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

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

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

2.1 Data preprocessing

(a) Reduction of noisy or irrelevant details

5

2.1 Data preprocessing

Example

TopoR_Node = 1 2 3 4 5 6

AA

1

0

Classification

A

4TopoRTyp

(b) Description of topology with node geometry

The direction of AA‘.2

The direction of AA‘.1

0

Classification

AAa

ß

r

A

A’

3TopoRTyp ,3TopoRWinkel )2,0[

.

A

A’

6

2.1 Data preprocessing

(c) Reduction of topological differences

A0

B1B2

B0

Point B 1 : B1 B0

Point B 2 : B 2 B0

Line B 1B2 : Delete

: objects in dataset 1

: objects in dataset 2

: improved objects in dataset 2

Improvement process:

7

2.2 Matching of road objects

Step1: Instantiation of the reference polyline

8

2.2 Matching of road objects

Step 2: Identification of possible matching candidates

Step 3: Exclusion of incorrect candidates

α s

A

G

O

O’

Matching candidates

Matching reference

Turning points&Buffer-P

Buffer-V

H

K

9

2.2 Matching of road objects

Step 4: Exactness inspection of the matching candidates

A

G

H

K

O

O’

Matching candidates

Matching reference

Turning points&Buffer-P

Buffer-V

)_,_(_ SimilarityTopoSimilarityGeoFSumWMatching

]1,0[_ SumWMatching0: an entirely wrong match

1: a perfect match. {

10

2.2 Matching of road objects

Step 5: Establishment of link file

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

2.4 Conflict solution

Conflict

SolutionCriterion: Larger or Largest

“Matching_SumW”

13

3. Matching result

Area (a) Area (b) Area (c) All

Number of line objects

Basis DLM 311 670 362 1343

TeleAtlas 319 733 459 1511

Number of points along road lines of TeleAtlas

804 2413 1611 4828

Number of matched points

Correct match 766 (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 seconds 65 seconds 42 seconds 133 seconds

(a) (b) (c)

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

}20,0;_{ SumWMatchingMin

SumWMatching _

15

4. Assessment of matching certainty

(b) Distribution of “Matching_Certainty”

0

2

4

6

8

10

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Matching_Certainty

Den

sity

falsely matched objects

overall matched objects

Matching_Certainty [0.00, 0.20] (0.20, 0.75) [0.75, 1.00]

Density distribution

Among the falsely matched objects ca. 90% ca. 10% 0 %

Among the overall matched objects ca. 25% ca. 50% ca. 25%

16

4. Assessment of matching certainty

(c) Classification of the Matching Certainty

Class of the matching certainty

perfect good possible

Matching_Certainty [0.75, 1.00] (0.20, 0.75) [0.00, 0.20]

17

5. Application – Integration of postal data

Integration process of postal data

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.