, AL078002, AL078003, AL074001, AL074002, AL013001, AL013002.
exclude <- c("AL088001", "AL088002", "AL088003", "AL088004", "AL088005", 
            "AL088006", "AL078001", "AL078002", "AL078003", "AL074001", 
            "AL074002", "AL013001", "AL013002") <- coplas2019 %>% 
  left_join(i20b) %>% 
  filter(!(code %in% exclude)) %>% 
  mutate(buffer_20 = case_when(
    code %in% c("GR140011", "GR140013") ~ 1,
    TRUE ~ buffer_20)) %>% 
  filter(buffer_20 == 1) %>% 

write_csv(, here::here("data/coplas2019sn.csv"))

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] DiagrammeR_1.0.6.1 sp_1.4-5           DT_0.17            flextable_0.6.3   
 [5] sf_0.9-7           finch_0.4.0        here_1.0.1         forcats_0.5.1     
 [9] stringr_1.4.0      dplyr_1.0.4        purrr_0.3.4        readr_1.4.0       
[13] tidyr_1.1.2        tibble_3.0.6       ggplot2_3.3.3      tidyverse_1.3.0   
[17] workflowr_1.6.2   

loaded via a namespace (and not attached):
 [1] fs_1.5.0           lubridate_1.7.10   RColorBrewer_1.1-2 httr_1.4.2        
 [5] rprojroot_2.0.2    tools_4.0.2        backports_1.2.1    bslib_0.2.4       
 [9] R6_2.5.0           KernSmooth_2.23-18 rgeos_0.5-5        DBI_1.1.1         
[13] lazyeval_0.2.2     colorspace_2.0-0   withr_2.4.1        tidyselect_1.1.0  
[17] curl_4.3           compiler_4.0.2     git2r_0.28.0       cli_2.3.0         
[21] rvest_0.3.6        jsonld_2.2         xml2_1.3.2         officer_0.3.16    
[25] sass_0.3.1         scales_1.1.1       classInt_0.4-3     rappdirs_0.3.3    
[29] systemfonts_1.0.0  digest_0.6.27      rmarkdown_2.6.6    base64enc_0.1-3   
[33] pkgconfig_2.0.3    htmltools_0.5.1.1  dbplyr_2.1.0       htmlwidgets_1.5.3 
[37] rlang_0.4.10       readxl_1.3.1       rstudioapi_0.13    visNetwork_2.0.9  
[41] jquerylib_0.1.3    generics_0.1.0     emld_0.5.1         jsonlite_1.7.2    
[45] crosstalk_1.1.1    zip_2.1.1          magrittr_2.0.1     Rcpp_1.0.6        
[49] munsell_0.5.0      gdtools_0.2.3      lifecycle_1.0.0    stringi_1.5.3     
[53] whisker_0.4        yaml_2.2.1         jqr_1.2.0          grid_4.0.2        
[57] EML_2.0.4          promises_1.2.0.1   crayon_1.4.1       lattice_0.20-41   
[61] haven_2.3.1        hms_1.0.0          knitr_1.31         pillar_1.4.7      
[65] uuid_0.1-4         reprex_1.0.0       glue_1.4.2         evaluate_0.14     
[69] V8_3.4.0           hoardr_0.5.2       data.table_1.13.6  modelr_0.1.8      
[73] vctrs_0.3.6        httpuv_1.5.5       cellranger_1.1.0   gtable_0.3.0      
[77] assertthat_0.2.1   xfun_0.20          broom_0.7.4        e1071_1.7-4       
[81] later_1.1.0.1      class_7.3-18       units_0.6-7        ellipsis_0.3.1