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I'm working with a two-column data.frame with the columns containing x and y coordinates (Longitude, Latitude). A simple way to visualize a density map is by using the 'stat_density_2d' function. When I run the code below on my data, I obtain large density values. To me, this density plot suggest that the densities are larger than the total number of observations, which does not make sense to me. I have provided a small subset of my data.

df <- structure(list(Lat = c(-24.1871741, -24.2069615, -24.2022726, 
    -24.2016188, -24.2152107, -24.1939073, -24.1913561, -24.198409, 
    -24.2088875, -24.2121186), Long = c(30.8839167, 30.8814249, 30.8788437, 
    30.8903969, 30.8883906, 30.8784664, 30.870561, 30.8800543, 30.8818679, 
    30.8914805)), row.names = c(NA, 10L), class = "data.frame")

ggplot(df, aes(Long, Lat)) +
    stat_density2d(geom="tile", aes(fill = ..density..), contour = FALSE) + 
    geom_point(colour = "white")

enter image description here

Even with only ten data points, the plot suggests a density range between 500 and 2000 points. I don't believe the output is wrong, but I'm just wondering if some could explain to me why these values are so larger on such a small dataset. How should I interpreted the density values from the legend?

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  • I would highly recommend projecting your data. Under the hood ggplot2::stat_density2d is calling MASS::kde2d for the density estimate and MASS:bandwidth.nrd as the automatic bandwidth plugin. I cannot imagine that the automatic bandwidth selector is coming up with anything relevant using lat/long coordinates. Geographic coordinates are degrees not distance.
    – Jeffrey Evans
    Commented Jul 8, 2020 at 16:19

1 Answer 1

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density is points per unit area and your area is about

(30.890-30.870)*(24.215-24.190)

0.0005 square degrees. There's 10 points, which means that's about 20,000 points per unit area. The smoothing done by the density estimation brings that down.

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