Effectiveness of animated choropleth and proportional symbol cartograms for epidemiological dashboards

References

• Aylin, P., Maheswaran, R., Wakefield, J., Cockings, S., Jarup, L., Arnold, R., Wheeler, G., & Elliott, P. (1999). A national facility for small area disease mapping and rapid initial assessment of apparent disease clusters around a point source: The UK small area health statistics unit. Journal of Public Health Medicine, 21(3), 289–298. https://doi.org/10.1093/pubmed/21.3.289
   PubMedGoogle Scholar
• Beale, L., Abellan, J. J., Hodgson, S., & Jarup, L. (2008). Methodologic issues and approaches to spatial epidemiology. Environmental Health Perspectives, 116(8), 1105–1110. https://doi.org/10.1289/ehp.10816
   PubMed Web of Science ®Google Scholar
• Berry, I., Soucy, J.-P., Tulte, A., & Fisman, D. (2020). Open access epidemiologic data and an interactive dashboard to monitor the COVID-19 outbreak in Canada. Canadian Medical Association Journal (CMAJ), 192(15), E420. https://doi.org/10.1503/cmaj.75262
   PubMed Web of Science ®Google Scholar
• Boscoe, F., & Pickle, L. (2003). Choosing geographic units for choropleth rate maps, with an emphasis on public health applications. Cartography and Geographic Information Science, 30(3), 237–248. https://doi.org/10.1559/152304003100011171
   Google Scholar
• Bostock, M., Oglevetsky, V. and Heer, J. (2011). D³ data-driven documents. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2301–2309. https://doi.org/10.1109/TVCG.2011.185
   PubMed Web of Science ®Google Scholar
• Boulos, M. K., & Geraghty, E. (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbreaks and epidemics.
   Google Scholar
• Centers for Disease Control and Prevention (CDC) Geography and Geospatial Science Working Group (GeoSWG). (2012). Cartographic guidelines for public health. Retrieved April 22, 2021, from https://www.cdc.gov/dhdsp/maps/GISX/resources/Cartographic_Guidelines.pdf
   Google Scholar
• Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43(3), 671–681. https://doi.org/10.2307/2532003
   PubMed Web of Science ®Google Scholar
• Cybulski, P., & Medyńska-Gulij, B. (2018). Cartographic redundancy in reducing change blindness in detecting extreme values in spatio-temporal maps. ISPRS International Journal of Geo-Information, 7(1), 8. https://doi.org/10.3390/ijgi7010008
   Web of Science ®Google Scholar
• Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases, 20(5), 533–534. https://doi.org/10.1016/S1473-3099(20)30120-1
   PubMed Web of Science ®Google Scholar
• Dorling, D., & Openshaw, S. (1992). Using computer animation to visualize space-time patterns. Environment and Planning B: Planning and Design, 19(6), 639–650. https://doi.org/10.1068/b190639
   Web of Science ®Google Scholar
• Duncan, I. K., Tingsheng, S., Perrault, S. T., & Gastner, M. T. (2021). Task-based effectiveness of interactive contiguous area cartograms. IEEE Transactions on Visualization and Computer Graphics, 27(2), 2136–2152. https://doi.org/10.1109/TVCG.2020.3041745
   PubMedGoogle Scholar
• Elliott, P., & Wartenberg, D. (2004). Spatial epidemiology: Current approaches and future challenges. Environmental Health Perspectives, 112(9), 998–1006. https://doi.org/10.1289/ehp.6735
   PubMed Web of Science ®Google Scholar
• Elmer, M. E. (2013, August 25–30). Symbol considerations for bivariate thematic maps. In Proceedings of the 26th International Cartographic Conference, Dresden, Germany.
   Google Scholar
• Fabrikant, S. I., & Goldsberry, K. (2005). Thematic relevance and perceptual salience of dynamic geovisualization displays. Proceedings, 22th ICA/ACI International Cartographic Conference, A Coruña, Spain.
