#Create a kernel density raster for one year of crime data

 

library(raster)

 

#Creating Traffic Crime for 2016 and 2017

TrafCrime2016 = TrafCrime[TrafCrime$date < as.Date('2017-01-01'),]

TrafCrime2017 = TrafCrime[TrafCrime$date >= as.Date('2017-01-01'),]

 

pixelsize = 100

box = round(extent(neighborhoods) / pixelsize) * pixelsize

template = raster(box, crs = project,

nrows = (box@ymax - box@ymin) / pixelsize, 

ncols = (box@xmax - box@xmin) / pixelsize)

 

TrafCrime2017$PRESENT = 1

raster2017 = rasterize(TrafCrime2017 , template, field = 'PRESENT', fun = sum)

 

TrafCrime2016$PRESENT = 1

raster2016 = rasterize(TrafCrime2016 , template, field = 'PRESENT', fun = sum)

 

#Create Kernel Density Map of traffic crimes in the city of portland during 2017

 

kernel = focalWeight(raster2017, d = 264, type = 'Gauss')

heat2017 = focal(raster2017, kernel, fun = sum, na.rm=T)

dev.new()

plot(heat2017,main = "Kernel Density Map of Traffic Crimes in the City of Portland in 2017")

plot(neighborhoods, border='#00000040', add=T)

 

#Create Kernel Density Map of traffic crimes in the city of portland during 2016

 

kernel = focalWeight(raster2016, d = 264, type = 'Gauss')

heat2016 = focal(raster2016, kernel, fun = sum, na.rm=T)

dev.new()

plot(heat2016,main = "Kernel Density Map of Traffic Crimes in the City of Portland in 2016")

plot(neighborhoods, border='#00000040', add=T)