Venue or business perspective

Here we will get a clear idea from a business perspective. Often the following questions will come into picture:

How many customers does venue X Receive per day? What about per hour? What is the pattern? Can we estimate business value based on the check-ins?

We will use a gym venue as an example here, with VenueID = 4aca718ff964a520f6c120e3. For this dataset, this gym has 118 check-ins. Although the data is small and cannot be generalizable in this particular VenueID, imagine it has enough data for a longer period of time. We can estimate the peak times of this gym as the following graph shows. There is a peak of check-ins at 14:00 and at 20:00:

This kind of business perspective analysis helps both decision makers and competitors to gain an insight into businesses. This is only an individual business example, but this can simply be extended to businesses in this dataset and look further into it. In fact, Foursquare predicted Chipotle's sales (link available in the information box), a Mexican grill, to drop 30% during the months of 2016 before the company announced its loss.

Let's now look at how location data science is different than data science in the next section.