1. Fast Point-Feature Label Placement Algorithm for Real Time Screen Maps
Missae Yamamoto
Gilberto Camara
Luiz Antonio Nogueira Lorena

2. Schedule Definition The combinatorial explosion of possible solutions
Label placement
potential label positions
cartographic preference
The combinatorial explosion of possible solutions
Conflict graph and adjacency matrix
FALP - Fast Algorithm for Label Placement
Results
Sample of label placement for 1000 points
Conclusion

3. Label placement (FONTE: Edmondson et. al. (1996, p. 14).

4. The label placement problem
We want our maps to be legible
Name placement can be one of the most time-consuming aspects of map production,
Names must not overlap
Names must be clearly associated with the features they annotate

5. A good map must be easily readable
Source: ESRI

6. Why fast algorithms for label placement?
Web maps are very popular
Map servers allow the user to combine many layers
Layers have text associated
Unfeasible to pre-compute all label arrangements
We need fast ways to generate good quality maps for web map servers

7. Source: Institute for Cartography and Geoinformation, University of Bonn

8. A set of potential label positions and their cartographic preference (best = 1; worse = 4)

9. the combinatorial explosion of possible solutions•
1 point
2¹ configurations
P1/L0
P1/L1
2 points
2² configurations
P2/L0
P2/L1
P2/L0
P2/L1
3 points
2³ configurations
P3/L0
P3/L1
P3/L0
P3/L1 • • • • • • • • • • • • • •
1000 points
2¹ººº configurations • • • •

10. Two points – potential label positions and corresponding conflict graph
Candidate label positions
Conflict graph (linked nodes
have conflicts)

11. A conflict graph as an adjacency matrix
Number of conflicts (node degree): L5 = 4

12. Fast Algorithm for Label Placement (FALP)

13. A fast algorithm for label placement
Create the conflict graph (done off-line).
Use the conflict graph to label as many points as possible.
Create a labeling for the remaining points
Use local search to improve map quality.

14. The maximum nonconflict labeling algorithm (Uses the conflict graph to label as many points as possible.)

15. Example of label arrangement

16. Adjacency matrix for example

17. Label with least conflicts is L24 Solution1 = {(P6, L24)}

18. Label with least conflicts is L02 Solution 2 = {(P6, L24), (P1, L02)}

19. Label with least conflicts is L05 Solution3 = {( P6, L24), (P1, L02), (P2, L05)}

20. Two labels with 4 conflicts (L12 or L18) P3 = {L11, L12 } or P5 = {L18 , L19, L20}? We choose {P3, L12} since P3 has two possible label positions and P5 has three

21. Label with least conflicts is L12 Solution4 = {( P6, L24), (P1, L02), (P2, L05), (P3, L12)}

22. Label with least conflicts is L15 S= {( P6, L24), (P1, L02), (P2, L05), (P3, L12), (P4, L15)}

23. Remaining label is L18 final solution S= {( P6, L24), (P1, L02), (P2, L05), (P3, L12), (P4, L15), (P5, L18)}

24. Processing labels with conflicts (Create a labeling for the remaining points)

25. Processing of labels with conflicts
S* = {(P1, L02), (P2, L05), (P3, 0), (P4, L14), (P5, 0), (P6, L23)}
Point P3 has candidate labels L = {(L09, 1), (L10, 2), (L11, 1), (L12,0)}
S* = {(P1, L02), (P2, L05), (P3, L12), (P4, L14), (P5, 0), (P6, L23)}
Point P5 has candidate labels L= {(L17, 3), (L18, 1), (L19, 1), (L20, 2)}
S* = {(P1, L02), (P2, L05), (P3, L12), (P4, L14), (P5, L18), (P6, L23)}

26. Local search algorithm (Use local search to improve map quality)

27. Local search algorithm
S* = {(P1, L02), (P2, L05), (P3, L12), (P4, L16), (P5, L18), (P6, L22)
P1 List = {(L01, 1), (L02, 0)}
S* = {(P1, L02), (P2, L05), (P3, L12), (P4, L16), (P5, L18), (P6, L22)}
P2 List = {(L05, 0)}
P3 List = {(L09, 1), (L10, 2), (L11, 2), (L12,1)}
S* = {(P1, L02), (P2, L05), (P3, L09), (P4, L16), (P5, L18), (P6, L22)}

28. Results

29. Standard sets of randomly generated points
conditions described by Christensen et al. (1995)
grid size of 792 by 612 units
fixed size label of 30 by 7 units
page size of 11 by 8.5 inch

30. Test set Number of the points: N = 100, 250, 500, 750, 1000
Configurations: For each problem size, 25 different configurations with random placement of point features using different seeds;
4 label positions were considered for each point

31. Percentage of labels without conflict for different number of points

32. Computational times to reach the good solutions for different number of points (sec)

33. After FALP application for 1000 random points (labels without overlap = 911)

34. Conclusion
The FALP showed quality results in label placement and excellent runtime performance
We recommend to use FALP to solve point feature label placement for real time screen maps