Lately I've been playing in R and have my own R statistics lab day every Saturday. Well today I downloaded Seattle's data set of >438k 911 police responses for the city and plotted them in R. You can find the data I used and more here: http://data.seattle.gov/
Here is the plot I created in R from this data:
First, compare my plot to this Google Map:
View Larger Map
I've read about Facebook doing something similar and seeing maps formed but I've never experienced it for myself. It was awesome to see it happen.
Also (and very unsurprisingly) notice how the darkest area with the most incidents is in downtown and how abruptly the density changes at the city limits.
Here's how to do it.
- Downloaded this specific dataset in CSV format: http://data.seattle.gov/Public-Safety/Seattle-Police-Department-911-Incident-Response/3k2p-39jp
- Load it into R by executing the following command: police_911 <- read.csv(file.choose())
- Find where you downloaded the file and open it
- Then I always use summary to quickly look at the data and get my bearings: summary(police_911)
- Notice the Longitude and Latitude columns? Already neatly parsed for us! Time to plot!
- plot(police_911$Longitude, police_911$Latitude, pch=20, cex=.01)
That's it! The pch and cex parameters allow me to set the point shape and size respectively. By executing length(police_911$Latitude) I can find out how many rows there are in that column... 438,512. Schiesse!
Well that's it. Just a quick update about something I found to be interesting.
Update: Here's a map with Fire responses added to it as well. Blue is police and red is fire.
