diff --git a/02_activities/assignments/assignment_2.md b/02_activities/assignments/assignment_2.md index 7bb5b2df7..269eab3b4 100644 --- a/02_activities/assignments/assignment_2.md +++ b/02_activities/assignments/assignment_2.md @@ -9,27 +9,36 @@ - You can find data visualizations at https://public.tableau.com/app/discover or https://datavizproject.com/, or anywhere else you like! - For each visualization (good and bad): - Explain (with reference to material covered up to date, along with readings and other scholarly sources, as needed) why you classified that visualization the way you did. - ``` - Your answer... - - - - - - - ``` + I used 01_materials and slides 01_course_intro.pdf and 04_choosing_the_right_visualization.pdf + + Good Visualization (Line Graph Example) https://public.tableau.com/app/profile/economic.research.service/viz/MeetinghoneydemandintheUnitedStates/HoneyDemand + + - I would classify this data visualization overall really good. + - Referring to its aesthetic quality, it is pleasing to look at. The lines are clearly distinguished with labels and the use of colour is not overwhelming. There is a title and axis label on the x-axis so I can clearly see what this graph is about and what data is being presented. + - Referring to its substantive quality, the visualization accurately and honestly presents the data. I can clearly see that there is a increase in honey production. There is a source for the information (USDA). The information in my opinion is presented objectively and there is no visuals to indicate some type of bias or emotional narrative. + - Referring to its perceptual quality, I can understand what message the maker of the visualization is attempting to convey. I can clearly see that the honey domestic consumption is elevated compared to sugar. + - The chart type is familiar, it is concise and it is explanatory. The interpretation of the data points are approximate but when you scroll over the line it is more accurate. + - The graph employs provenance rhetoric by citing the source of the data which helps the audience trust the visualization more, signalling transparency and trustworthiness. + + Bad Visualization (Bubble Map Example) https://www.flickr.com/photos/b_willers/6331225797/in/photostream/ + + - I would classify this data visualization as overall bad. + - Referring to its aesthetic quality, it is really pleasing to look at. The map is clean and the colours are not overwhelming. There is a clear legend for the different magnitudes. But, the content is a little unclear. There are no labels to indicate which country or region is affected. The bubbles overlap each other and the scale for some are so large that I cannot tell where the data bubble is supposed to be. It is hard to distingusih individual data bubbles and see them on their own. + - Referring to its substantive quality, I would say that the visualization does not accurately and honestly presents the data. I can see that the bubble map is displaying earhtquakes that result in 1000 or more deaths since 1900. But, this is such a broad classification of data. The bubbles do not describe how many deaths specifically, do not indicate which exact year the earthquake hit and also do not indicate what deaths mean if its immediate or long-term effects. It also does not indicate if there are freqeunt earthquakes in one specific area. There is a source for the information (www.guardian.co.uk/news/datablog/2010/feb/28/deadliest-ea...) but when I try to click into the source it is missing. The information in my opinion is presented relatively objectively and there is no specific visuals to indicate some type of bias or emotional narrative. I do personally think the title is a little insensitive for the data being conveyed as earthquakes are more than "Deadly Vibrations", and are instead a quite serious natural event. + - Referring to its perceptual quality, I can understand generally what message the maker of the visualization is attempting to convey. I understand that the visualization is showing earthquakes resulting in 1000 or more deaths since 1900. But, I am unsure why the magnitudes matter or whether the author is saying a certain magnitude results in more deaths or not. This is not clearly conveyed. + - The chart type is familiar, it is concise and it is exploratory. The interpretation of the data points are very approximate. + - How could this data visualization have been improved? - ``` - Your answer... - - - + + Good Visualization (Line Graph Example) https://public.tableau.com/app/profile/economic.research.service/viz/MeetinghoneydemandintheUnitedStates/HoneyDemand + - The data visualization can be improved in a few ways. I think it would be beneficial to specifically indicate the difference betweem honey and honey-sweetened products as I do feel the line graph is a little broad in its message. Additionally, it is a little unclear if the graph is showing domestic consumption or demand, although they are related they can be interpreted as two different metrics. I also would like some information on how the index was calculated and have a axis title for this value. It is also not super clear if the graphs are presenting values compared to the 1990 index or whether those values are the true values post 1990. Lastly, the presentation of the data points could be visually more accurate. + Bad Visualization (Bubble Map Example) https://www.flickr.com/photos/b_willers/6331225797/in/photostream/ + - The data visualization can be improved in a few ways. I think that each data bubble should be clearly labelled with a specific number for the number of deaths and also indicate which year the earthquakes occured. I think it would be useful to have a feature that seperates the different magnitudes instead of displaying them all at once for a better comparison of the data, for example comparing the number of deaths for just magnitude level 6 earthquakes. A better title and more reliable source using provenance rhetoric would be beneficial. Also, I am not sure if just displaying magnitudes 5.5 or up is very accurate as there are some earthquakles that are lower than this. Lastly, I would clearly label which region the earthquakes are affecting as there some areas which the bubble covers a whole country. - ``` - Word count should not exceed (as a maximum) 500 words for each visualization (i.e. 300 words for your good example and 500 for your bad example)