In a previous post, I showed how to use the Uber Movement Travel Times data to create isochrones. In this post, we will explore another use case of this dataset. Say you are concerned about loss of productivity due to long commute times of your employees and wonder if a change in office times might help them get to the office faster. A similar analysis can be done to see if a change in office location will result in better or worse commutes.
Here’s the hypothetical scenario “Given the location of an office and location of homes of employees, determine their current commute times for office timings of 9am-11am and 5pm-6pm. If the office timings were changed to non-peak timings of 7am-8am and 3pm-4pm, what would be the time savings?“
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.
When working with elevation data, sometimes you may discover that 2 datasets from different providers have very different elevation values for the same location. A common reason for this being each dataset being referenced to a different surface.
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.
This post is the continuation of Summary Aggregate and Spatial Filters in QGIS. I have been exploring aggregate functions more and have found interesting ways to automate tasks in QGIS. One such example is helping automatically keeping track of feature edits to help with Quality Assurance (QA).
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.
You may have seen a map where source and destination points are connected via curved lines. It is possible to create such a map in QGIS with a simple trick – using custom projections and densification of lines. I will outline the steps to create such a map.