Quantitative Measures for Cartogram Generation Techniques

Alam, Mohammad Jahangir; Kobourov, Stephen G.; Veeramoni, Sankar

doi:10.1111/cgf.12647

Cited by 27 publications

(35 citation statements)

References 40 publications

(89 reference statements)

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“…Combining ideas from previous work on tile maps [EvKSS15, MDS*17] and cartograms [AKV*15, KN16], we suggest that a tile map should preserve the following properties of the original map:…”

Section: Rationale and Overviewmentioning

confidence: 92%

Generating Tile Maps

McNeill

Hale

2017

Computer Graphics Forum

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Tile maps are an important tool in thematic cartography with distinct qualities (and limitations) that distinguish them from better‐known techniques such as choropleths, cartograms and symbol maps. Specifically, tile maps display geographic regions as a grid of identical tiles so large regions do not dominate the viewer's attention and small regions are easily seen. Furthermore, complex data such as time series can be shown on each tile in a consistent format, and the grid layout facilitates comparisons across tiles. Whilst a small number of handcrafted tile maps have become popular, the time‐consuming process of creating new tile maps limits their wider use. To address this issue, we present an algorithm that generates a tile map of the specified type (e.g. square, hexagon, triangle) from raw shape data. Since the ‘best’ tile map depends on the specific geography visualized and the task to be performed, the algorithm generates and ranks multiple tile maps and allows the user to choose the most appropriate. The approach is demonstrated on a range of examples using a prototype browser‐based application.

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“…Combining ideas from previous work on tile maps [EvKSS15, MDS*17] and cartograms [AKV*15, KN16], we suggest that a tile map should preserve the following properties of the original map:…”

Section: Rationale and Overviewmentioning

confidence: 92%

Generating Tile Maps

McNeill

Hale

2017

Computer Graphics Forum

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“…For cartograms, Tobler et al [Tob04] revisited definitions and algorithms used to construct them, as well as quality metrics to evaluate their performance and accuracy. Alam et al [AKV15] surveyed how distortion can affect effectiveness. For a cartogram to be effective, it needs to be readable and recognizable with respect to their shape and neighbor areas.…”

Section: Summary Of Approachesmentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

110

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The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi-and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

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“…This is measured in terms of "cartographic error." There is no "perfect" cartogram that is geographically accurate, preserves the topology, and also has zero cartographic error [9]. Some cartograms preserve shape at the expense of cartographic error, others preserve topology, still others preserve shapes and relative positions.…”

Section: Cartogram Typesmentioning

confidence: 99%

Evaluating Cartogram Effectiveness

Nusrat

Alam

Kobourov

2018

IEEE Trans. Visual. Comput. Graphics

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Cartograms are maps in which areas of geographic regions, such as countries and states, appear in proportion to some variable of interest, such as population or income. Cartograms are popular visualizations for geo-referenced data that have been used for over a century to illustrate patterns and trends in the world around us. Despite the popularity of cartograms, and the large number of cartogram types, there are few studies evaluating the effectiveness of cartograms in conveying information. Based on a recent task taxonomy for cartograms, we evaluate four major types of cartograms: contiguous, non-contiguous, rectangular, and Dorling cartograms. We first evaluate the effectiveness of these cartogram types by quantitative performance analysis (time and error). Second, we collect qualitative data with an attitude study and by analyzing subjective preferences. Third, we compare the quantitative and qualitative results with the results of a metrics-based cartogram evaluation. Fourth, we analyze the results of our study in the context of cartography, geography, visual perception, and demography. Finally, we consider implications for design and possible improvements.

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Quantitative Measures for Cartogram Generation Techniques

Cited by 27 publications

References 40 publications

Generating Tile Maps

Generating Tile Maps

Quality Metrics for Information Visualization

Evaluating Cartogram Effectiveness

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