Apresentação dos resultados para atividade de Espacial
Primeiro vamos carregar os pacotes necessários
require(maptools) ## Carregando pacotes exigidos: maptools## Carregando pacotes exigidos: sp## Checking rgeos availability: TRUE
## Please note that 'maptools' will be retired by the end of 2023,
## plan transition at your earliest convenience;
## some functionality will be moved to 'sp'.gpclibPermit() ## [1] FALSErequire(sp)
require(dplyr)## Carregando pacotes exigidos: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(spdep) ## Carregando pacotes exigidos: spdep## Carregando pacotes exigidos: spData## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`## Carregando pacotes exigidos: sf## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUErequire(rgdal)## Carregando pacotes exigidos: rgdal## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-32, (SVN revision 1176)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.4.3, released 2022/04/22
## Path to GDAL shared files: C:/Users/Gabriel/AppData/Local/R/win-library/4.2/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/Gabriel/AppData/Local/R/win-library/4.2/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.5-0
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.require(classInt) ## Carregando pacotes exigidos: classIntrequire(RColorBrewer) ## Carregando pacotes exigidos: RColorBrewerlibrary(raster) ##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
##
## selectlibrary(readxl)
library(skimr)##
## Attaching package: 'skimr'## The following object is masked from 'package:raster':
##
## bindpar.ori <- par(no.readonly=TRUE)Vamos agora carregar nossos arquivos
readxl::read_excel## function (path, sheet = NULL, range = NULL, col_names = TRUE,
## col_types = NULL, na = "", trim_ws = TRUE, skip = 0, n_max = Inf,
## guess_max = min(1000, n_max), progress = readxl_progress(),
## .name_repair = "unique")
## {
## path <- check_file(path)
## format <- check_format(path)
## read_excel_(path = path, sheet = sheet, range = range, col_names = col_names,
## col_types = col_types, na = na, trim_ws = trim_ws, skip = skip,
## n_max = n_max, guess_max = guess_max, progress = progress,
## .name_repair = .name_repair, format = format)
## }
## <bytecode: 0x0000022f27fa1080>
## <environment: namespace:readxl>Indicadores = read_xlsx("Data_BA.xlsx", sheet = 1, col_names = T ) # Carregando indicadores
Ba_shape <- readShapePoly("BA_Municipios_2021.SHP") # Carregando shape## Warning: readShapePoly is deprecated; use rgdal::readOGR or sf::st_readproj4string(Ba_shape) <- CRS("+init=epsg:4674")## Warning in showSRID(uprojargs, format = "PROJ", multiline
## = "NO", prefer_proj = prefer_proj): Discarded datum
## Sistema_de_Referencia_Geocentrico_para_las_AmericaS_2000 in Proj4 definitionAgora irei fazer as transformações para que sejá possível concatenar posteriormente nossos dados ao shape.
library(stringr)
Ba_shape@data$NM_MUN <- as.character(Ba_shape@data$NM_MUN )
Ba_shape@data$NM_MUN <- str_to_title(Ba_shape@data$NM_MUN)
Indicadores$Município <- str_to_title(Indicadores$Município)
head(Ba_shape@data$NM_MUN)## [1] "Abaíra" "Abaré" "Acajutiba" "Adustina"
## [5] "Água Fria" "Érico Cardoso"head(Indicadores$Município)## [1] "Abaíra" "Abaré" "Acajutiba" "Adustina"
## [5] "Água Fria" "Érico Cardoso"head(Indicadores)## # A tibble: 6 × 4
## Município GINI THEIL IDHM
## <chr> <dbl> <dbl> <dbl>
## 1 Abaíra 0.46 0.44 0.603
## 2 Abaré 0.55 0.59 0.575
## 3 Acajutiba 0.57 0.61 0.582
## 4 Adustina 0.54 0.55 0.546
## 5 Água Fria 0.53 0.54 0.55
## 6 Érico Cardoso 0.49 0.51 0.584Vamos plotar agora o mapa da Bahia e puxar algumas estatísticas a respeito do shape
class(Ba_shape)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"plot(Ba_shape)
title("Municípios da Bahia")
slotNames(Ba_shape)## [1] "data" "polygons" "plotOrder" "bbox" "proj4string"dim(Ba_shape)## [1] 417 4dim(Indicadores)## [1] 417 4#par(c(0,0,0,0))
plot(Ba_shape, add=T, lwd=1)
#head(Ba_shape@data)
#head(Indicadores)Agora iremos concatenar as duas bases de dados isso só é possível graças a transformação feita lá em cima.
