Clusters de Acidentes por Affinity Propagation

Clusters de Acidentes por Affinity Propagation

library(leaflet.extras)

## Loading required package: leaflet

library(apcluster)

## 
## Attaching package: 'apcluster'

## The following object is masked from 'package:stats':
## 
##     heatmap

library(magrittr)
library(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, union

library(leaflet)
library(rgdal)

## Loading required package: sp

## 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: 1.5-27, (SVN revision 1148)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/fagne/Documents/R/win-library/4.1/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/fagne/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/fagne/Documents/R/win-library/4.1/rgdal/proj

library(rgeos)

## rgeos version: 0.5-8, (SVN revision 679)
##  GEOS runtime version: 3.9.1-CAPI-1.14.2 
##  Please note that rgeos will be retired by the end of 2023,
## plan transition to sf functions using GEOS at your earliest convenience.
##  GEOS using OverlayNG
##  Linking to sp version: 1.4-5 
##  Polygon checking: TRUE

library(geojsonio)

## Registered S3 method overwritten by 'geojsonsf':
##   method        from   
##   print.geojson geojson

## 
## Attaching package: 'geojsonio'

## The following object is masked from 'package:base':
## 
##     pretty

library(mapview)
library(contoureR)

## Loading required package: geometry

library(geosphere)

## 
## Attaching package: 'geosphere'

## The following object is masked from 'package:geojsonio':
## 
##     centroid

library(cluster)
library(sp)
library(rgdal)
library(RColorBrewer)
library(sp)
library(foreign)

Carga de Dados

#dados = read.dbf("~/OneDrive/r-files/CIET/acidentes2020/_Base/acidentes_2014a2020_WGS84.dbf")
#dados = read.dbf("/Users/fagne/OneDrive/r-files/CIET/acidentes2020/_Base/acidentes_2014a2020_WGS84.dbf")
dados = read.dbf("/Users/fagne/OneDrive/r-files/CIET/acidentes2020/_Base/acidentes_2014a2020_WGS84.dbf")
#dados = read.dbf("/Users/fsmoura/OneDrive/r-files/CIET/acidentes2020/_Base/mercator_32722_2014_2019.dbf")
dados = dados[dados$ANO > 2014, ]
sort(unique(dados$ANO))

## [1] 2015 2016 2017 2018 2019 2020

anos = length(unique(dados$ANO))
anos

## [1] 6

class(dados)

## [1] "data.frame"

x2 <- cbind(dados$LONGITUDE, dados$LATITUDE)
x2 <- x2[complete.cases(x2), ]
dim(x2)

## [1] 75102     2

head(x2)

##           [,1]      [,2]
## [1,] -51.01213 -30.19741
## [2,] -51.03732 -30.21369
## [3,] -51.05466 -30.23513
## [4,] -51.05470 -30.23517
## [5,] -51.05470 -30.23517
## [6,] -51.05470 -30.23517

Preparação

x1 <- x2
#x2 <- x2[sample(nrow(x2), round(nrow(dados)*0.25, 0)), ]
#x2 <- x2[sample(nrow(x2), round(nrow(dados)*0.05, 0)), ]
#x2 = as.data.frame(x2)
load("data/x2-20000-90.rda")
load("data/apres2-20000-90.rda")
names(x2) = c("LONGITUDE", "LATITUDE" )
head(x2)

#save(x2, file = "data/x2-20000-90.rda")
dim(x1)

## [1] 75102     2

dim(x2)

## [1] 18776     2

Treino

load("data/apres2-20000-90.rda")
#apres <- apcluster(negDistMat(r=2), x2, q=0.999)
plot(apres, x2)

A caption

summary(apres)

##   Length    Class     Mode 
##     3668 APResult       S4

#save(apres, file = "data/apres2-20000-90.rda")

Obtenção de Centróides

centroides = unique(apres@exemplars)
poly = data.frame()
centr_indice = 0
for (i in centroides){
  centr_indice = centr_indice + 1
  centr_lat=x2[i,1]
  centr_lon=x2[i,2]
  poly = rbind(poly, c(centr_lat, centr_lon, centr_indice))
}
names(poly) = c("Lat", "Lon", "Cluster")
head(poly)

dim(poly)

## [1] 3668    3

exemplars = poly
#save(exemplars, file = "data/exemplars-20000-90.rda")

Classificação Global

predict.apcluster <- function(s, exemplars, newdata){
  simMat <- s(rbind(exemplars, newdata), sel=(1:nrow(newdata)) + nrow(exemplars))[1:nrow(exemplars), ]
  unname(apply(simMat, 2, which.max))
}
resultado <- list()

dados$cluster = 0
for(i in seq(from=1, to=length(dados$ID)-1000, by=1000)){
  inicio = i
  final = i+999
  resultado = predict.apcluster(negDistMat(r=2), x2[apres@exemplars, ],  dados[inicio:final, 2:3])
  dados$cluster[inicio:final] = resultado
}
controle = length(dados$cluster)  - final

resultado = predict.apcluster(negDistMat(r=2), x2[apres@exemplars, ],  dados[(final + 1):length(dados$cluster), 2:3])
dados$cluster[(final + 1):length(dados$cluster)] = resultado
head(dados)

tail(dados)

#save(dados, file = "data/acidentes-20000-90.rda")

Acidentes por Cluster

pal <- colorFactor(
  palette = 'Dark2',
  domain = dados$cluster
)

leaflet(dados) %>%
  addTiles(group="Mapa") %>% 
  addCircles(group="Acidentes", ~LONGITUDE, ~LATITUDE, weight = 0.1, radius=7, color=~pal(cluster),
             stroke = TRUE, fillOpacity = 0.8, popup=~paste("Cluster Nº: ", cluster,  
            "<br>Ano: ", ANO, "<br>Tipo: ", TIPO_ACID, "<br>Local: ", LOG1,  "<br>UPS: ", UPS,   sep = " ")) %>% 
  addLegend(group="Legenda", "topright", colors= "", labels=paste("Classificados em meio a ", summary(apres)[1], "Clusters"), title="Acidentes em Porto Alegre") %>% 
  addLayersControl(overlayGroups = c("Mapa", "Acidentes", "Legenda"),
                   options = layersControlOptions(collapsed = FALSE)) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter)

A caption

Enriquecimento Informacional

rm(apres)
clusters_encontrados = sort(unique(dados$cluster))
#clusters_encontrados
parq = dados
poly = data.frame()
for (i in clusters_encontrados){
  temp = parq[parq$"cluster" == i,  ]
  ch1 = convexHullAM_Indexes(temp[,2],temp[,3], includeColinear=FALSE,zeroBased = FALSE)
  #print(i)
  #print(ch1)
  poligono = temp[ch1, 2:3 ]
  area <- geosphere::areaPolygon(x = poligono)
  acidentes = nrow(temp)
  pol = temp
  coordinates(pol) = ~LONGITUDE+LATITUDE
  centr_lat=gCentroid(pol, byid=FALSE)$x
  centr_lon=gCentroid(pol, byid=FALSE)$y
  if(nrow(temp) >= anos) {
    for (ii in ch1) {
    polying = temp[ii,]
    polying$area = area
    polying$acidentes = acidentes
    polying$centroide_lat = centr_lat
    polying$centroide_lon = centr_lon
    poly = rbind(poly, polying)
    }  
  }
}
head(poly)

tail(poly)

mean(poly$area)

## [1] 9554.711

median(poly$area)

## [1] 4862.517

minimoquantil = quantile(poly$area, probs = 0.01)
maximoquantil = quantile(poly$area, probs = 0.90)
quantile(poly$area, probs = c(0.01, 0.25, 0.5,0.75,0.99))

##         1%        25%        50%        75%        99% 
##   115.9416  1651.0521  4862.5172  9498.2522 67387.6134

poly = poly[(poly$area < maximoquantil) & (poly$area > minimoquantil), ]
dim(poly)

## [1] 16191    48

class(poly)

## [1] "data.frame"

pol = poly

Combinando Informações

#save(pol, file = "data/pol.Rds")
#load("data/acidentes.rda")
#load("data/apres2.rda")
#load("data/exemplars.rda")
#load("data/x2.rda")
dados = poly[,c(1:9, 11:13,38,41,44:48)]
head(dados)

names(dados) = c("ID","lat", "lon", "log1", "Log2", "Pred", "Local", "Tipo", "Via", "Data", "Dia", "Hora", 
                 "Fx_horaria","UPS", "box_id", "Area", "Acidentes", "CentLon", "CentLat")
dados$id = (dados$box_id * 11)
dados$group = dados$id
head(dados)

dadostemp = dados[, c(15:21)]
coordinates(dados)=c("lat","lon")
df = dados
df

## class       : SpatialPointsDataFrame 
## features    : 16191 
## extent      : -51.26562, -51.0838, -30.23911, -29.95977  (xmin, xmax, ymin, ymax)
## crs         : NA 
## variables   : 19
## names       :     ID,               log1,                                    Log2,  Pred,      Local,         Tipo,                        Via,       Data,         Dia,  Hora, Fx_horaria, UPS, box_id,             Area, Acidentes, ... 
## min values  : 600975, AC A AV A J RENNER, AC DOIS VOLUNTARIOS-PRES CASTELO BRANCO,     0, Cruzamento, ABALROAMENTO, 0 AV JORGE BENJAMIN ECKERT, 01/01/2015,     DOMINGO, 00:00,          0,   1,      1, 117.435793273151,         6, ... 
## max values  : 683133, VDT LEONEL BRIZOLA,                             VDT OBIRICI, 90100, Logradouro,   TOMBAMENTO,         VDT LEONEL BRIZOLA, 31/12/2020, TERCA-FEIRA, 23:59,         23,  13,   3666, 16967.1165241543,       290, ...

data <- data.frame(box_id=unique(df$box_id),row.names=unique(df$id))
head(data)

dadostemp2 = dados[!duplicated(dados$id),]
#head(dadostemp2, 15)
data = as.data.frame(cbind(data, dadostemp2@data))

Criação de Polígonos

points2polygons <- function(df,data) {
  get.grpPoly <- function(group,ID,df) {
    Polygon(coordinates(df[df$id==ID & df$group==group,]))
  }
  get.spPoly  <- function(ID,df) {
    Polygons(lapply(unique(df[df$id==ID,]$group),get.grpPoly,ID,df),ID)
  }
  spPolygons  <- SpatialPolygons(lapply(unique(df$id),get.spPoly,df))
  SpatialPolygonsDataFrame(spPolygons,match.ID=T,data=data)
}

#Criamos o SpatialPolygonsDataFrame
data$Log2 = NULL
spDF <- points2polygons(df,data)
spDF

## class       : SpatialPolygonsDataFrame 
## features    : 2580 
## extent      : -51.26562, -51.0838, -30.23911, -29.95977  (xmin, xmax, ymin, ymax)
## crs         : NA 
## variables   : 19
## names       : box_id,     ID,                        log1,  Pred,      Local,         Tipo,                        Via,       Data,         Dia,  Hora, Fx_horaria, UPS, box_id.1,             Area, Acidentes, ... 
## min values  :      1, 601030, AC CIRIO AMARAL DE OLIVEIRA,     0, Cruzamento, ABALROAMENTO, 1 AV EDVALDO PEREIRA PAIVA, 01/01/2017,     DOMINGO, 00:00,          0,   1,        1, 117.435793273151,         6, ... 
## max values  :   3666, 683078,          VDT LEONEL BRIZOLA, 21010, Logradouro,   TOMBAMENTO,         VDT LEONEL BRIZOLA, 31/10/2019, TERCA-FEIRA, 23:55,         23,  13,     3666, 16967.1165241543,       290, ...

class(spDF)

## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"

spDF@data$group = 1
spDF@data$box_id = NULL
dim(spDF@data)

## [1] 2580   18

dadostemp = unique(dadostemp)
spDF@data = merge(spDF@data, dadostemp, by = "box_id")
dim(spDF@data)

## [1] 2580   24

plot(spDF,col=spDF$box_id+1)

A caption

library(rgdal)
rgdal::writeOGR(obj = spDF,
                dsn = "data/myParq.json",
                layer = "myParq",
                driver = "GeoJSON",
                overwrite_layer = TRUE)

Acidentes por Cluster

#carregamos os dados SpatialPolygonsDataFrame
parqs <- geojsonio::geojson_read("data/myParq.json", what = "sp")
#Verificamos o objeto
parqs

## class       : SpatialPolygonsDataFrame 
## features    : 2580 
## extent      : -51.26562, -51.0838, -30.23911, -29.95977  (xmin, xmax, ymin, ymax)
## crs         : +proj=longlat +datum=WGS84 +no_defs 
## variables   : 24
## names       : box_id,     ID,                        log1,  Pred,      Local,         Tipo,                        Via,       Data,         Dia,     Hora, Fx_horaria, UPS,           Area.x, Acidentes.x,    CentLon.x, ... 
## min values  :      1, 601030, AC CIRIO AMARAL DE OLIVEIRA,     0, Cruzamento, ABALROAMENTO, 1 AV EDVALDO PEREIRA PAIVA, 01/01/2017,     DOMINGO, 00:00:00,          0,   1, 117.435793273151,           6,   -51.264331, ... 
## max values  :   3666, 683078,          VDT LEONEL BRIZOLA, 21010, Logradouro,   TOMBAMENTO,         VDT LEONEL BRIZOLA, 31/10/2019, TERCA-FEIRA, 23:55:00,         23,  13, 16967.1165241543,         290, -51.08445544, ...

dim(parqs)

## [1] 2580   24

library(raster)

## 
## Attaching package: 'raster'

## The following object is masked from 'package:dplyr':
## 
##     select

## The following object is masked from 'package:magrittr':
## 
##     extract

projection(parqs)

## [1] "+proj=longlat +datum=WGS84 +no_defs"

library(mapview)
mapviewPalette(name = "Viridis")
library(RColorBrewer)
mapview(parqs, zcol = "Acidentes.x", col.regions=brewer.pal(9, "YlOrRd"))

Acidentes por m2

parqs@data$densidade = (parqs@data$Acidentes.x/parqs@data$Area.x)*1000000
mapview(parqs, zcol = "densidade", col.regions=brewer.pal(9, "YlOrRd"))

Acidentes por poligono

hist(parqs@data$Acidentes.x, col = "magenta")

A caption

Acidentes por KM2

hist(parqs@data$densidade, col = "orange")

A caption

Locais mais densos

quantile(parqs$densidade, probs = 0.99)

##      99% 
## 103194.5

temp = parqs[parqs$densidade > quantile(parqs$densidade, probs = 0.99), ]
mapview(temp, zcol = "densidade")

Calculando a matriz de vizinhanças

projection(parqs)

## [1] "+proj=longlat +datum=WGS84 +no_defs"

parqstemp = parqs
require(sf)

## Loading required package: sf

## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1

shape <- read_sf(dsn = ".", layer = "mercator_32722_2014_2019")
projection(shape)

## [1] "+proj=utm +zone=22 +south +datum=WGS84 +units=m +no_defs"

projection(parqstemp) = projection(shape)

ccods = coordinates(parqs)
temps = as.data.frame(ccods)
cord.dec = SpatialPoints(cbind(temps$V1, temps$V2), proj4string=CRS("+proj=longlat"))
cord.UTM <- spTransform(cord.dec, CRS("+init=epsg:32722"))
ccods = as.data.frame(cord.UTM)
points = cbind(ccods[,1],ccods[,2])
head(points)

##          [,1]    [,2]
## [1,] 481049.1 6680143
## [2,] 489433.0 6677792
## [3,] 482481.2 6677908
## [4,] 487301.7 6671709
## [5,] 477498.2 6673973
## [6,] 478011.1 6678011

library(spdep)

## Loading required package: spData

## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`

distNeighbors = 400
dnb = dnearneigh(points,0,distNeighbors)
class(dnb)

## [1] "nb"

subsets = as.data.frame(matrix(dnb))
class(subsets)

## [1] "data.frame"

subsets = subsets$V1
lengths(subsets)

##    [1] 20  3 19  3  6 30 27  9 22 13  1 13 23 26 21  6  2  7 17 13  8  8  4  6
##   [25] 21 11 16 19  8 10  3  7  1  6  5 18 11 12 16 14  6 21 10  6 17 23  1 13
##   [49]  8  6 16 19  5 16  4  3 23  2  6  1 20 13 26 17  5 18 15 10 12 11 19 22
##   [73] 15 14 16 12 11 18 14  1  5  4  8 27  6  7 15 19  7  2 20  7  7 18  3  4
##   [97] 18  8  9  4 21 11  5  6 17 18  9  1 10 16  9 10  8  6 16  3 15 20 13  6
##  [121]  6  1  1  5 10 25 24 13 12 16  8  5  1 23 30 17 10 22 16 27  6  9  7  8
##  [145] 24 11 11  3 18 25 15  5  3 13 23 17  7  2 11  2  7 10  1  1  5 12  4  3
##  [169] 15 11  2 16 16 12 12  6 20  8 18 20 24  4 14  8  9 13  7  7  1 16 20 16
##  [193]  5  3 19  8 19 22 18  6  9  7 24 21  8 13 25  7 18  5 22 12  6  5  7 13
##  [217]  3 19 17 17 12 12 11  6 13  8 14 12  7 25  1  3 26 19 16  8  9 23 15  9
##  [241] 19 25 14 21  3  7  9 15  9 15 20  6  8 20  8  7  5 18  4  1 12  6  6 15
##  [265]  1 16 19  7  8  8 19 15  5 27 17 15 23  5  5  3  3 13 25 18  5 20 20 13
##  [289] 15 10 15 10 23 13 20 12  3  6  4  4 11  6 11 16  8 19 10  4 22  3 13 12
##  [313]  7 21 18  1 11  4 23 12 24 17 16  6  8  3  8  1 17  4 17  1  1 11  2  9
##  [337] 21  1 20  7 12 19 11  4  5 13 16  5  9  7  1 12  6  1 19 16  9  2  3  6
##  [361] 11  7 13  6  7  3  3 25  3  3 18  9  2 17 22 10 12 20 11 21 16 10 17 20
##  [385] 16  7  1 19  1  7 15 10  9 11  5  8 16  7  5 15  1  6 23 12  5 15 21  9
##  [409] 12  9  4  7 13  1  1  1 19 19  5  9  4 32 17  2 29  1  5  6 23  6  7 21
##  [433] 10  5 14  9  6  5 23  6 10  4 14 14  5  9 14  7 11  4 13 19 12  8 16  7
##  [457] 16 16  7  9 14 17  3 11  8  9  5  5 11 10 23 16  6 16  3  4 34  6 17 14
##  [481] 16  6  8 12 17 29  4 24  6  5 12 15  6  1  4  9  2 16 15  8 19  3  3  1
##  [505] 21 31  1 19  2  2 16 11 18 15  5  5  9 19  7  9  2 20 16  6 20  9  5 13
##  [529] 15  3  7  7 14 13  2 26 17 10  9 14  1  8 17  3 20 15  1  1 20 18 17 24
##  [553]  6  3  1 11 13  1  9 22  4 10 20  7  4 12  7 13 26 21  6  7  9 16 18  3
##  [577] 20 12 10 12 13 15 19  7  5  6 18  5 21  2  5 10 13  2 14  6  1 18 15 27
##  [601] 23  9  5 11  1 13 21  8 13  6 18 20  8  7 15  8 16  3 19 24 21  3 18 12
##  [625] 11  3 23 13 17  7  3 14 20 10 15 14  3  7 16 15  1 19 13 20 21 16 18  6
##  [649]  9  5  9 16 13 13  3  3  8 16  4  9 23 23 15  6 26  5  7 20 11 10  6 26
##  [673] 22  9 14 15  5  2  9  9  2 12 15  5 11  8 12 16 12  1 16 10 18 16 10 15
##  [697] 15 12 17 15 13 13  8 14  9 31 13  6 26 23  9  1 13 21 24 21  3  9 22  1
##  [721]  8  6 10 14  5 12 20 14 11  7  4 10 15  9  8 17  5 17 12  6  4  9 13 19
##  [745] 23 14 13  4 10 10 20  2 17  3 16  6 23 15 18 15 19  2 11 19  5 15  6 20
##  [769] 12  3 20  1 17 15  8 14  9  1  5  9 16  8 16 12  1  4 19  4 14  6 20  4
##  [793]  9  8 13 25  2  4  3  9 24 24 16  3 23  8 18 15 13 22 15 10 16  7  3  1
##  [817]  1 13  1  8  9 11  9  3 25  5 24  2  9 12 12  6 16  2  1 18  4 15  8  3
##  [841]  5  4  8  6  2  6  4  7 19 13  3  3 12  6 24 23 10  1 14  6 12  6 17 20
##  [865] 12 19 20 15 21 17 12  4 15  7  8 25  3  8 15 12  5  1 24 10 17 17 17  2
##  [889] 18 17 10 18 17  3 17 15  5  1 21 11 27  5  5 18 20 18  4 16 19  7 13  6
##  [913]  9 21 17  1 13 24  6  6 25  4  6 16 10 16 19 11 14 11  3  5  4  3 10 20
##  [937] 11  1 17 14 16 14  4 16 17  9  7 19 11 23 15  6 12  8 14  5 15 10 18 17
##  [961] 14 14  9 11  6 17 20 12  7  7 12 11  9  8  9 12 26 19  9  9 16 15  8 16
##  [985] 12 16 13  8 17 10 12  9  6  3 20 10  2 11 11  8 11 18 21  4 12 10  5  4
## [1009] 16 19  6 22 17 11 11  9 12 32 12 14 18 12 11 12 20 12  3  7  9 13  1  7
## [1033]  5  4  6  5 10 23 19 12 15 14  7 10 10 17  3 21  5  5  6 13  8 14  2  8
## [1057] 12 17  8  8  4 14  5  6  9 23  3 15  5  1  5 16  5 12  5 22 24 13 17 20
## [1081]  6  9 13 11  5  5  3 28  6  2 15 11 11 11 20 29 12 13 19 34  8  9  7 15
## [1105] 12 10  5  9 21 17 22  6 26  4  4 14 23  4  9 14 15  9 19  6 16  6  3  5
## [1129]  5  6  5 21  5 10 11  6 30  7 18 14 19  7  5 22 13 17 11 11 12 10 13  3
## [1153] 12  8  2  8  1 13  9  8  3  5 17  8 17 19 17  7  6  1  5  8  4 17 13 11
## [1177] 16 13  3 12 10 21 10 13  8 13  6  5  4  8  9 15 12  3  3  9  1 18 10 13
## [1201] 14  9 19  2  4  1 10  6 12 15 23 13  9 15  5 10  6 10  3 30  2  7  9  9
## [1225]  9 16  5 20  1  9  5 12 18 16  5 22  5 27  9 17  9 11  3  7  7 19  8  3
## [1249]  3  4  6 12 11 15  1 23  2 21  3 17 11 10 17 13 23 14 15  9 17 10 19 21
## [1273]  7 19  8 11  2 10  2  9  8  1  4  6 15 16 12 25  4  6  9 19 17  3  4 10
## [1297]  8  7 10  4  3  3 14  6 19 19 13 24 13 22 12 10 12 22 19 21  3 18  3  7
## [1321]  8  1 19 15  7  6  2  5 18  9 17  7 14 12 16 23 19  4  5 13 19 12 31 16
## [1345] 12  3 14  2  6  1 11 11 30  3 29 14 13 15  5 21 23 19  5  6  1 15  5 14
## [1369]  6  7 19 10  4 20 21  7 13  4  1  3 11 17  4 19 10 14 14  9  6 21 15  5
## [1393] 12  3  7  5 23  4  9 17 17  1  4  6 11 12 11  8 18 15  2  2 11 20  5 17
## [1417]  7  1  4  1 16  8  7  2 17  6 12 14 20  8 11 16 13 30  3  6 16 24 19  3
## [1441]  9  7  4  4 17 20 21  6 10 11  6 12  6  6 21  8 18  6 17 14  9  5  3 21
## [1465]  4  6  9 11  5  8 16  7 13  6 10 25  7 12 23 13  6 15 10  3  8 23  7  8
## [1489] 16 13 10 18 13 10 17  5 18  4 12  3  1  6 20  2  8  5 12 14 14  6  8 22
## [1513] 19 10  5 20 15 13  2 14  5  2 24 16  1  4  3 11  5  9 12  3 12 10  8 25
## [1537] 10  2  6 10 16  5  4  7 17 13 10  9 26 17  3  4 13  4 14  5  6 18  6  4
## [1561] 20 10  7 10 13 16 26  9 29 12  3 15 13  4  5 17 10 13  4 11 15  2 17 21
## [1585] 23  8  5 13  2 13 16  1 10 16 10 12  7  9 15  3 13  1 20  5  8 10  5 15
## [1609]  7  6 10 14 22 21 10 12 13 27 22 15 12 15 10 14 16 15 14  9 11  6  3 12
## [1633] 15 17 19  3  5 18 13  2 20  1  5 12  9  8 11  6 20 18 10  7  4 20 10  5
## [1657] 17 13 23  1 24  3 25 13  4 14 14  5  6 20 28 10 34 12  7  5 14  6 13 10
## [1681]  6 10 10  6  2 13  6 12 19  5 10  5  4 24 19  1  3 12 11  6  3  8 11  1
## [1705]  4 18  6  4  4  5 22  5 23  3  3 10  6 17  8 22 14 23  2  7  3 19 19  6
## [1729]  5 15  4 15 12  2 16 12 13 10 10  4  4 11  6  5  7 15  8 16 18 11  4 24
## [1753] 19 11  1 11 23 10  5  5 19 18 18  7  4  2 12  4  7 16  2  7  7 18  4  8
## [1777] 14 15 20 22  6  4  4  5 24  4 20 16  6 15  5  7  1  1 22 19  6 15 11 10
## [1801] 22 19 27  8 31  9 27 16 13  1  6 14 11 26 17  7  7  2 25  8  1 16 20 15
## [1825] 13 21  7  2 19  9 10 18  4 21 22 13 17  7  1 10  3 17  9 17 20 18 23 15
## [1849]  7  1  1  1  4 14 23 10  4 15  3 13  9 13 20 11 21  2 12  5 19  8 14 17
## [1873] 18 10 18 20 21 19 16  3  3  3 15  7 15  3 14 23  1 11 21  5  6  6 15  6
## [1897] 18 16 20 19  5  6 16 20  2 26 14  9 17 17 12 11  5 19 23  2  6 19 20  9
## [1921] 23 13 13  6  7 15  4  2  1  4 18  7 21 16 15  7 23  4 12 19 23 22  4 25
## [1945]  6  2  6 26 10  9  1 15  1 11 10  6 18  4 16 14  7 13 23  3  3  5 11 16
## [1969]  3  4 14 13  3 17 10 20 21  5 16  3  2 16 12 15 17 21 15 11  4 21  9  8
## [1993] 16 23 16  3 20  1 24  3 19 19  2  3  8 15  5 13 11 10  9  8 28 16 18 10
## [2017]  6  4  4 15  8 12 14 21  8  9 11  8  5  6 17  8  3 10  3 12  4 10 14 12
## [2041] 10  4 14  3 33 21 12 12 15  6 11 11 26  3 25  8 20 11 16 18  2  5  6 15
## [2065] 10  4  2  3 11  6 34 14 12 20  1 16  6 20  2 17 13 17 24 18 22  5  5 31
## [2089] 12  8 24  7  3  3 21 21  9  4 18  2 27 13 23 20  6 14  5  5  4 14 11  7
## [2113] 12 19 11 14  3  1 21 15  5  9 26 12 14  9  6  8  8  4 13  9  4  3  7 11
## [2137]  6  7 20 14  9  5 20  6 20 16 21  5  9  7 23 13 25 11 18  7  7 14 12  7
## [2161]  7 28 10 12  2  4  8  4  5 12 16 18  6  9 17 19 19  7 15 17  3 18 22 14
## [2185]  2 12 16 14  2  2  8  6  7  8  5  1  1 10 11 14 16 13  7  9  5 17  9 21
## [2209] 12 19 22  6 11  3 16  3 11 15 10  8  5 13  3 22  6  1  4  1 16 19 19 19
## [2233]  5  8  4 25  3 24 15 12 26  5 14 11  6  5 14 18 12 24  2 10  6  1 22 23
## [2257]  4  9 27 19 15 17 16  2 18 19  5 17 13 13 25 14  7  9 15  8 19  7  7 12
## [2281] 18  5 17 18 14  7 22  7 15  6  5 17 16 18  1  6  1 14 13 23  7 10  2  4
## [2305] 20  6 14 11  8 14 11  7 12  3 19 12  4  6  6 13  3  6 21  6 13  4 25  6
## [2329]  5 10  6 10 19  5  7  5  9 18 10 21  5  9 10 23  2 16 13  8 12  5  5 14
## [2353] 24 14  4 12 12 17 10  7  6 10  9 12 15 18 16 12  3 16 17  6 20  2  4 19
## [2377]  5 17 31 18 21  9 14  7  9 19  1 22  2  2 10  5 28 21  1  7 21  6 18  7
## [2401] 12 13  4 15  8  9 11  8 11  5 16 13 14  6  9 13  5  9 14 16  5  5 14  7
## [2425] 21  2  4 11 14  5 17  3  7 24 18  3 10  9 26 16  4 10  9  2  9  8  8 10
## [2449] 23 10  6 21  1 17  5  6 22  3  6 22 11  9 22  6 19  7  8 10  6  1  6 16
## [2473] 11 14 19 22  3 20 14 12  3  4  5 23 14  6  2 16  3 19  4  8  6  2 22 18
## [2497]  8 13  3 19  4  4  4 16 29 20 14 18 19 18  5  8  7  1  1 12 18 13  6 14
## [2521]  3  2  1 11 12 10 14 11  7  2  2  1  5 11 23 16 11  5  2 23  5  6 10 19
## [2545]  3  7  6  5  4  1  4 15 11  7 22 12  1 19 10 12  9 11  7 21  7 34 10  8
## [2569] 14 10  5 18 13  8 22  1 21  9 29  5

parqs$n = 1
sub = which(subsets == '0')
sub

##  [1]   47   60   80  108  163  265  316  351  401  414  416  504  548  597  605
## [16]  690  720  785  819  835  898 1070 1170 1229 1322 1350 1418 1420 1525 1642
## [31] 1755 1793 1810 1821 1851 1852 1929 1953 1998 2228 2254 2387 2470 2514 2515
## [46] 2523 2532

parqs$n[sub] = 0
length(parqs)

## [1] 2580

parqs = parqs[parqs$n > 0,]
length(parqs)

## [1] 2533

length(dnb)

## [1] 2580

#dim(ccods)
ccods = ccods[-sub, ]
dim(ccods)

## [1] 2533    2

points = cbind(ccods[,1],ccods[,2])
head(points)

##          [,1]    [,2]
## [1,] 481049.1 6680143
## [2,] 489433.0 6677792
## [3,] 482481.2 6677908
## [4,] 487301.7 6671709
## [5,] 477498.2 6673973
## [6,] 478011.1 6678011

#dnb = dnearneigh(points,0,2000)
dnb = dnearneigh(points,0,distNeighbors)
dnb

## Neighbour list object:
## Number of regions: 2533 
## Number of nonzero links: 29174 
## Percentage nonzero weights: 0.4547007 
## Average number of links: 11.51757

length(dnb)

## [1] 2533

Matriz de Vizinhanca

Matriz Binária

W.Bin= nb2mat(neighbours = dnb, style = "B")
#parqs <- parqs[!sub,]

Matriz Normalizada

W.Normal= nb2mat(neighbours = dnb, style = "W")
#head(W.Normal)

KNN

vizinhos_4 <- knearneigh(points, k = 4)
class(vizinhos_4)

## [1] "knn"

head(vizinhos_4$nn)

##      [,1] [,2] [,3] [,4]
## [1,] 1792  312 1789  367
## [2,]  779  753  476  581
## [3,] 1985 1212 2432   69
## [4,]  302 2262 1542 1869
## [5,]  530 2438  615 1557
## [6,]  690  997 2064  694

vizinhanca_4 <- knn2nb(vizinhos_4)
class(vizinhanca_4)

## [1] "nb"

Preparação para Análisis Global e Local

mv_simpl = st_as_sf(parqs)
plot(mv_simpl)

A caption

class(mv_simpl)

## [1] "sf"         "data.frame"

library(dplyr)
mv_simpl =  mv_simpl %>% dplyr::select(Acidentes.y)
#mv_simpl <- st_simplify(mv_simpl, preserveTopology = FALSE,                  dTolerance = 1)
class(mv_simpl)

## [1] "sf"         "data.frame"

mapview::mapview(mv_simpl)

sf::sf_use_s2(FALSE)#trips and tiks

## Spherical geometry (s2) switched off

mv_simpl = st_as_sf(mv_simpl)
vizinhanca_neig <- poly2nb(mv_simpl)

## although coordinates are longitude/latitude, st_intersects assumes that they are planar

ShapeNEIG = parqs
ShapeNEIG$vizinhos = card(vizinhanca_neig)
ShapeNEIG <- subset(ShapeNEIG, parqs$vizinhos != 0)
#vizinhanca2neig <- poly2nb(ShapeNEIG)

Calculando o Índice de Moran Global

Os índices de autocorrelção espacial global calculados pelos testes de normalidade e permutação.

Pelo teste de Normalidade

moran.test(parqs$Acidentes.y,listw=nb2listw(dnb, style = "W"), randomisation= FALSE)

## 
##  Moran I test under normality
## 
## data:  parqs$Acidentes.y  
## weights: nb2listw(dnb, style = "W")    
## 
## Moran I statistic standard deviate = 14.989, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic       Expectation          Variance 
##      0.1601123927     -0.0003949447      0.0001146697

Pelo teste de Permutação ou Teste de pseudo-significˆancia

 moran.test(parqs$Acidentes.y,listw=nb2listw(dnb, style = "W"), randomisation= TRUE)

## 
##  Moran I test under randomisation
## 
## data:  parqs$Acidentes.y  
## weights: nb2listw(dnb, style = "W")    
## 
## Moran I statistic standard deviate = 15.055, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic       Expectation          Variance 
##      0.1601123927     -0.0003949447      0.0001136709

Por simulação de Monte-Carlo

moran.mc(parqs$Acidentes.y, listw=nb2listw(dnb, style = "W"), nsim=999)

## 
##  Monte-Carlo simulation of Moran I
## 
## data:  parqs$Acidentes.y 
## weights: nb2listw(dnb, style = "W")  
## number of simulations + 1: 1000 
## 
## statistic = 0.16011, observed rank = 1000, p-value = 0.001
## alternative hypothesis: greater

Pelo teste de Permutação

Diferente dos demais testes globais o teste para o EBI é exclusivo para taxas e tem-se apenas a opção de teste da permutação

EBImoran.mc(parqs$Acidentes.y,parqs$Area.y,
            nb2listw(dnb, style="B", zero.policy=TRUE), nsim=999, zero.policy=TRUE)

## The default for subtract_mean_in_numerator set TRUE from February 2016

## 
##  Monte-Carlo simulation of Empirical Bayes Index (mean subtracted)
## 
## data:  cases: parqs$Acidentes.y, risk population: parqs$Area.y
## weights: nb2listw(dnb, style = "B", zero.policy = TRUE)
## number of simulations + 1: 1000
## 
## statistic = 0.04604, observed rank = 1000, p-value = 0.001
## alternative hypothesis: greater

Por simulação de Monte-Carlo

shapeCG.p=parqs$Acidentes.y/parqs$Area.y
moran.mc(shapeCG.p, nb2listw(dnb, style="B", zero.policy=TRUE),
         nsim=999, zero.policy=TRUE)

## 
##  Monte-Carlo simulation of Moran I
## 
## data:  shapeCG.p 
## weights: nb2listw(dnb, style = "B", zero.policy = TRUE)  
## number of simulations + 1: 1000 
## 
## statistic = 0.042389, observed rank = 1000, p-value = 0.001
## alternative hypothesis: greater

Calculando a Estatística C de Geary Global

Pelo teste de Normalidade

geary.test(parqs$Acidentes.y, listw=nb2listw(dnb, style = "W"), randomisation= FALSE)

## 
##  Geary C test under normality
## 
## data:  parqs$Acidentes.y 
## weights: nb2listw(dnb, style = "W") 
## 
## Geary C statistic standard deviate = 13.446, p-value < 2.2e-16
## alternative hypothesis: Expectation greater than statistic
## sample estimates:
## Geary C statistic       Expectation          Variance 
##      0.8502252871      1.0000000000      0.0001240747

Pelo teste de Permutação

geary.test(parqs$Acidentes.y, listw=nb2listw(dnb, style = "W"), randomisation=TRUE)

## 
##  Geary C test under randomisation
## 
## data:  parqs$Acidentes.y 
## weights: nb2listw(dnb, style = "W") 
## 
## Geary C statistic standard deviate = 9.9245, p-value < 2.2e-16
## alternative hypothesis: Expectation greater than statistic
## sample estimates:
## Geary C statistic       Expectation          Variance 
##       0.850225287       1.000000000       0.000227749

Por simulação de Monte-Carlo

geary.mc(parqs$Acidentes.y, listw=nb2listw(dnb, style = "W"),nsim=999)

## 
##  Monte-Carlo simulation of Geary C
## 
## data:  parqs$Acidentes.y 
## weights: nb2listw(dnb, style = "W") 
## number of simulations + 1: 1000 
## 
## statistic = 0.85023, observed rank = 1, p-value = 0.001
## alternative hypothesis: greater

Calculando Índice de Getis e Ord Global

Getis-Ord é um indicador que mede a concentração local de uma variável de atributo distribuída espacialmente

globalG.test(parqs$Acidentes.y, nb2listw(dnb, style="B"))

## 
##  Getis-Ord global G statistic
## 
## data:  parqs$Acidentes.y 
## weights: nb2listw(dnb, style = "B") 
## 
## standard deviate = 19.366, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Global G statistic        Expectation           Variance 
##       6.355133e-03       4.548803e-03       8.699842e-09

Getis e Ord Local

localG(parqs$Acidentes.y, nb2listw(dnb, style="B"), zero.policy=NULL, spChk=NULL, return_internals=FALSE)

