1. The Role Of Data Matching Tools When Migrating To Automated Cartographic Production Systems
Patrick Revell
Ordnance Survey Research
International Cartographic Conference
November 2009

2. Ordnance Survey Mapping Products
Large Scale Database
OS MasterMap®
(Topography, ITN (road), Address, Imagery Layers)
1:10 000
Raster
Vector
1:25 000
1:50 000
(Vector), Gazetteer 1:
Vector, Gazetteer 1:
(Vector)

3. Automatic Generalisation
Aim to Capture Once (at most detailed scale)
Use Many Times (to derive all smaller scale products)
…then propagate updates to derived products
Most efficient, cost-effective and flexible production method.
Ordnance Survey Research have been investigating automatic generalisation for at least six years.

4. 1:50 000 Scale Generalisation

5. 1:25 000 Scale Generalisation

6. All involve comparing spatial datasets…
BUT…
Automatic gen products are not totally consistent with manual ones.
How do we ensure no information is lost? (eg. names)
Not all the required information is available at the most detailed scale.
How do we get this information into our database? (eg. r.o.w., tourist)
Manually generated (legacy) products still need updating.
How do we make this as efficient as possible?
All involve comparing spatial datasets…

7. Conflation/Spatial Data Integration
Conflation is a process carried out to integrate spatial datasets
Pre-Processing. eg. raster to vector conversion.
Feature matching. Finding corresponding features between datasets.
Integration. When correct matches have been found, can then do:
Correction of differences
Information transfer (geometry/attributes)
Geometry alignment

8. Conflation Software Hardly any tools in mainstream GIS.
Open-source software:
GeOxygene, COGIT laboratory, IGN France
open framework for creating/deploying GIS applications.
various tools for spatial data handling, including data matching
Java Conflation Suite (JCS) from Vivid Solutions
tools for common geospatial data integration problems.
based on the Java Topology Suite (JTS), 2D spatial functions for geometric operations.
Both can match road network datasets and polygon datasets

9. Application 1 Matched Large Scale Small Scales Updates
Data matching to improve update efficiency of small scale mapping
Matched
Large Scale
Small Scales
Updates

10. Matching 1:25 000 Scale Buildings
(a) one-to-one match (simplification)
(b) many-to-one match (amalgamation, simplification)
(c) one-to-nothing match (elimination)
(d) another many-to-one match (amalgamation, displacement)
(e) many-to-many match (typification/simplification)
(f) poor match - requires an update

11. Building Matching Results
GeOxygene
Java Conflation Suite
1: Scale matched
1: Scale unmatched
Large Scale matched
Large Scale unmatched

12. Evaluating Building Matches
Size. Areas are similar.
Position. Centres of gravity are close.
Shape. Building shapes are similar:
Convexity. Total area divided by the area of the convex hull.
Elongation. The Smallest Minimum Bounding Rectangle’s (SMBR’s) length divided by its width.
SMBR:
Orientation. Building orientations are similar:
SMBR Orientation. Orientation of the longest side of the SMBR.
Wall Statistical Weighting Calculated from the orientation of each wall, weighted by the wall lengths.

13. Matching 1: Scale Roads
GeOxygene results

14. Application 2 Matched Large Scale Small Scales Attribute Transfer
Data matching to enhance large scale data.
Matched
Large Scale
Small Scales
Attribute Transfer

15. Enhancing The Large Scale Rail Network
Large scale rail network has no classification.
1: scale rail data, classified as:
Narrow Gauge Railway:
Multi Track Railway (generalised)
Single Track Railway/Siding (generalised)

16. Matching Rail Networks
Tried road network matching tools.
Now developing custom matching tool.

17. Application 3 Matched Large Scale Names Small Scale Names
Improving the consistency of names between different scale products using data matching.
Matched
Large Scale Names
Small Scale Names
Manual Correction

18. Matching Different Names Datasets

19. Large Scale Topographic Features Large Scale Cartographic Names
Application 4
Attaching cartographic names to topographic features
Matched
Large Scale Topographic Features
Large Scale Cartographic Names
Attribute Transfer

20. Attaching Names to a Hydrology Network

21. Conclusions
Data matching is an essential tool to help migrate towards generating all mapping products from a single database.
Allows our large scale database to be enriched with all the required information for automatic generalisation.
Enables improved consistency across products.
Can give an insight into the manual generalisation decision-making process.
Can help make the update of manually generalised products more efficient.

22. Contact for further information:
Thanks for your attention!
Contact for further information:
Patrick Revell
Research (C530) Ordnance Survey Romsey Road SOUTHAMPTON United Kingdom SO16 4GU
Website: