Data exploration in GRASS GIS – boxplots

I am currently working on some exercises for which I need data about municipalities in the Netherlands. A good place to look for such data is the CBS (Dutch Central Bureau of Statistics). One data layer is vector layers of the dutch municipalities and neighborhoods, which include demographic data.

One of the first things I normally do when exploring new data is to look at the distribution of the data. For example by creating a histogram using the d.vect.colhist addon (see my earlier post). But what if I want to compare the distribution of different groups or samples? In such a case I find boxplots more convenient. However, there is no tool in GRASS GIS to create boxplots, so I had a look at the d.vect.colhist addon code and adapted the code to create boxplots instead of histograms.

An example

Let’s for example look at the average population densities of the municipalities.

The average population density (number of inhabitants / km2) per municipality in 2017. Source: CBS.

What if I want to compare the distribution of the average population density per provinces Dutch provinces? You can install the addon (see the end of this post) and run d.vect.colbp on the command line or the console. This will open a window with different tabs.

In the first tab, you can define a column in the attribute table to plot (here BEV_DICHTH, which is the column with the population density) and a column that will be used to group the data (here provincie, which gives the names of the provinces the municipality belongs to). As you can see in the screenshot above, you have a few options to change the plot (layout). In this case, I choose to rotate the x-axis labels so they do not overlap. The resulting plot looks like:

The distribution of the average population densities of the Dutch municipalities per province.

You can of course also use the command line. In this case I will plot the boxplots horizontally using the ‘h flag’.

d.vect.colbp -h map=gemeenten@CBS column=BEV_DICHTH \
    where="AANT_INW > 1" plot_output=example_1.png \
    group_by=provincie order=ascending --overwrite

With will give you the plot below.

The distribution of the average population densities of the Dutch municipalities per province.

The add-on does not provide further options to change the appearance of the plot, as the main idea is to use this for quick exploration of your data, similar to the other plotting tools in GRASS GIS. However, you can save the plot as a svg file, and further edit it in e.g., Inkscape.

Testing the add-on

I probably will try to integrate the option to create boxplots in the existing d.vect.colhist add-on, but for now it is available as a separate addon on Github for testing. For GRASS GIS version 7.2 and above, you can use g.extension; simply type on the command line:

g.extension d.vect.colbp \
    url=https://github.com/ecodiv/d.vect.colbp

I am sure there is a lot to improve as this was a rather quick hack, so any feedback will be most welcome. If you try it out and run into problems, please let me know (suggestions for improvements are of course also welcome).

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Draw a histogram of vector attribute column in GRASS GIS

GRASS GIS has convenient tools to draw histograms of raster values. As similar tool to draw a histogram of values in a vector attribute table lacks. But you can easily add this functionality by installing the d.vect.colhist addon by Moritz Lennert. Read this short post on Ecodiv.earth tutorials.

bitmap

Plotting GRASS data in Python

GRASS GIS offers some useful but basic plotting options for raster data. However, for plotting of data in attribute tables and for more advanced graphs, we need to use other software tools. In this tutorial I explore some of the possibilities offered by Pandas plot() and how we can further tune plots using matplotlib / pyplot library.

map_municipals
Map of the municipals in Wake County, North Carolina, and for each municipal the distribution of distances to the nearest school (data source: North Carolina sample data set).