Demonstration of generalisation in action Sales Manager Customer Services Manager 16 th July 2009

Generalisation Business Function Dependencies An effective Generalisation solution requires: –An efficient and accredited Workflow Workflow Allows Information Distribution Facilitates Data Management Supports Product Generation – 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 Framework Data Quality Management Data Configuration Model Generalisation Cartographic Generalisation Text Placement Digital Data Model Digital Cartographic Product Cartographic Publishing Product Generation Data Management Information Distribution Web Mapping

Data Quality Management Data Configuration 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

Data Quality Management Data Configuration 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 Data Configuration

Data Management Demonstration Data Quality Management Data Configuration The Generalisation Framework: Data Management

Model Generalisation Cartographic Generalisation Text Placement Digital Data Model Product Generation The Generalisation Framework: Product Generation Model Generalisation

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 Generalisation Framework: Product Generation

The lake is to small to be represented 1:50,000 Centre point of the lake is identified The 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

Model Generalisation Cartographic Generalisation Text Placement Digital Data Model Product Generation The Generalisation Framework: Product Generation Cartographic Generalisation

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

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 The symbol is displaced above the road. The buffer is now in conflict with the road above. Score = 6 The 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

Snapping Lines (to be parallel)

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 other Points show location of the objects Point symbols buffered to identify conflicts Location for the typified symbol identified 3 symbols typified to 1 for representing at 1:50,000

Line typification

Point displacement

Line / area snapping

Area simplification

Results

Generalisation Framework: Product Generation Model Generalisation Cartographic Generalisation Text Placement Digital Data Model Product Generation Text Placement

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 to the 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 provided ii.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 Generation Avoid Overlaps with Buildings Avoid overlaps with Roads Abbreviate and Re-paragraph

Road Label Orientation and Position

XML representation

Postscript output

Digital Cartographic Product Cartographic Publishing 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 source dataset to be kept up-to-date Reduce time and cost of manual checking and processing Documented set of known (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 Framework FeatureAdvantageBenefit