BASE DE DATOS ATLANTIC
DIEGO CAMILO CELADA LOZADA
17/3/2021
BASE DE DATOS ATLANTIC
En este Rpbs se trata la base de datos Atlantics.
1. Cargar libreria y la base de datos
Cargamos la libreria sp, disponible para el tratamiento de datos espaciales y la libreria readr para la carga de la base de datos con extención .CSV
library(sp) #Libraria para el tratamiento de datos espaciales
library(readr)Cargamos la base de datos “data_atlantic”, la observamos y vemos sus dimensiones
data_atlantic<- read_csv("data_atlantic_1998_2012.csv")
View(data_atlantic) # Observar la base de datos
dim(data_atlantic) #Observar las dimensiones de la base de datos## [1] 666 9names(data_atlantic) # Da el nombre de las variables de la base de datos## [1] "FIPS" "x" "y" "Rate" "POV" "SMOK" "PM25" "NO2" "SO2"class(data_atlantic) #Tipo de datos de la base de datos ("data.frame")## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"Observamos los primeros seis datos y los ultimos dos
head(data_atlantic) # Permite observar las seis primeras filas de la base de datostail(data_atlantic) # Permite observar las seis ultimas filas de la base de datosObservamos el resumen estadistico de la base de datos
summary(data_atlantic) # Resumen de datos estadisticos## FIPS x y Rate
## Min. :10001 Min. : 949877 Min. : 932261 Min. : 39.00
## 1st Qu.:24010 1st Qu.:1248238 1st Qu.:1332531 1st Qu.: 62.00
## Median :37126 Median :1425055 Median :1675880 Median : 69.00
## Mean :35728 Mean :1426696 Mean :1669654 Mean : 69.97
## 3rd Qu.:51033 3rd Qu.:1617599 3rd Qu.:1970044 3rd Qu.: 77.00
## Max. :54109 Max. :1916559 Max. :2623675 Max. :120.00
## POV SMOK PM25 NO2
## Min. : 3.26 Min. :13.51 Min. : 7.323 Min. : 0.5954
## 1st Qu.:11.35 1st Qu.:24.57 1st Qu.:10.508 1st Qu.: 1.2608
## Median :14.54 Median :26.52 Median :11.652 Median : 1.7766
## Mean :15.21 Mean :26.06 Mean :11.394 Mean : 2.2015
## 3rd Qu.:18.91 3rd Qu.:28.18 3rd Qu.:12.384 3rd Qu.: 2.6475
## Max. :35.26 Max. :34.96 Max. :14.133 Max. :14.6895
## SO2
## Min. :0.001209
## 1st Qu.:0.027947
## Median :0.049337
## Mean :0.072520
## 3rd Qu.:0.091953
## Max. :0.4208472. Conversión a datos espaciales
Convertimos las columnas (x,y) de la base de datos data_atlantic en un objeto espacial
coordinates(data_atlantic) = c("x", "y") # Convierte el data.frame a un objeto espacial de tipo SPACIAPOINTDATAFRAME
class(data_atlantic)## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"Diagramamos la base de datos data_atlantic
plot(data_atlantic) # Diagrama la base de datos 
plot(data_atlantic, pch=1, main="Dato espacial: Punto") # Diagrama la base de datos con forma de puntos
3. Tratamiento de los datos espaciales
Para unir los datos en forma de linea ejecutamos las siguientes lineas
# Union de datos en forma de linea
cc = coordinates(data_atlantic)
m.sl = SpatialLines(list(Lines(list(Line(cc)), "line1")))
plot(m.sl,main="Dato espacial: Línea" )
Diagramamos la distrbución espacial de la variable NO2, por medio de la libreria “laticce”
#DISTRIBUCI?N ESPACIAL DE LA VARIABLE ZINC
library(lattice)
spplot(data_atlantic, c("NO2"))
Diagramamos la distrbución espacial conlas coordenadas (x,y)
#Distribuci?n espacial con coordenadas (x,y)
library(ggplot2)
methods(fortify)## [1] fortify.cld* fortify.confint.glht*
