AUTOMATED SCHEMATIZATION CASESTUDY

Suchith Anand Jerry Swan

Mike JacksonMark Ware

Presentation OverviewOverview

IntroductionIntroductionGeneralization backgroundAutomated SchematizationConclusions

Th C t f G ti l S i (CGS) The Centre for Geospatial Science (CGS)

Established November 2005 as a cross-Established November 2005 as a cross-faculty post-graduate research centre.

Research focus:– spatial data infrastructures (SDI), – geospatial intelligence, – geospatial interoperability

– location-based services.location based services.

Creating Intelligent ApplicationsCreating Intelligent Applications

Goal: Make it easier for geospatial researchers to incorporate proven geospatial techniques into their incorporate proven geospatial techniques into their workflow.

The idea is to create generic frameworks that are customized with the problem-specific details.customized with the problem specific details.

Initial efforts have concentrated on an object-level Initial efforts have concentrated on an object level optimization framework using state-space search.

Bigger picture

Geospatial Standards (for ex. OGC spec.)

Maturity of open source software (for ex. OSGeo stack)

OS Geo Product development statistics 2008

http://wiki.osgeo.org/wiki/Project_Stats

CGS Optimization Framework• Currently implemented:

– Hillclimbing, Simulated AnnealingHillclimbing, Simulated Annealing– (Reactive) Tabu Search– Simple Genetic AlgorithmSimple Genetic Algorithm

• Implementation targets JVM and hence easily integrated ith Geotools\52North WPS etcintegrated with Geotools\52North WPS etc.

h G li i ?Why – Generalization ?

The process of simplifying the form or shape of map features, usually carried out when the map is changed from a large scale to a small scale, is referred to as generalisation.

Map generalisation is a process of extracting the important p g p g pand relevant spatial information from reality.

M G li ti tMap Generalization operators

SimplificationlAmalgamation

EliminationTypificationExaggerationExaggerationDisplacement

Si lifi tiSimplification

Douglas-Peucker algorithm (1973)

Amalgamation

DeLucia and Black (1987) - triangulation-based area amalgamation procedures. These ideas are taken up and advanced in Jones et al (1995)

Su et al (1997) - A raster-based aggregation method (forms the basis of ESRI's AreaAggregate function)

Ai and van Oosterom (2002) - displacement vectors prior to amalgamationa d a Ooste o ( 00 ) d sp ace e t ecto s p o to a a ga at o

Elimination

Regnauld (2001) – area features (includes deletion and Regnauld (2001) area features (includes deletion and aggregation of features)

fTypification

Feature clusturing - (Mackaness 1994, Ormsby and Mackaness 1999, Mackanessand Mackechnie 1999)

Sester (2003) and Moulin (2003) -Kohonen Self Organizing Maps

Regnauld (1996) - Minimum Spanning Trees

Exaggerationgg

Mackaness (1995) - alpha analysis for classifying urban road ac a ess ( 995) a p a a a ys s o c ass y g u ba oadnetworks hierarchically, providing a means for removing roads at smaller scale while still conveying essential characteristics of the network

Displacement

Lonergan and Jones (2001) - map quality is measured in terms of minimum distance violations, and polygon displacement achieved by calculating displacement vectors in an iterative fashion

Li et al (2002) - polygon displacement using a two-level agent-based architecture.

Harry Beck’s Schematic Tube Map

Source: London Transport Museum

Schematic Map - Characteristics

T l i ll i t t•Topologically consistent.

•Simplified lines (Douglas-Peucker).

•May be desirable to re-orient lines so that they are horizontal, vertical or diagonal.

•Scale in congested areas expanded at the expense of scale in areas that are less soexpense of scale in areas that are less so.

Graphic manipulations for producingGraphic manipulations for producing a schematic map

Lines are simplified and re-oriented to conform to a regular grid. Congested areas areincreased in scale at the expense of scale in areas of lesser node density

Constraints

TopologicalOrientationClearanceAngleAngleRotationDisplacementLengthg

TopologicalTopological

Original network and derived schematic map should be topologically consistent

Topological – original (Left), topological error (Middle) and acceptable solution (Right)acceptable solution (Right)

Orientation

If possible, network edges should lie in horizontal, vertical or diagonal directionor diagonal direction

Orientation – original (L) and schematized (R)

AngleAngle

If possible, the angle between a pair of connected edges should be greater than some minimum angle

Angle – edges re-oriented but Angle constraint violated (L) and acceptable solutionand acceptable solution

Rotation

An edge’s orientation should remain as close to its starting orientation as possible

:Rotation – original (L), acceptable solution (M) and better solution (R)

Clearance

If possible, the distance between disjoint features should be greater than some minimum distanceg

Clearance – constraint violated (L) and resolved (R)

Displacement

Vertices should remain as close to their starting positions as possibleVertices should remain as close to their starting positions as possible.

Displacement – original (L), acceptable solution (M) and better solution (R)

Length

Length – original (L) and congestion reduced by enforcing Length constraint (R)

Core Process

•Evaluate– For each vertex:

Count topological errorsMeasure constraint violationsH i ti l i th f th bHeuristic value is the sum of the above

ModifyModifyDisplace vertices

Demo – Original Featureset

Demo – Schematized Featureset

Conclusions

•Implements a usecase for automatic production of schematic maps

•Proof-of-concept implemented for WFS, using schematization as transformation exemplar.p

Thank You