##    [1] -4.652452e-01 -1.261236e+00  3.968726e-01  1.652944e-01 -1.767821e-01
##    [6]  2.660078e+00  5.059171e+00  6.993543e-01  1.090253e-03  2.010997e+00
##   [11] -6.821314e-01  8.388251e-01  4.617553e+00  5.688581e+00  8.827077e+00
##   [16]  4.772195e-01  2.008967e-01 -1.965612e-01  1.662408e+00  4.190492e+00
##   [21] -1.672306e+00 -1.413914e+00 -9.988479e-01 -1.465424e+00  4.741375e-01
##   [26]  8.428524e-01 -2.057758e+00  2.121978e+00  3.992186e-01  7.241474e-01
##   [31] -4.159438e-01  8.578805e-01  4.972814e-02 -3.830458e-01 -5.439677e-01
##   [36] -8.798341e-01 -7.322343e-01  4.127778e-01  3.492601e-01 -4.974646e-01
##   [41] -7.567489e-01  1.110007e+00 -5.093232e-01  1.018814e+00  3.231003e+00
##   [46] -1.707192e-01 -6.041080e-02 -1.671190e-01  4.067155e+00  1.946279e+00
##   [51]  2.652817e+00  2.891138e-02 -7.066065e-01 -1.273200e+00 -7.327333e-01
##   [56]  3.653210e-01 -6.414056e-01 -1.398641e-01  3.450216e+00  1.519474e+00
##   [61]  1.903917e+00  1.393385e+00 -6.256827e-01  1.496038e+00  2.644201e+00
##   [66] -2.145725e-01 -1.322733e+00 -8.014778e-01  5.460869e-01  1.418826e+00
##   [71] -1.688272e+00  1.277104e+00 -4.770155e-01 -1.547067e+00 -1.479448e+00
##   [76]  6.866635e-01  1.937996e+00 -4.405056e-01  1.680646e-01 -2.071264e-02
##   [81]  1.510108e+00  2.529316e-01 -4.215186e-01  1.213461e-01  9.331207e+00
##   [86]  4.637453e-02 -5.120191e-01 -7.044560e-02 -5.775618e-01  2.951769e+00
##   [91]  1.685931e-01 -8.109518e-01  5.569483e-01  2.603026e+00 -4.745728e-01
##   [96] -7.518227e-01 -6.558549e-01  1.523016e+00 -1.346348e-02 -2.371788e-01
##  [101] -4.586701e-02  1.573471e+00  2.440527e-01 -4.290005e-01 -8.132395e-01
##  [106]  1.460278e+00  1.812980e+00  1.547337e+00  9.929501e-01 -5.132601e-01
##  [111]  7.638680e-01 -4.951711e-01  1.143003e+00  1.364426e+00  3.064663e+00
##  [116] -9.621185e-01  4.954719e-01  4.460703e-03 -7.735533e-01 -5.643848e-01
##  [121] -1.421938e+00  1.629070e+00  4.039356e-01  2.483901e+00 -1.691724e+00
##  [126]  3.980696e-02 -1.202705e+00 -1.464979e+00 -8.650667e-01  6.213175e-01
##  [131]  4.205511e+00  1.896646e+00 -7.449082e-02 -2.853371e-01  2.539546e+00
##  [136]  3.795771e+00 -5.301214e-01  1.173156e+00  1.494554e-01 -7.805267e-01
##  [141]  5.704962e+00 -2.855389e-01  6.119664e-01 -1.287646e+00  1.584959e+00
##  [146]  5.163565e-01 -7.291033e-01 -1.177128e+00 -7.057495e-01 -1.664532e+00
##  [151] -6.042639e-01  8.955069e-01  3.734033e-01 -6.089672e-01  4.688971e-01
##  [156] -9.325712e-01 -6.101870e-01 -8.564378e-01  4.949946e-02 -1.959054e-01
##  [161]  1.535305e+00 -9.531458e-01 -8.644048e-01 -4.822725e-01  4.504654e+00
##  [166] -7.383162e-01 -1.118845e+00  1.953483e+00  1.749467e+00  1.497178e-01
##  [171]  8.505011e-01  2.839457e+00 -8.298920e-01 -2.123420e+00  9.617606e+00
##  [176]  1.764302e+00  1.921006e-01  1.277380e+00 -8.298440e-01 -1.742597e+00
##  [181] -6.038009e-01 -1.806397e+00 -1.803859e-01 -3.620269e-01  7.497031e-01
##  [186]  4.095983e-01  3.521845e+00 -1.054543e+00 -4.418693e-01  2.071509e+00
##  [191]  5.942738e-01  8.100804e+00  2.624214e+00  7.406646e-01 -6.248821e-01
##  [196] -7.664765e-01 -1.425465e+00  7.220352e-01  2.493736e+00  5.127170e-01
##  [201] -8.378364e-01  1.850903e+00  9.950062e-03  1.898896e+00 -8.905651e-01
##  [206]  4.240202e+00 -1.746199e+00 -9.979502e-01 -1.013719e+00  6.329839e-01
##  [211] -1.524695e+00  2.185767e-01  1.721993e-02 -6.625287e-01 -2.383011e-01
##  [216] -5.686699e-01  2.159090e+00  2.502665e+00  2.811560e-02 -1.168025e+00
##  [221] -1.672251e-01 -1.669889e-01  5.294090e-01  5.472275e-01 -2.072281e-01
##  [226] -8.650667e-01  2.411182e+00  2.084135e+00 -3.833117e-03  5.681681e-01
##  [231]  1.678418e+00 -6.452199e-01  4.885459e+00  6.333856e-02  1.538101e+00
##  [236]  1.882259e+00  4.431677e+00  1.155254e-01  3.313304e+00 -1.392985e+00
##  [241] -1.339073e+00 -1.543619e+00  1.189604e+00 -7.968896e-01 -8.945349e-01
##  [246] -1.754520e-01 -4.952077e-01 -4.911009e-01  3.660804e+00  2.213360e-01
##  [251]  1.725189e+00 -1.075985e+00 -1.151648e+00 -1.044455e+00 -6.361984e-01
##  [256]  1.455138e+00 -9.809141e-01 -8.316014e-01  7.479457e+00  7.100622e+00
##  [261]  7.645746e-01 -1.442603e+00  1.467908e+00  5.290886e-01  1.490933e+00
##  [266]  1.667561e+00  7.003916e-02  1.423507e+00  1.684442e+00  1.571223e+00
##  [271]  2.192306e+00 -6.859441e-01 -3.167148e-02 -1.022656e+00 -1.577613e+00
##  [276] -7.349685e-01  3.759337e+00 -3.073911e-01 -1.076038e+00 -2.699161e-01
##  [281]  6.719581e+00 -1.306752e+00  2.655066e+00 -5.507686e-01  1.168058e+00
##  [286]  2.301610e+00  8.227204e-01  6.880197e-01 -6.035498e-01  9.570207e-02
##  [291] -1.366624e+00 -1.204543e+00 -1.319101e+00  8.082424e-01  2.181395e+00
##  [296] -1.372090e+00  1.647860e-01 -1.904886e-01 -8.312497e-01  1.333395e+00
##  [301] -7.489706e-02 -1.978012e-01  8.084081e-01 -1.234489e+00 -1.003164e+00
##  [306]  2.254232e-01 -1.771794e+00  1.565527e+00 -7.927512e-01 -7.883859e-01
##  [311] -1.181799e+00 -2.401028e-01  7.936874e-01  1.434825e+00  1.272287e-01
##  [316] -5.921985e-01 -1.952035e+00 -7.986814e-01 -9.175906e-01 -1.007717e+00
##  [321] -6.820478e-01  6.918276e-01 -1.204265e+00 -3.959407e-01 -7.274439e-01
##  [326] -2.705988e-01 -1.518263e+00 -7.383506e-01  4.245489e-01  3.886488e-01
##  [331] -4.069257e-01  2.081997e+00 -7.641947e-02 -1.315160e-01  2.017134e+00
##  [336] -3.741179e-01 -9.071638e-01 -1.055738e+00  1.072041e+00  1.178306e+00
##  [341] -1.136934e+00 -1.040889e+00  7.194209e-01  7.033911e-01  7.009143e-01
##  [346] -7.734404e-01  1.157452e+00 -5.450666e-01  1.507395e+00 -2.529834e-01
##  [351] -9.704918e-01 -4.014396e-01 -1.963036e+00 -5.414989e-01 -9.639498e-01
##  [356] -7.003830e-01  3.741723e-01 -1.101934e+00 -7.593891e-01  1.590809e+00
##  [361]  3.298057e-02 -7.852531e-01 -7.174967e-01 -6.592930e-01 -1.029111e+00
##  [366]  6.077830e-02 -6.575924e-01 -8.857257e-01 -1.574316e+00 -2.181086e-01
##  [371] -2.210746e+00  2.101211e+00  6.602281e-01  3.477533e-01  1.473012e+00
##  [376]  1.978465e+00  4.624879e-01 -5.425321e-01 -6.363506e-01  6.174573e-01
##  [381] -1.790852e-01 -6.640162e-01  1.673051e+00 -1.550108e+00 -1.484564e+00
##  [386] -1.396245e+00  2.751431e-01 -2.312914e-01 -2.126153e-01 -1.009298e+00
##  [391] -3.986964e-01  2.217172e+00 -1.672527e+00  2.718892e+00 -1.360285e+00
##  [396] -9.738779e-01  1.293536e+00 -8.713637e-01 -9.339353e-01 -8.455084e-01
##  [401] -1.255653e+00 -3.118090e-01 -5.606730e-01 -4.886871e-02 -6.818725e-01
##  [406]  1.080834e+01  1.948390e+00 -1.156891e+00  2.271908e-01 -1.616369e+00
##  [411]  5.473499e+00 -5.966238e-03 -1.190999e+00  5.606581e+00 -5.904313e-01
##  [416] -1.628892e+00  6.843131e-02  5.183887e+00  5.141556e-01  2.260050e+00
##  [421]  5.020602e+00 -2.000791e+00 -1.668915e+00 -1.200612e-01 -2.478349e-01
##  [426] -1.466981e+00 -1.424172e+00  1.049649e-01 -1.054060e+00 -3.498077e-01
##  [431] -2.435848e-01 -1.198144e+00 -3.770501e-01 -1.177728e+00 -9.025409e-01
##  [436]  2.264181e-01 -8.708434e-01 -8.217141e-02 -9.073002e-01  1.644000e+00
##  [441]  7.229497e-01  8.357575e-01 -3.936910e-01 -1.899042e-01 -1.373793e+00
##  [446]  1.352059e+00 -5.335319e-01 -8.377896e-01 -4.868516e-02 -1.429450e+00
##  [451]  9.969687e-01 -1.234534e+00  4.688971e-01  1.727435e+00  1.123270e-02
##  [456] -1.280975e+00 -9.123243e-01 -1.589633e+00 -1.189376e+00  3.629716e+00
##  [461]  1.058905e+00  6.463832e-01  1.759653e+00 -8.385325e-01 -9.074924e-01
##  [466]  5.659617e+00  7.198393e-01  7.178233e-01  3.089185e-01  3.142150e-01
##  [471] -5.501477e-01  4.482003e-01 -3.438641e-01  2.054647e-01  1.293759e+00
##  [476] -1.525268e+00  1.535900e+00 -1.560084e+00 -4.006279e-01 -3.681534e-01
##  [481]  1.851759e+00  2.345288e+00 -8.191631e-01 -5.636983e-01  1.706602e+00
##  [486] -7.383162e-01  1.311640e+00  1.286293e+00  4.367299e+00  8.285253e-01
##  [491] -1.365913e+00  3.235390e-01 -1.194777e+00  5.602070e+00 -8.763743e-02
##  [496]  2.751019e-02 -6.414404e-01 -9.972511e-01  3.986073e-01 -2.488094e-01
##  [501] -5.562415e-01 -6.838409e-01 -2.149954e-01  1.504473e+00 -5.955567e-01
##  [506]  7.333989e-01  1.203967e+00 -1.087143e+00 -1.190695e+00  1.126423e+00
##  [511] -3.497573e-01 -6.253140e-01  1.721307e+00 -1.070371e+00 -9.725356e-01
##  [516] -9.906108e-01 -1.050513e+00 -5.214678e-01 -3.356488e-01 -5.604633e-01
##  [521]  6.649250e-01  8.277762e-01 -9.162448e-02  2.724357e+00 -5.182344e-01
##  [526]  3.164147e-01 -1.391517e+00 -1.075948e+00 -8.648582e-01 -2.146987e-01
##  [531]  8.265694e-02 -9.747780e-02 -6.399830e-01 -5.496548e-02 -4.196954e-02
##  [536]  1.024293e+00  1.310418e+00  3.320300e+00  4.261210e+00 -4.586701e-02
##  [541]  6.727135e-03 -8.190105e-01  1.035430e+00 -8.622013e-01 -9.107933e-01
##  [546] -7.518872e-01 -7.940760e-01 -1.456145e+00 -7.687886e-01 -2.911198e-01
##  [551]  1.320102e-01 -1.090399e+00 -1.316526e-02  4.949523e-01  1.654260e+00
##  [556]  2.617521e+00  8.807783e-01 -1.055274e+00 -6.302708e-01 -3.840175e-01
##  [561]  1.783615e+00 -4.582048e-02 -8.647742e-01  1.022855e+00 -7.265942e-01
##  [566]  6.783298e-01  4.843910e+00  1.755406e+00 -3.199267e-02  8.154934e+00
##  [571] -9.065955e-01 -1.157352e+00  1.691106e+00  1.156122e-01 -5.028561e-01
##  [576] -5.493141e-01 -8.678130e-01 -1.336657e-01 -7.696600e-01 -6.256526e-02
##  [581] -9.321681e-01 -6.959291e-01 -1.210898e-01  2.385789e+00  2.702076e+00
##  [586]  1.419378e+00 -5.435989e-01  1.324433e+00 -7.881300e-01  1.659103e+00
##  [591]  2.344154e+00  1.110318e+00 -1.122191e+00 -5.835336e-01 -1.391746e+00
##  [596]  7.408669e-01  1.228811e+00 -1.023684e-01  8.084273e-01  2.179371e-01
##  [601]  1.742582e-01 -1.061374e+00 -7.330218e-01  1.711676e+00 -8.215681e-02
##  [606]  9.458706e+00 -8.911023e-01  1.467330e+00  7.819032e-01 -1.200244e+00
##  [611] -3.626184e-01  2.973254e-01  2.585088e-01 -1.085693e+00 -3.173014e-01
##  [616] -9.178054e-01 -9.637341e-01 -1.315080e+00 -4.805449e-01 -5.643264e-01
##  [621] -1.438798e-01 -3.100660e-01 -3.351569e-01  1.758730e+00  7.369554e-01
##  [626] -7.735831e-01  4.982508e-02 -1.510240e+00  1.484443e+00  5.537585e+00
##  [631] -1.062510e+00 -1.388488e+00  2.681794e+00  1.112231e+00 -1.567241e+00
##  [636] -2.017062e-01  