Quantify effect of snow on cityeconomy ?

The 'beast from the east' caused a lot of snow in London recently.

Gyana is a platform that is revolutionising how big data is used by business by creating a self service platform that does not require technical expertise. It is also easily providing insights that were not easy to get before.

Using Gyana we were able to get a sense of what were the busiest commuter stations in London and how their travellers increased/reduced.

So, here is the total footfall per week shown below for the top 5 busiest London stations combined: London Waterloo, London Victoria, London Liverpool Street, London Bridge, London Euston.

The following is showing how the footfall changed in the stations commencing the week 29/10/17 to 25/02/18.

29-Oct-17      13.8 Mn

05-Nov-17     14.2 Mn

12-Nov-17     14.4 Mn

19-Nov-17     14.7 Mn

26-Nov-17     15.0 Mn

03-Dec-17     15.2 Mn

10-Dec-17     15.0 Mn

17-Dec-17      13.2 Mn

24-Dec-17      5.0 Mn

31-Dec-17      9.4 Mn

07-Jan-18      13.3 Mn

14-Jan-18      13.9 Mn

21-Jan-18      13.8 Mn

28-Jan-18      12.6 Mn

04-Feb-18     13.6 Mn

11-Feb-18     13.4 Mn

18-Feb-18     13.8 Mn

25-Feb-18     12.0 Mn

From this we can see macro-economic trends:

1. The travel rush before Christmas -consistent increase of half to 1 million every week from end of October building up to the week of 17 Dec- machine like consistency- can this be used for targeted campaign strategies?
2. The reduced travel during Christmas(47%) and before New Years-(50%) - good time to downgrade advertising rates or maybe get focussed attention since lesser people?
3. In recent months London has experienced a lot of snowfall with the arrival of the ‘Beast from the East’. We can see how these weather conditions affected stations. There was an approximately 15% reduction in travel in the week 25/3/18 to 3/3/18 due to snow. What does this mean for an urban economic planning perspective?

This allows us to quantify the effect of snow and holidays on travel and potentially the economy!

We may be able to make much more informed and detailed predictions for travel and brands that sell to travel or policy makers for urban commute or even urban travel design.

This will be helpful to many people and organisations including businesses, the government and the general public. It’ll help us plan for the future and create better solutions and prevention methods to travel disruptions now that we can quantify impacts of these weather changes on individual aspects of the city's life.