ML/Statistical Model to Analyse the Distribution

Consider a Sample Data-set provided below;

|ShopID|    |Transactions|    |dist_to_shop|
   S1           15478               0 
   S2           12345              0.41
   S3           17865              0.11
   S4           35479              0.57
   S5           74589              0.35

The data-set consist of ShopID, Transactions and dist_to_shop (In Meters) fields. Assuming all the Shops belong to one retailer, I would like to find out the distribution of Transaction/People Visits to the other shops, by assigning weights/business rules on the basis of the distance.

For Example, the weights can be given as;

0-200 Meters = 40% 
201-400 Meteres = 30%
401  Above = 20%

Question - What would happen if the ShopID S3 is closed and the possible transactions/people_count splitting or distributing in other shops based on the distance weights provided. It's similar to What-If Analysis. I would like to know the spread of the Transactions if a Shop Closes down.

My data-set almost looks similar to the one I have provided above. I would like to know the best approach to solve this scenario. Which ML/Statistical Model will be best suitable.

Any inputs will prove really valuable.

Edit: Changes made on 25/Feb/2019