ind <- match(Ba_shape@data$NM_MUN, Indicadores$Município)
head(ind)## [1] 1 2 3 4 5 6Passando os indices para o shape e transformando os indicadores em numéricos
Indicadores <- Indicadores[ind,]
row.names(Indicadores) <- (Ba_shape$NM_MUN)## Warning: Setting row names on a tibble is deprecated.row.names(Indicadores) = row.names(Ba_shape)## Warning: Setting row names on a tibble is deprecated.Ba_shape <- spCbind(Ba_shape, Indicadores)
#table(Ba_shape)
names(Ba_shape)## [1] "CD_MUN" "NM_MUN" "SIGLA" "AREA_KM2" "Município" "GINI"
## [7] "THEIL" "IDHM"head(Ba_shape@data)## CD_MUN NM_MUN SIGLA AREA_KM2 Município GINI THEIL IDHM
## 0 2900108 Abaíra BA 538.677 Abaíra 0.46 0.44 0.603
## 1 2900207 Abaré BA 1604.923 Abaré 0.55 0.59 0.575
## 2 2900306 Acajutiba BA 181.475 Acajutiba 0.57 0.61 0.582
## 3 2900355 Adustina BA 629.099 Adustina 0.54 0.55 0.546
## 4 2900405 Água Fria BA 742.775 Água Fria 0.53 0.54 0.550
## 5 2900504 Érico Cardoso BA 735.249 Érico Cardoso 0.49 0.51 0.584min(Ba_shape$IDHM)## [1] 0.486max(Ba_shape$IDHM)## [1] 0.759Ba_shape$IDHM = as.numeric(Ba_shape$IDHM)
Ba_shape$THEIL = as.numeric(Ba_shape$THEIL)
Ba_shape$GINI = as.numeric(Ba_shape$GINI)
Indicadores$Municípi## Warning: Unknown or uninitialised column: `Municípi`.## NULLIndicadores %>% dplyr::select(c(IDHM,Município)) %>% arrange(desc(IDHM))## # A tibble: 417 × 2
## IDHM Município
## <dbl> <chr>
## 1 0.759 Salvador
## 2 0.754 Lauro De Freitas
## 3 0.721 Barreiras
## 4 0.716 Luís Eduardo Magalhães
## 5 0.712 Feira De Santana
## 6 0.712 Itabuna
## 7 0.708 Madre De Deus
## 8 0.7 Santo Antônio De Jesus
## 9 0.699 Cruz Das Almas
## 10 0.694 Camaçari
## # … with 407 more rowsfazendo a estatística descritiva dos dados
skim(Indicadores$IDHM)| Name | Indicadores$IDHM |
| Number of rows | 417 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| data | 0 | 1 | 0.59 | 0.04 | 0.49 | 0.57 | 0.59 | 0.61 | 0.76 | ▁▇▅▁▁ |
Visualizando o mapa para o indice IDH
INT1 <- classIntervals(Ba_shape$IDHM, n=4, style="quantile")
COLORES1 <- (c('red',"#964b00",'lightgrey','green2',"green4"))
COL1 <- findColours(INT1, COLORES1)
INT1## style: quantile
## one of 518,665 possible partitions of this variable into 4 classes
## [0.486,0.566) [0.566,0.589) [0.589,0.614) [0.614,0.759]
## 101 107 100 109#border=NA
plot(Ba_shape, col=COL1)
#sp::wkt(Ba_shape)
#locator(1)
TB1 <- attr(COL1, "table")
names(TB1)= c('Baixo','Médio','Alto','Muito Alto')
legtext <- paste(names(TB1))
legend(-48.7785, -15.95352, fill=attr(COL1, "palette"), legend=legtext,
bty="n",cex=1)
#locator(1)
scalebar(100, xy=c(-35.97071, -16.87172),
type="bar", below="km",
cex=1, lonlat=T,divs=4)
#locator(1)
compassRose(-35.53014, -13.28069, cex=1)
title("IDH dos municípios da Bahia")
Visualizando o mapa para o indice de Theil
quantile(Ba_shape$THEIL)## 0% 25% 50% 75% 100%
## 0.29 0.46 0.52 0.58 0.98min(Ba_shape$THEIL)## [1] 0.29max(Ba_shape$THEIL)## [1] 0.98INT2 <- classIntervals(Ba_shape$THEIL, n=4, style="quantile")
COLORES2<- (c('red',"#964b00",'lightgrey','green2',"green4"))
COL2 <- findColours(INT2, COLORES2)
#border=NA
plot(Ba_shape, col=COL2)
#locator(1)
TB2 <- attr(COL2, "table")
names(TB2)= c('Baixo','Médio','Alto','Muito Alto')
legtext <- paste(names(TB2))
legend(-48.7785, -15.95352, fill=attr(COL2, "palette"), legend=legtext,
bty="n",cex= 1)
#locator(1)
scalebar(100, xy=c(-35.97071, -16.87172),
type="bar", below="km",
cex=0.8, lonlat=T,divs=4)
#locator(1)
compassRose(-35.53014, -12.28069, cex=1)
title("Indice de theil por municípios da Bahia")
Visualizando o mapa para o indice de Gini
INT3 <- classIntervals(Ba_shape$GINI, n=4, style="quantile")
COLORES3 <- (c('red',"#964b00",'lightgrey','green2',"green4"))
COL3 <- findColours(INT3, COLORES3)
#border=NA
plot(Ba_shape, col=COL3)
#locator(1)
TB3 <- attr(COL3, "table")
names(TB3)= c('Baixo','Médio','Alto','Muito Alto')
legtext <- paste(names(TB3))
legend(-48.7785, -15.95352, fill=attr(COL3, "palette"), legend=legtext,
bty="n",cex=0.6)
#locator(1)
scalebar(100, xy=c(-35.97071, -16.87172),
type="bar", below="km",
cex=0.8, lonlat=T,divs=4)
#locator(1)
compassRose(-35.53014, -12.28069, cex=0.8)
title("Indice de Gini por municípios da Bahia")
Agora iremos calcular as vizinhanças
require(spdep)
Ba_shape.nb1 <- poly2nb(Ba_shape)
class(Ba_shape.nb1)## [1] "nb"Ba_shape.nb1[[1]]## [1] 6 222 264 298 329 331Ba_shape.nb1[[6]]## [1] 1 94 234 290 329 331Ba_shape@data[Ba_shape.nb1[[6]],5]## [1] "Abaíra" "Caturama"
## [3] "Livramento De Nossa Senhora" "Paramirim"
## [5] "Rio De Contas" "Rio Do Pires"class(Ba_shape.nb1)## [1] "nb"Ba_shape.nb1 = poly2nb(Ba_shape)
vizinhanca = nb2listw(Ba_shape.nb1, style="W",
