This dataset is downloaded from UCI Machine Learning repository. The purpose of this analysis is to visualize the road network of Denmark. The attributes provided are latitude, longitude and altitude of the road. Visualization is done using R packages plotGoogleMaps and googleVis.
For detailed analysis and code visit my Github repository Create a new category for Altitude field
spatial_network$altitude_category<-NA
spatial_network$altitude_category[spatial_network$altitude<0]<-"Very Low" spatial_network$altitude_category[spatial_network$altitude>0 & spatial_network$altitude<50]<-"Low" spatial_network$altitude_category[spatial_network$altitude>50 & spatial_network$altitude<100]<-"High" spatial_network$altitude_category[spatial_network$altitude>100]<-"Very High" Mapping using plotGoogleMaps and R
#Plotting using plotGoogleMaps
library(plotGoogleMaps) spatial2<-spatial_network[1:50000,] coordinates(spatial2)<- ~ longitude+latitude proj4string(spatial2) =CRS("+proj=longlat+datum=WGS84") ic<iconlabels(attribute=spatial_network$altitude_category,colPalette=rainbow(4),icon=TRUE,at=NULL,height=10,scale=0.6) spatial3<-SpatialPointsDataFrame(spatial2,data = data.frame(ID=row.names(spatial2))) m<-plotGoogleMaps(spatial3,filename = "myMap1.html",iconMarker = ic) Mapping using googleVis and R
# Plotting using googleVis
library(googleVis) spatial_network$latlon <- paste(spatial_network$latitude, spatial_network$longitude, sep = ":") map <- gvisMap(spatial_network, locationvar = "latlon", tipvar = "altitude_category", options = list(showTip = T, showLine = F, enableScrollWheel = TRUE, useMapTypeControl = TRUE, width = 1400, height = 800, allowHtml = TRUE)) plot(map)
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