Mapa de interpolacion con poligonos de Thiessen
install.packages("spatstat")
library(spatstat)
library(tmap)
Magdal <- shapefile("C:/Users/vale_/Desktop/UNAL/6to semestre/GB/magda2.shp")
Precipitacion <- shapefile("C:/Users/vale_/Desktop/UNAL/6to semestre/GB/prec2.shp")
Precipitacion@bbox <-Magdal@bbox
th <- as(dirichlet(as.ppp(Precipitacion)), "SpatialPolygons")
crs(th) <- crs(Precipitacion)
crs(Magdal) <- crs(Precipitacion)
crs(th)
CRS arguments:
+proj=tmerc +lat_0=4.59620041666667
+lon_0=-74.0775079166667 +k=1 +x_0=1000000
+y_0=1000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0
+units=m +no_defs
crs(Precipitacion)
CRS arguments:
+proj=tmerc +lat_0=4.59620041666667
+lon_0=-74.0775079166667 +k=1 +x_0=1000000
+y_0=1000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0
+units=m +no_defs
th.z <- over(th, Precipitacion, fn=mean)
th.spdf <- SpatialPolygonsDataFrame(th, th.z)
th.clp <- raster::intersect(Magdal,th.spdf)
tm_shape(th.clp) +
tm_polygons(col="rainfall", palette="RdBu", midpoint=25.0,
title="Polígonos Thiessen \nPrecipitación prevista \n(en mm)") +
tm_legend(legend.outside=TRUE)

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=Spanish_Colombia.1252
[2] LC_CTYPE=Spanish_Colombia.1252
[3] LC_MONETARY=Spanish_Colombia.1252
[4] LC_NUMERIC=C
[5] LC_TIME=Spanish_Colombia.1252
attached base packages:
[1] stats graphics grDevices utils datasets
[6] methods base
other attached packages:
[1] spatstat_1.64-1 rpart_4.1-15
[3] nlme_3.1-144 spatstat.data_1.4-3
[5] RColorBrewer_1.1-2 gstat_2.0-6
[7] tmap_3.0 SpatialPosition_2.0.1
[9] cartography_2.4.1 rgeos_0.5-3
[11] readxl_1.3.1 lwgeom_0.2-4
[13] leaflet_2.0.3 scales_1.1.1
[15] sf_0.9-3 forcats_0.5.0
[17] stringr_1.4.0 dplyr_1.0.0
[19] purrr_0.3.4 readr_1.3.1
[21] tidyr_1.1.0 tibble_3.0.1
[23] ggplot2_3.3.1 tidyverse_1.3.0
[25] elevatr_0.2.0 rgdal_1.5-8
[27] rgl_0.100.54 rasterVis_0.47
[29] latticeExtra_0.6-29 lattice_0.20-38
[31] raster_3.1-5 sp_1.4-2
loaded via a namespace (and not attached):
[1] backports_1.1.7 lazyeval_0.2.2
[3] splines_3.6.3 jqr_1.1.0
[5] crosstalk_1.1.0.1 digest_0.6.25
[7] htmltools_0.4.0 rsconnect_0.8.16
[9] leaflet.providers_1.9.0 fansi_0.4.1
[11] magrittr_1.5 tensor_1.5
[13] modelr_0.1.8 xts_0.12-0
[15] jpeg_0.1-8.1 colorspace_1.4-1
[17] blob_1.2.1 rvest_0.3.5
[19] haven_2.3.0 xfun_0.14
[21] leafem_0.1.1 crayon_1.3.4
[23] jsonlite_1.6.1 hexbin_1.28.1
[25] zoo_1.8-8 glue_1.4.1
[27] stars_0.4-1 polyclip_1.10-0
[29] gtable_0.3.0 webshot_0.5.2
[31] V8_3.2.0 abind_1.4-5
[33] DBI_1.1.0 miniUI_0.1.1.1
[35] Rcpp_1.0.4.6 isoband_0.2.2.9000
[37] viridisLite_0.3.0 xtable_1.8-4
[39] units_0.6-6 foreign_0.8-75
[41] intervals_0.15.2 htmlwidgets_1.5.1
[43] httr_1.4.1 FNN_1.1.3
[45] ellipsis_0.3.1 pkgconfig_2.0.3
[47] XML_3.99-0.3 farver_2.0.3
[49] deldir_0.1-25 dbplyr_1.4.4
[51] crul_0.9.0 tidyselect_1.1.0
[53] rlang_0.4.6 manipulateWidget_0.10.1
[55] later_1.0.0 tmaptools_3.0
[57] munsell_0.5.0 cellranger_1.1.0
[59] tools_3.6.3 cli_2.0.2
[61] generics_0.0.2 broom_0.5.6
[63] evaluate_0.14 fastmap_1.0.1
[65] goftest_1.2-2 yaml_2.2.1
[67] leafsync_0.1.0 knitr_1.28
[69] fs_1.4.1 mime_0.9
[71] xml2_1.2.5 compiler_3.6.3
[73] rstudioapi_0.11 curl_4.3
[75] png_0.1-7 e1071_1.7-3
[77] spatstat.utils_1.17-0 reprex_0.3.0
[79] spacetime_1.2-3 stringi_1.4.6
[81] Matrix_1.2-18 classInt_0.4-3
[83] markdown_1.1 vctrs_0.3.0
[85] slippymath_0.3.1 pillar_1.4.4
[87] lifecycle_0.2.0 geojsonio_0.9.2
[89] maptools_1.0-1 httpuv_1.5.3.1
[91] R6_2.4.1 promises_1.1.0
[93] KernSmooth_2.23-16 codetools_0.2-16
[95] dichromat_2.0-0 assertthat_0.2.1
[97] withr_2.2.0 httpcode_0.3.0
[99] mgcv_1.8-31 parallel_3.6.3
[101] hms_0.5.3 geojson_0.3.4
[103] grid_3.6.3 class_7.3-15
[105] rmarkdown_2.1 shiny_1.4.0.2
[107] lubridate_1.7.8 base64enc_0.1-3
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