zero.policy=TRUE)
vizinhanca## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 417
## Number of nonzero links: 2360
## Percentage nonzero weights: 1.357188
## Average number of links: 5.659472
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 417 173889 417 156.5982 1724.023Calulando o indice de Moran global
Mglobal1 = moran.test(Ba_shape$IDHM, listw=nb2listw(Ba_shape.nb1))
Mglobal1##
## Moran I test under randomisation
##
## data: Ba_shape$IDHM
## weights: nb2listw(Ba_shape.nb1)
##
## Moran I statistic standard deviate = 12.958, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.3832125066 -0.0024038462 0.0008856233is.numeric(Ba_shape$THEIL)## [1] TRUEMglobal2 = moran.test(Ba_shape$THEIL, listw=nb2listw(Ba_shape.nb1))
Mglobal2##
## Moran I test under randomisation
##
## data: Ba_shape$THEIL
## weights: nb2listw(Ba_shape.nb1)
##
## Moran I statistic standard deviate = 9.4815, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.2796889641 -0.0024038462 0.0008851704Mglobal3 = moran.test(Ba_shape$GINI, listw=nb2listw(Ba_shape.nb1))
Mglobal3##
## Moran I test under randomisation
##
## data: Ba_shape$GINI
## weights: nb2listw(Ba_shape.nb1)
##
## Moran I statistic standard deviate = 10.175, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.3006368779 -0.0024038462 0.0008870922Calculando o indice de Moran local para o IDH
Ba_shape.mloc1 <- localmoran(Ba_shape$IDHM, listw=vizinhanca,
zero.policy=T,
alternative = "two.sided")
dim(Ba_shape.mloc1)## [1] 417 5head(Ba_shape.mloc1)## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 0 -0.001272182 -0.0001171665 0.008044022 -0.01287808 0.98972506
## 1 0.037910379 -0.0005060820 0.105210572 0.11843707 0.90572135
## 2 0.216727888 -0.0002006788 0.020765359 1.50538308 0.13222562
## 3 0.979391381 -0.0032497872 0.267547587 1.89974117 0.05746709
## 4 0.634438250 -0.0027297299 0.139505292 1.70591977 0.08802302
## 5 -0.034595336 -0.0001389114 0.009536702 -0.35283455 0.72421248min(Ba_shape.mloc1)## [1] -3.130361max(Ba_shape.mloc1)## [1] 10.88678Checando a amplitude da estatística de Moran local para IDH
list_w <- vizinhanca
signif = 0.05
Sd_1 <- (Ba_shape$IDHM) - mean(Ba_shape$IDHM)
mI_1 <- Ba_shape.mloc1[, 5]
C_mI <- mI_1 - mean(mI_1) # MAS N?O QUEREMOS CENTRAR! Apenas o sinal importa
quadrant <- vector(mode = "numeric", length = nrow(Ba_shape.mloc1))
# builds a data quadrant
quadrant[Sd_1 >0 & mI_1>0] <- 4
quadrant[Sd_1 <0 & mI_1<0] <- 1
quadrant[Sd_1 <0 & mI_1>0] <- 2
quadrant[Sd_1 >0 & mI_1<0] <- 3
quadrant[Ba_shape.mloc1[,5]>signif] <- 0.05
# plot in r
brks <- c(0,1,2,3,4)
colors <- c("white","blue",rgb(0,0,1,alpha=0.4),rgb(1,0,0,alpha=0.4),"red")
plot(Ba_shape,border="lightgray",col=colors[findInterval(quadrant,brks,
all.inside=FALSE)])
title("Indice de Moran Local para o IDH")
legend(-50.92708, -12.95683, legend = c("Não significativo","Baixo-baixo","Baixo-Alto","Alto-Baixo","Alto-Alto"),
fill=colors,bty="n",cex=0.6)
scalebar(100, xy=c(-35.97071, -16.87172),
type="bar", below="km",
cex=0.6, lonlat=T,divs=4)
compassRose(-35.53014, -12.28069, cex=0.6)
Calculando o indice de Moran local para o Theil
Ba_shape.mloc2 <- localmoran(Ba_shape$THEIL, listw=vizinhanca,
zero.policy=T,
alternative = "two.sided")
dim(Ba_shape.mloc2)## [1] 417 5head(Ba_shape.mloc2)## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 0 0.24395151 -1.811387e-03 0.124149308 0.6974999 0.4854900
## 1 0.21780741 -9.891000e-04 0.205526931 0.4826208 0.6293650
## 2 0.49816735 -1.707178e-03 0.176385083 1.1902278 0.2339569
## 3 0.02342064 -1.372147e-04 0.011331850 0.2213020 0.8248573
## 4 -0.04697900 -4.596629e-05 0.002355470 -0.9670296 0.3335292
## 5 0.07617027 -6.435609e-05 0.004418575 1.1468620 0.2514387Checando a amplitude do Moran local para o Theil
list_w <- vizinhanca
Sd_2 <- (Ba_shape$THEIL) - mean(Ba_shape$THEIL)
mI_2 <- Ba_shape.mloc2[, 5]
C_m2 <- mI_2 - mean(mI_2) # MAS N?O QUEREMOS CENTRAR! Apenas o sinal importa
quadrant <- vector(mode = "numeric", length = nrow(Ba_shape.mloc2))
# builds a data quadrant
quadrant[Sd_2 >0 & mI_2>0] <- 4
quadrant[Sd_2 <0 & mI_2<0] <- 1
quadrant[Sd_2 <0 & mI_2>0] <- 2
quadrant[Sd_2 >0 & mI_2<0] <- 3
quadrant[Ba_shape.mloc2[,5]>signif] <- 0.05
# plot in r
#brks <- c(0,1,2,3,4)
colors <- c("white","blue",rgb(0,0,1,alpha=0.4),rgb(1,0,0,alpha=0.4),"red")
plot(Ba_shape,border="lightgray",col=colors[findInterval(quadrant,brks,
all.inside=FALSE)])
title("Indice de Moran Local para o Theil")
legend(-50.92708, -12.95683, legend = c("insignificant","low-low","low-high","high-low","high-high"),
fill=colors,bty="n",cex=0.6)
