Automatic Typographic Maps

Shehzad Afzal*, Ross Maciejewski†, Yun Jang‡, Niklas Elmqvist*, David S. Ebert* Purdue University*, Arizona State University†, Sejong University in Seoul ‡

Spatial Text Visualization Using Automatic Typographic Maps

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Motivation

AxisMaps.com

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Motivation

• Typographic Map: Map made entirely of the geographical labels (i.e., “Type”)

• Aesthetically pleasing

• Exists only for handful of cities

• Printed map sold from $30-$150’

AxisMaps.comSan Francisco

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MotivationHow are these maps designed?• Manual text placement using Adobe Illustrator over an

‘OpenStreetMap’ image• Text options are modified based on underlying spatial

features• Process takes several weeks to complete one map

• Focus of their current approach is ‘purely aesthetic’

• Our work “Automates the Typographic Map Generation Process”

• Potential of visualizing data using spatialized text

Image Courtesy: AxisMaps.com

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Related Work• Maharik et al. (2011) introduced ‘calligrams’

(digital micrography images)

• ‘TagMaps’ by Yahoo: Word clouds on top of graphical features

• ‘Wordle’ by Viegas et al (2009), ‘ManiWordle’ by Koh et al (2010) & ‘SparkClouds’ by Lee et al (2010)

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System Overview

OSM Parser

Data Cleaning/Filtering

Build Graphical Objects & Layers

Optimizations

Region Generation

Path/Road Generation

Open Street Map(OSM) File

Open Street Map(OSM) File

Visual Properties/Style Sheet

SVG FileSVG File

SVG Renderer

Typographic Map

SVG Code Generation

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System Overview

OSM Parser

Data Cleaning/Filtering

Build Graphical Objects & Layers

Optimizations

Region Generation

Path/Road Generation

Open Street Map(OSM) File

Open Street Map(OSM) File

Visual Properties/Style Sheet

SVG FileSVG File

SVG Renderer

Thema-Typographic

MapSpatial Statistical Dataset

SVG Code Generation

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Data Model• Layers: Particular class of geographical objects

• e.g., Highways, Primary roads, park etc.• Visual Attributes such as font size, color, weight etc.

• Graphical Objects belong to exactly one layer • 1D paths(roads) or 2D paths(polygons)

• Ordering Layers: • Layers are drawn in ascending order of priority

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Data Model• Ordering Layers:

• 1D Paths have higher priority than 2D Paths• Ordering Graphical Objects within Layers:

• Horizontal Paths have higher priority than vertical paths

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Optimizations• Divided lanes having the same label are merged together to

form a single lane

• Font size for polygonal areas adjusted according to the area

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Limitations• Definitions of polygonal areas are not always consistent in

OSM. e.g., Rivers & lakes boundaries

• Segments of same road have different names or category and they often overlap

• OSM data is not completely defined for some geographic regions

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Paths as Text• Rendering Path using Text:

• Fit Text to path and repeat it for the duration of path’s length• Rotate characters to align with path normal• Path thickness is controlled by font size

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Paths as Text• Visual Clutter - Path Overlap:

Clutter from Label Overlap Character Mask Character Halo

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Regions as Text

Bounding Box filled with Oriented Text Clipped Text using Region Path

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Regions as Text

Adjacent Regions: Vary the orientation of the lines in adjacent regions resulting in visual continuity between regions

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Comparison with AxisMap Output

AxisMaps Map of San Francisco, CA Automatic Typographic Map of San Francisco, CA

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Comparison with AxisMap Output

AxisMaps Map of San Francisco, CA Automatic Typographic Map of San Francisco, CA

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Comparison with AxisMap Output

AxisMaps Map of San Francisco, CA Automatic Typographic Map of San Francisco, CA

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Comparison with AxisMap Output

AxisMaps Map of San Francisco, CA Automatic Typographic Map of San Francisco, CA

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Typographic Map - Seattle

Automatic Typographic Map – Seattle near VisWeek 2012 Venue

Visweek Venue

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Typographic Map - Chicago

Automatic Typographic Map – Chicago, IL

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Thema-Typographic Maps

Thematic Maps:

Geographic Maps where Geospatial variable is visually encoded on the map

Thema-Typographic Maps: Modify Font attributes on per character level to convey the value of a statistical variable at each character’s spatial location

Font Attributes: Typically Size, but color, intensity etc.

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Thema-Typographic Maps Showing Crime Rate

KDE Map for West Lafayette, INshowing Crime Activity

Thema-Typographic Map Statistical variable visualized is

Crime Rate

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Thema-Typographic Maps in SVG

• Scaling individual characters: Internally we need to calculate where characters end on a path in 2D Space. This helps in following ways:

• Correct Lookup of Mapping Variable in Spatial Dataset• # of characters required to fill the path can be calculated• Stroke width of the background mask is now defined as an average of

the minimum and maximum font size

Other Applications: Traffic Intensity, Demographics, political data can be overlaid on a typographic map

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Conclusions and Future Work

• Automatic Generation of Typographic Maps for any Geographic Region within seconds

• ‘Thema-Typographic Maps’: Combines Typographic Maps technique with spatial datasets

Future Work:• Spatial data features as a means of visualizing data• Support Navigation, drilling down and changing map layout

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Acknowledgements• AxisMaps for their helpful feedback/discussions and permission

to use their Typographic Map image in our paper

• This work was supported in part by the U.S. Department of Homeland Security’s VACCINE Center under Award no.

103659 / 2009-ST-061-CI0001 and the Defense Threat Reduction Agency under Award no. HDTRA 1-10-1-0083

Thank you

[Web Service Client]

http://web.ics.purdue.edu/~safzal/typomaps.html

Shehzad Afzalsafzal@purdue.edu

Spatial Text Visualization Using

Automatic Typographic Maps