6.5 Map sequences

This activity concerns map sequences – the creation of a set of maps showing infectious disease distribution through time. Such sequences are a useful exploratory technique that can help identify anomalous data in a time series, seasonal patterns or foci for disease outbreaks. Sequences can be based on individual cases, disease rates within regions, or rates of change. Traditionally, such sequences have been created using a static series of ‘snapshots’ through time, which may then be placed side by side and compared. All maps within a sequence share a common cartographic design for ease of comparison. Since the early 1990s, animation of maps (‘map movies’) has become more common as an alternative to such static displays. However, the advantages of such animations in helping interpret patterns of disease spread remain a subject of research.

Google Earth has a time function that is particularly useful for this type of map animation and this has been used for some examples of this type of data visualisation.  Within ArcGIS, the time animation toolbar performs a similar function.

Activity

  1. If you are using ArcGIS Desktop, create a map sequence, depicting patterns of influenza spread in French departments using this zip file.  If you are using ArcGIS Pro, create a time animation of weekly COVID-19 cases using the data and instructions in this zip file.
  2. Read the reference and view the web site below for examples of both static and animated map sequences being used with infectious disease data. For your own personal study, make some brief notes on the possible advantages of map animation (if any) over more static web pages.

References (Essential reading for this learning object indicated by *)

The healthmaps site provides spatio-temporal visualisations of georeferenced, validated reports of disease outbreaks, such as for Ebola: https://www.healthmap.org/ebola/#timeline

* Cifuentes, E., Hernandez, J. E., Venczel, L., and Hurtado, M. (1999) Panorama of acute diarrhoeal diseases in Mexico. Health and Place 5, 247-255.

* Castronov, D., Chui, K., and Naumova, E. (2009) Dynamic maps: a visual-analytic methodology for exploring spatio-temporal disease patterns. Environmental Health 8, 61. http://www.ehjournal.net/content/8/1/61

This article above has some clickable links where you can view dynamic maps of hospitalization rates for cases of salmonella.

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