Enhancing GeoHealth: A step-by-step procedure for spatiotemporal disease mapping

Submitted: 29 March 2024
Accepted: 15 September 2024
Published: 23 October 2024
Abstract Views: 286
PDF: 67
Supplementary Materials: 14
HTML: 8
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Cartography, or geographical visualization of disease is an essential aspect of the field of GeoHealth, yet there is limited guidance on the visualization of spatiotemporal disease maps. In order to adequately contribute to understanding disease outbreaks, disease maps should be crafted carefully and according to relevant cartographic guidelines. This article aims to increase the understanding of space-time visualization techniques that are relevant to the field of GeoHealth, by providing a step-by-step framework for the creation of space-time disease visualizations. This study introduces a systematic approach to spatiotemporal disease mapping by integrating operations from the Generalized Space Time Cube (GSTC) Framework with established cartographic symbology guidelines. This resulted in an overview table that contains both the relevant GSTC operations and cartographic guidelines, as well as a step-by-step procedure that guides users through the process of creating informative spatiotemporal disease maps. The practical application of this step-by-step procedure is demonstrated with an example using Dutch COVID-19 data. By providing a clear, practical step by step procedure, this study enhances the capacity of public health professionals, policymakers, and researchers to monitor, understand, and respond to the spatial and temporal dynamics of diseases.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Aigner W, Miksch S, Schumann H, Tominski C, 2011. Visualization of time-oriented data. Springer London. DOI: https://doi.org/10.1007/978-0-85729-079-3
Andrienko G, Andrienko N, Demsar U, Dransch D, Dykes J, Fabrikant SI, Jern M, Kraak M-J, Schumann H, Tominski C, 2010. Space, time and visual analytics. Int J Geograph Inform Sci 24:1577–600. DOI: https://doi.org/10.1080/13658816.2010.508043
Andrienko N, Andrienko G, Gatalsky P, 2003. Exploratory spatio-temporal visualization: An analytical review. J Visual Lang Computing 14:503–541. DOI: https://doi.org/10.1016/S1045-926X(03)00046-6
Bach B, Dragicevic P, Archambault D, Hurter C, Carpendale S, 2016. A descriptive framework for temporal data visualizations based on generalized space‐time cubes. Computer Graphics Forum 36:36–61. DOI: https://doi.org/10.1111/cgf.12804
Berry BJL, 1964. Approaches to regional analysis: A synthesis. Ann Assoc Am Geogr 54:2–11. DOI: https://doi.org/10.1111/j.1467-8306.1964.tb00469.x
Bertin J, 1967. Sémiologie graphique. Les diagrammes Les réseaux Les cartes. Paris: Gauthier-Villars.
Boyandin I, Bertini E, Lalanne D, 2012. A qualitative study on the exploration of temporal changes in flow maps with animation and small‐multiples. Computer Graphics Forum 31:1005–14. DOI: https://doi.org/10.1111/j.1467-8659.2012.03093.x
Buckley A, Hardy P, Field K, 2022. Cartography. In W. Kresse & D. Danko (Red.), Springer Handbook of Geographic Information (pp. 315–352). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-53125-6_13
Calvo L, Cucchietti F, Pérez-Montoro M, 2023. Measuring the effectiveness of static maps to communicate changes over time. IEEE Transactions on Visualization and Computer Graphics 29:4243–55. DOI: https://doi.org/10.1109/TVCG.2022.3188940
Carroll LN, Au AP, Detwiler LT, Fu T, Painter IS, Abernethy NF, 2014. Visualization and analytics tools for infectious disease epidemiology: A systematic review. J Biomed Inform 51:287–98. DOI: https://doi.org/10.1016/j.jbi.2014.04.006
Cromley EK, McLafferty SL, 2011. Gis and public health. Guilford Press.
Hägerstrand T, 1970. What about people in Regional Science? Papers of the Regional Science Association 24:6–21. DOI: https://doi.org/10.1007/BF01936872
Hazen H, Anthamatten P, 2011. An introduction to the geography of health. Routledge. DOI: https://doi.org/10.4324/9780203877463
Kraak MJ, 2003. The space-time cube revisited from a geovisualization perspective. ICC 2003: Proceedings of the 21st international cartographic conference: cartographic renaissance (pp. 1988-1996). International Cartographic Association.
Kraak MJ, 2014. Mapping time: illustrated by Minard's map of Napoleon's Russian campaign of 1812. ESRI.
Kraak MJ, Ormeling F, 2020. Cartography: Visualization of geospatial data, fourth edition. CRC Press. DOI: https://doi.org/10.1201/9780429464195
Lan Y, Desjardins MR, Hohl A, Delmelle E, 2021. Geovisualization of covid-19: State of the art and opportunities. Cartographica 56:2–13. DOI: https://doi.org/10.3138/cart-2020-0027
Langran G, 1992. Time in geographic information systems. Geocarto Int 7:40–40. DOI: https://doi.org/10.1080/10106049209354371
Monmonier M, 1990. Strategies for the visualization of geographic time‐series data. Cartographica 27:30-45. DOI: https://doi.org/10.3138/U558-H737-6577-8U31
Mooney P, Juhász L, 2020. Mapping COVID-19: How web-based maps contribute to the infodemic. Dialog Human Geogr 10:265–270. DOI: https://doi.org/10.1177/2043820620934926
Pena-Araya V, Pietriga E, Bezerianos A, 2019. A comparison of visualizations for identifying correlation over space and time. IEEE Transactions on Visualization and Computer Graphics, 1–1. DOI: https://doi.org/10.1109/TVCG.2019.2934807
Peuquet DJ, 1994. It’s about time: A conceptual framework for the representation of temporal dynamics in geographic information systems. Annals of the Association of American Geographers 84:441–461. DOI: https://doi.org/10.1111/j.1467-8306.1994.tb01869.x
RIVM. (2024). COVID-19 dataset. Rijksinstituut voor Volksgezondheid en Milieu. Available from: https://data.rivm.nl/covid-19/
Rodrigues S, Figueiras A, Alexandre I, 2019. Once upon a time in a land far away: Guidelines for spatio-temporal narrative visualization. 2019 23rd International Conference Information Visualisation (IV), 44–49. DOI: https://doi.org/10.1109/IV.2019.00017
Souris M, 2019. Epidemiology and geography: Principles, methods and tools of spatial analysis (1ste dr.). Wiley. DOI: https://doi.org/10.1002/9781119528203
Tobler WR, 1970. A computer movie simulating urban growth in the detroit region. Econ Geogr 46:234–40. DOI: https://doi.org/10.2307/143141
Zhang Y, Sun Y, Padilla L, Barua S, Bertini E, Parker AG, 2021. Mapping the landscape of covid-19 crisis visualizations. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–23. DOI: https://doi.org/10.1145/3411764.3445381
Zhong C, Wang T, Zeng W, Müller Arisona S, 2012. Spatiotemporal visualisation: A survey and outlook. In Arisona SM, Aschwanden G, Halatsch J, Wonka P (Red.), Digital Urban Modeling and Simulation. Springer; pp. 299–317. DOI: https://doi.org/10.1007/978-3-642-29758-8_16

How to Cite

Roelofs, B., & Weitkamp, G. (2024). Enhancing GeoHealth: A step-by-step procedure for spatiotemporal disease mapping. Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1287

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.