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Demonstration of Demonstration of generalisation in generalisation in
actionaction
Martin [email protected]
Sales Manager
Customer Services Manager1616thth July 2009 July 2009
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Generalisation Business Function Dependencies
• An effective Generalisation solution requires:– An efficient and accredited Workflow
Workflow
Allows
InformationDistribution
Facilitates
DataManagement
Supports
ProductGeneration
– to facilitate quality controlled Data Management– that will support quality assured Product Generation and – allow online & interoperable Information Distribution
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Agenda
Data Management Data quality management & data configuration Demonstration
Product Generation Model and Cartographic generalisation Demonstration Text placement
Information Distribution Cartographic publishing Web mapping Digital products
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The Generalisation FrameworkData Quality Management
DataConfiguration
ModelGeneralisation
CartographicGeneralisation
TextPlacement
DigitalData Model
DigitalCartographic
Product
CartographicPublishing
ProductGeneration
Data Management
Information Distribution
Web Mapping
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Data Quality Management
DataConfiguration
Data Management
Ensure that the source data is clean and fit for generalisation Data Unification, to ensure the data is in a single coherent dataset.
Validating the data to ensure it conforms to the specification, matches the business rules and is geometrically correct.
Data Quality validation based on conformance checking using data specifications expressed as business rules
The Generalisation Framework:Data Management
Data Quality Management
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Data Quality Management
DataConfiguration
Data Management
Building structures in the data that are required by the generalisation process. For example:
Data must be continuous
Clusters and partitions, to identify and group related objects and enable regions of data to be processed independently
Building references between objects (references between parent and child classes) for fast traversal
The Generalisation Framework:Data Management
DataConfiguration
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Data Management
Demonstration
Data Quality Management
DataConfiguration
The Generalisation Framework:Data Management
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ModelGeneralisation
CartographicGeneralisation
TextPlacement
DigitalData Model
ProductGeneration
The Generalisation Framework:Product Generation
ModelGeneralisation
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Clarity Demo
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Model Generalisation is the reduction of the amount of source data to a level suitable for the target scale. This is achieved by;
Removing feature classes that are not visualised at the target scale
Amalgamating or removing small features while retaining topological connectivity& positional accuracy
Filtering unwanted detail from features
Features should retain their real world coordinates (not displaced or exaggerated etc).
The Generalisation Framework:Product Generation
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The lake is to small to be represented 1:50,000Centre point of the lake
is identifiedThe rivers are extended
to the centre point
Area Merging (merging base areas)
Example requirement Delete lake objects that are too small for representing at 1:50,000
If the lake has a connection with two or more rivers then connectivity must be maintained
Merge all the parts of the deleted lake into the surrounding areas
The lake object is deleted leaving a hole
Vacated regions are merged into the
surrounding areas
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Area Merging (merging base areas)
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Area Merging (merging base areas)
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Line Filtering(Point Reduction)
Area Merging
Geometry Change(Area to Point)
Line Filtering(Point Reduction)
Area Merging
BaseDLM DLM50.1
Model Generalisation Results
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ModelGeneralisation
CartographicGeneralisation
TextPlacement
DigitalData Model
ProductGeneration
The Generalisation Framework:Product Generation
CartographicGeneralisation
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Cartographic Generalisation is concerned with the detection and resolution of conflicts between map objects for representation at the target scale.
Using AGENT technology, map objects (like roads, buildings) become Agents, making them self and context-aware Generalisation of clusters of objects Agents co-operate to achieve an acceptable cartographic generalised result through:
Simplification, Enlargement, Diffusion Exaggeration, Typification, Displacement
Agents enact different generalisation algorithms: Rule or Goal driven to find and keep the best result
The Generalisation Framework:Product Generation
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Clarity Demo
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Agent Approach
Map objects (e.g Roads, Buildings) are made Agents, making them self aware
Measures: Indicating the state and surroundings of the object
“How big am I?”
“How close am I to my nearest neighbour?”
Constraints: Asserting the target values
“I am too small for the target scale”
“I am too near the next building”
Algorithms: Change the state in order improve the situation
Agents enact different generalisation algorithms, to find and keep the best result
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Agent Lifecycle
5
6
2
8
The symbol is displaced above the road. The buffer is now in conflict with
the road above. Score = 6The buffer around the point symbol is
in conflict with the road below. Score = 5
The symbol is displaced above and to west of the road. The buffer is now in
conflict with the both roads. Score = 2
The symbol is displaced above and to the east of the road. The symbol is no
longer in conflict with either road. Score = 8
The best result is kept
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Snapping Lines (to be parallel)
Example requirement Lines that run close together should be snapped parallel
The line object with the highest priority should remain fixed
The two objects are displaced to an appropriate distance for representing at 1:50,000
Adjacent objects are diffused to preserve topological relationships
Adjacent roads do not have parallel geometries
The first road is snapped parallel
Second road snapped parallel and displaced
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Snapping Lines (to be parallel)
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Snapping Lines (to be parallel)
Roads are snapped parallel
Parallel roads displaced from under the fixed road
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Typification (of identical point symbols)
Requirement
Point symbols that overlap should be detected and using typification reduced in number for displaying at 1:50,000
The number of symbols maintained should be determined by Topfer's Radix Law
The placement of remaining symbols should be representative of original placement of symbols
Point symbols are in conflict with each otherPoints show location of
the objectsPoint symbols buffered to
identify conflictsLocation for the typified
symbol identified3 symbols typified to 1 for representing at 1:50,000
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Line typification
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Point displacement
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Line / area snapping
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Area simplification
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Results
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Results
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Results
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Results
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Generalisation Framework:Product Generation
ModelGeneralisation
CartographicGeneralisation
TextPlacement
DigitalData Model
ProductGeneration
TextPlacement
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Text Placement with Radius ClearText
ClearText responds to the need to automatically place text (labels) into blank spaces on a map so that:
Labels are clear to read Label placement takes into account visual associations e.g a building label should not be separated from its building by a road
ClearText uses Gothic’s OO abilities to express relationships between objects – providing a broad set of placement preferences (rules)
Identifying candidate locations Evaluate those candidate locations in a sympathetic way with respect tothe neighbouring map features and or text features Identify the best location of each label
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1. Label Generation – how labels are created and associated with a real world feature
2. Label Placement – Algorithm’s applied in accordance with some user defined constraints in order to re-position the generated labels to the ideal place
3. Resolving Conflicts - Navigate to areas of conflict between labels and adjust the positioning manually/by running algorithms on only those areas. Edit the label text – e.g. create abbreviations
Text Placement with Radius ClearText
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Label Generation
Label objects are created in either of the following ways: 1. By processing features in the dataset that must be labelled and creating
matching label objects string is created from attributes set during the process the number of candidate labels can be specified determining the how many
placement options are tried desired frequency of labels along the feature can be specified whether the text must be inside or outside the feature
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2. OR by importing text and creating label objects for that text, then matching up these label objects with the corresponding features
Text labels can be imported from an external source in two ways:i. From flat text files using the interface providedii. From other GIS file formats using the Gothic FME importer
Notes: Text labels can also be generated manually using Cleartext digitising tools References are maintained between labels and their associated feature, so that
the geometry of the feature can be obtained for placement and display purposes Text labels can be imported without having a specific feature relationship defined
Label Generation
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ClearText decides how to place labels so that they are cartographically as clear as possible, and decides how to handle conflicts Geographical features rule out some of the label positions Plans are invoked to adjust each remaining label so that relative to the features and labels around, each label is of reasonable cartographic quality
Label Placement
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• Knowledge of which label has been chosen from each candidate groups may make it possible to improve the position of a label by moving it slightly, into a position that was not one of the original candidates• ClearText makes this adjustment via a set of constraints based on the cartographic quality of the labels in terms of their relationships to both features and to other labels
Label Placement – Polishing
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Initial GenerationInitial GenerationAvoid Overlaps with BuildingsAvoid Overlaps with BuildingsAvoid overlaps with RoadsAvoid overlaps with RoadsAbbreviate and Re-paragraphAbbreviate and Re-paragraph
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Road Label Orientation and Position
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XML representation
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Postscript output
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DigitalCartographic
Product
CartographicPublishing
Information Distribution
Web Mapping
The Generalisation Framework:Information Distribution
To complete the Generalisation Framework and facilitate dissemination of information, 1Spatial works with partners' solutions such as those provided by:• Autodesk – Autodesk Mapguide• Oracle - Database• Safe Software - FME• Star Informatic – Mercator
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Reducing time-to-market, improving service to customers
Increase confidence in and reputation of products
and organisation
Opening new sales opportunities
Reduce maintenance costs
Free cartographers to work on mission-critical tasks
Saving time in recording and centralising internal processes
Increased delivery cycles, closer concurrency
in product range
Consistent and reliable results
Quickly derive new products from existing data
Requires only one sourcedataset to be kept up-to-date
Reduce time and cost of manual checking and processing
Documented set ofknown (manufacturing and
specification) rules
High Speed Processing environment
Sustainable and reproducible
Scalable, easy to use, customisable
Automated process
Automatic identification and resolution of conflict
Rules driven
The Generalisation FrameworkFeature Advantage Benefit