demonstration of generalisation in action [email protected] sales manager...

Demonstration of Demonstration of generalisation in generalisation in

actionaction

Martin [email protected]

Sales Manager

[email protected]

Customer Services Manager1616thth July 2009 July 2009

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

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

The Generalisation FrameworkData Quality Management

DataConfiguration

ModelGeneralisation

CartographicGeneralisation

TextPlacement

DigitalData Model

DigitalCartographic

Product

CartographicPublishing

ProductGeneration

Data Management

Information Distribution

Web Mapping

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



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


DataConfiguration

Data Management

Demonstration


DataConfiguration


ModelGeneralisation


TextPlacement

DigitalData Model

ProductGeneration

The Generalisation Framework:Product Generation

ModelGeneralisation

Clarity Demo

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 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

Area Merging (merging base areas)

Line Filtering(Point Reduction)

Area Merging

Geometry Change(Area to Point)

Line Filtering(Point Reduction)

Area Merging

BaseDLM DLM50.1

Model Generalisation Results

ModelGeneralisation


TextPlacement

DigitalData Model

ProductGeneration



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


Clarity Demo

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

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

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


Roads are snapped parallel

Parallel roads displaced from under the fixed road

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

Line typification

Point displacement

Line / area snapping

Area simplification

Results

Generalisation Framework:Product Generation

ModelGeneralisation


TextPlacement

DigitalData Model

ProductGeneration

TextPlacement

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

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

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

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

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

• 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

Initial GenerationInitial GenerationAvoid Overlaps with BuildingsAvoid Overlaps with BuildingsAvoid overlaps with RoadsAvoid overlaps with RoadsAbbreviate and Re-paragraphAbbreviate and Re-paragraph

Road Label Orientation and Position

XML representation

Postscript output

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

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

demonstration of generalisation in action [email protected] sales manager...

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