## [3] fortify.data.frame* fortify.default*
## [5] fortify.formula* fortify.function*
## [7] fortify.glht* fortify.grouped_df*
## [9] fortify.Line* fortify.Lines*
## [11] fortify.lm* fortify.map*
## [13] fortify.NULL* fortify.Polygon*
## [15] fortify.Polygons* fortify.sfc*
## [17] fortify.sfg* fortify.SpatialLinesDataFrame*
## [19] fortify.SpatialPolygons* fortify.SpatialPolygonsDataFrame*
## [21] fortify.summary.glht* fortify.tbl*
## [23] fortify.tbl_df*
## see '?methods' for accessing help and source codem = as(data_atlantic, "data.frame")
ggplot(m, aes(x, y)) + geom_point() + coord_equal()
Diagramamos la caja de bloques de la variable NO2
boxplot(x = data_atlantic$NO2)# Caja de bigotes de la variable NO2
4. Crear los centroides de un poligono
library(rgdal) # Incluimos la libreria ## rgdal: version: 1.5-23, (SVN revision 1121)
## 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/asus/Documents/R/win-library/4.0/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/asus/Documents/R/win-library/4.0/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 rgdal.
## Overwritten PROJ_LIB was C:/Users/asus/Documents/R/win-library/4.0/rgdal/projAbrimos un archivo .shape, creamos y ploteamos los centroides
Y = readOGR(file.choose()) # abrir un archivo shp## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\asus\Documents\DOCTORADO\MATERIAS\PROFUNDIZACION_I\R\ANTLANTIC\COUNTY_ATLANTIC.shp", layer: "COUNTY_ATLANTIC"
## with 666 features
## It has 12 fields
## Integer64 fields read as strings: IDclass(Y)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"plot(Y)
centroides = coordinates(Y)
class(centroides)## [1] "matrix" "array"cent=as.data.frame(centroides)
class(cent)## [1] "data.frame"head(cent)coordinates(cent) = c("V1","V2")
class(cent)## [1] "SpatialPoints"
## attr(,"package")
## [1] "sp"plot(cent,pch=1,add=T)
5. Crea y guardar archivos espaciales
crear un archivo espacial a partir de los centroides
fg=SpatialPointsDataFrame(cent,data_atlantic)
ds=“C:/Users/asus/Documents/DOCTORADO/MATERIAS/PROFUNDIZACION_I/R/ANTLANTIC”
writeOGR(fg, dsn=ds, driver=“ESRI Shapefile”,“puntos”)
Guardar archivos en tipo .RData
save(centroides,cent,file=“Prueba.RData”)
Cargar archivos en tipo .RData
load(“Prueba.RData”)
6. Libreria GWmodel
#Cargamos el archivo County Atlantic
Condado = readOGR(file.choose()) ## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\asus\Documents\DOCTORADO\MATERIAS\PROFUNDIZACION_I\R\ANTLANTIC\COUNTY_ATLANTIC.shp", layer: "COUNTY_ATLANTIC"
## with 666 features
## It has 12 fields
## Integer64 fields read as strings: IDPloteamos el archivo condado
plot(Condado)
class(Condado) #Observamos el Tipo de datos de condado ("SpatialPolygonsDataFrame")## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"Cargamos el archivo State Atlantic
Estado = readOGR(file.choose()) ## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\asus\Documents\DOCTORADO\MATERIAS\PROFUNDIZACION_I\R\ANTLANTIC\STATE_ATLANTIC.shp", layer: "STATE_ATLANTIC"
## with 11 features
## It has 9 fields
## Integer64 fields read as strings: ID ID2plot(Estado)
Ploteamos el archivo Estado