scalebar(100, xy=c(-35.97071, -16.87172),
type="bar", below="km",
cex=0.6, lonlat=T,divs=4)
compassRose(-35.53014, -12.28069, cex=0.6)
Calculando o indice de Moran local para Gini
Ba_shape.mloc3 <- localmoran(Ba_shape$GINI, listw=vizinhanca,
zero.policy=T,
alternative = "two.sided")
dim(Ba_shape.mloc3)## [1] 417 5head(Ba_shape.mloc3)## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 0 0.86591059 -4.716150e-03 0.322296139 1.5335730 0.12513474
## 1 0.21486593 -6.498684e-04 0.135083213 0.5863790 0.55762084
## 2 0.64379311 -2.154673e-03 0.222520411 1.3693438 0.17089185
## 3 0.05471855 -2.258872e-04 0.018653198 0.4022973 0.68746524
## 4 -0.02610869 -2.085359e-05 0.001068636 -0.7980372 0.42484889
## 5 0.57832096 -1.390195e-03 0.095321748 1.8776544 0.06042846Calculando a amplitude do indice de Moran local para Gini
list_w <- vizinhanca
Sd_3 <- (Ba_shape$GINI) - mean(Ba_shape$GINI)
mI_3 <- Ba_shape.mloc3[, 5]
C_m3 <- mI_3 - mean(mI_3) # MAS N?O QUEREMOS CENTRAR! Apenas o sinal importa
quadrant <- vector(mode = "numeric", length = nrow(Ba_shape.mloc3))
# builds a data quadrant
quadrant[Sd_3 >0 & mI_3>0] <- 4
quadrant[Sd_3 <0 & mI_3<0] <- 1
quadrant[Sd_3 <0 & mI_3>0] <- 2
quadrant[Sd_3 >0 & mI_3<0] <- 3
quadrant[Ba_shape.mloc3[,5]>signif] <- 0.05
# plot in r
#brks <- c(0,1,2,3,4)
colors <- c("white","blue",rgb(0,0,1,alpha=0.4),rgb(1,0,0,alpha=0.4),"red")
plot(Ba_shape,border="lightgray",col=colors[findInterval(quadrant,brks,
all.inside=FALSE)])
title("Indice de Moran Local para o GINI")
legend(-50.92708, -12.95683, legend = c("insignificant","low-low","low-high","high-low","high-high"),
fill=colors,bty="n",cex=0.6)
scalebar(100, xy=c(-35.97071, -16.87172),
type="bar", below="km",
cex=0.6, lonlat=T,divs=4)
compassRose(-35.53014, -12.28069, cex=0.6)
Checando a amplitude do moran
#Checando a amplitude da estatística de moran local
list_w <- vizinhanca
Sd_1 <- (Ba_shape$IDHM) - mean(Ba_shape$IDHM)
mI_1 <- Ba_shape.mloc1[, 5]
C_mI <- mI_1 - mean(mI_1) # MAS NÃO QUEREMOS CENTRAR! Apenas o sinal importa
quadrant <- vector(mode = "numeric", length = nrow(Ba_shape.mloc1))
quadrant[Sd_1 > 0 & mI_1 > 0] <- 1
quadrant[Sd_1 < 0 & mI_1 > 0] <- 2
quadrant[Sd_1 > 0 & mI_1 < 0] <- 3
quadrant[Sd_1 < 0 & mI_1 < 0] <- 4
signif <- 0.05
# places non-significant Moran's in the category '5'
quadrant[Ba_shape.mloc1[, 5] > signif] <- 5
colors <- c("red", "blue", "lightpink", "skyblue2", "white")
#par(mar=c(0,0,0,0))
plot(Ba_shape, col = colors[quadrant])
#locator
legend(-48.92708, -12.95683, legend = c("alto-alto",
"baixo-baixo", "alto-baixo", "baixo-alto","Não Sig."),
fill = colors, bty = "n", cex = 0.7, y.intersp = 1, x.intersp = 1)
###
nobs <- length(Ba_shape)
Ne.nb <- poly2nb(Ba_shape, queen = T)
coords <- coordinates(Ba_shape)
#par(mar = c(0,0,0,0))
plot(Ba_shape, border = "grey")
plot(Ne.nb, coords, add = T, col = "red")
plot(Ba_shape, add = T, lwd = 2)
#locator(1)
scalebar(500, xy=c(-35.95657, -13.52004),
type="bar", below="km",
cex=0.8, lonlat=T,divs=4)
compassRose(-49.95657, -13.52004)
vizinhança = nb2listw(Ne.nb, style = "W", zero.policy = T)
vizinhança## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 417
## Number of nonzero links: 2360
## Percentage nonzero weights: 1.357188
## Average number of links: 5.659472
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 417 173889 417 156.5982 1724.023Ne.nb <- poly2nb(Ba_shape, queen = T)
### indice de moran global para para detecar a presença de dependencias antes
### da regressão espacial
Mglobal1 <- moran.test(Ba_shape$IDHM, nb2listw(Ne.nb), alternative = "two.sided")
Ba_shape.mloc1## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 0 -0.0012721820 -1.171665e-04 8.044022e-03 -0.012878082 9.897251e-01
## 1 0.0379103786 -5.060820e-04 1.052106e-01 0.118437071 9.057214e-01
## 2 0.2167278884 -2.006788e-04 2.076536e-02 1.505383081 1.322256e-01
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## 401 0.0340132903 -2.375820e-05 2.458830e-03 0.686416416 4.924506e-01
