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Covid-19 lockdown restrictions have led to an unprecedented level of 'working from home' all around the world. As restrictions begin to be lifted over the months ahead, both employers and employees will face new challenges related to commuting safely and risk minimization.
In this use case we outline some of the ways in which Alteryx can be used to help answer these questions, and support safe 'back to work' strategies, whatever they may look like. To do this we will use the imaginary example of Acme Corp, that employs around 1,000 employees across Greater London, and operates 5 London offices.
The workflows used here include a range of macros from TravelTime which can be found in the Alteryx Gallery here.
The first question we want to answer is what are the options for our employees to commute to these various offices, particularly for different transport modes? There are two main reasons for this:
Our data is in two simple Excel spreadsheets, one containing the employee postcodes, and one the offices:
Here is the first workflow we need to run in Alteryx:
The steps in the workflow are as follows:
We now have a separate matrix for each office, with the travel time for each employee via the four different transport options. The output below shows the first few entries of the matrix for the Southwark office.
We can now use this data to spot sensible opportunities for employees to commute to one of the five different offices by a method of transport other than public transport. For example at the Southwark office, Employee 107 faces a 42 minute commute by public transport, but only a 25 minute cycle.
To assess the second point of allocating those employees who still wish to commute by public transport to the nearest office, we run an almost identical workflow as before:
The only change here is in the final Filter and Cross Tab tools. This time we separate out the results by transport type instead of office, and set the columns in the matrix as the Office ID.
We now have a separate matrix for each method of transport, and examining the public transport matrix allows us to establish which is the best office for each employee in terms of shortest commute time:
For example with Employee 132, if we wanted to encourage public transport commute times of no more than 30 minutes, they could use either the Paddington office (24 minutes) or even better the Shepherd’s Bush office (17 minutes).
Another aspect of our plan for employees returning to the office safely is to try reduce the need for particularly high risk individuals to travel through the busiest underground stations. To do this we add a third column to our employee data called ‘Risk Level’ and then run the following workflow:
The steps in the workflow are as follows:
Use the Filter tool with a custom filter to select any route legs that involve one of the five busiest underground stations that we want to highlight, as shown below
The first output we can use is the Browse tool directly from the Routes tool:
Using this visual output we can investigate which routes run through the stations we are looking to avoid. We can do even better than this though, by using the output from the second Filter tool:
This is a list of all of the combinations of high risk employees and offices which involve a route passing through one of the five busiest stations.
We can use this output to ensure that if they need to travel using public transport, we can allocate high risk employees to offices which at the very least avoid public transport routes that involve changing at busy stations.
A similar approach could be used to avoid whole tube lines by filtering the ‘Instructions’ field in the Routes tool output for the name(s) of the lines to be avoided.
Having assessed the travel options for our employees returning to one of the five offices, we decide that it would be sensible to reduce our real estate portfolio by closing one of the offices.
Going forwards, we want to encourage walking and cycling to work as much as possible, so we want to take accessibility by these modes into account when deciding which office to shut.
Below is the workflow we run for this analysis:
The steps in the workflow are as follows:
The resulting matrix allows us to compare how many employees can walk or cycle to each office within 30 minutes. The True column is the count of how many employees can make the journey in under 30 minutes, while the False column is the count of those who can’t.
Using this analysis we can see which office provides the worst access for our current employees based on walking and cycling.
Canary Wharf turns out to be the least accessible of the offices for both options:
Based on where our current employees live, Canary Wharf is clearly the best option for an office to be closed, in terms of accessibility by walking and cycling. However, taking a more long-term perspective, we also need to consider two additional factors:
To assess the impact of potentially closing the Canary Wharf office given these two additional considerations, we run the following workflow:
The steps in the workflow are as follows:
The results of this workflow are below:
The highlighted isochrone here corresponds to the reachable area for the Canary Wharf office. Comparing this to the reachable areas covered by the other offices we can see that by closing the Canary Wharf office we will lose some accessibility to the East of London.
However, the majority of the area served by the Canary Wharf office is also served by one of the four other offices, so we can be confident that closing it will not have too detrimental an impact on our ability to attract talent from outside of London, who want to commute by public transport.
To explore these isochrones in more detail, they can be easily exported into Tableau.
Good case study and nice presentation.
Awesomeness!