class(Estado) #Observamos el Tipo de datos de Estado ("SpatialPolygonsDataFrame")## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"Datos = read.csv(file.choose(), header=T) # Cargamos la base de datos
SPDF = merge(Condado, Datos, by = "FIPS") # unir base de datos con el SpatialPolygonsDataFrame "Condado"
names (SPDF) # Observamos los nombres de la base de datos creada## [1] "FIPS" "ID" "x.x" "y.x" "REGION_ID"
## [6] "DIVISION_I" "STATE_ID" "COUNTY_ID" "REGION" "DIVISION"
## [11] "STATE" "COUNTY" "x.y" "y.y" "Rate"
## [16] "POV" "SMOK" "PM25" "NO2" "SO2"Creamos un lista para los graficos e incluimos la libreria GWmodel
polys = list("sp.lines", as(Estado, "SpatialLines"), col = "grey", lwd=0.8, lty=1)
library(GWmodel)## Loading required package: maptools## Checking rgeos availability: TRUE## Loading required package: robustbase## Loading required package: Rcpp## Loading required package: spatialreg## 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')`##
## Attaching package: 'spData'## The following object is masked _by_ '.GlobalEnv':
##
## polys## Loading required package: Matrix## Registered S3 methods overwritten by 'spatialreg':
## method from
## residuals.stsls spdep
## deviance.stsls spdep
## coef.stsls spdep
## print.stsls spdep
## summary.stsls spdep
## print.summary.stsls spdep
## residuals.gmsar spdep
## deviance.gmsar spdep
## coef.gmsar spdep
## fitted.gmsar spdep
## print.gmsar spdep
## summary.gmsar spdep
## print.summary.gmsar spdep
## print.lagmess spdep
## summary.lagmess spdep
## print.summary.lagmess spdep
## residuals.lagmess spdep
## deviance.lagmess spdep
## coef.lagmess spdep
## fitted.lagmess spdep
## logLik.lagmess spdep
## fitted.SFResult spdep
## print.SFResult spdep
## fitted.ME_res spdep
## print.ME_res spdep
## print.lagImpact spdep
## plot.lagImpact spdep
## summary.lagImpact spdep
## HPDinterval.lagImpact spdep
## print.summary.lagImpact spdep
## print.sarlm spdep
## summary.sarlm spdep
## residuals.sarlm spdep
## deviance.sarlm spdep
## coef.sarlm spdep
## vcov.sarlm spdep
## fitted.sarlm spdep
## logLik.sarlm spdep
## anova.sarlm spdep
## predict.sarlm spdep
## print.summary.sarlm spdep
## print.sarlm.pred spdep
## as.data.frame.sarlm.pred spdep
## residuals.spautolm spdep
## deviance.spautolm spdep
## coef.spautolm spdep
## fitted.spautolm spdep
## print.spautolm spdep
## summary.spautolm spdep
## logLik.spautolm spdep
## print.summary.spautolm spdep
## print.WXImpact spdep
## summary.WXImpact spdep
## print.summary.WXImpact spdep
## predict.SLX spdep## Welcome to GWmodel version 2.2-3.
## The new version of GWmodel 2.2-4 now is ready##
## Attaching package: 'GWmodel'## The following objects are masked from 'package:stats':
##
## BIC, fittedCreando una paleta de colores
col.palette = colorRampPalette(c("blue", "sky blue", "green", "yellow", "red"), space = "rgb", interpolate = "linear")la funci?n gwss Calcula medidas descriptivas del conjunto de datos geogr?ficamente ponderadas
incluye variables: Rate, PM2.5, el kernel utilizado es bisquare y ancho de banda =48
gwss.rate.pm25 = gwss(SPDF, vars = c("Rate","PM25"), kernel = "bisquare", adaptive = TRUE, bw = 99)## Warning in proj4string(data): CRS object has comment, which is lost in outputgwss.rate.pm25## ***********************************************************************
