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Discussing State‐of‐the‐Art Spatial Visualization Techniques Applicable for the Epidemiological Surveillance Data on the Example of Campylobacter spp. in Raw Chicken Meat
Author(s) -
PlazaRodríguez C.,
Appel B.,
Kaesbohrer A.,
Filter M.
Publication year - 2016
Publication title -
zoonoses and public health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.87
H-Index - 65
eISSN - 1863-2378
pISSN - 1863-1959
DOI - 10.1111/zph.12231
Subject(s) - visualization , raw data , campylobacter , sample (material) , chart , data visualization , geography , data collection , epidemiology , spatial epidemiology , data science , computer science , environmental health , data mining , cartography , statistics , medicine , biology , pathology , mathematics , programming language , chemistry , genetics , chromatography , bacteria
Summary Within the European activities for the ‘Monitoring and Collection of Information on Zoonoses’, annually EFSA publishes a European report, including information related to the prevalence of Campylobacter spp. in Germany. Spatial epidemiology becomes here a fundamental tool for the generation of these reports, including the representation of prevalence as an essential element. Until now, choropleth maps are the default visualization technique applied in epidemiological monitoring and surveillance reports made by EFSA and German authorities. However, due to its limitations, it seems to be reasonable to explore alternative chart type. Four maps including choropleth, cartogram, graduated symbols and dot‐density maps were created to visualize real‐world sample data on the prevalence of Campylobacter spp. in raw chicken meat samples in Germany in 2011. In addition, adjacent and coincident maps were created to visualize also the associated uncertainty. As an outcome, we found that there is not a single data visualization technique that encompasses all the necessary features to visualize prevalence data alone or prevalence data together with their associated uncertainty. All the visualization techniques contemplated in this study demonstrated to have both advantages and disadvantages. To determine which visualization technique should be used for future reports, we recommend to create a dialogue between end‐users and epidemiologists on the basis of sample data and charts. The final decision should also consider the knowledge and experience of end‐users as well as the specific objective to be achieved with the charts.