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

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality

<p>Abstract</p> <p>Background</p> <p>Kulldorff's spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S.</p> <p>Results</p> <p>We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results.</p> <p>Conclusion</p> <p>The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales.</p> <p>Method</p> <p>We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.</p

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

Geographically weighted visualization: Interactive graphics for scale-varying exploratory analysis

We introduce a series of geographically weighted (GW) interactive graphics, or geowigs, and use them to explore spatial relationships at a range of scales. We visually encode information about geographic and statistical proximity and variation in novel ways through gw-choropleth maps, multivariate gw-boxplots, gw-shading and scalograms. The new graphic types reveal information about GW statistics at several scales concurrently. We impement these views in prototype software containing dynamic links and GW interactions that encourage exploration and refine them to consider directional geographies. An informal evaluation uses interactive GW techniques to consider Guerry's dataset of 'moral statistics', casting doubt on correlations originally proposed through visual analysis, revealing new local anomalies and suggesting multivariate geographic relationships. Few attempts at visually synthesising geography with multivariate statistical values at multiple scales have been reported. The geowigs proposed here provide informative representations of multivariate local variation, particularly when combined with interactions that coordinate views and result in gw-shading. We argue that they are widely applicable to area and point-based geographic data and provide a set of methods to support visual analysis using GW statistics through which the effects of geography can be explored at multiple scales

Mapping Bolivia’s socio-political climate: Evaluation of multivariate design strategies

This study investigated the effectiveness of multivariate mapping in displaying functional relationships using a case example of social and political data from Bolivia. The effectiveness of four approaches to multivariate mapping (combined sequential schemes, separable graduated circles, choropleth/proportional symbols, and pair of sequential schemes) in communicating these relationships was evaluated. A paper map survey in English and Spanish was administered to thirty-four participants in the United States and thirty in Bolivia using four multivariate maps and one control (separate maps). Significant results showed that viewing the datasets displayed in separate maps best transmitted the map message. The pair of sequential schemes approach received the highest scores when considering the multivariate mapping methods only. This approach also yielded higher scores from the Bolivian sample, suggesting that readers lacking map experience can benefit from this form of multivariate mapping. Cultural differences revealed among the two sample groups shows that when creating a study using a map based evaluation, it is best to fully investigate cultural characteristics that surround map reading prior to creating the evaluation tool. While this study did reveal benefits of certain approaches to multivariate mapping, these approaches should be further investigated based on regional and characteristic differences among groups