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

Techniques for Representation of Regional Clusters in Geographical In-formation Systems

This paper provides an overview of visualization techniques adapted for regional clusters presentation in Geographic Information Systems. Clusters are groups of companies and insti-tutions co-located in a specific geographic region and linked by interdependencies in providing a related group of products and services. The regional clusters can be visualized by projecting the data into two-dimensional space or using parallel coordinates. Cluster membership is usually represented by different colours or by dividing clusters into several panels of a grille display. Taking into consideration regional clusters requirements and the multilevel administrative division of the Romania’s territory, I used two cartograms: NUTS2- regions and NUTS3- counties, to illustrate the tools for regional clusters representation.Geographic Information Systems, Regional Clusters, Spatial Statistics, Geographic Data Visualisation

Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods

Techniques for Representation of Regional Clusters in Geographical In-formation Systems

This paper provides an overview of visualization techniques adapted for regional clusters presentation in Geographic Information Systems. Clusters are groups of companies and insti-tutions co-located in a specific geographic region and linked by interdependencies in providing a related group of products and services. The regional clusters can be visualized by projecting the data into two-dimensional space or using parallel coordinates. Cluster membership is usually represented by different colours or by dividing clusters into several panels of a grille display. Taking into consideration regional clusters requirements and the multilevel administrative division of the Romania’s territory, I used two cartograms: NUTS2- regions and NUTS3- counties, to illustrate the tools for regional clusters representation

Choropleth map legend design for visualizing community health disparities

<p>Abstract</p> <p>Background</p> <p>Disparities in health outcomes across communities are a central concern in public health and epidemiology. Health disparities research often links differences in health outcomes to other social factors like income. Choropleth maps of health outcome rates show the geographical distribution of health outcomes. This paper illustrates the use of cumulative frequency map legends for visualizing how the health events are distributed in relation to social characteristics of community populations. The approach uses two graphs in the cumulative frequency legend to highlight the difference between the raw count of the health events and the raw count of the social characteristic like low income in the geographical areas of the map. The approach is applied to mapping publicly available data on low birth weight by town in Connecticut and Lyme disease incidence by town in Connecticut in relation to income. The steps involved in creating these legends are described in detail so that health analysts can adopt this approach.</p> <p>Results</p> <p>The different health problems, low birth weight and Lyme disease, have different cumulative frequency signatures. Graphing poverty population on the cumulative frequency legends revealed that the poverty population is distributed differently with respect to the two different health problems mapped here.</p> <p>Conclusion</p> <p>Cumulative frequency legends can be useful supplements for choropleth maps. These legends can be constructed using readily available software. They contain all of the information found in standard choropleth map legends, and they can be used with any choropleth map classification scheme. Cumulative frequency legends effectively communicate the proportion of areas, the proportion of health events, and/or the proportion of the denominator population in which the health events occurred that falls within each class interval. They illuminate the context of disease through graphing associations with other variables.</p

Options and recommandations related to further development of an Espon Cartographic Language

In this 5th part of Espon Cartographic Language Final Report, our aim is to identify good practices, as well in the development of interactive cartographic environments such as atlases, as in innovative cartographic constructions. Our proposals target several levels:- The level of applications themselves: which functionalities have to be use, for what applications and what objectives?-The level of cartographic representations, meaning the possibilities to introduce elements of animation and interactivity in maps, depending on data and objectives: what innovations for which representation?To achieve such aims, we use two types of resources:- a collection of interactive atlases, considered as the most representative of the diversity in european statistical atlases, which we have analyzed and compared.- the collection of maps presented in Task 4, that we propose to enrich with functions of interaction and animation.The first part of Task 5 deals with recommendations, coming from a comparative analysis of european statistical atlases. These recommendations depend on the type of environment to be made (environment of visualization, analysis or exploration), and on the desired interactivity level.The second part deals with recommendations to create interactive and animated maps. They are illustrated by concrete proposals, in the form of summary datasheet.The final part deals with a comparison of computer tools that can be used to make innovative cartographic applications