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I have a question with regard to spatial aggregation in R. What I am trying to do is aggregate a point dataset to a grid. I am unsure however how to do this as I have little experience with this sort of stuff. I was hoping anyone of you might have some useful guidance/a possible solution.

My vantage point is a dataset containing georeferenced data on conflict events in Africa (see www.acleddata.com). The points are georeferenced with latitude/longitude coordinates and contain data on event type and time. What I want to do is aggregate these points to a 1x1 degree grid.

Thus a grid-cell should contain the information of the data points if a event happened to occur within that grid-cell. The eventual product of this should be a data frame or something that I can export to a csv-file as the data is intended to be used in a panel data-set for statistical analysis.

So far I loaded and plotted the data and the shapefile using the code below. I believe that I should use the over function from the sp package to aggregate but I do not know how. Hope one of you can help.

The code I used so far can be found here with the corresponding visual result over there.

Suggestion for doing this in QGIS are welcome as well.

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This is a fast simple operation requiring nothing more than a little arithmetic. But what format do you want the output in? "CSV" only suggests that it should be a relational table, but this presents a problem: when you aggregate, each cell will potentially correspond to a varying number of points. Usually you select one of two options: you either output one record per point (including the ID of its containing cell) or you output one record per cell and include some statistical summaries of the points it contains. Which do you need? –  whuber Jan 18 '13 at 14:33
Sorry I didn't specify that. What I need is one record per cell. I use the csv-file to make panel data in cell-year format. –  BlankUsername Jan 18 '13 at 15:28
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2 Answers

up vote 8 down vote accepted

The data as downloaded contain some frank locational errors, so the first thing to do is limit the coordinates to reasonable values:

data.df <- read.csv("f:/temp/All_Africa_1997-2011.csv", header=TRUE, sep=",",row.names=NULL)
data.df <- subset(data.df, subset=(LONGITUDE >= -180 & LATITUDE >= -90))

Computing grid cell coordinates and identifiers is merely a matter of truncating the decimals from the latitude and longitude values. We can combine these coordinates into unique identifiers, attaching them to the input dataframe, and write the augmented dataframe out as a CSV file. There will be one record per point:

data.df$X <- floor(data.df$LONGITUDE)
data.df$Y <- floor(data.df$LATITUDE)
data.df$Cell <- data.df$X + 360 * data.df$Y
write.csv(data.df, "f:/temp/africa.csv")

You might instead want output that summarizes events within each grid cell. To illustrate this, let's compute the counts per cell and output those, one record per cell:

counts <- by(data.df, data.df$Cell, function(d) c(d$X[1], d$Y[1], nrow(d)))
counts.m <- matrix(unlist(counts), nrow=3)
rownames(counts.m) <- c("X", "Y", "Count")
write.csv(as.data.frame(t(counts.m)), "f:/temp/grid.csv")

For other summaries, change the function argument in the computation of counts. (Alternatively, use spreadsheet or database software to summarize the first output file by cell identifier.)

As a check, let's map the counts using the grid centers to locate the map symbols. (The points located in the Mediterranean Sea, Europe, and the Atlantic Ocean have suspect locations: I suspect many of them result from mixing up latitude and longitude in the data entry process.)

count.max <- max(counts.m["Count",])
colors = sapply(counts.m["Count",], function(n) hsv(sqrt(n/count.max), .7, .7, .5))
plot(counts.m["X",] + 1/2, counts.m["Y",] + 1/2, cex=sqrt(counts.m["Count",]/100),
     pch = 19, col=colors,
     xlab="Longitude of cell center", ylab="Latitude of cell center",
     main="Event counts within one-degree grid cells")

Africa map

This workflow is now

  • Thoroughly documented (by means of the R code itself),

  • Reproducible (by rerunning this code),

  • Extensible (by modifying the code in obvious ways), and

  • Reasonably fast (the whole operation takes less than 10 seconds to process these 53052 observations).

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Code is perfectly reproducible. I have one additional question though. Instead of a summary, how do I attach the information from the input data file to the cell in the created grid? –  BlankUsername Jan 18 '13 at 15:58
That is not possible to do with an output table, because the full information for cells has variable length. The proper way to record that is with the first form of output I exhibited: one record per point with a cell identifier attribute. One of these two formats--the per-point and per-cell tables--will be expected by whatever statistical program you are using. –  whuber Jan 18 '13 at 16:07
Ah ok. I see what you mean. Only have to create a grid for all cells and merge it. Thanks for the help. –  BlankUsername Jan 18 '13 at 16:31
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Well, what you want is a basic so called "Spatial Join", which matches two shapefiles to each other and allocates the sum (count number) to the resulting attribute-table. If you search for "Spatial Join in R" you'll find numerous examples even here on GIS.Stackexchange. I quickly googled and found for example this code posted on a mailing list.

If you want to achieve a spatial attribute join in QGIS, then do the following:

  • Save your shapes as .shp files (command writeOGR from the rgdal package)
  • Load them in QGIS. Recreate your vector grid via the MMQGIS plugin (Create -> Create Grid Layer) with appropriate scaling.
  • Use the "Join Attributes" tool from the Vector -> Data Management menu. Select an attribute of your point layer (this could be a simple column representing TRUE (1) or FALSE (0) values for different conflict events).
  • Select your grid and Sum all occurrences and execute. Afterwards i would also clip your grid with a shape of the African continent.

If the Join somehow fails (doesn't work for me everytime), then stick to SEXTANTE and look for the SAGA toolbox, which also has very good joining functions.

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Although this is a solution, it's a particularly complex and inefficient one given that summarizing points to a grid is just a matter of a few simple arithmetic operations, which R excels at. Using shapefiles, rgdal, QGIS, and Sextante is a bit like recommending that someone hire out a modern automated industrial plant in order to nail two boards together :-). –  whuber Jan 18 '13 at 14:36
I will try this approach this weekend. In the near future I might want to combine various shape-files with each other so this could be useful. Thanks for the input and the suggestions. –  BlankUsername Jan 18 '13 at 16:33
@whuber: Thats true, but if you want to distribute and maybe style your output, then a shapefile is the obvious choice. Nevertheless, nice R example! –  Curlew Jan 18 '13 at 16:35
I finally tried it. But the problem with this approach is that it sums all the observations to the polygon. While I ideally want to keep the information on different events over time. But it could be that I did something wrong. –  BlankUsername Feb 17 '13 at 14:41
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