## * Package GWmodel *
## ***********************************************************************
##
## ***********************Calibration information*************************
##
## Local summary statistics calculated for variables:
## Rate PM25
## Number of summary points: 666
## Kernel function: bisquare
## Summary points: the same locations as observations are used.
## Adaptive bandwidth: 99 (number of nearest neighbours)
## Distance metric: Euclidean distance metric is used.
##
## ************************Local Summary Statistics:**********************
## Summary information for Local means:
## Min. 1st Qu. Median 3rd Qu. Max.
## Rate_LM 63.601 67.120 70.595 72.102 75.026
## PM25_LM 10.556 11.259 11.436 11.805 12.198
## Summary information for local standard deviation :
## Min. 1st Qu. Median 3rd Qu. Max.
## Rate_LSD 10.18465 11.13278 11.38412 11.86204 13.8873
## PM25_LSD 0.73052 1.05446 1.15478 1.26483 1.6269
## Summary information for local variance :
## Min. 1st Qu. Median 3rd Qu. Max.
## Rate_LVar 103.72716 123.93877 129.59810 140.70788 192.8575
## PM25_LVar 0.53365 1.11189 1.33352 1.59979 2.6467
## Summary information for Local skewness:
## Min. 1st Qu. Median 3rd Qu. Max.
## Rate_LSKe 0.14786 0.31226 0.45736 0.59853 0.7895
## PM25_LSKe -1.43289 -0.85131 -0.39649 -0.20993 0.1126
## Summary information for localized coefficient of variation:
## Min. 1st Qu. Median 3rd Qu. Max.
## Rate_LCV 0.147086 0.156275 0.164858 0.175433 0.1913
## PM25_LCV 0.060252 0.088918 0.101306 0.111091 0.1533
## Summary information for localized Covariance and Correlation between these variables:
## Min. 1st Qu. Median 3rd Qu. Max.
## Cov_Rate.PM25 -2.2003666 -0.8874850 -0.3896257 -0.0545890 0.5514
## Corr_Rate.PM25 -0.1408757 -0.0638106 -0.0291851 -0.0039810 0.0457
## Spearman_rho_Rate.PM25 -0.1137968 -0.0305331 0.0055412 0.0501639 0.0890
##
## ************************************************************************#visualizamos las diferentes medidas, por ejemplo, para la media geograficamente ponderada de la variable Lung rate seria:
Mean.Rate = spplot(gwss.rate.pm25$SDF, "Rate_LM", main = "Media Local de tasa cancer", sp.layout = list(polys), col = "transparent", col.regions = col.palette(100))
Mean.Rate
#visualizamos la media local de pobresa de la base de datos:
col.palette = colorRampPalette(c("blue", "sky blue", "green", "purple", "black"), space = "rgb", interpolate = "linear")
gwss.pov.pm25 = gwss(SPDF, vars = c("POV","PM25"), kernel = "bisquare", adaptive = TRUE, bw = 48)## Warning in proj4string(data): CRS object has comment, which is lost in outputMean.Rate_F = spplot(gwss.pov.pm25$SDF, "POV_LM", main = "Media Local de Pobresa", sp.layout = list(polys), col = "transparent", col.regions = col.palette(100))
Mean.Rate_F
#visualizamos la correlación entre la pobresa y el material particulado, tamaño 2.5 micrómetros:
###########################################################################33
col.palette = colorRampPalette(c("Red", "yellow", "green", "blue", "black"), space = "rgb", interpolate = "linear")
gwss.pov.pm25 = gwss(SPDF, vars = c("POV","PM25"), kernel = "bisquare", adaptive = TRUE, bw = 48)## Warning in proj4string(data): CRS object has comment, which is lost in outputMean.Rate_corr = spplot(gwss.pov.pm25$SDF, "Corr_POV.PM25", main = "Correlación Pobresa vs PM25", sp.layout = list(polys), col = "transparent", col.regions = col.palette(100))
Mean.Rate_corr