1.609137e+00  6.376870e-01  2.137444e+00 -6.271775e-01
##  [641] -1.392606e+00  2.244712e+00 -1.062775e+00 -5.410950e-01 -1.561121e+00
##  [646]  1.406709e+00  4.135614e-01  9.625042e-01 -8.500653e-01  6.335587e-01
##  [651] -1.424235e+00 -3.529502e-01  5.300224e-01 -4.961570e-01  1.219233e+00
##  [656] -5.503156e-01  5.861039e-01 -5.973407e-01 -1.057822e-01  1.803284e+00
##  [661]  1.236254e+00 -9.387914e-02 -5.117458e-01  3.479056e-01 -8.740792e-01
##  [666] -1.255971e+00  8.608680e-01 -1.688335e+00 -1.536310e-01 -7.881816e-01
##  [671] -7.331275e-01 -4.750351e-01  1.850120e+00  1.923452e+00 -9.870286e-02
##  [676]  4.676234e+00 -1.473965e+00  1.380029e+00 -3.065978e-01 -9.798152e-01
##  [681]  1.459902e-01 -1.453161e-01  1.794498e+00 -4.809067e-01  4.706886e+00
##  [686] -1.282988e+00  2.876846e-01  2.048754e+00  1.800023e-01  2.782612e+00
##  [691]  4.049574e-02 -2.334702e-01  5.212545e+00  2.473847e+00 -7.978833e-01
##  [696] -9.107606e-01  1.930871e-01  2.565107e+00  9.475347e-01  5.121189e+00
##  [701]  4.563778e-01 -6.362477e-02  8.008506e-01  3.515267e-01  1.774671e-01
##  [706]  2.632844e-02 -1.087809e+00 -6.456235e-01  1.389811e+00 -1.008270e+00
##  [711] -1.818745e-01 -8.698453e-01 -9.755321e-01 -4.958097e-01 -9.411157e-01
##  [716]  2.029520e+00 -1.718495e-01 -1.012397e-01 -8.383487e-02 -3.191407e-02
##  [721]  1.040881e+00 -3.926239e-01  1.304353e+00  4.658503e-01 -5.533108e-01
##  [726] -4.944129e-01 -1.618112e-01  3.150298e+00  8.423759e-01  1.821688e+00
##  [731] -5.413438e-01 -6.689217e-01  2.011551e-01 -2.074386e-01  1.331333e+00
##  [736] -3.929260e-01 -1.207953e+00 -1.737664e+00 -1.484946e+00  6.420182e-01
##  [741] -1.677684e+00  1.586956e+00 -1.652116e+00 -9.276327e-01 -9.970517e-01
##  [746] -1.242610e-01  5.664105e+00 -1.238916e+00  4.324040e+00 -8.685387e-01
##  [751]  2.188008e+00 -3.412483e-01 -1.393183e+00  8.903247e-01 -6.363239e-01
##  [756] -2.621285e-01  1.981072e+00 -7.013705e-01 -6.717266e-01  1.005242e+00
##  [761] -6.821604e-01 -6.051274e-01  4.236159e-01  3.898182e+00 -3.131394e-01
##  [766]  4.295819e-01 -1.177303e+00 -1.089981e+00 -1.932302e-01 -1.203816e+00
##  [771] -6.224228e-01 -4.203665e-01  3.676082e+00 -3.123181e-01  1.142367e+00
##  [776]  2.387956e-01  3.445822e-01  1.343235e+00 -9.322009e-01 -1.021490e+00
##  [781] -3.628466e-01 -7.959376e-02  2.037806e+00 -1.351757e-02 -6.611479e-01
##  [786] -1.023492e+00  1.551055e+00 -1.493953e+00 -2.842477e-01  2.170470e+00
##  [791] -1.510430e+00  2.828558e+00  1.991991e+00  4.619138e-01 -9.479441e-01
##  [796]  1.429665e+00 -4.153997e-01 -6.363778e-01 -7.733878e-01  8.038357e-01
##  [801] -1.154964e+00 -1.240317e+00 -5.400842e-01  3.172718e-01 -1.208171e+00
##  [806]  8.669813e-01 -6.458320e-01 -2.154660e-01 -9.322345e-01  6.986746e-01
##  [811]  3.174166e-01  7.943616e-01 -1.017758e+00  3.246381e-01 -3.179029e-01
##  [816]  1.715243e+00  4.651654e-01 -3.155285e-01 -1.397065e+00 -6.270256e-01
##  [821] -5.234106e-01 -1.616265e+00 -8.955031e-01 -2.698938e-01  2.010354e+00
##  [826] -5.695952e-01  2.134383e-01 -3.359573e-01  3.776211e+00 -8.354636e-01
##  [831] -1.524979e+00 -1.393235e+00 -1.070962e+00 -1.298807e+00  3.979370e+00
##  [836]  2.899126e+00 -1.218530e+00  8.268271e-01  1.389103e+00  2.288898e+00
##  [841]  6.884869e-01 -1.560084e+00  3.011231e+00 -1.774727e-01 -4.497562e-01
##  [846]  1.645135e-01  1.629405e+00 -2.563415e-01  4.314439e-01 -6.717940e-01
##  [851] -4.627797e-01 -1.295495e+00  2.123195e+00 -9.226112e-01 -4.743543e-01
##  [856]  7.273213e-01 -9.177172e-01 -5.718678e-01 -1.137347e-01  1.086792e+00
##  [861] -6.464365e-01 -7.272954e-01  4.807956e+00 -8.800062e-02  8.501907e-01
##  [866]  1.740395e+00 -6.956598e-01 -8.355123e-01  1.530181e+00  1.120862e+00
##  [871]  5.044583e-01  1.414030e+00  1.607484e-01 -1.261084e+00 -7.622450e-01
##  [876] -9.993062e-01 -8.705919e-01  1.601701e+00 -6.912958e-01  4.381449e-01
##  [881]  9.265639e-02 -1.076318e+00 -8.685116e-01  7.048545e-01 -3.838010e-01
##  [886] -2.668808e-01  1.495652e+00 -6.346927e-01 -6.539023e-03  3.836779e-01
##  [891]  1.710095e+00 -5.070444e-01 -3.919804e-01  1.529780e+00 -2.244787e-01
##  [896]  1.819057e+00  1.501171e+00 -1.185766e+00 -5.507670e-01  3.530219e-01
##  [901] -1.433175e+00  2.139637e+00 -9.354223e-01 -8.120324e-01  3.913611e+00
##  [906]  1.564178e+00  6.481390e-01 -4.880270e-01 -1.478750e+00 -1.102581e+00
##  [911]  1.928219e-01 -1.044276e+00 -2.045030e-01 -7.393647e-01  1.921252e-01
##  [916] -2.895958e-02  1.866597e-01  1.172931e+00 -1.516434e+00  1.345338e+00
##  [921]  5.179884e-01 -6.558093e-01 -1.485241e+00  5.114889e-02 -1.360902e+00
##  [926]  2.415656e+00  8.912926e-01 -1.576064e+00  3.966037e-01  7.629594e-01
##  [931] -9.985160e-01  3.966727e-01 -1.235316e+00 -8.426459e-01 -9.528154e-01
##  [936]  7.545724e-02 -1.174489e+00 -1.508120e+00 -7.406480e-01  4.194695e+00
##  [941] -6.593994e-01 -4.598082e-01 -6.505277e-01  7.006245e-01  4.942677e-01
##  [946]  1.495618e+00  5.958988e-01  3.231223e-01 -8.552749e-01 -5.284912e-02
##  [951]  1.367798e+00 -8.282154e-01 -7.491581e-01 -1.071818e+00  3.842688e-01
##  [956]  7.979220e-01  2.787877e+00  2.839631e-02  9.429059e-01  7.441199e-01
##  [961]  9.033824e-01 -8.148699e-01  8.405261e-01 -8.197847e-01 -6.938871e-01
##  [966]  1.656613e+00 -1.446149e+00  3.297839e+00 -3.059396e-01 -9.656529e-01
##  [971] -7.948460e-02 -1.447476e+00 -1.181672e+00 -9.870066e-01 -7.838372e-01
##  [976] -5.761520e-01  1.574056e+00  2.059954e-01 -7.005477e-01  8.190748e-02
##  [981]  1.057350e+00  6.696755e-01  1.701685e-01  1.360341e-02 -9.713080e-01
##  [986] -1.157895e+00  7.629439e-01  1.104889e+00  3.953899e+00 -2.736072e-02
##  [991]  1.225460e+00  6.954264e-01  5.235560e-01  3.893541e-01  1.583756e+00
##  [996]  6.632018e-01  2.438241e+00 -1.455176e+00 -6.349719e-01  1.445262e+00
## [1001]  8.624522e-01  9.643407e-02  3.507152e+00  1.475479e+00 -2.241673e-01
## [1006] -1.102581e+00  1.516169e+00 -5.371283e-01  3.103136e+00 -5.449016e-01
## [1011] -1.374311e+00 -6.869345e-01 -4.957656e-01 -8.495600e-01 -2.166697e-01
## [1016] -5.527775e-01  2.992938e-01 -4.451428e-01 -4.226349e-01  5.839058e-01
## [1021]  1.240032e+00  4.963729e-01  6.638447e-01 -1.747351e-01  8.510119e-01
## [1026]  4.565598e-01  3.187397e+00 -7.889009e-01 -1.444337e+00 -8.123662e-02
## [1031]  4.970535e-01  2.704921e-01  7.472289e+00 -7.061545e-01 -2.967293e-01
## [1036] -1.031281e+00  6.468284e+00  1.079330e+00  1.078719e-01 -1.752262e-01
## [1041] -4.261929e-01 -1.096244e+00  9.621655e-01 -1.092035e-01  1.109851e+00
## [1046] -8.116946e-01 -9.546787e-01 -5.031809e-01 -6.866302e-01  2.510406e-01
## [1051] -6.252018e-01 -1.032026e+00 -1.383119e+00  3.563461e+00  1.483533e+00
## [1056] -7.865523e-01  6.390234e-01  1.742446e+00 -8.873272e-01  4.542498e+00
## [1061]  1.477067e+00 -2.498590e-01 -7.487108e-01 -9.943430e-01 -1.472071e+00
## [1066]  1.992122e+00  7.958205e-01 -8.996255e-01  3.723602e+00  2.893914e-01
## [1071] -4.706161e-01  5.807401e-01  1.752625e+00  4.113981e+00  1.230360e+00
## [1076] -2.773946e-01  8.292706e-01  6.179363e+00 -5.212270e-02 -1.254926e+00
## [1081]  3.225621e-01 -3.844126e-01 -7.402151e-01 -5.528417e-01 -1.443617e+00
## [1086] -2.329129e-01  3.624125e+00  1.796199e+00 -2.251411e-01 -1.390653e-01
## [1091]  3.139476e+00  7.755746e-02 -1.318512e+00 -6.963835e-01  1.723885e+00
## [1096] -1.250374e+00  1.357495e+00 -3.890933e-01 -1.725036e+00 -1.408707e-01
## [1101]  8.597904e-01  4.208633e-01  2.366004e+00  4.393797e-01 -1.076172e+00
## [1106] -1.833480e+00 -1.976402e+00 -1.747343e+00  2.983724e-02  3.978278e+00
## [1111] -4.209585e-01 -9.582140e-01 -5.670473e-01  5.513640e-01  4.860257e+00
## [1116]  7.374873e-01  4.062420e-01 -4.253720e-01  1.042798e+01  1.532804e+00
## [1121]  1.686617e+00  2.380246e+00 -1.374506e-01 -7.293571e-01 -3.321709e-01
## [1126]  1.450399e+00 -1.136313e+00 -3.210214e-01  2.861692e+00 -7.591024e-01
## [1131]  6.669919e-02 -7.650495e-01 -1.094104e+00 -1.397381e+00 -2.702339e-01
## [1136]  2.200749e+00  2.713219e-01  1.226225e+00 -9.440830e-01  2.730436e+00
## [1141] -1.372300e+00 -1.445749e+00 -1.652877e+00  2.095006e+00  3.289475e-01
## [1146] -1.045180e+00 -3.268937e-01 -1.526373e+00 -3.761776e-02 -9.991005e-01
## [1151]  7.069282e-01 -9.651867e-01  2.913949e+00  2.492697e+00 -1.612048e+00
## [1156] -9.970350e-01  1.137480e+00  7.074447e-01  2.493944e+00  1.316669e+00
## [1161]  6.481125e-02 -4.089894e-01 -1.434956e+00  1.448075e+00 -5.229723e-01
## [1166] -2.643030e-01  9.662310e-01 -7.964844e-02  1.494785e+00 -1.400969e+00
## [1171]  6.789590e-03 -1.234774e+00 -3.388067e-01 -4.076358e-01  2.007020e+00
## [1176] -1.566866e+00  6.131646e-01 -9.280451e-01  7.287932e-01  1.177785e+00
## [1181] -1.255307e+00  4.656647e-01 -7.278281e-01 -3.063549e-01 -8.681053e-01
## [1186] -5.275204e-01  3.105022e-01  1.442156e-01  3.731017e-01  1.478249e+00
## [1191]  2.192534e+00 -1.505215e+00  1.388349e+00  7.192768e-01 -5.531033e-01
## [1196] -1.339792e+00  5.580519e+00 -8.992348e-01  4.608383e-02  1.336251e-01
## [1201]  2.347529e+00 -6.894053e-01  1.153186e+00 -1.137070e-02  1.506594e+00
## [1206] -1.715112e-01 -1.587831e+00 -1.626430e+00 -1.674529e-01  2.688848e+00
## [1211] -1.813084e+00  2.337182e-01  5.087266e-02  3.977374e+00 -1.408537e+00
## [1216] -1.530599e+00 -9.180084e-01  5.451937e-02  1.934680e+00 -7.857078e-01
## [1221] -9.929923e-01  3.663468e-01 -9.601929e-01 -9.961164e-01 -4.160936e-01
## [1226]  9.909237e-02 -8.867439e-01 -2.557591e-02  1.297603e+00  1.697350e-01
## [1231] -4.073377e-01 -5.062721e-01 -9.972096e-01  1.994027e-01 -1.128730e+00
## [1236]  1.140302e+00  3.576730e+00 -1.494440e+00  2.352031e+00  1.832462e+00
## [1241]  7.948747e-01 -7.559435e-01  1.688547e+00 -9.947872e-01  2.614524e-01
## [1246]  6.642982e-01  5.460630e+00 -7.835863e-01  1.619643e+00 -1.064091e+00
## [1251] -1.026833e-01 -1.796917e+00 -8.353997e-01 -2.331194e-01 -9.972096e-01
## [1256]  1.328157e+00  4.478744e-01 -7.276660e-01  5.338208e-01 -1.746996e+00
## [1261] -1.843683e+00  6.351331e-01 -8.851263e-01  4.216744e+00 -7.465743e-01
## [1266] -1.709970e+00 -3.546304e-01  5.012902e-01  1.163163e+00 -9.176320e-01
## [1271] -9.304188e-01 -1.116150e+00 -4.750288e-01  