In NUDEPU/GeoNUDEPU:

knitr::opts_chunk$set(cache = FALSE, echo = FALSE, warning = FALSE, 
                      message = FALSE, fig.align = "center", out.width = "75%")
knitr::knit_hooks$set(mysize = function(before, options, envir) {
  if (before) 
    return(options$size)
})
library(readxl)
library(sf)
library(leaflet)
library(leafem)
library(lwgeom)
library(raster)
library(knitr)
library(kableExtra)
library(appraiseR)
library(units)
install_symbolic_unit(name = "Reais", warn = TRUE, dimensionless = TRUE)
library(geosphere)
library(GeoNUDEPU)

# 1. Digitação das Coordenadas
#
coords <- read_excel(params$data)
#
#
# 2. Criação da feição  
#
# Atentar para a definição do crs apropriado
# 
#
# Exemplos:
# WGS 84 = 4326
# SAD 69 22S = 29192
# SIRGAS 2000 22S = 31982
#
#pl1 <- st_sf(geom = st_sfc(st_polygon(list(area1))))
#pl2 <- st_sf(geom = st_sfc(st_polygon(list(area2))))
#spl <- st_sfc(geom = st_combine(rbind(pl1, pl2)), crs = 29192)
spl <- st_sfc(st_polygon(list(as.matrix(coords[, c("E", "N")]))), crs = params$crs)
#
# 3. Criação dos atributos  
#
vigencia <- as.POSIXlt(params$inicio)
vigencia$year <- vigencia$year + params$prazo
attr <- data.frame(
  destinacao = params$destinacao,
  tipo = params$tipo,
  rip = params$rip,
  interessado = params$interessado,
  area = st_area(spl),
  area_uniao = st_area(spl),
  inicio = params$inicio,
  prazo = params$prazo,
  vigencia = vigencia ,
  cnpj = params$cnpj,
  nup = params$nup,
  protocolo = params$protocolo,
  ref = params$ref,
  concedida = params$concedida,
  municipio = params$municipio,
  logradouro = params$logradouro,
  trecho = params$trecho,
  aval = set_units(params$aval, Reais/m^2)*st_area(spl),
  dataaval = params$dataaval,
  refaval = params$refaval,
  onerosa = params$onerosa,
  geometriaAproximada = params$geometriaAproximada)
#
# 4. União dos atributos à geometria
#
#
spl_df <- st_sf(attr,
                geometry = spl) %>%
  st_transform(crs = 4326) # Transforma para WGS84.

# Leitura de dados da SPU-SC-NUDEM
#
data("polUniao")
data("lpmHomologada")
data("lpmDermarcada")
data("lpmPresumida")
data("lltmHomologada")
data("lltmDemarcada")
data("lltmPresumida")
#
# Leitura de dados das camadas existentes
#
ocupacoes <- st_read("https://raw.githubusercontent.com/geoSPU/SPU-SC-NUPRIV/master/ocupacoes.geojson")
autobras <- st_read("https://github.com/geoSPU/SPUData/raw/master/autobras.geojson")
cessoes <- st_read("https://raw.githubusercontent.com/geoSPU/SPUData/master/cessoes.geojson")
entregas <- st_read("https://raw.githubusercontent.com/geoSPU/SPUData/master/entregas.geojson")
certdisp <- st_read("https://github.com/geoSPU/SPUData/raw/master/certdisp.geojson")
ranchos <- st_read("https://raw.githubusercontent.com/geoSPU/SPUData/master/ranchos_de_pesca.geojson")
aeroportos <- st_read("https://github.com/geoSPU/SPUData/raw/master/aeroportos.geojson")
portos <- st_read("https://github.com/Cadastro-Marinho/BrasilData/raw/master/portos.geojson")

Poligonal em análise -- Ortofotos

ext <- extent(spl_df)
m <- spl_df %>% 
  leaflet() %>% 
  # Adiciona ortofotos do Estado de SC (SIG-SC) como base
  addWMSTiles(baseUrl = "http://sigsc.sc.gov.br/sigserver/SIGSC/wms", 
              layers = list("OrtoRGB-Landsat-2012")) %>%
  # Adiciona como camada de base Fotos de satélite da ESRI
  # addProviderTiles(providers$Esri.WorldImagery) %>%
  # Adiciona o(s) polígono(s) em análise
  addPolygons(stroke = TRUE, weight = 2, color = "blue", 
              group = "Em análise") %>%
  # Adiciona outras camadas existentes
  # addPolygons(data = cessoes, color = "red") %>% 
  # addPolygons(data = certdisp, color = "white", fillColor = "blue", 
  #             dashArray = "10 10") %>% 
  # addPolygons(data = autobras, color = "white", fillColor = "grey", 
  #             dashArray = "10 10") %>% 
  # addPolygons(data = entregas, color = "white", fillColor = "white", 
  #             dashArray = "10 10") %>% 
  # Adiciona polígonos da União
  addPolygons(data = polUniao, color = "yellow", group = "Terras de Marinha") %>% 
  # Adiciona Linhas de Marinha
  # addPolylines(data = lltmHomologada, color = "red") %>% 
  # addPolylines(data = lpmHomologada, color = "blue") %>% 
  # addPolylines(data = lltmDemarcada, color = "red", dashArray = "10 10") %>%
  # addPolylines(data = lpmDemarcada, color = "blue", dashArray = "10 10") %>%
  addPolylines(data = lltmPresumida, color = "red", dashArray = "10 10",
               opacity = .2, weight = 3) %>%
  addPolylines(data = lpmPresumida, color = "blue", dashArray = "10 10",
               opacity = .2, weight = 3) %>%
  # Zoom no imóvel em análise
  fitBounds(lng1 = ext[1], lat1 = ext[3], lng2 = ext[2], lat2 = ext[4]) %>% 
  # Adiciona Legendas
  addLegend(labels = "Terras de Marinha", colors = "yellow", 
            group = "Terras de Marinha", position = "topright") %>% 
  addLegend(labels = "Em análise", colors = "blue", 
            group = "Em análise", position = "topright") %>% 
  # Adiciona Minimapa
  addMiniMap(position = "topleft", 
             tiles = providers$Esri.WorldGrayCanvas) %>% 
  addMouseCoordinates() %>%
  addLogo("SPU.jpg", src = "local", position = "bottomleft", 
                  offset.x = 15, offset.y = 15, 
                  width = 75, height = 75)
name <- paste(st_geohash(spl_df, 10), ".png", sep = "")
mapview::mapshot(m, file = name, remove_url = FALSE)
knitr::include_graphics(name)

Poligonal em análise -- ESRI Imagery

centroide <- st_coordinates(st_centroid(spl_df))
m <- spl_df %>% 
  leaflet() %>% 
  # Adiciona ortofotos do Estado de SC (SIG-SC) como base
  # addWMSTiles(baseUrl = "http://sigsc.sc.gov.br/sigserver/SIGSC/wms", 
  #             layers = list("OrtoRGB-Landsat-2012")) %>%
  # Adiciona como camada de base Fotos de satélite da ESRI
  addProviderTiles(providers$Esri.WorldImagery) %>%
  # Adiciona o(s) polígono(s) em análise
  addPolygons(stroke = TRUE, weight = 2, color = "blue", 
              group = "Em análise") %>%
  # Adiciona outras camadas existentes
  # addPolygons(data = cessoes, color = "red") %>% 
  # addPolygons(data = certdisp, color = "white", fillColor = "blue", 
  #             dashArray = "10 10") %>% 
  # addPolygons(data = autobras, color = "white", fillColor = "grey", 
  #             dashArray = "10 10") %>% 
  # addPolygons(data = entregas, color = "white", fillColor = "white", 
  #             dashArray = "10 10") %>% 
  # Adiciona polígonos da União
  addPolygons(data = polUniao, color = "yellow", group = "Terras de Marinha") %>% 
  # Adiciona Linhas de Marinha
  # addPolylines(data = lltmHomologada, color = "red") %>% 
  # addPolylines(data = lpmHomologada, color = "blue") %>% 
  # addPolylines(data = lltmDemarcada, color = "red", dashArray = "10 10") %>%
  # addPolylines(data = lpmDemarcada, color = "blue", dashArray = "10 10") %>%
  addPolylines(data = lltmPresumida, color = "red", dashArray = "10 10",
               opacity = .2, weight = 3) %>%
  addPolylines(data = lpmPresumida, color = "blue", dashArray = "10 10",
               opacity = .2, weight = 3) %>%
  # Zoom no imóvel em análise
  setView(lng = centroide[1], lat = centroide[2], zoom = 17) %>% 
  # Adiciona Legendas
  addLegend(labels = "Terras de Marinha", colors = "yellow", 
            group = "Terras de Marinha", position = "topright") %>% 
  addLegend(labels = "Em análise", colors = "blue", 
            group = "Em análise", position = "topright") %>% 
  # Adiciona Minimapa
  addMiniMap(position = "topleft", 
             tiles = providers$Esri.WorldGrayCanvas) %>% 
  addMouseCoordinates() %>%
  addLogo("SPU.jpg", src = "local", position = "bottomleft", 
                  offset.x = 15, offset.y = 15, 
                  width = 75, height = 75)
name <- paste(st_geohash(spl_df, 10), "_2.png", sep = "")
mapview::mapshot(m, file = name, remove_url = FALSE)
knitr::include_graphics(name)

