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?“
Mapshaper is a free and open-source tool that is best known for fast and easy simplification. Other tools for simplification – like QGIS or ogr2ogr – do not preserve topology while simplifying. This means you may get sliver polygons or missing intersections. Mapshaper performs topologically-aware simplification and gives you much more control on the process.