Generating pseudo-random data is important for many aspects of research work. QGIS provides for many methods of generating random points to facilitate this.

Recently, I ran into a problem where I wanted to generate random points inside a polygon – but I wanted the random points to have a certain distribution. I wanted to generate a dataset showing employee home locations for a company. Given a city boundary and the location of office, I wanted to have a point layer that showed where the employees lived. A simple ‘Random points within Polygon’ algorithm would not work here, since the distribution of points would not be uniform within the city.

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Table Joins are a way to join 2 separate layers based on a common attribute value. QGIS has a Join Attributes By Field Value algorithm that allows you to table joins. A limitation of this algorithm is that the field values must match exactly. If the values differ slightly – the join will fail. There are many times where you are trying to join 2 layers from different sources and they contain values which are similar but may not match exactly. Fortunately QGIS now has built-in fuzzy string matching functions that can be used – along with Aggregate function – to do table join based on fuzzy matches.

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When you want to buffer features that are spread across a large area (such as global layers), there is no suitable projection that can give you accurate results. This is the classic case for needing Geodesic Buffers – where the distances are measured on an ellipsoid or spherical globe. This post explains the basics of geodesic vs. planar buffers well.

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