1. 1 Implementation of a generic road-matching approach for the integration of postal data M. Zhang, L. Meng Department of Cartography, TU Munich 1. Motivation 2. The matching strategy 3. Matching results 4. Assessment of matching certainty 5. Application – Integration of postal data 6. Conclusion and future work ICA workshop on geospatial analysis and modeling Vienna, 2006-07-08

2. 2 Task Enriching Basis DLM with post addresses, which are bound to the road layers of TeleAtlas. 1. Motivation - Project “Postal data integration” Problem Absence of some important road attributes, such as street name, etc. Solution Matching between Basis DLM und TeleAtlas

3. 3 2. The matching strategy Data Matching (1) Data preprocessing (2) Matching of road objects (3) Unsymmetrical Buffer Growing (4) Conflict solution An integrative matching process Data integration

4. 4 2.1 Data preprocessing (a) Reduction of noisy or irrelevant details

5. 5 2.1 Data preprocessing Example TopoR_Node = 123456 1 0 Classification (b) Description of topology with node geometry The direction of AA‘. 2 1 0 Classification.

6. 6 2.1 Data preprocessing (c) Reduction of topological differences A 0 B 1 B 2 B 0 Point B 1 : B 1 B 0 Point B 2 : B 2 B 0 Line B 1 B 2 : Delete : objects in dataset 1 : objects in dataset 2 : improved objects in dataset 2 Improvement process:

7. 7 2.2 Matching of road objects Step1: Instantiation of the reference polyline

8. 8 2.2 Matching of road objects Step 2: Identification of possible matching candidates Step 3: Exclusion of incorrect candidates α s

9. 9 2.2 Matching of road objects Step 4: Exactness inspection of the matching candidates 0: an entirely wrong match 1: a perfect match. {

10. 10 2.2 Matching of road objects Step 5: Establishment of link file

11. 11 2.3 Unsymmetrical Buffer Growing 46 M 41 M A B C D The mean and variance of the position discrepancies between the matched pairs can be used as the indicators to capture the global deviation in the current matching area.

12. 12 2.4 Conflict solution Conflict Solution Criterion: Larger or Largest “Matching_SumW”

13. 13 3. Matching result Area (a)Area (b)Area (c)All Number of line objects Basis DLM 311670362 1343 TeleAtlas 319733459 1511 Number of points along road lines of TeleAtlas 804241316114828 Number of matched points Correct match766 (95.3%) 2292 (95.0%) 1583 (98.3) 4641 (96.1%) Poor match 8 (1.0%)16 (0.7%)16 (1.0%) 40 (0.8%) Number of points with no match 30 (3.7%)105(4.3%)12 (0.7%) 147 (3.1%) Computing time 26 seconds65 seconds42 seconds 133 seconds (a) (b) (c)

14. 14 4. Assessment of matching certainty (a) Definition of the Variable „Matching_Certainty“ Definition { IF Conflicting match THEN Matching_Certainty = ELSE Matching_Certainty = END IF } ? ? Matching Errors

15. 15 4. Assessment of matching certainty (b) Distribution of “Matching_Certainty” Matching_Certainty[0.00, 0.20](0.20, 0.75)[0.75, 1.00] Density distribution Among the falsely matched objectsca. 90%ca. 10%0 % Among the overall matched objectsca. 25%ca. 50%ca. 25%

16. 16 4. Assessment of matching certainty (c) Classification of the Matching Certainty Class of the matching certainty perfectgoodpossible Matching_Certainty[0.75, 1.00](0.20, 0.75)[0.00, 0.20]

17. 17 5. Application – Integration of postal data Integration process of postal data

18. 18 6. Conclusion and future work Future work  Refining the current matching approach  Dealing with the special matching cases Conclusion  Unsymmetrical Buffer Growing - generic nature  Definition of Matching Certainty and classification of matching results - a more comfortable interaction.