Clutter Reduction Methods for Point Symbols in Map Mashups Jari - - PowerPoint PPT Presentation
Clutter Reduction Methods for Point Symbols in Map Mashups Jari Korpi, Paula Ahonen-Rainio 28.8.2013 Contents 1. Aim of the study: Clutter reduction for map mashups 2. Classification of the clutter reduction methods 3. Criteria for
Jari Korpi, Paula Ahonen-Rainio 28.8.2013
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= Content from different sources are overlaid on top of each other, typically thematic information
= Often interactive tools for exploring the thematic information
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Map mashups can vary in... Common problem with mashups: Clutter of symbols
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symbology density usage
We needed to find methods that are suitable for reducing clutter in an interactive map mashup To be successful in choosing a method for a cluttered map the characteristics and needs of the case and the strengths and limitations of different methods must be known
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Because map mashups have characteristics from both maps and information visualization, methods from both disciplines should be considered Maps generalization operators Information visualization clutter reduction methods
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Map Mashups Cartography Information visualisation
Selection Selection Filtering Refinement Refinement Sampling Displacement Displacement Displacement Aggregation Aggregation Clustering Typification Typification Clustering Symbolisation Symbolisation Classification Change size Change opacity Spatial distortion Topological distortion Animation Animation
Map Mashups Cartography Information visualisation
Selection Selection Filtering Refinement Refinement Sampling Displacement Displacement Displacement Aggregation Aggregation Clustering Typification Typification Clustering Symbolisation Symbolisation Classification Change size Change opacity Spatial distortion Topological distortion Animation Animation
Map Mashups Cartography Information visualisation
Selection Selection Filtering Refinement Refinement Sampling Displacement Displacement Displacement Aggregation Aggregation Clustering Typification Typification Clustering Symbolisation Symbolisation Classification Change size Change opacity Spatial distortion Topological distortion Animation Animation
Map Mashups Cartography Information visualisation
Selection Selection Filtering Refinement Refinement Sampling Displacement Displacement Displacement Aggregation Aggregation Clustering Typification Typification Clustering Symbolisation Symbolisation Classification Change size Change opacity Spatial distortion Topological distortion Animation Animation
cluttered changing
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Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
Map Mashups Cartography (McMaster & Shea 1992) Information visualisation (Ellis & Dix 2007) Reduces complexity
Reducing complexity
Avoids hidden symbols
Avoids overlap
Keeps spatial information
Maintaining spatial accuracy Keeps spatial information
Can be localised
Can be localised
Is scalable
Is scalable
Is controllable
Is adjustable
Keeps attribute values
Maintaining attribute accuracy Can show point/line attribute
Can access individual items
Can discriminate points/lines
Improves aesthetic quality
Maintaining aesthetic quality
Keeps logical hierarchy
Maintaining a logical hierarchy
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yes mainly partially no
Primary criteria for the case: 1.Effect must be targetted to cluttered areas 2.Individual items must be accessible 3.Attribute values must be shown To supplement the limitations
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For map mashups, clutter reduction methods and requirements are derived from cartography and information visualization Knowing the general strengths and limitations of the methods helps in finding the suitable methods for each case None of the clutter reduction methods is perfect; each has its strengths and limitations
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Further information: jari.korpi@aalto.fi The Cartographic Journal 50(3) pp. 257-265.
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