2D vs VR
with Water Data
By Liza Kolev 2023
This page is based on the Spring 2023 Project 1 In-Class activity done by Liza Kolev. The handout can be found here or here if you would like to do the activity. The handout has links to the 2D visualization that will be described below as well as links to download the APK files of the two VR visualizations.
2D Visualization of Countries' Water Data
Renewable Internal Freshwater Resources
The first 2D dataset is of the renewable internal freshwater resources per capita in cubic meters per country for the year 2007, 2012, and 2014, and it is provided by the World Bank. The World Bank defines the renewable internal freshwater resources as the internal renewable resources (internal river flows and groundwater from rainfall) in the country, and it is calculated per capita using the World Bank's population estimates.
Population Percentage Using Safely Managed Water Services
The other 2D dataset used in this project was of the population percentage per country that was using safely managed water services. The data is provided by the World Bank, but, initially, it came from WHO/UNICEF Joint Monitoring Programme ( JMP ) for Water Supply, Sanitation and Hygiene. The population percentage of people using safely managed drinking water services is defined as the percentage of people using drinking water from an improved sources (e.g. piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged/delivered water) that is accessible on premises, available when needed, and free from faecal and priority contamination.
The original aim of the project was to compare countries' renewable water resources to their water economy, but there is no explicit water economy data to be found due to the broadness of the term 'water economy'. According to Ariel Dinar and Yacov Tsur, the water economy consists of the water sources, the water users, the physical infrastructure connecting these sources and users, and the institutions governing water allocation. Thus, in an effort to find data that encompassed this understanding of the water economy, I looked for countries' data that showed how many users per country have access to drinkable water.
VR Visualizations of Countries' Water Data
This visualization just takes the data for each country as is. The red object is a scale object, an attempt to help users understand the renewable internal freshwater resources data for each country. The population percentage using safely managed water services can be found as an info card on top of each country.
This visualization takes the data for each country and scales it according to the land area of the country. Once again, the red object is a scale object, an attempt to help users understand the renewable internal freshwater resources data for each country. The population percentage using safely managed water services can be found as an info card on top of each country.
In-Class Activity Results
The entry form did not have an objective in terms of comparing 2D vs VR visualization as it was completed before students saw the visualizations. However, it was intended to see what factors students considered when answering the questions.
Students brough up governmental structure (i.e. is the government stable enough to provide these resources?), general geography knowledge (e.g. a dessert location), and news they might have read recently (e.g. California has been having a water shortage or there was an article about S. Africa's water shortage). Naturally, the questions forced students to also think about population and the land area of each country.
N.B. While looking through the brief explanations students put in for each answer, it became clear that one or two students confused questions. Thus, the data here is not entirely correct, unfortunately.
Exit Form and Discussion of Results
After users explored the data in 2D and in VR, they were asked to rate the effectiveness of the visualizations from 1-5, where 1 was "Not Effective At All" and 5 was "Amazingly Effective". Interestingly, there was overlap in the results; in fact, there seemed to be more agreement on the 2D visualizations' effectiveness while there was a wider range for the VR visualization. Another interesting outcome was what people preferred out of the 2D visualization options. For renewable resources, the majority preferred the table, while for population precentage, the majority preferred the map. Granted, this may have been caused by the fact that the supposed table for population percentage was not working properly for users. All the charts related to these results can be seen in Exit Form Graph Results Part 1.
Another aspect I wanted to explore in this VR project was whether or not it made a major difference if the data was in per capita or in per capita per sq km (i.e. taking into account a country's land size) in terms of getting information from the data. When users were asked if their pair discussions shed any light on major differences in their understanding of the data, the majority said that there was a major difference! One point that was brought up, as I had expected, was that smaller countries that may have higher renewable resources would get weighted much higher, leading to them having disproportional height compared to their large land-size neighbors. However, due to the nature of the VR visualizations, some also said it was hard to compare the data since it required both people to walk around the map to visually confirm data.
2D vs VR Discussion
Please refer to Exit Form Graph Results Part 2 for both Group 1 and Group 2 above. Given that users in Group 1 nor in Group 2 reached unanimous decisions based of their respective VR visualizations, it suggests that the VR visualization may not have improved the understanding of data. However, since I had not asked users the same questions when observing the 2D visualization, if the VR visualization was worse or better than the 2D visualization in terms of communicating data cannot be determined. However, I had asked users "Comparing the VR to 2D visualizations, what attributes did you prefer/dislike?"
Based on the answers to the questions, it appears that the majority of users enjoyed viewing the data in 2D compared to VR. Some felt the 2Ds were more user friendly and that they were easier to read/analyze. In addition, some felt that it was easier to see the changes in the data over time in the 2D due to the rollover maps and the line graphs that were available, while in VR, it just switched between each year. Also, in VR, sometimes countries were right behind each other, making it a bit harder to view the data or be able to read the card stating the population percentage, while the 2Ds showed all the countries data individually without any overlap. One final negative that was brought up about the VR was the navigation. This is probably connected to the user friendliness: users felt it was difficult to understand the data completely due to having to teleport around as well as having to go on top of each country's bar to learn about the population percentage.
When later asked how the VR visualizations could be improved, most of the comments were about the functionality rather than about the visualization of the data. One comment though that I think would be great to implement in terms of the data visualization was to "use a sqrt() or log() height scale" to make the bars more legible or understandable. In terms of the functionality comments, flying and using joystick instead of teleporing was suggested. Also, having a summary or simply more annotations for users to understand what was being viewed was requested as well.
It was originally aimed that population percentage be shown in some other visual rather than a label, but time, general VR knowledge, and simply not having a good idea for showing the population percentage led to the unoriginal labels on top of coutry bars. For a future direction, it would be great to remodel the VR project to see if that changes everyone's perspective on 2D vs VR visualization for water data.
“The Water Economy.” The Economics of Water Resources: A Comprehensive Approach, by Ariel Dinar and Yacov Tsur, Cambridge University Press, Cambridge, 2021, pp. 45–63.