Track and analyze trash data from containers using sensors to determine density, type, and neighborhood patterns to optimize waste collection. Machine learning techniques like clustering can analyze the data to predict garbage percentages, compare to real data, and improve the system by determining the best locations for new containers to reduce waste and make collection more efficient through data-driven environmental management.

1. What do we not see in our cities?

2. Track your Trash!

3. Optimize & Increase Visibility

4. Data for Everyday Environmental Fitness
Track your Trash!

5. Sources of Data

6. Locations of Containers

7. People per container

9. How we use data to generate information?

10. Graphs makes our life easier
Density
Type of Trash
Neighborhood
Now Decide your data analyse
technique!
(Clustering)

11. Know all about your data (linear regression, Density and boxplot)

12. Where we should put next
Wasteba

13. OTA
LBS
PWMS
TECH

14. SUM UP

15. Output:
Predicting the percentage of garbage sensor and compare with real data and
improve the system
Reduce generated waste with controlling and educating people.
Efficient source and IT Service Management.

16. Machine Learning system

17. Thank you!

18. Sources! GIS Data: CBS and 4Orange

19. Municipality Data Source
- Afvalapartcontaine File