4.083567e-01 -1.190179e+00
## [1276] -2.892942e-01 -7.329364e-01  1.274368e+00  9.822912e-01 -5.324760e-01
## [1281]  1.218075e+00  7.378844e-01  4.340614e-01  3.535339e+00  1.527347e+00
## [1286]  4.006176e-01 -4.886402e-01 -1.188919e+00  1.680393e+00  6.322981e+00
## [1291] -3.400389e-01  2.688546e-01 -9.704069e-01 -2.428292e-01  4.038529e-01
## [1296]  5.302757e-01 -7.015507e-01  6.699386e-01  1.200253e+00 -9.064331e-01
## [1301] -1.148222e+00 -1.029050e+00 -9.123763e-01 -5.137297e-01 -1.011008e+00
## [1306] -2.174003e-01  1.315831e-01 -9.402267e-01 -6.074271e-01 -3.398986e-01
## [1311] -5.721726e-01  1.701741e+00 -1.136150e+00 -1.132419e-01 -7.727276e-01
## [1316]  1.818497e+00  2.301922e+00  3.750021e+00  5.204239e-01  6.762906e-01
## [1321] -7.858432e-01  8.970782e-01 -4.472203e-01  2.307584e+00 -7.327333e-01
## [1326] -1.476777e+00  4.063089e+00 -5.477311e-01  4.033573e+00 -2.766453e-01
## [1331]  9.553805e-01  1.259441e+00  1.175374e+00  3.982707e-01  8.999518e-01
## [1336]  9.033989e-01 -1.096294e+00 -1.580547e-01 -9.107606e-01  8.704591e-01
## [1341] -1.792549e+00 -8.061138e-01 -1.821872e+00  2.725333e-02 -7.194692e-01
## [1346]  7.081320e-01 -4.040516e-01 -7.111946e-01  1.242121e+00 -3.533653e-01
## [1351] -3.550792e-01 -1.067625e+00 -8.192784e-01  1.274464e+00 -5.095366e-01
## [1356]  1.095386e+00 -7.012958e-01  1.092348e+00 -3.785350e-01  2.719035e-01
## [1361] -9.037550e-01 -8.885890e-01 -1.391746e+00  5.647833e+00  1.153355e+01
## [1366] -1.239487e+00  2.646172e+00 -2.311143e-01 -4.904939e-01 -6.257295e-01
## [1371]  4.907295e-01 -1.615943e+00  1.651725e-01  3.098152e+00 -5.219191e-03
## [1376] -4.077763e-01 -1.158875e+00 -6.261127e-01  1.242402e+00  1.086715e+00
## [1381] -6.642031e-01  4.323577e-01 -1.115783e+00 -1.050191e+00 -1.563560e-01
## [1386] -9.645707e-01  1.272621e+00 -1.768211e+00 -2.367054e-01  2.007617e+00
## [1391]  8.239908e-01 -1.456352e+00  8.751611e-01 -2.132739e-02 -4.037847e-02
## [1396] -1.557996e-01 -1.241084e+00  1.404819e-01 -3.435330e-01  7.086992e-01
## [1401]  1.536740e+00  5.940265e-01 -1.172386e+00  2.102330e+00  2.775958e+00
## [1406]  4.177047e+00 -1.180744e+00 -1.766939e-01 -8.790672e-01  2.273216e-01
## [1411]  1.012757e+00 -1.207759e+00 -2.623251e-01 -1.910142e+00 -5.872716e-01
## [1416] -1.089364e+00  1.323152e+00  1.496592e+00  5.369893e+00 -9.061193e-01
## [1421]  2.620384e+00  7.448840e-01 -5.864561e-01 -1.467852e+00 -8.810383e-03
## [1426]  9.434793e-01  2.683754e+00  9.694726e-01  1.032827e+00 -1.223480e+00
## [1431]  8.415076e+00  8.479487e-01 -9.956804e-01 -2.985750e-01 -9.442609e-01
## [1436] -5.259148e-02  1.679250e-01 -2.332839e-01 -5.675494e-01 -1.617026e+00
## [1441]  1.516258e-01  1.064002e+00 -6.059349e-02  6.331048e-01 -5.700608e-01
## [1446]  1.111797e+00 -6.968769e-01  2.183936e+00 -9.923995e-01 -1.243288e+00
## [1451] -5.442405e-01  6.624473e-02 -4.205671e-01 -1.486736e+00 -5.956109e-01
## [1456] -1.314005e+00 -1.687977e+00  2.250033e+00  3.742687e-01  7.244164e-01
## [1461]  3.650958e+00  1.170637e+00 -1.661213e-02 -5.451895e-01  1.289209e+00
## [1466] -1.667459e+00  7.059755e-01 -1.116033e+00  5.253304e-01 -1.318551e+00
## [1471]  6.485703e-01 -1.287494e+00 -8.650036e-01 -8.868798e-01  1.395012e+00
## [1476] -6.733367e-01 -6.205489e-01  5.407411e-01  4.119433e-01  7.497556e-01
## [1481]  1.325848e+00 -1.018063e+00 -3.131956e-01  1.035504e+01  2.258930e+00
## [1486] -6.252664e-01 -1.301319e+00  1.711915e+00  9.852442e-01 -7.481446e-02
## [1491] -8.997392e-01 -1.696927e-01 -6.465375e-01 -1.563840e-01  9.605912e-01
## [1496]  2.275818e+00  1.609126e+00 -7.592624e-01  6.947274e-02 -7.885937e-01
## [1501]  9.127754e-01  1.204058e+00 -9.177609e-01  1.032327e+00 -1.901291e-01
## [1506] -2.967293e-01  7.594929e-01 -1.000229e+00 -9.972511e-01 -6.255494e-01
## [1511]  2.909101e+00  1.439087e+00 -1.546776e+00 -1.159077e+00 -1.218386e+00
## [1516]  1.450762e+00  1.221333e+00  2.026016e+00  2.109095e-01  9.265304e-01
## [1521]  1.595903e+00  4.443236e+00 -1.524671e+00 -1.193224e+00 -1.296332e+00
## [1526] -4.874467e-01  1.108106e-01  1.130998e+00  3.609327e+00 -8.128597e-01
## [1531] -1.043451e+00  4.089198e-01 -6.965340e-01  7.550865e-01  8.675066e-01
## [1536] -9.773605e-01 -9.001042e-01  3.687425e+00 -7.038718e-01  3.792442e+00
## [1541] -4.762816e-01 -6.006923e-01  8.278125e+00  1.032668e-01 -1.227222e+00
## [1546] -9.530019e-01  3.198265e+00 -1.580623e+00 -5.826388e-01 -1.159238e+00
## [1551] -1.740906e+00 -1.333985e+00  1.040330e+00  1.585399e+00  1.330299e+00
## [1556]  1.484515e+00 -2.805960e-01 -8.702460e-01 -1.867383e+00 -7.385314e-01
## [1561] -5.571526e-01  1.577847e+00 -5.906797e-01 -1.335104e+00  9.901185e-01
## [1566] -1.392893e+00  4.368762e-01 -2.491917e-01 -1.253859e-01  1.211409e-01
## [1571] -7.588124e-01 -1.255568e+00 -8.193090e-01  2.511756e+00 -5.157361e-02
## [1576] -1.250222e+00 -9.148281e-01 -2.161592e-01  5.603000e-01 -1.077833e+00
## [1581] -6.259528e-01  5.677908e-02 -2.295653e-01 -1.019792e+00  2.977037e-01
## [1586] -2.198087e-01  6.748540e-01 -9.524747e-01  3.302163e+00  4.889618e-01
## [1591] -1.522250e+00  1.204335e+00 -1.050351e+00  7.083634e-01  2.905701e+00
## [1596] -8.448416e-01  1.388081e+00 -1.945130e+00 -6.429229e-01 -1.009616e+00
## [1601] -4.758026e-01 -9.442609e-01 -7.144640e-01  2.820468e+00 -1.028677e+00
## [1606]  2.713638e+00 -4.688377e-01  9.102202e-02  1.413543e+00  1.045750e+00
## [1611] -4.474390e-01  1.741368e+00  8.270783e-01 -2.241673e-01  5.948333e-01
## [1616] -6.207291e-01 -9.678873e-01 -9.998923e-01 -9.985387e-01  3.277067e+00
## [1621] -5.384862e-01 -6.987180e-01 -1.661963e+00  1.912910e+00 -1.363707e+00
## [1626] -8.306754e-01  2.640148e-01 -1.498511e+00 -3.338373e-01 -8.650667e-01
## [1631] -8.346691e-01 -1.313854e+00  1.195493e+00  1.925637e-01 -8.844719e-01
## [1636]  1.619094e+00 -1.247226e+00  2.749841e-01 -1.784571e+00  2.777984e+00
## [1641]  5.618257e+00  6.212314e-01  6.219374e+00  3.967864e-01 -1.322010e+00
## [1646]  1.135316e+00  2.115189e-01  1.525207e+00  1.870462e+00  1.232409e+00
## [1651] -1.128689e+00  3.742994e-01 -1.116345e+00  1.075226e+00 -1.126442e+00
## [1656] -1.155239e+00 -9.804892e-01 -5.955515e-01  1.248298e+00 -9.362975e-02
## [1661] -4.080551e-01 -1.095717e+00 -1.479174e+00 -2.524224e-01 -7.401136e-01
## [1666] -7.277444e-01 -9.971287e-01 -2.116876e+00 -8.851492e-01 -2.334233e-01
## [1671] -9.438782e-01 -4.749704e-01 -7.039324e-01 -8.647548e-01 -4.727603e-01
## [1676]  9.570034e-01  1.774671e-01 -8.380317e-01 -1.532304e-02 -4.408025e-01
## [1681]  6.458251e-01  1.340586e+00  2.416244e+00 -4.952864e-01 -1.365969e+00
## [1686] -1.101761e+00 -9.249722e-01  1.299457e+00  1.084739e-01  1.322117e+00
## [1691]  3.326496e-01  9.773102e-01 -8.994335e-01  1.144710e-01 -8.647340e-01
## [1696]  1.704768e+00  3.996470e+00 -1.147354e+00 -1.137486e+00  4.915387e-01
## [1701] -1.044749e+00  1.589289e+00 -1.847416e-01 -9.648299e-01  1.691041e+00
## [1706] -9.782339e-01  6.770944e-01 -3.017984e-02 -6.971785e-01 -1.525268e+00
## [1711] -1.616369e+00  6.758178e-01  1.205193e+00 -1.608427e+00 -1.131188e+00
## [1716] -1.085030e+00 -1.969007e-01  6.593257e-01 -4.377045e-01  4.268950e+00
## [1721] -7.923562e-01 -2.038625e-01  5.215546e+00 -1.064438e+00  5.414780e-01
## [1726]  1.554409e+00 -1.116197e+00 -1.157684e+00 -8.709647e-01 -9.088603e-01
## [1731]  2.114108e+00  4.280932e-01  4.110961e+00 -9.754148e-01 -9.320447e-01
## [1736] -6.531901e-02 -1.341505e+00 -1.564369e+00  7.508045e-01 -8.678912e-01
## [1741] -9.574285e-01  4.647360e+00  3.205854e-01  3.078735e-02 -1.041252e+00
## [1746] -1.137621e+00  9.976402e-01  9.031264e-02  1.466930e+00 -1.578707e+00
## [1751] -1.181901e+00  1.518068e+00 -8.914274e-01  8.460063e-01 -1.113431e+00
## [1756] -8.025355e-01 -4.084573e-01  4.204943e-01  4.062065e-01 -7.066021e-01
## [1761] -1.268898e+00 -8.648582e-01  8.530327e-01 -1.055356e+00 -1.055830e+00
## [1766] -1.038433e+00 -1.161492e+00  4.614711e-01  4.134760e+00  2.387729e+00
## [1771]  3.885466e+00  2.257539e-01  5.079193e+00  3.474897e-01  2.346435e+00
## [1776]  7.296982e-02 -3.040907e-01 -7.181071e-01  5.536776e-01 -4.977951e-01
## [1781]  8.488592e-01  1.584316e+00  1.706530e+00  1.034535e+00 -3.178741e-01
## [1786]  4.441128e+00 -8.633724e-02  1.680171e-01 -4.339428e-01  8.441948e-01
## [1791] -8.609130e-02 -1.735519e+00 -6.124714e-01 -6.410581e-01  2.165688e+00
## [1796] -7.208010e-01 -8.128644e-01 -2.864798e-01 -1.342084e+00  2.494847e-01
## [1801]  1.721121e+00 -1.255917e-01  7.394875e-01 -2.146885e-01 -1.791471e-01
## [1806]  9.968765e-01 -9.442609e-01  1.941212e-01  3.477475e-01 -1.151583e+00
## [1811]  3.538416e+00  2.228086e-01  2.223234e+00  7.857828e-01  5.481145e-01
## [1816] -7.274835e-01 -4.038911e-01 -1.174377e+00  5.276489e+00 -1.842167e+00
## [1821] -1.296026e+00 -1.406110e+00 -8.906834e-01  1.883969e+00 -1.010761e+00
## [1826] -1.446451e+00  1.885428e+00 -1.203009e+00  1.439835e-01 -3.177340e-01
## [1831]  5.409821e-02  9.082088e-02  4.334104e+00 -4.735428e-01  3.342297e-01
## [1836]  8.010172e+00  3.195454e+00 -1.035169e-01 -1.517091e+00  1.362443e+00
## [1841]  4.794458e-01  2.775256e+00  1.588220e+00 -3.894625e-01 -8.907459e-01
## [1846] -1.524499e+00  1.542820e-02  1.134757e+00 -9.088653e-01 -5.206811e-01
## [1851] -1.393705e+00  3.571522e+00 -8.193090e-01 -5.954105e-01 -6.401056e-01
## [1856] -1.525972e+00  1.000350e+00  2.359180e-01 -3.521996e-01  2.337729e-01
## [1861]  7.317061e-01  3.386874e+00  3.876104e-02  2.260794e+00 -1.137221e+00
## [1866]  1.130342e+00  3.611731e-01  5.198324e-01 -5.441993e-01  3.860247e+00
## [1871]  2.146467e+00  1.264062e+00  3.486950e+00 -1.006657e+00  1.470012e+00
## [1876]  1.740595e+00 -9.114791e-01  1.863204e+00  3.209334e+00 -9.001912e-01
## [1881] -8.116788e-01  2.311567e+00  1.600481e+00 -1.564145e-01 -4.888851e-01
## [1886] -8.376866e-01 -8.589840e-02  8.693477e-01 -7.333197e-01  1.699239e+00
## [1891] -1.501536e+00 -5.440527e-01 -1.479514e+00  2.125517e+00 -6.978594e-01
## [1896]  1.941250e+00  8.985338e-01  3.182334e+00 -1.975876e-01  2.404328e+00
## [1901]  4.423210e-01 -7.873471e-02  3.133070e+00  4.082032e+00 -1.371218e+00
## [1906] -3.813832e-01  2.685634e-01 -4.387598e-01 -7.056997e-01  1.225008e+00
