Transform point shapefile to SpatStat object

August 19, 2014
By

(This article was first published on R Video tutorial for Spatial Statistics, and kindly contributed to R-bloggers)

Today I wanted to do some point pattern analysis in R using the fantastic package spatstat.
The problem was that I only had a point shapefile, so I googled a way to transform a shapefile into a ppp object (which is the point pattern object used by spatstat).
I found a method that involves the use of as.ppp(X) to transform both spatial points and spatial points data frames into ppp objects. The problem is when I tested with my dataset I received an error and I was not able to perform the transformation.

So I decided to do it myself and I now want to share my two lines of code for doing it, maybe someone has has encountered the same problem and does not know how to solve it. Is this not the purpose of these blogs?

First of all, you need to create the window for the ppp object, which I think it is like a bounding box. To do that you need to use the function owin.
This functions takes 3 arguments: xrange, yrange and units.

Because I assumed you need to give spatstat a sort of bounding box for your data, I imported a polygon shapefile with the border of my area for creating the window.
The code therefore looks like this:

library(raster)
library(spatstat)

border <- shapefile("Data/britain_UTM.shp")

window <- owin(xrange=c(bbox(border[1,1],bbox(border[1,2]),
yrange=c(bbox(border)[2,1],bbox(border)[2,2]),
unitname=c("metre","metres"))

 

Then I loaded my datafile (i.e. WindData) and used the window object to transform it into a point pattern object, like so:

WindData <- shapefile("Data/WindMeanSpeed.shp")

WindDataPP <- ppp([email protected][,1],
[email protected][,2],
[email protected]$MEAN,
window=window)

 

Now I can use all the functions available in spatstat to explore my dataset.

summary(WindDataPP)



@fveronesi_phd

To leave a comment for the author, please follow the link and comment on their blog: R Video tutorial for Spatial Statistics.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)