1. Extracting Places from Traces of Locations
Algorithms featured by:
Jong Hee Kang
William Welbourne
Denjamin Stewart
Gaetano Dorriello
Present by:
Yuan Liu

2. Introduction What are these algorithms for?
Extracting locations from cluster points
What are these algorithms trying to achieve?
Significantly reduce the server load
Maintain data accuracy
Translate coordinates to locations

3. Recall of My Senior Design problem

4. Recall of My Senior Design problem (continue)
What did our customer wants:
Plug collected GPS points into Google Map
Can group these points manually or automatically
Customize these grouped points (name, etc)
Our Approach:
Use Area grouping (draws a circle)
Use a automatic filter

5. Article proposed Algorithms
Time based Clustering:
clustering locations along the time axis
New location is compared with previous locations
If the new location is moving away starts a new cluster
Then ignore the clusters with short time duration

6. Article proposed Algorithms (continue)

7. Article proposed Algorithms (continue)
Frequency clustering:
Time based Clustering algorithm only consists data with longer time duration
People may visit important place frequently, but not for a long time (such as ATM, mailbox)
Need two threshold value: one determines duration, the other determines the frequency of visiting.
May yield false information (stop at traffic light)

8. How would these algorithms may help my team
Wheelchair people usually does not move around in a big radius, and they tend to stay at one place for a long period of time, therefore the Time clustering algorithms applies the best.
The server we were using is quite weak, and we didn’t assume everyone has broadband. Such algorithms will greatly reduce the calculation time.

9. Questions?