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I am an absolute beginner in analyzing geographic data with R.

I have a database with longitude and latitude of around a million buildings in a country. I would like to find the average density of those buildings. For that I have divided the whole country into several cells using Raster and applied projection with the appropriate proj4 profile. Now I want to check which buildings are coming under which cell to find the density.

> metersCoordinates <- totalMeterDatabase[,c("longitude","latitude")]
> coordinates(metersCoordinates) <- ~longitude+latitude
> r <- raster(ncols=6000, nrows=2000)
> r[] <- 0
> crs(r) <- "+proj=somerc +lat_0=46.95240555555556 +lon_0=7.439583333333333 +k_0=1 +x_0=6000 +y_0=2000 +ellps=bessel +towgs84=674.374,15.056,405.346,0,0,0,0 +units=m +no_defs"

> projection(r)

Could you please tell me how can I find it?

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  • Thanks for the link. The link helped me to find the right answer with the help of Amadou Kone answer. I have edited the Amadou Kone answer with your example. – Vamsi Sep 18 '15 at 7:02
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If I understand your question, you can use the extract() function. First, give each raster cell a unique value when you create the raster. I'd also recommend giving the raster the extent of your coordinates. So:

 metersCoordinates <- totalMeterDatabase[,c("longitude","latitude")]
 coordinates(metersCoordinates) <- ~longitude+latitude
 r <- raster(matrix(seq(1,6000*2000), ncol=6000, nrow=2000), 
        xmx=max(metersCoordinates$longitude),
        xmn=min(metersCoordinates$longitude),
        ymx=max(metersCoordinates$latitude),
        ymn=min(metersCoordinates$latitude))
 totalMeterDatabase$cellnumber <- extract(r, metersCoordinates)

And use that to do your aggregation/analysis.

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