   Google Scholar
• Fabrikant, S. I., Rebich-Hespanha, S., Andrienko, N., Andrienko, G., & Montello, D. R. (2008). Novel method to measure inference affordance in static small-multiple map displays representing dynamic processes. The Cartographic Journal, 45(3), 201–215. https://doi.org/10.1179/000870408X311396
   Web of Science ®Google Scholar
• Fareed, N., Swoboda, C. M., Chen, S., Potter, E., Wu, D. T. Y., & Sleck, C. J. (2021). U.S. COVID-19 state government public dashboards: An expert review. Applied Clinical Informatics, 12(2), 208–221. https://doi.org/10.1055/s-0041-1723989
   PubMedGoogle Scholar
• Field, K. (2020, February 25). Mapping coronavirus, responsibly. ArcGis Blog. Retrieved April 10, 2021, from https://www.esri.com/arcgis-blog/products/product/mapping/mapping-coronavirus-responsibly/
   Google Scholar
• Fish, C., Goldsberry, K. P., & Battersby, S. (2011). Change blindness in animated choropleth maps: An empirical study. Cartography and Geographic Information Science, 38(4), 350–362. https://doi.org/10.1559/15230406384350
   Web of Science ®Google Scholar
• Forster, F. (1966). Use of a demographic base map for the presentation of areal data in epidemiology. British Journal of Preventative and Social Medicine, 20(4), 165–171. https://doi.org/10.1136/jech.20.4.165
   PubMedGoogle Scholar
• Gastner, M. T., & Newman, M. E. J. (2004). Diffusion-based method for producing density-equalizing maps. PNAS, 101(20), 7499–7504.
   PubMed Web of Science ®Google Scholar
• Gastner, M. T., Seguy, V., & More, P. (2018). Fast flow-based algorithm for creating density-equalizing map projections. PNAS, 115(10), E2156–E2164.
   PubMed Web of Science ®Google Scholar
• Geospatial @ UCSF. (2020). Mapping the coronavirus (blog posted Mar 3, 2020). Retrieved May 17, 2021, from https://maps.ucsf.edu/news/mapping-coronavirus
   Google Scholar
• Gillihan, A. F. (1927). Population maps. American Journal of Public Health, 17(4), 316–319.
   Google Scholar
• Griffin, A. L., MacEachren, A. M., Hardisty, F., Steiner, E., & Li, B. (2006). A comparison of animated maps with static small-multiple maps for visually identifying space-time clusters. Annals of the Association of American Geographers, 96(4), 740–753. https://doi.org/10.1111/j.1467-8306.2006.00514.x
   Web of Science ®Google Scholar
• Hannen, T. (2020, May 7). Coronavirus coxcombs to waffles: What Florence Nightingale can teach us about the best ways to map the spread of disease. Interview of Kenneth Field by Alan Smith. Financial Times. Retrieved April 20, 2021, from https://www.ft.com/video/18bce4f1-83a5-4a6a-afff-e477c9b2ba45
   Google Scholar
• Harrower, M. (2003). Tips for designing effective animated maps. Cartographic Perspectives, 44(44), 63–65. https://doi.org/10.14714/CP44.516
   Google Scholar
• Harrower, M. (2007). Unclassed animated choropleth maps. The Cartographic Journal, 44(4), 313–320. https://doi.org/10.1179/000870407X241863
   Google Scholar
• Harrower, M., Fabrikant S. (2008). The role of map animation in geographic visualization. 49–65.
   Google Scholar
• Juergens, C. (2020). Trustworthy COVID-19 mapping: Geo-spatial data literacy aspects of choropleth maps. Journal of Cartography and Geographic Information, 70(4), 155–161. https://doi.org/10.1007/s42489-020-00057-w
   PubMedGoogle Scholar
• Kantor, M. (2020). The Science and scientist behind the John Hopkins coronavirus dashboard. Interview with Lauren Gardner. Podcast. Retrieved March 27, 2021, from https://www.esri.com/about/newsroom/podcast/the-science-and-scientist-behind-the-johns-hopkins-coronavirus-dashboard/
   Google Scholar
• Koivisto, M., & Revonsuo, A. (2003). An ERP study of change detection, change blindness, and visual awareness. Psychophysiology, 40(3), 423–429.