## 402 0.2572523320 -6.914078e-04 9.557640e-02 0.834352674 4.040823e-01
## 403 0.0128125274 -2.158473e-05 1.482033e-03 0.333377973 7.388490e-01
## 404 -0.1954769625 -1.198876e-03 8.221931e-02 -0.677543046 4.980615e-01
## 405 0.6942758968 -2.630161e-03 2.714962e-01 1.337495578 1.810609e-01
## 406 0.5251999304 -4.268931e-03 2.918648e-01 0.980052929 3.270600e-01
## 407 0.2328036423 -5.060820e-04 4.177927e-02 1.141438231 2.536876e-01
## 408 -0.0423069838 -2.143385e-03 2.213571e-01 -0.085366181 9.319703e-01
## 409 -0.1230663737 -8.847331e-05 6.074274e-03 -1.577901421 1.145883e-01
## 410 1.7036277600 -3.697281e-03 3.812405e-01 2.765137431 5.689882e-03
## 411 -0.2864328032 -4.046624e-04 2.777395e-02 -1.716287050 8.610952e-02
## 412 -1.6239586546 -1.001521e-02 5.080969e-01 -2.264201389 2.356173e-02
## 413 0.0321484520 -6.750271e-05 5.575087e-03 0.431464609 6.661306e-01
## 414 -0.0373633963 -5.262462e-05 3.613156e-03 -0.620713079 5.347884e-01
## 415 0.4871708854 -3.526810e-03 1.596936e-01 1.227920544 2.194768e-01
## 416 0.2386945536 -1.122762e-04 9.272541e-03 2.479975293 1.313915e-02
## attr(,"call")
## localmoran(x = Ba_shape$IDHM, listw = vizinhanca, zero.policy = T,
## alternative = "two.sided")
## attr(,"class")
## [1] "localmoran" "matrix" "array"
## attr(,"quadr")
## mean median pysal
## 1 High-Low High-High High-Low
## 2 Low-Low Low-Low Low-Low
## 3 Low-Low Low-Low Low-Low
## 4 Low-Low Low-Low Low-Low
## 5 Low-Low Low-Low Low-Low
## 6 Low-High Low-High Low-High
## 7 Low-High Low-High Low-High
## 8 High-High High-High High-High
## 9 High-High High-High High-High
## 10 Low-Low Low-High Low-High
## 11 High-High High-High High-High
## 12 High-High High-High High-High
## 13 Low-Low Low-Low Low-Low
## 14 Low-Low Low-Low Low-Low
## 15 Low-Low Low-Low Low-Low
## 16 Low-High Low-High Low-High
## 17 High-High High-High High-High
## 18 Low-High Low-High Low-High
## 19 Low-Low High-Low Low-Low
## 20 Low-High Low-High Low-High
## 21 High-Low High-Low High-Low
## 22 Low-Low Low-Low Low-Low
## 23 Low-High Low-High Low-High
## 24 Low-Low Low-Low Low-Low
## 25 Low-High Low-High Low-High
## 26 Low-Low Low-Low Low-Low
## 27 Low-High Low-High Low-High
## 28 Low-Low Low-Low Low-Low
## 29 Low-High Low-High Low-High
## 30 Low-High Low-High Low-High
## 31 Low-Low Low-High Low-High
## 32 Low-Low Low-Low Low-Low
## 33 Low-Low Low-Low Low-Low
## 34 Low-Low Low-Low Low-Low
## 35 Low-Low Low-Low Low-Low
## 36 Low-Low Low-High Low-High
## 37 High-High High-High High-High
## 38 Low-High Low-High Low-High
## 39 High-High High-High High-High
## 40 High-High High-High High-High
## 41 High-Low High-Low High-Low
## 42 High-High High-High High-High
## 43 High-High High-High High-High
## 44 Low-Low Low-Low Low-Low
## 45 Low-Low Low-Low Low-Low
## 46 Low-Low Low-Low Low-Low
## 47 Low-Low Low-Low Low-Low
## 48 High-Low High-Low High-Low
## 49 Low-Low Low-Low Low-Low
## 50 High-High High-High High-High
## 51 Low-Low Low-Low Low-Low
## 52 High-Low High-Low High-Low
## 53 Low-High Low-High Low-High
## 54 High-Low High-Low High-Low
## 55 Low-Low High-Low Low-Low
## 56 Low-Low Low-Low Low-Low
## 57 High-Low High-Low High-Low
## 58 High-High High-High High-High
## 59 Low-Low Low-Low Low-Low
## 60 Low-Low Low-Low Low-Low
## 61 Low-High Low-High Low-High
## 62 High-High High-High High-High
## 63 High-High High-High High-High
## 64 Low-Low Low-Low Low-Low
## 65 Low-Low Low-Low Low-Low
## 66 High-High High-High High-High
## 67 Low-Low Low-Low Low-Low
## 68 High-Low High-Low High-Low
## 69 Low-Low Low-Low Low-Low
## 70 Low-Low Low-Low Low-Low
## 71 High-High High-High High-High
## 72 Low-Low Low-Low Low-Low
## 73 Low-Low Low-Low Low-Low
## 74 Low-High Low-High Low-High
## 75 Low-High Low-High Low-High
## 76 Low-Low Low-Low Low-Low
## 77 Low-Low High-Low Low-Low
## 78 Low-High Low-High Low-High
## 79 High-High High-High High-High
## 80 Low-High High-High Low-High
## 81 High-Low High-Low High-Low
## 82 Low-Low Low-Low Low-Low
## 83 Low-Low Low-Low Low-Low
## 84 High-Low High-Low High-Low
## 85 High-Low High-Low High-Low
## 86 Low-Low Low-Low Low-Low
## 87 High-High High-High High-High
## 88 Low-High Low-High Low-High
## 89 Low-High Low-High Low-High
## 90 Low-High Low-High Low-High
## 91 High-Low High-High High-High
## 92 Low-High Low-High Low-High
## 93 High-High High-High High-High
## 94 Low-High Low-High Low-High
## 95 High-High High-High High-High
## 96 High-Low High-High High-Low
## 97 Low-Low Low-Low Low-Low
## 98 High-Low High-Low High-Low
## 99 High-High High-High High-High
## 100 High-High High-High High-High
## 101 High-High High-High High-High
## 102 High-High High-High High-High
## 103 High-High High-High High-High
## 104 High-High High-High High-High
## 105 Low-Low Low-Low Low-Low
## 106 Low-Low Low-Low Low-Low
## 107 Low-Low Low-Low Low-Low
## 108 Low-High High-High Low-High
## 109 Low-Low Low-Low Low-Low
## 110 High-High High-High High-High
## 111 Low-Low Low-Low Low-Low
## 112 High-High High-High High-High
## 113 Low-High High-High Low-High
## 114 High-Low High-Low High-Low
## 115 Low-Low Low-Low Low-Low
## 116 High-Low High-High High-High
## 117 High-High High-High High-High
## 118 Low-High Low-High Low-High
## 119 Low-Low Low-Low Low-Low
## 120 High-High