Visualizing and Analysing Public-Health Data Using Value-by-Area Cartograms
Toward a New Synthetic Framework
Datos Bibliográficos
| ID | 14701366 |
|---|---|
| Autores | Daniel Z Sui (0009-0006-1129-021X, autor de correspondencia), James B Holt |
| Año | 2008 |
| Volumen | 43 |
| Número | 1 |
| Páginas | 3-20 |
| Fecha de publicación | 2008-01-01 |
| Peer Reviewed | Sí |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Cartographica The International Journal for Geographic Information and Geovisualization (JOURNAL) |
| Identificadores de la revista | ISSN: 0317-7173 • E-ISSN: 1911-9925 |
| Editorial | University of Toronto Press Inc. (UTPress) (PUBLISHER) |
| DOI | 10.3138/carto.43.1.003 |
| OpenAlex | W4244440127 |
| Idioma | EN |
| Referencias citadas | 67 |
Although the first use of cartograms for visualizing public-health data can be traced back to the late 1920s, there still exists no systematic study on the effectiveness of this non-conventional cartographic technique in public health. Situated in the literature of three dominating paradigms regarding the nature of maps, this article develops a comprehensive framework for a better understanding of cartograms in public health. Using data from the US Behavioral Risk Factor Surveillance System (BRFSS), we conducted a series of cognitive tests on the effectiveness of cartograms for visualizing public-health data. The effects of using cartograms in spatial statistical analysis were evaluated by comparing analytical results derived from cartograms with those based on choropleth maps. Our results indicate that a comprehensive understanding of cartograms must include cognitive, analytical, and critical dimensions
Cartography · Data mining · Data science · Geography · Public health · Situated · Visualization · Computer Science · Data Visualization and Analytics · Data-Driven Disease Surveillance · Medicine · Spatial and Panel Data Analysis · Artificial Intelligence
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| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |