The State of the Art in Cartograms

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges

Evaluating Cartogram Effectiveness

Cartograms are maps in which areas of geographic regions (countries, states) appear in proportion to some variable of interest (population, income). Cartograms are popular visualizations for geo-referenced data that have been used for over a century and that make it possible to gain insight into 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 different types of cartograms: contiguous, non-contiguous, rectangular, and Dorling cartograms. Specifically, we evaluate the effectiveness of these cartograms by quantitative performance analysis, as well as by subjective preferences. We analyze the results of our study in the context of some prevailing assumptions in the literature of cartography and cognitive science. Finally, we make recommendations for the use of different types of cartograms for different tasks and settings

California's Political Geography

Based on PPIC Statewide Survey data, outlines regional variations in party representation in the U.S. Congress and the state legislature, residents' party affiliation, and ideology on social and fiscal issues. Considers electoral implications

The use of cartograms for BGS data and information representation

Cartograms are shown to be superior to both choropleth maps and bar charts, as they are designed to deliver geospatial statistics effectively and efficiently. This report presents the results of research in establishing the potential for the use of area cartograms for science information delivery. The research involved establishing an overview of all the types of cartogram used; noting the types of data used in their creation and identifying similar sources of data that may prove appropriate for use in cartograms. Test examples were created and presented along with an evaluation as to their effectiveness and efficiency in communicating spatially orientated datasets. The work was carried out as part of the Data Representation and Presentation project for the Geospatial Capture and Solutions team. This forms part of a wider research project investigating new ways in which BGS can display science and information more effectively to a broad range of audiences

The association between county political inclination and obesity: Results from the 2012 presidential election in the United States.

ObjectiveWe examined whether stable, county-level, voter preferences were significantly associated with county-level obesity prevalence using data from the 2012 US Presidential election. County voting preference for the 2012 Republican Party presidential candidate was used as a proxy for voter endorsement of personal responsibility approaches to reducing population obesity risk versus approaches featuring government-sponsored, multi-sectoral efforts like those recommended by the Centers for Disease Control Centers for Disease Control (CDC, 2009).MethodCartographic visualization and spatial analysis were used to evaluate the geographic clustering of obesity prevalence rates by county, and county-level support for the Republican Party candidate in the 2012 U.S. presidential election. The spatial analysis informed the spatial econometric approach employed to model the relationship between political preferences and other covariates with obesity prevalence.ResultsAfter controlling for poverty rate, percent African American and Latino populations, educational attainment, and spatial autocorrelation in the error term, we found that higher county-level obesity prevalence rates were associated with higher levels of support for the 2012 Republican Party presidential candidate.ConclusionFuture public health efforts to understand and reduce obesity risk may benefit from increased surveillance of this and similar linkages between political preferences and health risks

Election Data Visualisation

Visualisations of election data produced by the mass media, other organisations and even individuals are becoming increasingly available across a wide variety of platforms and in many different forms. As more data become available digitally and as improvements to computer hardware and software are made, these visualisations have become more ambitious in scope and more user-friendly. Research has shown that visualising data is an extremely powerful method of communicating information to specialists and non-specialists alike. This amounts to a democratisation of access to political and electoral data. To some extent political science lags behind the progress that has been made in the field of data visualisation. Much of the academic output remains committed to the paper format and much of the data presentation is in the form of simple text and tables. In the digital and information age there is a danger that political science will fall behind. This thesis reports on a number of case studies where efforts were made to visualise election data in order to clarify its structure and to present its meaning. The first case study demonstrates the value of data visualisation to the research process itself, facilitating the understanding of effects produced by different ways of estimating missing data. A second study sought to use visualisation to explain complex aspects of voting systems to the wider public. Three further case studies demonstrate the value of collaboration between political scientists and others possessing a range of skills embracing data management, software engineering, broadcasting and graphic design. These studies also demonstrate some of the problems that are encountered when trying to distil complex data into a form that can be easily viewed and interpreted by non-expert users. More importantly, these studies suggest that when the skills balance is correct then visualisation is both viable and necessary for communicating information on elections

Density-equalizing maps for simply-connected open surfaces

In this paper, we are concerned with the problem of creating flattening maps of simply-connected open surfaces in $\mathbb{R}^3$. Using a natural principle of density diffusion in physics, we propose an effective algorithm for computing density-equalizing flattening maps with any prescribed density distribution. By varying the initial density distribution, a large variety of mappings with different properties can be achieved. For instance, area-preserving parameterizations of simply-connected open surfaces can be easily computed. Experimental results are presented to demonstrate the effectiveness of our proposed method. Applications to data visualization and surface remeshing are explored