High-High High-High
## 121 Low-High High-High Low-High
## 122 High-High High-High High-High
## 123 High-High High-High High-High
## 124 Low-High Low-High Low-High
## 125 High-Low High-High High-High
## 126 Low-Low Low-Low Low-Low
## 127 Low-Low Low-Low Low-Low
## 128 High-High High-High High-High
## 129 Low-Low Low-Low Low-Low
## 130 Low-Low Low-Low Low-Low
## 131 High-High High-High High-High
## 132 Low-Low Low-High Low-Low
## 133 Low-Low Low-High Low-High
## 134 Low-High Low-High Low-High
## 135 High-Low High-Low High-Low
## 136 High-Low High-Low High-Low
## 137 High-High High-High High-High
## 138 Low-Low Low-Low Low-Low
## 139 Low-High High-High Low-High
## 140 Low-High Low-High Low-High
## 141 High-High High-High High-High
## 142 Low-Low Low-Low Low-Low
## 143 High-High High-High High-High
## 144 Low-High Low-High Low-High
## 145 Low-Low Low-Low Low-Low
## 146 Low-Low Low-Low Low-Low
## 147 High-High High-High High-High
## 148 High-Low High-Low High-Low
## 149 Low-Low High-Low Low-Low
## 150 Low-Low Low-Low Low-Low
## 151 High-Low High-High High-High
## 152 Low-High Low-High Low-High
## 153 Low-Low Low-Low Low-Low
## 154 Low-Low Low-Low Low-Low
## 155 High-High High-High High-High
## 156 Low-Low Low-Low Low-Low
## 157 Low-Low Low-High Low-Low
## 158 High-High High-High High-High
## 159 High-Low High-Low High-Low
## 160 High-High High-High High-High
## 161 High-High High-High High-High
## 162 Low-Low Low-Low Low-Low
## 163 Low-Low Low-Low Low-Low
## 164 High-High High-High High-High
## 165 Low-Low Low-Low Low-Low
## 166 Low-High Low-High Low-High
## 167 High-Low High-Low High-Low
## 168 Low-Low Low-Low Low-Low
## 169 Low-Low High-High Low-Low
## 170 Low-Low Low-Low Low-Low
## 171 Low-Low Low-Low Low-Low
## 172 High-High High-High High-High
## 173 High-Low High-Low High-Low
## 174 High-High High-High High-High
## 175 High-High High-High High-High
## 176 High-Low High-Low High-Low
## 177 High-High High-High High-High
## 178 Low-High Low-High Low-High
## 179 Low-Low Low-Low Low-Low
## 180 Low-Low Low-Low Low-Low
## 181 Low-Low Low-Low Low-Low
## 182 High-High High-High High-High
## 183 Low-Low Low-Low Low-Low
## 184 Low-Low High-Low Low-Low
## 185 High-High High-High High-High
## 186 High-Low High-High High-High
## 187 Low-Low Low-Low Low-Low
## 188 Low-High Low-High Low-High
## 189 Low-High Low-High Low-High
## 190 High-High High-High High-High
## 191 High-High High-High High-High
## 192 High-High High-High High-High
## 193 Low-High Low-High Low-High
## 194 High-High High-High High-High
## 195 Low-Low Low-Low Low-Low
## 196 Low-High Low-High Low-High
## 197 Low-Low Low-Low Low-Low
## 198 High-High High-High High-High
## 199 Low-Low Low-Low Low-Low
## 200 High-High High-High High-High
## 201 Low-Low Low-Low Low-Low
## 202 High-Low High-Low High-Low
## 203 Low-High Low-High Low-High
## 204 High-Low High-Low High-Low
## 205 Low-Low High-Low Low-Low
## 206 High-High High-High High-High
## 207 Low-High High-High Low-High
## 208 High-Low High-Low High-Low
## 209 Low-Low Low-Low Low-Low
## 210 High-High High-High High-High
## 211 Low-High Low-High Low-High
## 212 Low-Low Low-Low Low-Low
## 213 High-Low High-Low High-Low
## 214 Low-Low Low-Low Low-Low
## 215 Low-Low Low-Low Low-Low
## 216 Low-High Low-High Low-High
## 217 Low-High High-High Low-High
## 218 High-High High-High High-High
## 219 Low-Low Low-Low Low-Low
## 220 Low-Low Low-Low Low-Low
## 221 Low-High Low-High Low-High
## 222 High-Low High-Low High-Low
## 223 High-High High-High High-High
## 224 Low-High Low-High Low-High
## 225 Low-High Low-High Low-High
## 226 High-High High-High High-High
## 227 Low-Low Low-Low Low-Low
## 228 Low-Low Low-Low Low-Low
## 229 Low-Low Low-Low Low-Low
## 230 High-High High-High High-High
## 231 High-High High-High High-High
## 232 High-Low High-Low High-Low
## 233 High-High High-High High-High
## 234 High-High High-High High-High
## 235 High-High High-High High-High
## 236 Low-Low Low-Low Low-Low
## 237 High-Low High-Low High-Low
## 238 High-High High-High High-High
## 239 High-Low High-Low High-Low
## 240 High-High High-High High-High
## 241 Low-Low Low-Low Low-Low
## 242 Low-High Low-High Low-High
## 243 Low-Low Low-Low Low-Low
## 244 Low-High Low-High Low-High
## 245 Low-Low Low-Low Low-Low
## 246 Low-Low Low-Low Low-Low
## 247 High-Low High-Low High-Low
## 248 High-Low High-Low High-Low
## 249 High-High High-High High-High
## 250 Low-Low High-Low Low-Low
## 251 Low-Low Low-Low Low-Low
## 252 Low-Low Low-Low Low-Low
## 253 High-High High-High High-High
## 254 Low-High Low-High Low-High
## 255 High-High High-High High-High
## 256 Low-Low Low-Low Low-Low
## 257 High-Low High-Low High-Low
## 258 Low-Low Low-Low Low-Low
## 259 Low-Low Low-Low Low-Low
## 260 Low-Low Low-Low Low-Low
## 261 Low-Low Low-Low Low-Low
## 262 Low-Low Low-Low Low-Low
## 263 High-Low High-Low High-Low
## 264 High-High High-High High-High
## 265 High-High High-High High-High
## 266 Low-Low Low-Low Low-Low
## 267 Low-Low High-Low Low-Low
## 268 High-High High-High High-High
## 269 Low-High Low-High Low-High
## 270 High-High High-High High-High
## 271 High-Low High-Low High-Low
## 272 High-High High-High High-High
## 273 Low-Low Low-Low Low-Low
## 274 Low-Low Low-Low Low-Low
## 275 Low-Low Low-Low Low-Low
## 276 High-High High-High High-High
## 277 Low-Low Low-Low Low-Low
## 278 Low-Low Low-Low Low-Low
## 279 Low-Low Low-Low Low-Low
## 280 Low-Low Low-Low Low-Low
## 281 High-High High-High High-High
## 282 High-Low High-Low High-Low
## 283 Low-Low Low-Low Low-Low
## 284 Low-Low Low-Low Low-Low
## 285 Low-Low Low-Low Low-Low
## 286 High-Low High-Low High-Low
## 287 Low-Low Low-Low Low-Low
## 288 Low-High Low-High Low-High
## 289 High-High High-High High-High
## 290 High-Low High-High High-High
## 291 Low-High High-High Low-High
## 292 Low-Low Low-Low Low-Low
## 293 Low-High Low-High Low-High
## 294 High-Low High-Low High-Low
## 295 Low-Low Low-Low Low-Low
## 296 Low-High Low-High Low-High
## 297 Low-Low Low-Low Low-Low
## 298 Low-High Low-High Low-High
## 299 Low-Low Low-Low Low-Low
## 300 High-High High-High High-High
## 301 Low-Low Low-Low Low-Low
## 302 High-Low High-Low High-Low
## 303 Low-Low Low-Low Low-Low
## 304 Low-Low Low-Low Low-Low
## 305 Low-Low Low-Low Low-Low
## 306 Low-Low Low-Low Low-Low
## 307 Low-Low Low-Low Low-Low
## 308 High-Low High-Low High-Low
## 309 High-High High-High High-High
## 310 Low-Low Low-Low Low-Low
## 311 High-High High-High High-High
## 312 High-High High-High High-High
## 313 High-High High-High High-High
## 314 High-High High-High High-High
## 315 Low-Low Low-Low Low-Low
## 316 Low-Low Low-Low Low-Low
## 317 Low-Low High-Low Low-Low
## 318 Low-Low Low-Low Low-Low
## 319 Low-Low Low-Low Low-Low
## 320 Low-Low Low-Low Low-Low
## 321 Low-Low Low-Low Low-Low
## 322 High-High High-High High-High
## 323 Low-High Low-High Low-High
## 324 High-High High-High High-High
## 325 High-High High-High High-High
## 326 Low-Low Low-Low Low-Low
## 327 High-Low High-Low High-Low
## 328 Low-High Low-High Low-High
## 329 High-High High-High High-High
## 330 Low-High Low-High Low-High
## 331 High-Low High-Low High-Low
## 332 Low-Low Low-Low Low-Low
## 333 High-High High-High High-High
## 334 High-Low High-Low High-Low
## 335 High-High High-High High-High
## 336 High-High High-High High-High
## 337 Low-High Low-High Low-High
## 338 Low-Low Low-Low Low-Low
## 339 High-High High-High High-High
## 340 High-Low High-Low High-Low
## 341 Low-Low Low-Low Low-Low
## 342 High-Low High-Low High-Low
## 343 Low-Low Low-Low Low-Low
## 344 High-High High-High High-High
## 345 High-High High-High High-High
## 346 Low-High High-High Low-High
## 347 High-High High-High High-High
## 348 Low-High Low-High Low-High
## 349 High-High High-High High-High
## 350 High-High High-High High-High
## 351 High-Low High-Low High-Low
## 352 Low-High Low-High Low-High
## 353 High-High High-High High-High
## 354 High-High High-High High-High
## 355 High-High High-High High-High
## 356 High-High High-High High-High
## 357 High-High High-High High-High
## 358 Low-High High-High Low-High
## 359 High-High High-High High-High
## 360 Low-High Low-High Low-High
## 361 Low-Low Low-Low Low-Low
## 362 Low-High High-High Low-High
## 363 High-High High-High High-High
## 364 High-High High-High High-High
## 365 Low-Low Low-Low Low-Low
## 366 High-High High-High High-High
## 367 Low-Low Low-Low Low-Low
## 368 High-High High-High High-High
## 369 High-High High-High High-High
## 370 High-Low High-Low High-Low
## 371 High-High High-High High-High
## 372 Low-Low Low-Low Low-Low
## 373 High-Low High-Low High-Low
## 374 Low-High Low-High Low-High
## 375 High-Low High-Low High-Low
## 376 Low-Low High-Low Low-Low
## 377 High-High High-High High-High
## 378 Low-High Low-High Low-High
## 379 Low-Low Low-Low Low-Low
## 380 High-High High-High High-High
## 381 Low-High High-High Low-High
## 382 Low-High Low-High Low-High
## 383 Low-Low Low-Low Low-Low
## 384 High-High High-High High-High
## 385 High-High High-High High-High
## 386 Low-Low Low-Low Low-Low
## 387 High-Low High-Low High-Low
## 388 High-High High-High High-High
## 389 High-High High-High High-High
## 390 Low-Low Low-Low Low-Low
