Map Design

Map Design

5610.3 - Using cartograms to make space for dense data

Wednesday, July 5
3:30 PM - 3:50 PM
Location: Coolidge

Maps are designed to a specific spatial scale. The larger the scale, the more space is available on the map to represent map content. At the vast majority of scales, arguably at all scales (e.g. for phenomena with fractal properties) there will be locations within the map extent where the phenomenon to be depicted on the map is too dense for complete representation. In these cases we typically generalise, but we can also locally create more space for these objects, making for a multi-scaled map (the generalisation process of displacement - moving features into available space to reduce clutter - is closest to this in concept).
Cartograms are also multi-scaled maps, typically using an area-based attribute (e.g. census meshblock-based population count) to render that area proportional in size to that attribute in the map. It follows that we could apply cartograms to the space underlying dense data, giving data more room for uncluttered display. Cartograms have been used non-conventionally in this sense (a distorted flow map with many more short flows than long flows) and as a map zooming mechanism. As a linear cartogram, the London Underground map could be regarded as the result of the distortion of areal space underlying the lines of the train network, expanding to make room for the high density of stations in Central London, with a corresponding contraction of space for the less-dense suburban ring of stations.
We have applied this principle of space distortion to transport-to-school flow data from the Built Environment and Active Transport to School (BEATS) study, a project examining factors associated with walking and cycling modes of transport to school. Flow data was derived from digitised hand-drawn routes for 740 students from all 12 Dunedin high schools. Superimposed student routes were transformed into frequency values, subsequently rendered as proportional-width flow lines.
A generalised Voronoi diagram was calculated from the Dunedin street network. The Voronoi polygons were turned into a cartogram with student frequency as the distorting attribute (using the Gastner-Newman algorithm in ScapeToad free software). The resulting cartogram was used to geometrically transform the road network, which formed the basis for a distorted flow map. This flow map had the desired spatial properties, creating space along routes of high flows and contracting space where there were low or no flows.
This is an instance of distorting space by design rather than directly through data. Use of map projections could produce a multi-scaled map (e.g. Hagerstrand's logarithmic azimuthal projection) but the featured use of cartograms affords finer control on distortion. Challenges and extensions will be discussed, including the effect of cartogram distortion on map interpretation relative to requirement for prior knowledge of the area being mapped. Extensions include the schematicization of the flow network for more uncluttered rendering (so that it approaches the linear geometric simplicity of a tube map). This will be situated relative to recent research on automated solutions to this 'metro map problem" as well as cartographic guidelines on origin-destination (flow) map display.

Antoni B. Moore

Associate Professor
University of Otago / School of Surveying

I teach and research in GIScience at the School of Surveying, University of Otago, New Zealand. My main area of research is Geovisualisation. I have been exploring diverse approaches to the challenge of displaying mapped data: turning large and complex space-time datasets into simplified diagrams, geographic virtual reality (VR), augmented reality (AR) and art. One of the projects I am involved with is to evaluate an AR sandbox-based terrain teaching tool (where sand can be moulded into landforms that are sensed and transformed into a 3D map in real time), a collaboration with computer scientists and education specialists at Otago.


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Judy Rodda

University of Otago / School of Surveying

Dr Judith Rodda is a recent PhD graduate in spatial ecology at the School of Surveying. Judy completed a Bachelor of Science degree in Physics and Master’s of Science in Oceanography in the United States. She was awarded the Environmental Systems Research Institute (Esri) Young Scholar Award for 2016. Judy is currently working as a research assistant responsible for the Geographic Information Systems analysis for the BEATS Study.


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Sandy Mandic

University of Otago / School of Physical Education, Sport and Exercise Sciences

Dr Sandy Mandic is a Senior Lecturer in the School of Physical Education, Sport and Exercise Sciences, University of Otago. Her research expertise is in the area of Physical Activity and Health. Current research encompasses multidisciplinary and multi-sector approach to physical activity and health with implications for transport, urban design and education sectors. Sandy is the primary investigator for the BEATS Study and an academic leader of the Active Living Laboratory at the University of Otago. Sandy has extensive experience in implementation and coordination of research projects, establishing collaborations with local organizations, and working in multidisciplinary research teams.


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Alzbeta Brychtova

UX Designer
University of Zurich / Lufthansa Systems

Alžběta Brychtová is currently an UX designer and cartographic visualization expert in Lufthansa Systems since 2016. Before, she was a postdoctoral researcher with the Geographic Information Visualization and Analysis group of the GIScience Center of the University of Zurich.
She completed her PhD in Geoinformatics and Cartography at the Department of Geoinformatics, Faculty of Science, Palacký University Olomouc in Czech Republic. During her PhD studies she was a visiting researcher at the ETH Zurich, University of Zurich (multiple times), and University of St Andrews (UK). Her primary research interests are in cognitive and usability issues in geovisualizations.


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5610.3 - Using cartograms to make space for dense data

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