   PubMed Web of Science ®Google Scholar
• Kronenfeld, B. J. (2018). Manual construction of continuous cartograms through mesh transformation. Cartography and Geographic Information Science, 45(1), 76–94. https://doi.org/10.1080/15230406.2016.1270775
   Web of Science ®Google Scholar
• Kronenfeld, B. J., & Wong, D. W. S. (2017). Visualizing statistical significance of disease clusters using cartograms. International Journal of Health Geographics, 16(1), 19. https://doi.org/10.1186/s12942-017-0093-9
   PubMedGoogle Scholar
• Lan, Y., Desjardins, M. R., Hohl, A., & Delmelle, E. (2021). Geovisualization of COVID-19: State of the art and opportunities. Cartographica: The International Journal for Geographic Information and Geovisualization, 56(1), 2–13. https://doi.org/10.3138/cart-2020-0027
   Google Scholar
• MacNab, Y. C., Farrell, P. J., Gustafson, P., & Wen, S. (2004). Estimation in Bayesian disease mapping. Biometrics, 60(4), 865–873. https://doi.org/10.1111/j.0006-341X.2004.00241.x
   PubMed Web of Science ®Google Scholar
• Merrill, D. W., Selvin, S., Close, E. R., & Holmes, H. H. (1996). Use of density equalizing map projections (DEMP) in the analysis of childhood cancer in four California counties. Statistics in Medicine, 15(17), 1837–1848. https://doi.org/10.1002/(SICI)1097-0258(19960915)15:17<1837:AID-SIM395>3.0.CO;2-E
   PubMed Web of Science ®Google Scholar
• Milner, G. (2020). Creating the dashboard for the pandemic. ArcUser, summer, 56–59.
   Google Scholar
• Mooney, P., & Juhász, L. (2020). Mapping COVID-19: How web-based maps contribute to the infodemic. Dialogues in Human Geography, 10(2), 265–270. https://doi.org/10.1177/2043820620934926
   Web of Science ®Google Scholar
• Moon, S., Kim, E.-K., & Hwang, C.-S. (2014). Effects of spatial distribution on change detection in animated choropleth maps. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 32(6), 571–580. https://doi.org/10.7848/ksgpc.2014.32.6.571
   Google Scholar
• Nelson, E. S. (1999). Using selective attention theory to design bivariate point symbols. Cartographic Perspectives, 32(1), 6–28.
   Google Scholar
• Nguyen, K. T., Hatzenbuehler, M. L., & Goldstein, N. D. (2019). Potential relationship between HIV criminalization and structural stigma related to sexual orientation in the United States. Journal of Acquired Immune Deficiency Syndrome, 80(5), e106–108. https://doi.org/10.1097/QAI.0000000000001961
   PubMed Web of Science ®Google Scholar
• Nusrat, A., Alam, M. J., Scheidegger, C., & Kobourov, S. (2018). Cartogram visualization for bivariate geo-statistical data. IEEE Transactions on Visualization and Computer Graphics, 24(10), 2675–2688. https://doi.org/10.1109/TVCG.2017.2765330
   PubMed Web of Science ®Google Scholar
• Rensink, R. A. (2002). Internal vs. external information in visual perception. In Proceedings of the 2nd international symposium on Smart graphics, Hawthorne, New York, USA.