## 391 Low-Low Low-Low Low-Low
## 392 Low-High Low-High Low-High
## 393 Low-Low Low-Low Low-Low
## 394 Low-Low Low-Low Low-Low
## 395 High-Low High-Low High-Low
## 396 Low-Low Low-Low Low-Low
## 397 High-Low High-Low High-Low
## 398 Low-Low High-Low Low-Low
## 399 High-High High-High High-High
## 400 Low-Low Low-Low Low-Low
## 401 Low-Low Low-Low Low-Low
## 402 High-High High-High High-High
## 403 High-High High-High High-High
## 404 Low-Low High-Low Low-Low
## 405 High-Low High-Low High-Low
## 406 High-High High-High High-High
## 407 Low-Low Low-Low Low-Low
## 408 Low-Low Low-Low Low-Low
## 409 Low-High Low-High Low-High
## 410 Low-High Low-High Low-High
## 411 High-High High-High High-High
## 412 Low-High Low-High Low-High
## 413 High-Low High-Low High-Low
## 414 Low-Low Low-Low Low-Low
## 415 High-Low High-Low High-Low
## 416 Low-Low Low-Low Low-Low
## 417 Low-Low Low-Low Low-Lowindice de moran global para para detecar a presença de dependencias antes da regressão espacial
Mglobal1 <- moran.test(Ba_shape$IDHM, nb2listw(Ne.nb), alternative = "two.sided")
head(Ba_shape.mloc1)## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 0 -0.001272182 -0.0001171665 0.008044022 -0.01287808 0.98972506
## 1 0.037910379 -0.0005060820 0.105210572 0.11843707 0.90572135
## 2 0.216727888 -0.0002006788 0.020765359 1.50538308 0.13222562
## 3 0.979391381 -0.0032497872 0.267547587 1.89974117 0.05746709
## 4 0.634438250 -0.0027297299 0.139505292 1.70591977 0.08802302
## 5 -0.034595336 -0.0001389114 0.009536702 -0.35283455 0.72421248Gráfico de espalhamento de moran para IDH
moran.plot(Indicadores$IDHM, listw=vizinhanca,zero.policy=T,
labels=as.character(Indicadores$Município),
xlab="IDHM",
ylab="Spatial Lag",
pch=16, col="black",cex=.5, quiet=TRUE)
Gráfico de espalhamento de moran para theil
moran.plot(Indicadores$THEIL, listw=vizinhanca,zero.policy=T,
xlab="Theil",
ylab="Spatial Lag",
pch=16, col="black",cex=.5, quiet=TRUE)
Gráfico de espalhamento de moran para Gini
moran.plot(Indicadores$GINI, listw=vizinhanca,zero.policy=T,
xlab="Gini",
ylab="Spatial Lag",
pch=16, col="black",cex=.5, quiet=TRUE)
LISA MAP Independentes para IDH
LISA1 <- classIntervals(Ba_shape.mloc1[,5], style="fixed",
intervalClosure = c( "right"),
fixedBreaks=c(0, 0.001, 0.01, 0.05, by=1))
colors <- c("red", "blue", "lightpink", "skyblue2", "white")
COL_Lisa1 <- findColours(LISA1, colors)
# window(6,4) # quando quiser salva uma imagem com boa qualidade
#par(mar=c(0,0,0,0))
plot(Ba_shape, col=COL_Lisa1,border=NA)
#title("LISA Map")
TB5 <- attr(COL_Lisa1, "table")
legtext <- paste(names(TB5))
legend("bottomright", fill=attr(COL_Lisa1, "palette"),
legend=c("0.1%", "1.0%", "5.0%", "N.sgf"), bty="n", cex=0.8)
plot(Ba_shape,add=TRUE,lwd=1)
scalebar(500, xy=c(-47.97892, -17.23938),
type="bar", below="km",
cex=0.8, lonlat=T,divs=4)
compassRose(-48.45524, -14.00634,cex=0.8)
title("Lisa MAP para o IDH dos municípios do estado da Bahia ")
LISA MAP Independentes para theil
LISA2 <- classIntervals(Ba_shape.mloc2[,5], style="fixed",
intervalClosure = c( "right"),
fixedBreaks=c(0, 0.001, 0.01, 0.05, by=1))
colors <- c("red", "blue", "lightpink", "skyblue2", "white")
COL_Lisa2 <- findColours(LISA2, colors)
# window(6,4) # quando quiser salva uma imagem com boa qualidade
#par(mar=c(0,0,0,0))
plot(Ba_shape, col=COL_Lisa2,border=NA)
#title("LISA Map")
TB5 <- attr(COL_Lisa2, "table")
legtext <- paste(names(TB5))
legend("bottomright", fill=attr(COL_Lisa2, "palette"),
legend=c("0.1%", "1.0%", "5.0%", "N.sgf"), bty="n", cex=0.8)
plot(Ba_shape,add=TRUE,lwd=1)
scalebar(500, xy=c(-47.97892, -17.23938),
type="bar", below="km",
cex=0.8, lonlat=T,divs=4)
compassRose(-49.45524, -14.00634,cex=0.6)
title("Lisa MAP para o indice de THEIL dos municípios do estado da Bahia ")
LISA MAP Independentes para Gini
LISA3 <- classIntervals(Ba_shape.mloc3[,5], style="fixed",
intervalClosure = c( "right"),
fixedBreaks=c(0, 0.001, 0.01, 0.05, by=1))
colors <- c("red", "blue", "lightpink", "skyblue2", "white")
COL_Lisa3 <- findColours(LISA3, colors)
# window(6,4) # quando quiser salva uma imagem com boa qualidade
#par(mar=c(0,0,0,0))
plot(Ba_shape, col=COL_Lisa3,border=NA)
#title("LISA Map")
TB5 <- attr(COL_Lisa3, "table")
legtext <- paste(names(TB5))
legend("bottomright", fill=attr(COL_Lisa3, "palette"),
legend=c("0.1%", "1.0%", "5.0%", "N.sgf"), bty="n", cex=0.8)
plot(Ba_shape,add=TRUE,lwd=1)
scalebar(500, xy=c(-47.97892, -17.23938),
type="bar", below="km",
cex=0.8, lonlat=T,divs=4)
compassRose(-49.45524, -14.00634,cex=0.6)
title("Lisa MAP para o indice de GINI dos municípios do estado da Bahia")