Tabela de coordenadas

df <- st_coordinates(spl_df)[, c(1, 2)]
df_SIRGAS <- st_coordinates(st_transform(spl_df, crs = 31982))[, c(1, 2)]
vertices <- paste("VT", seq_len(dim(df_SIRGAS)[1] - 1), sep = "")
row.names(df_SIRGAS) <- make.names(c(vertices, vertices[1]), unique = TRUE)
# kable(df,
#       format.args = list(nsmall = 2),
#       col.names = c("E", "N"))
#df_SIRGAS
azimutes <- vector(mode = "numeric", length = dim(df)[1]-1)
distances <- vector(mode = "numeric", length = dim(df)[1]-1) # Curso é o azimute de 0 a 360

for (i in seq_len(dim(df)[1])) {
  if (i != dim(df)[1]){

      azimutes[[i]] <- st_geod_azimuth(st_sfc(st_point(df[i, ]), 
                                          st_point(df[i + 1, ]), crs = 4326))

      distances[[i]] <- st_distance(st_point(df_SIRGAS[i, ]),
                               st_point(df_SIRGAS[i + 1, ]))
  }
}

azimutes <- set_units(azimutes, rad)
azimutes_deg <- set_units(azimutes, degrees)
course <- (as.vector(azimutes_deg) + 360) %% 360
conv_merid <- -1.06277192089
planar_course <- course - conv_merid

df_SIRGAS <- cbind(df_SIRGAS, 
                   AzReal = c(course, NA),
                   AzPlano = c(planar_course, NA),
                   Dist = c(distances, NA))
df_SIRGAS <- as.data.frame(df_SIRGAS)

# Para grau, minutos e segundos
#sp::dd2dms(as.vector(course[c(7, 8, 1, 2, 3, 4, 5, 6)]))

kable(df_SIRGAS, digits = 2,
      format.args = list(nsmall = 2, decimal.mark = ",", big.mark = "."),
      col.names =  c("E", "N", "Real", "Plano", "(m)"),
      longtable = TRUE, 
      booktabs = TRUE) %>%
  add_header_above(c("Vértice", "Coordenadas" = 2, "Azimutes" = 2, "Distância")) %>% 
  kable_styling(font_size = 3)

Tabela de atributos

kable(t(st_drop_geometry(spl_df)), col.names = NULL, booktabs = TRUE)%>% 
  kable_styling(font_size = 6)

Memorial

writeLines("Inicia-se este memorial pelo vertice ", 
           con = 'memorial.md', sep = "")
for (i in seq_len(nrow(df_SIRGAS) - 1)) {
  cat("**", rownames(df_SIRGAS)[i], "** com coordenadas **N ", 
  brf(df_SIRGAS[i, "Y"]), "** e **E ", brf(df_SIRGAS[i, "X"]), 
  "**. Deste segue com azimute ", brf(df_SIRGAS[i, "AzPlano"]), " graus e 
  distancia ",  brf(df_SIRGAS[i, "Dist"]), "m ate o vertice ", 
  sep = "", file = 'memorial.md', append = TRUE)
}
cat("**", rownames(df_SIRGAS)[1], "** origem deste memorial.", sep = "",
           file = 'memorial.md', append = TRUE)

readLines('memorial.md')

Exportar

# Escrever no disco em formato geojson
st_write(spl_df, "04972.009718.2013-58.geojson", delete_dsn = TRUE)

NUDEPU/GeoNUDEPU documentation built on Jan. 17, 2022, 5:18 p.m.