I have a grid data of evenly spaced points that gives the number of people with a facebook account within a certain radius (approximately equal to the mid-distance between to points). From that I'd like to draw a smoothed map of my variable density of facebook users.

I read that available interpolation methods for that were :

  • natural neighbours
  • kriging, or IDW in the simpler way
  • rectangular interpolation

1) Kriging seems to be a good idea in any case, still I wonder if rectangular interpolation could be more appropriate and far less complex since I have perfectly evenly spaced values ?

2) Are there any interpolation models that takes also as input the radius around each data point ? From what I understand all of the above methods use only X-Y coordinates and the value of the interest feature, not any radius around X-Y in which the value is the same.

From @Spacedman's comment, I see that I misunderstood my dataset. What I actually have is z values for a set of discs defined by a set of points (X,Y) and a unique radius r*. Which means, at that point I don't have a true grid to interpolate on.

An easy workaround is to assume that the density at each point (let's call them f(X,Y) by contrast with z(X,Y,r*)) is uniform in my disc, so that f(X,Y) = a in disc A. Since I know that z(A) = a*pi*r^2, I know that a = z(A)/(pi*r^2). I know have values for a set of points, which are still consistent with the values of the disc they belong to.

What if the discs overlap ? I can't assume anymore that f(X,Y) = a around a whole disc since I may have different values for a same (X,Y). Would taking the mean be outrageous ?

  • Have a look at the point density tool if you are using ArcGIS desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/…, or heat mapping if you are using QGIS.qgistutorials.com/en/docs/creating_heatmaps.html
    – GBG
    Commented Nov 8, 2018 at 20:32
  • Your data is number of people in a circle of area pi*r^2 where r is the circle radius, that gives you a density. You have that on a regular grid, so you can create a raster with that grid resolution. What you then want to do is - I guess - downscale (or is it upscale) to a higher resolution? By what factor? What is the resolution and size of your source sample grid?
    – Spacedman
    Commented Nov 9, 2018 at 8:19
  • @Spacedman each point is separeted by 20km, which makes a really poor resolution in urban areas so I'd like to increase the resolution (let's say by a x10 factor in order to have a grid of point separated by 2km (1km radius). The thing is : I can ask for a grid of points positionned every 2km, but I can't ask for a radius for the value of less than 10km around each point. In this case, circle densities would overlap. I wonder if i can use that overlapping to increase precision or if it is just redundant information
    – Romain
    Commented Nov 9, 2018 at 12:53
  • @GBG thanks, I'll have a look at that. Do you know which interpolation algorithm does the tool use ? I guess that it is IDW since I can give a radius in the parameters ?
    – Romain
    Commented Nov 9, 2018 at 13:01

1 Answer 1


if you have demographic data at hand, co-kriging with population density computed on the same radius would be very powerful.

Otherwise, I would consider bilinear interpolation between the points, because your density is not a punctual measure.

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