## [1911]  5.715966e+00 -1.758789e-01 -5.216009e-01 -8.650667e-01  5.864373e-01
## [1916] -1.949130e+00 -9.143727e-01 -1.578962e+00  1.446372e+00  6.938901e-01
## [1921] -1.142252e+00 -4.147378e-01  2.536063e-01  3.395478e+00  2.457366e+00
## [1926] -1.128945e+00  5.960447e-02 -3.803904e-01 -5.106615e-01  1.517308e+00
## [1931] -5.210885e-01 -5.182613e-01  1.816124e+00 -3.038146e-01 -1.102536e+00
## [1936]  9.200816e-01  2.878866e-01 -1.180442e+00 -1.385011e+00 -1.730604e+00
## [1941] -9.022560e-01 -5.211208e-01  2.269058e+00  2.939712e+00  2.050749e+00
## [1946] -1.391761e-01  3.393187e-01  2.276570e-01 -8.478039e-01  8.208518e-02
## [1951] -1.388007e+00  1.361754e+00  4.085487e-01 -4.576487e-01  3.360366e-01
## [1956]  7.184981e-05  9.454606e-01 -6.265892e-01  1.034128e+01  3.110679e-03
## [1961]  1.916120e-01  4.649570e-01 -6.153208e-01 -1.158780e+00 -3.630540e-01
## [1966]  1.711715e+00 -7.663143e-01  2.967531e-02  3.601990e+00  2.568106e+00
## [1971]  3.981120e+00 -5.075758e-01 -3.927044e-01  4.208286e+00  1.302916e-01
## [1976]  1.460586e-01 -2.922971e-01 -8.125380e-01 -9.990483e-01  1.289466e+00
## [1981]  9.429750e-01 -6.046011e-01 -7.906557e-01  6.336751e-02  4.722637e-01
## [1986] -4.261215e-01 -3.762050e-03 -6.077474e-01 -6.843109e-01 -5.637339e-01
## [1991] -2.337074e-01 -5.403214e-01  8.078112e-01  3.501729e-01 -9.723706e-01
## [1996] -5.743218e-01  1.244074e+00 -1.067377e+00  2.878307e-01 -1.846760e+00
## [2001]  1.428774e+00  2.886352e-01 -9.297065e-01  2.687043e+00 -8.648566e-01
## [2006]  5.766381e+00  3.806653e+00  9.684570e-01 -1.177461e+00  1.508537e+00
## [2011]  1.244434e+00 -6.907382e-01 -8.162236e-01  3.734093e+00 -8.914860e-01
## [2016]  2.731809e+00 -2.030419e-02  5.507683e-01  1.780838e+00  1.646847e-01
## [2021]  2.004248e-01 -5.120516e-01 -1.668665e+00 -2.319446e-01  1.448175e+00
## [2026] -1.762334e-01 -3.353869e-01 -5.120191e-01 -1.234675e+00  3.633066e+00
## [2031]  1.149167e+00  3.244044e+00 -1.711870e+00  8.740947e-01  2.538649e+00
## [2036] -8.649433e-01  1.861250e+00 -7.005877e-01 -2.997127e-01 -9.972511e-01
## [2041]  1.320143e+00 -6.843217e-01  9.855059e-01  3.488613e+00  1.136657e+00
## [2046]  1.153477e+00 -1.076203e+00 -3.598344e-01  3.888579e+00 -1.494616e+00
## [2051]  1.614350e+00  2.627006e-01  5.813262e-01 -1.208218e+00 -9.177609e-01
## [2056]  3.705759e+00  3.579668e-01 -5.986872e-01  2.134383e-01  6.540872e-01
## [2061] -5.766300e-01  4.260537e+00 -5.815341e-01  2.477999e+00  7.134117e-02
## [2066]  1.317452e+00 -7.211234e-01  6.230845e-01  1.112436e-01 -9.302223e-01
## [2071] -1.085345e+00 -9.261746e-01  1.141880e-01  1.058154e+00 -2.393758e-02
## [2076]  1.284922e+00  2.036156e+00 -4.424659e-01 -7.276168e-01  2.281954e+00
## [2081]  3.011482e+00 -8.099036e-01  9.275049e-01  3.456710e+00  2.870823e+00
## [2086]  1.288740e+00  2.560575e-01  2.860046e-02  2.381189e-01 -9.922774e-01
## [2091] -1.250746e+00 -1.091325e+00  5.617066e-01 -6.090048e-01 -7.064442e-01
## [2096]  2.242138e+00  3.245788e+00  5.708679e-01 -1.494353e+00  2.677100e+00
## [2101]  6.512082e-01  1.049712e+00  5.001111e-01  1.423785e+00 -2.705593e-01
## [2106] -6.597959e-01 -1.005012e+00  2.703165e+00 -5.290780e-02  9.436338e-01
## [2111] -1.633077e+00  5.403597e+00  6.247175e-01  7.205709e-01 -4.842355e-01
## [2116]  7.189706e-01  6.329446e-01  4.526035e+00  1.952054e+00 -1.570363e-01
## [2121] -2.311629e-01 -5.602116e-01  1.367232e+00  2.872846e-01 -9.113384e-01
## [2126] -1.288265e+00 -7.473594e-01  3.675610e-01 -1.136050e+00 -1.567303e+00
## [2131]  2.643273e-01  1.431293e-01 -1.128673e+00 -2.696299e-02  1.020253e+00
## [2136] -2.612143e-01 -7.832276e-01 -1.394385e+00  2.518161e+00 -1.509427e-01
## [2141] -1.419444e+00 -1.155446e+00 -6.967261e-01  1.319629e+00 -1.125200e+00
## [2146]  2.640504e-01  2.169347e+00  4.555504e+00  5.172162e-02 -1.094186e+00
## [2151] -9.325712e-01 -6.988940e-02  7.195227e-01  2.363047e-01 -2.143526e-01
## [2156] -8.307291e-01  1.866446e-01  1.787183e+00  1.026245e+00  5.801629e-01
## [2161] -9.532010e-01 -8.438642e-01  1.375382e+00 -4.213799e-01 -1.637372e+00
## [2166] -4.818844e-01 -1.194238e+00 -4.906714e-01 -5.328953e-01  6.642338e-01
## [2171] -1.308610e+00  1.498212e+00 -1.018399e+00  1.477074e+00 -1.023445e+00
## [2176]  2.124938e+00 -1.155543e+00  1.352458e+00 -1.734797e-01 -1.029938e+00
## [2181] -7.984375e-01 -7.885937e-01 -1.460378e+00 -4.688377e-01 -4.504354e-01
## [2186] -3.058621e-01 -8.650348e-01  2.834907e-01  2.540351e+00 -6.989247e-01
## [2191]  2.480685e+00  1.281738e+00 -1.567430e+00  2.541042e-01 -1.364337e+00
## [2196]  1.233523e+00 -5.742842e-01  1.285986e+00 -5.652951e-01  9.162191e-01
## [2201]  1.570011e+00 -2.780205e-01  1.995075e+00 -1.665434e-01  2.607265e+00
## [2206] -1.740608e-01 -1.633329e-02  6.016155e-02 -2.370783e-01  1.126701e+00
## [2211] -7.057577e-01 -3.210214e-01 -1.447476e+00  1.761015e+00  6.470108e-01
## [2216] -7.245782e-01 -2.232570e+00  1.331036e+00  1.817389e+00  1.045703e+00
## [2221] -5.729830e-01  5.473210e+00 -6.090671e-01 -1.453875e+00  2.249325e+00
## [2226]  9.086096e-02 -9.399915e-01 -1.384350e+00  1.340191e+00  3.874317e+00
## [2231]  2.833867e+00  5.119493e-01 -1.759663e+00  6.339049e+00 -6.942579e-02
## [2236] -1.025532e+00 -4.206530e-01  2.502160e+00  5.251958e+00 -6.997767e-02
## [2241] -3.390199e-01 -1.452819e+00 -2.533280e-01 -2.180687e-01 -8.550329e-01
## [2246] -1.976163e-02  1.152027e+00  1.391894e+00 -7.193786e-01 -9.525592e-01
## [2251]  1.058567e+00  1.735668e+00  1.649980e+00 -7.275255e-01  1.336649e+00
## [2256]  9.640025e-01  1.533929e+00  2.505408e+00 -6.307766e-02 -2.831124e-01
## [2261] -2.338149e-01 -3.817149e-01 -1.090399e+00 -2.804309e-01 -5.507670e-01
## [2266]  9.710478e-01 -3.159816e-01 -1.170060e+00  2.847292e+00 -8.424404e-01
## [2271] -1.795464e-01 -1.164233e+00 -7.592215e-01  1.060182e+00 -1.589324e-01
## [2276] -6.554643e-01  2.147558e-01 -1.635225e+00 -1.333752e+00 -1.181904e+00
## [2281] -1.185589e+00  1.192947e+00 -1.616400e+00  1.464209e+00 -1.044455e+00
## [2286]  1.756936e+00 -7.557074e-01 -8.288444e-01 -1.707145e-02  2.065992e+00
## [2291] -2.478893e-01 -1.340440e+00 -1.280314e+00 -1.372816e+00 -1.178529e+00
## [2296]  7.610561e-02  3.804418e+00  4.149504e-02 -4.326410e-01 -1.301260e+00
## [2301]  5.201675e+00  3.317132e+00  3.112316e+00 -7.060129e-01  3.351629e+00
## [2306]  7.029079e-01  3.088590e+00  2.778618e+00 -2.567981e-01  4.587462e-01
## [2311]  4.683744e-01  3.650273e+00  1.495928e-01 -1.158924e+00  2.024104e+00
## [2316]  2.379875e-01  9.501890e-01  2.582075e-01 -9.049112e-01 -2.703319e-01
## [2321]  6.495564e-01  1.035213e-01  2.963326e+00  1.093337e+00  2.716224e+00
## [2326]  1.038617e+00 -1.427362e+00 -1.101794e+00  1.425647e+00 -1.439327e+00
## [2331] -1.111826e+00  6.545437e-01 -6.091362e-01 -2.207485e-01 -7.306802e-01
## [2336]  1.723196e-01  2.820059e+00  3.079548e+00 -2.968145e-01  1.381169e+00
## [2341] -7.805755e-01  7.012000e-01 -5.877789e-02 -1.026272e+00  1.375500e+00
## [2346] -2.363053e-01 -1.093556e+00 -4.149742e-01 -6.395568e-01 -1.137272e+00
## [2351]  4.887307e+00  4.285014e+00 -8.649731e-01  2.277300e+00  7.098228e-01
## [2356]  2.737361e+00 -9.209153e-01 -8.025233e-01  1.470331e+00  2.200808e+00
## [2361] -7.464706e-01  3.603640e-01 -1.267027e+00  4.862945e-01  1.367396e+00
## [2366] -9.266945e-01 -4.281889e-01 -2.776735e-01 -1.256730e+00  2.432122e-01
## [2371] -4.600931e-01 -2.886201e-01 -3.239309e-01 -5.954151e-01  2.541011e-01
## [2376]  3.928912e-01  2.183020e+00  6.469528e-01  3.157548e-01  3.160278e-01
## [2381]  5.426304e-01 -5.780284e-01 -1.603683e-01  5.542581e-03 -1.089507e+00
## [2386] -8.024098e-01 -1.147846e+00 -3.798185e-01  8.068131e-01 -5.213238e-01
## [2391]  1.318193e-01  1.575842e+00  1.650703e+00 -9.178967e-01  1.909566e+00
## [2396] -4.003092e-01  7.801095e-01  1.999094e+00  8.539941e-01 -1.102414e+00
## [2401] -9.198768e-01 -6.413045e-01  4.543429e-01 -3.129174e-01 -5.393784e-01
## [2406] -6.103372e-01 -2.396556e-01 -6.529194e-01 -8.332183e-02  1.780425e+00
## [2411] -7.278281e-01 -7.632389e-01 -1.157224e+00 -1.821872e+00 -1.079390e-01
## [2416] -4.947802e-01 -1.148798e+00  1.391037e+00  1.877570e+00  2.109644e-01
## [2421]  1.039033e+00 -1.429231e+00 -2.353360e-01 -1.460828e+00 -2.473753e-01
## [2426] -2.989476e-02 -9.244884e-01  3.276645e-01 -1.418116e+00  1.945383e-01
## [2431]  1.179742e+00  3.747444e-01  1.056837e+00 -3.102720e-01 -8.543426e-01
## [2436]  1.693825e+00  6.481241e-01  2.447283e-01 -1.296226e+00  6.031027e-01
## [2441]  5.516282e+00  4.697026e-01 -8.314347e-01 -8.672642e-01 -2.697202e-01
## [2446] -8.385758e-01 -2.246490e-01  2.600650e-01 -1.542809e+00 -1.298215e+00
## [2451] -1.028817e+00  4.834281e+00  2.201138e+00 -1.283940e+00  7.515727e-01
## [2456] -9.963398e-01  1.599590e+00 -1.296174e+00 -9.987063e-01 -4.726764e-01
## [2461]  9.009315e-01  1.891191e+00  5.379232e+00 -1.125454e+00  8.687133e+00
## [2466]  2.680490e+00 -1.181950e+00 -9.942883e-01  2.551985e-01 -5.774149e-01
## [2471]  2.949954e+00 -4.376231e-01  1.161045e+00 -1.821872e+00 -2.547518e-01
## [2476]  1.221369e+00 -7.707962e-01 -6.356703e-01  1.964320e+00 -7.692356e-01
## [2481]  1.031648e+00  2.074828e+00 -1.200837e+00 -9.647905e-01 -1.158276e+00
## [2486] -1.949398e-01 -5.656417e-01  1.617446e+00  1.632493e+00  1.787741e-01
## [2491]  9.042421e-02 -6.415115e-01  1.299197e-01 -1.260001e+00 -7.562863e-01
## [2496] -4.945271e-01  1.798724e-01 -9.437284e-01 -2.451191e-02  6.570329e-02
## [2501] -1.321609e+00 -1.021535e+00 -8.649142e-01 -1.685104e+00  3.595580e-01
## [2506]  2.091977e-01  7.027913e-01 -6.661144e-01  1.044879e+00  3.696566e-01
## [2511]  4.391361e-01  2.291831e-01 -5.807878e-01  4.349734e-02 -9.396623e-01
## [2516] -6.973918e-01 -6.131179e-01  1.343061e+00  5.052926e+00  6.945633e-01
## [2521] -7.340517e-01  1.841881e+00  4.068088e-01 -1.219401e+00  5.598758e-01
## [2526]  3.166197e+00 -1.412858e+00  3.073088e+00 -8.192194e-01 -1.233443e+00
## [2531]  1.583823e+00  1.330207e+00 -1.178360e+00
## attr(,"gstari")
## [1] FALSE
## attr(,"call")
## localG(x = parqs$Acidentes.y, listw = nb2listw(dnb, style = "B"), 
##     zero.policy = NULL, spChk = NULL, return_internals = FALSE)
## attr(,"class")
## [1] "localG"

