Mapping Cambridge

mapping_cam.r

library(ggmap)

getLineColor <- function(val) {
	#pal <- colorRampPalette(c("#333333", "#ffffff", "#b5310c"))
	pal <- colorRampPalette(c("#ffffff", "red"))
	colors <- pal(80)
	val.log <- log(val)
	
	if (val > 50) {
		col <- colors[80]
	} else {
		colindex <- max( 1, round( 80 * val / 50 ) )
		col <- colors[colindex]
	}
	
	return(col)
}

distance <- read.csv("nearest-rest.csv", stringsAsFactors=FALSE)

# Calculate colors
pts0 <- mapproject(unlist(distance[, 'lng']), unlist(distance[, 'lat']))
pts1 <- mapproject(unlist(distance[, 'lngnear']), unlist(distance[, 'latnear']))
seg.colors <- sapply(distance$dist_miles, FUN=getLineColor)

#
# Map It
#

cam <- read.csv("nearest-rest.csv", stringsAsFactors=FALSE)

# The box that the data points are within, just top left (lat and long) and bottom right are fed in
# This box will be taken from google maps. You can also use other mapping sites - see ggmap help for full list.
bbox <- c(0.104994, 52.215382, 0.149626, 52.195868)
#get the map from google
basemap <- get_map(location=bbox, zoom=14, source='google', maptype="terrain", color="bw") 


#distance to line map
ggmap(basemap) + geom_segment(aes(x=lng, xend=lngnear, y=lat, yend=latnear, colour=dist_miles), size=1, data=cam)  + scale_colour_gradient(low="white", high="red", limits=c(0, 0.5))

#contour map
ggmap(basemap) + stat_density2d(aes(x=lngnear, y=latnear), data=cam, bins=30, color="red", alpha=0.8)

#density map
ggmap(basemap) + stat_density2d(aes( x=lngnear,y=latnear, fill=..level.. , alpha=..level.. ), data=cam, bins=8 ,alpha=0.3, geom = "polygon"  )