I have time series location data (latitude/longitude) from users who send their location every minute. Basically the history of different users moving on a map.
+---------+-------------------------+---------------------+
| user_id | point(lat,lon) | date |
+---------+-------------------------+---------------------+
| 111 | 26.453121,44.297412 | 2020-07-24 13:17:00 |
| 222 | 26.453155,44.297489 | 2020-07-24 13:17:00 |
| 111 | 26.453132,44.297455 | 2020-07-24 13:18:00 |
| 222 | 26.453191,44.297468 | 2020-07-24 13:18:00 |
| ... | | |
+---------+-------------------------+---------------------+
Based on this location data I need to predict how many users will be in a certain location.
For example given the point (lat=26.453121,lon=44.297412) (which in the image below is the center of the green circle) and a radius of 30 meters predict how many points (users) will be inside the perimeter based on past data. In the image below I have 3 points (users) inside.
The challenging part is that the center of the area (circle) and the radius are variable parameters and can get any value.
My questions:
Is it feasible to have the center of the circle and the radius as variable parameters? If not then how would someone approach a problem like this?
I am considering making the radius a fixed value but the fact that the center of the circle can be any point on a map is still giving me headaches.