Moran Local

Todas as análises feitas até o momento foram de escala global. No entanto, é necessário que seja feita também uma análise local do estudo. Essa análise pode ser feita pelo índice local de autocorrelaçãoo espacial (LISA). Para isso é preciso calcular o índice de Moran local.

ShapePB.mloc <- localmoran(parqs$Acidentes.y, listw=nb2listw(dnb, style="W")) 
head(ShapePB.mloc)

##              Ii          E.Ii       Var.Ii       Z.Ii Pr(z != E(Ii))
## 1  0.0752020015 -2.090568e-04 2.627279e-02  0.4652452    0.641755844
## 2  0.6624101685 -3.273876e-04 2.761154e-01  1.2612357    0.207223955
## 3  0.0003718575 -6.632609e-09 8.779430e-07  0.3968726    0.691461399
## 4 -0.0345645540 -5.167709e-05 4.359596e-02 -0.1652944    0.868712295
## 5 -0.0002955308 -6.632609e-09 2.794535e-06 -0.1767821    0.859679558
## 6  0.2668642164 -1.207058e-04 1.007360e-02  2.6600778    0.007812261

Mapa das probabilidades (Signific?ncias do I de Moral Local)

Por meio dos valor-p do éndice de Moran local é possível construir um mapa de probabilidades.

library(classInt)
INT4 <- classIntervals(ShapePB.mloc[,5], style="fixed", 
                       fixedBreaks=c(0,0.01, 0.05, 0.10))
CORES.4 <- c(rev(brewer.pal(3, "Reds")), brewer.pal(3, "Blues"))
COL4 <- findColours(INT4, CORES.4)
parqs$COL = COL4  
parqs$p_valor = ifelse(parqs$COL == "#DE2D26", "[0,0.01)", ifelse(parqs$COL == "#EEE5E4", "[0.01,0.05)", "[0.05,0.1]"))
plot(parqs, col=COL4)
title("P-valores do I de Moran Local por Distäncia de Centróides")
TB4 <- attr(COL4, "table")
legtext <- paste(names(TB4))
legend("bottomright", fill=attr(COL4, "palette"), legend=legtext, 
       bty="n", cex=0.7, y.inter=0.7)

A caption

mapview(parqs, zcol = "p_valor", col.regions=c("red", "orange", "green"))

temp = parqs[parqs$p_valor != "[0.05,0.1]", ]
mapview(temp, zcol = "p_valor", col.regions=c("red", "orange"))

Montando matrix W de vizinhança

ShapeCG.nb1.mat <- nb2mat(dnb)

Incidência de acidentes padronizada

Acidentes_SD <- scale(parqs$Acidentes.y)

Média das incidências de acedentes padronizada

Acidentes_W <- ShapeCG.nb1.mat %*% Acidentes_SD

Diagrama de espalhamento de Moran

plot(Acidentes_SD, Acidentes_W,xlab="Z",ylab="WZ")
abline(v=0, h=0)
title("Diagrama de Espalhamento de Moran por Distancia de Centróides")

A caption

Q <- vector(mode = "numeric", length = nrow(ShapePB.mloc))
Q[(Acidentes_SD>0  & Acidentes_W > 0)] <- 1            
Q[(Acidentes_SD<0  & Acidentes_W < 0)] <- 2
Q[(Acidentes_SD>=0 & Acidentes_W < 0)] <- 3
Q[(Acidentes_SD<0  & Acidentes_W >= 0)]<- 4
signif=0.05
parqs$Q = Q

Mapa LISA

Q[ShapePB.mloc[,5]>signif]<-5
CORES.5 <- c("blue", "green" , "red", "yellow", "gray", rgb(0.95,0.95,0.95))
#CORES.5 <- c(1:5, rgb(0.95,0.95,0.95))
parqs$cores5Q = CORES.5[Q]
plot(parqs, col=CORES.5[Q])
title("Mapa LISA por Distancia Centroides")
legend("bottomright", c("Q1(+/+)", "Q2(-/-)", "Q3(+/-)", "Q4(-/+)","NS"), 
       fill=CORES.5)

A caption

CORES.5[Q][1:5]

## [1] "gray" "gray" "gray" "gray" "gray"

head(CORES.5[Q])

## [1] "gray" "gray" "gray" "gray" "gray" "blue"

#save(parqs, file = "parqsFinal-975.Rds")

parqs$cores5 =  ifelse(parqs$cores5Q == "blue", "A-A", ifelse(parqs$cores5Q == "green", "B-B", 
            ifelse(parqs$cores5Q == "red", "A-B", ifelse(parqs$cores5Q == "yellow", "B-A", "NA"))))
mapview(parqs, zcol = "cores5", col.regions=c("red", "orange", "green", "yellow", "grey"))

temp = parqs[parqs$cores5 == "A-A", ]
mapview(temp, zcol = "cores5", col.regions=c("red", "orange", "green", "yellow", "grey"))

mapview(temp, zcol = "Acidentes.x", col.regions=brewer.pal(9, "YlOrRd"))