Exame Final

#PREPARANDO O ESPAÇO DE TRABALHO

#Instalação dos pacotes que serão utilizados durante o trabalho:

library(gstat)

library(lattice)

library(sp)

library(readxl)

library(latticeExtra)

data(meuse.all)

edit(meuse.all)

#1

path = "C://Users//kamil//Desktop//GEOLOGIA//Geoestat//"

dados <- (meuse.all)

View(dados)

#2

coordinates(dados)=c("x","y")

bubble(dados,"lead", maxsize = 1.5, col = "orange", xlab="UTM X", ylab="UTM Y")

#3

summary(dados$lead)

#4

h<-hist(dados$lead, col = "orange")

xhist<-c(min(h$breaks),h$breaks)

yhist<-c(0,h$density,0)

xfit<-seq(0,700, by=1.0)

yfit<-dnorm(xfit,mean=mean(dados$lead),sd=sd(dados$lead))

plot(xhist,yhist,type='s',ylim=c(0,max(yhist,yfit)),main="Histograma do teor de chumbo", xlab="Distribuição espacial",ylab="Densidade", col = "orange")

lines(xfit,yfit,col="blue")

#5

boxplot(dados$lead,range=1.5, col = "orange",main="Teor do Pb")

#6

coordinates(dados)

g <- gstat(id="lead", formula=V, locations=~x+y,data = dados)

graf<-variogram(g)

plot(graf, xlab="Pb", main = " Distribuição espacial de Chumbo",ylab='Semivariância', col="orange", cex=3)

#7

path="C://Users//kamil//Desktop//GEOLOGIA//Geoestat//"

vgm1=variogram(lead, locations=~x+y,data= amostra)

f<-fit.variogram(vgm1,vgm(8.15,"Sph",20,8))

ff<-variogramLine(f,maxdist=600,n=300,min=1.0e-6)

plot(ff,col="orange")

points(vgm1[,2],vgm1[,3], col="orange")

print(f)

#8

caminho="C:/Users/kamil/Desktop/GEOLOGIA/Geoestat/"

pts=read.table(paste(caminho,"coordenadas.txt",sep=""), header=TRUE) 

pts=rbind(pts, pts[1,])

spdf=SpatialPolygons(list(Polygons(list(Polygon(pts)),1)))

plot(spdf)

grd=makegrid(spdf, n = 10000,c(10,10))

colnames(grd)=c('x','y')

grd_pts=SpatialPoints(coords = grd)

grd_pts_in=grd_pts[spdf, ]

novogrid=grd_pts_in

gridded(novogrid)=TRUE

plot(novogrid,col="orange")

#MAPA DA DENSIDADE 

m=vgm( 8.15,"Sph", 20, 8.00)

pred=krige("lead", ~x+y, model = m,data = amostra, newd = novogrid)

l2 = list("SpatialPolygonsRescale", layout.north.arrow(), offset = c(179844,329800),scale = 190 )

scale <- list("SpatialPolygonsRescale", layout.scale.bar(height=0.05), offset = c(408200,329800),scale = 590, fill=c("transparent","black")) #escala do mapa

text1 <- list("sp.text", c(179900,329800), "0m")#escala do mapa

text2 <- list("sp.text", c(180984,329800), "500m")#escala do mapa

spplot(pred["var1.pred"], sp.layout=list(l2,scale,text1,text2), col.regions=bpy.colors(20),key.space=list(x=0.5,y=.90,corner=c(0,1)), main = "MAPA DENSIDADE", xlab="x", ylab= "y" ,xlim=c(177984,181400),ylim=c(329500,333750))

#9

s.grid=GridTopology(c(406900,87900),c(60,40),c(50,50))

s.grid=SpatialPoints(s.grid)

m=vgm( 8.15,"Sph", 20, 8.00)

pred=krige(V~1, ~UTMX+UTMY, model = m,data = amostra, newd = s.grid)

dfpred=as.data.frame(pred)

mz=matrix(dfpred[,3], nrow=50, ncol=50,byrow=FALSE)

nmz=matrix(nrow=50, ncol=50)

for(i in 1:50)

  for(j in 1:50)

  {nmz[i,j]=mz[i,51-j]}

persp(x = seq(407000,409940, by = 60), y=seq(88600, 90599,by=40),nmz,xlab="UTMX",ylab="UTMY", main="Diagrama de Bloco", theta=30, phi=30, r=100, d=20, scale=TRUE, col="orange")

#11

s.grid=GridTopology(c(181390,333610),c(20,20),c(30,30))

s.grid=SpatialPoints(s.grid)

pred <- krige(lead~1,~UTMX+UTMY, model = m, data= amostra, newd = novogrid, nsim=4, nmax=10)

spplot(pred["sim1"],xlab="UTMX",ylab="UTMY",main="Primeira simulação Gausiana")

x11()

spplot(pred["sim2"],xlab="UTMX",ylab="UTMY",main="Segunda simulação Gausiana")

x11()

spplot(pred["sim3"],xlab="UTMX",ylab="UTMY",main="Terceira simulação Gausiana")

x11()

spplot(pred["sim4"],xlab="UTMX",ylab="UTMY",main="Quarta simulação Gausiana")

x11()

#12

logarítmica (a)

polinomial (b)

hiperbólico (c)

linear (d)