   Google Scholar
• Reyes Nuñez, J. J., & Juhász, B. (2015). Hungarian survey on the use of cartograms in school cartography. International Journal of Cartography, 1(1), 5–17. https://doi.org/10.1080/23729333.2015.1055538
   Google Scholar
• Rezk, A. A., & Hendawy, M. (2023). Informative cartographic communication: A framework to evaluate the effects of map types on users’ interpretation of COVID-19 geovisualizations. Cartography and Geographic Information Science, 1–18. https://doi.org/10.1080/15230406.2022.2155249
   Web of Science ®Google Scholar
• Roth, R. E. (2013). Interactive maps: What we know and what we need to know. Journal of Spatial Information Science, 6(6), 59–115. https://doi.org/10.5311/JOSIS.2013.6.105
   Google Scholar
• Sack, D. E., Gange, S. J., Althoff, K. N., Pettit, A. C., Kheshti, A. N., Ransby, I. S., Nelson, J. J., Turner, M. M., Sterling, T. R., & Rebeiro, P. F. (2022). Visualizing the geography of HIV observational cohorts with density-adjusted cartograms. Journal of Acquired Immune Deficiency Syndrome, 89(5), 473–480. https://doi.org/10.1097/QAI.0000000000002903
   PubMed Web of Science ®Google Scholar
• Schiffmann, A., & Conlon, D. (2020). Retrieved February 8, 2021, from nCoV2019.live
   Google Scholar
• Soetens, L., Hahné, S., & Wallinga, J. (2017). Dot map cartograms for detection of infectious disease outbreaks: An application to Q fever, the Netherlands and Pertussis, Germany. Euro Surveillance, 22(26), ii=30562. https://doi.org/10.2807/1560-7917.ES.2017.22.26.30562
   PubMedGoogle Scholar
• Sui, D. Z., & Holt, J. B. (2008). Visualizing and analysing public-health data using value-by-area cartograms: Toward a new synthetic framework. Cartographica: The International Journal for Geographic Information and Geovisualization, 43(1), 3–20. https://doi.org/10.3138/carto.43.1.3
   Google Scholar
• Sun, S. (2013). An optimized rubber-sheet algorithm for continuous area cartograms. The Professional Geographer, 65(1), 16–30. https://doi.org/10.1080/00330124.2011.639613
   Web of Science ®Google Scholar
• Thorpe, A., Scherer, A. M., Han, P. K., Burpo, N., Shaffer, V., Scherer, L., & Fagerlin, A. (2021). Exposure to common geographic COVID-19 prevalence maps and public knowledge, risk perceptions, and behavioral intentions. JAMA Network Open, 4(1), e2033538. https://doi.org/10.1001/jamanetworkopen.2020.33538
   PubMed Web of Science ®Google Scholar
• Tobler, W. (2004). Thirty five years of computer cartograms. Annals of the Association of American Geographers, 94(1), 58–73. https://doi.org/10.1111/j.1467-8306.2004.09401004.x
   Web of Science ®Google Scholar
• Tran, N. K., & Goldstein, N. D. (2019). Jointly representing geographic exposure and outcome data using cartograms. American Journal of Epidemiology, 188(9), 1751–1750. https://doi.org/10.1093/aje/kwz141
   PubMed Web of Science ®Google Scholar
• Traun, C., Schreyer, ML, Wallentin, G. (2021). Empirical insights from a study on outlier preserving value generalization in animated choropleth maps. ISPRS International Journal of Geo-Information, 10(4), 208.
   Web of Science ®Google Scholar
• Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human-Computer Studies, 57(4), 247–262. https://doi.org/10.1006/ijhc.2002.1017
   Web of Science ®Google Scholar
• Wallace, J. W. (1926). Population map for health officers. American Journal of Public Health, 14(10), 1023.
   Google Scholar
• Yau, Y. C., & Gastner, M. T. (2021). Mapping the inequality of the global distribution of seasonal influenza vaccine. Environment & Planning A: Economy & Space, 53(6), 1249–1252. https://doi.org/10.1177/0308518X21998356
   Web of Science ®Google Scholar
• Yoo, K.-I., & Kronenfeld, B. (2020). An evaluation of COVID-19 dashboards from cartographic and epidemiological perspectives. Proceedings, AutoCarto 2020. https://cartogis.org/autocarto/autocarto-2020/program/
   Google Scholar