Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

For my research on the effect of power outages on fertility , we study a period of extensive power rationing that lasted for almost a whole year and affected most of Latin America, but in particular, it affected Colombia. The key difficult was to determine which areas were exposed to the power-outage and the extent to which this was the case. This is not straightforward, since there does not exist household- or even municipality level consumption data.

But here is how R and Satellite Data can help. In particular, we study the night light series obtained from the Defense Meterological Sattelite Program, which has been discussed by Jeffrey before.

We simply look for abnormal variation in municipality level light-emitting intensity from 1992 to 1993.

Here is some code that generates some Raster-Maps using the package rasterVis , and uses jQuery to generate a fancy before and after comparison to highlight the year-on-year changes in light intensity of 1992 compared to 1993.

tif<-"F101992.v4b_web.stable_lights.avg_vis.tif"
f151 = raster(tif)

tif<-"F101993.v4b_web.stable_lights.avg_vis.tif"
f152 = raster(tif)

##crop a smaller window to plot

e = extent(-78,-72,2,8)
#e = extent(-80,-78,-4.6,-2)
rn= crop(f151, e)
rn2= crop(f152, e)

### do a logarithmic transformation to highlight places that receive not much, but some light.

rn<-log(rn+1)
png("1992.png")
p <- levelplot(rn, layers=1, margin=FALSE,col.regions = gray(0:100/100))
p + layer(sp.polygons(COLPOB, lwd=.25, linetype=2, col='darkgray'))
dev.off()

rn2<-log(rn2+1)
png("1993.png")
p <- levelplot(rn2, layers=1, margin=FALSE,col.regions = gray(0:100/100))
p + layer(sp.polygons(COLPOB, lwd=.25, linetype=2, col='darkgray'))
dev.off()

Now with this together, you can create a fancy slider as I have seen on KFOR -- comparing satellite pictures of towns before and after a tornado went through them.

The code is essentially just borrowed from that TV station and it loads the javascript from their server; it is essentially just a clever use of jQuery and is maybe something that could or is already implemented in an R reporting package? Do you know of such a function?

Anyways, all you need is a slider.html page that contains the code referring to the two picture sources; the code is simple:

<!DOCTYPE html>
<html>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<style type="text/css">.sample1 {width:725px; height:725px;}.sample2 {width:725px; height:725px;}.sample3 {width:725px; height:725px;}</style>
<body>
<div id="wrapper">
<div class="container_6 clearfix">
<section class="main-section grid_6">
<div class="main-content">
<section class="clearfix">
<div>
<div class="container" style="position:relative">
alt="Before"
height="725px"
width="725px">
alt="After"
height="725px"
width="725px">
</div>
</div>
<script>
$(window).load(function() {$('.sample1').ClassyCompare({
defaultgap:50,
leftgap:0,
rightgap:10,
caption: true,
reveal: 0.5
});
});
</script> </div>
</section>
</div>
</section>
</div>
</div>

</body>
</html>

This is how it looks -- I know the stuff is not perfectly aligned, partly because when cropping the picture I made a mistake and could not be bothered with fixing it.

Have fun!