Project 1

Water Availability Visualizations with AR Water Simulations

By: Aarav Kumar (2025)

Project Overview Documents

Proposal Phase:

Project Description and Goals:

In this project, I aim to build visualizations representing water availability per household in water-scarce regions, simulating water levels in a life-size see-through container in AR, and showing the level change (rise/fall) to represent water availability changes over time. I aim to compare the AR visualization with 2D representations of the same water-availability data (in tables, bar charts, etc.) to evaluate the differences between non-immersive 2D media and immersive 3D media. In particular, I aim to compare viewers’ comprehension of the data, their ability to relate to the data, and the ease of understanding provided by the visualizations, noting any trade-offs.

Through this project, I aim to better understand how to evaluate software to meet a particular goal (in this case build simulations of water based on water availability data), and be able to develop enough skills in my selected software to run a tutorial on how to use it, and particularly how to use it to build visualizations of fluids and water-related data.

Class activity proposal:

Interactive activity where students choose from a list of water-scarce areas and see a (scaled) transparent cube fill up with the total amount of water available to a household in that area per day. Students can then choose to compare with similar visualizations of how much water daily activities like showering may take. Following this, students will see the same data in 2D charts and tables on their laptops, and fill out an evaluative form comparing the two mediums and noting any differences in their understanding or experience.

Timeline (with Milestones, Deliverables, and Wiki Additions)

Project 1 Timeline

Reference Datasets:

Primary Dataset: World Bank Data (1992 - 2014) - Renewable internal freshwater resources per capita
Datasets for potential testing or further prototyping:
- Worldometers - Water Use by Country
- Water Footprint Calculator - Water Footprint Comparisons by Country
- FAO Aquastat Data - Water-use data by country and year
  - Better summarized by the World Population Review
- OECD - Water Withdrawal Data (per capita)

Existing 2D Visualizations to Compare Against

(of the datasets listed and more)

Our World in Data (most recent revision in 2024) - Water Use + Stress
Our World in Data (most recent revision in 2024) - Clean Water
WorldMapper - No Water Access Per Capita

Essential Need - World Water Data for Safe Drinking

Resources for Getting Started:

Meta XR SDK for Unity – URL
Unity Visual Effect Graph Documentation - URL
Shader Graph in Unity for Water Effects - URL
Real-time Fluid Simulation Guide - URL
Unity Visual Effect Graph Documentation - URL
Shader Graph in Unity for Water Effects - URL
Real-time Fluid Simulation Guide - URL

Software Comparison:

Completed in AR Software for Fluid Simulations
Tabular software comparison here

Development Phase:

Criteria and Usability Targets for AR Water Visualization:

After initial prototyping and research, these are the main criteria I came up with to assess how effective my final AR data visualization will be.

Accuracy to Real-Life Volumetric Data (e.g. 1m3 actually appears as what 1m3 would look like)

Realism of Water Appearance and Texture

Resemblance to Real Fluid Movement and Dynamics

Ability to Compare Water Availability Between Countries

Feasibility of Visualization in Terms of Scale

Ease of Navigation (in this case, switching between countries’ data)

Ability to Relate to the Information (Data) Provided by the Visualization

Retention of Information (i.e. ability to recall the data)

Interactivity & User Engagement

The criteria in bold are the ones I want to focus on most, as I am really aiming to assess how AR may be able to provide more relatability to volumteric data through scaled visual references, and how that experience may help users compare data in a more realistic and familiar way. It may be the case that AR doesn't help in these things at all, and that other forms of data visualization could be more effective, so my aim is to analyze it without bias and compare my final visualization with other 2D visualizations. Thus, I hope to be able to assess the tradeoffs between AR and 2D visualizations of volumetric data, assessing the pros and cons of each.

The descriptors (1 to 5) for each criteria are described as below:

Fluid Visualization Criteria and Targets

Resources Used for Development

Setting up Unity for XR Development on Meta Quest 3

Building (HDRP) Water Simulations in Unity — CAUTION: NOT COMPATIBLE WITH ANDROID / META QUEST !!

Unity Documentation
Water System Introduction
- - https://www.youtube.com/watch?v=xH-9ZEX9n6A
  - https://www.youtube.com/watch?v=AF2lKk3t79A
Realtime Fluid Simulation - https://www.youtube.com/watch?v=zbBwKMRyavE&t=1s

Building (URP) Water Simulations in Unity - Compatible with Quest

Water Effects - https://www.youtube.com/watch?v=3CcWus6d_B8

Water Shaders (Shader Graph)

Free Customizable Water Shaders

Shader 1: https://assetstore.unity.com/packages/2d/textures-materials/water/simple-water-shader-urp-191449
- - Video Tutorial / Demo: https://www.youtube.com/watch?v=mWg4CE6ybKE
Shader 2: https://assetstore.unity.com/packages/vfx/shaders/urp-stylized-water-shader-proto-series-187485
- - Video Tutorial / Demo: https://www.youtube.com/watch?v=fHuN7WkrmsI&t=1s

System Keyboard Integration

(system keyboard is the local keyboard on the deployed device)

Virtual Keyboard (OVRVirtualKeyboard) Integration

(Oculus virtual keyboard - exists in the scene itself, not local to the device)

Debugging Help

Virtual keyboard not showing up - https://discussions.unity.com/t/xri-virtual-keyboard-not-showing-up/911021
System keyboard not showing up - https://stackoverflow.com/questions/68539289/how-do-i-enable-touch-keyboard-in-unity-vr
Controller tracking not working - https://discussions.unity.com/t/controllers-wont-track/920416
SideQuest debugging - https://sidequestvr.com/space/142/p/74477/i-have-this-problem-when-i-try-to-sideload-a-game-it-says-bird-runners-datalocaltmp_streamapk-could-not-be-installed-install_failed_update_incompatible-package-comdoodlestudiosbirdrunners

Final Class Activity:

Link to final class activity page

Evaluation Phase:

Class Activity Results:

Memory and Recall: Many participants struggled to recall specific numerical values from the AR version, often stating they couldn’t remember or had difficulty reading the data. While AR was more immersive, numerical data retention didn’t improve and estimations were slightly more off compared to the 2D version.
Comparative Ability in AR: The ability to compare countries dropped significantly in AR (by 4 points on a 10-point scale), suggesting that AR is better for immersive understanding rather than structured, side-by-side data analysis.
Navigation Preference Split: The preference for navigation was evenly split (4 for AR, 5 for 2D, 1 Both, 1 Neither). Some participants found searching in AR easier, while others preferred traditional UI elements in 2D. This suggests that AR interaction methods may need refinement to match the familiarity and efficiency of standard UI.
Synthesis and Summary of Data: The 2D version was seen as more flexible, providing more data, better view options, and making comparisons easier. Participants felt that the 2D format allowed for better synthesis and summary of information, making it effective for analyzing trends and extracting insights.
Engagement and Relatability in AR: AR was almost unanimously rated as more engaging (by 10/11 participants) and more relatable (8+ for AR vs. 5-6 for 2D, on a 10-point scale). This suggests that AR may be better suited for exploratory learning and experiential engagement rather than analytical tasks. It also suggests that being able to see volumetric data in a physical, real-life context may be more effective and communicative than pure numerical data.
Surprises: A notable surprise was that some participants didn’t prefer the 2D version at all, citing that they were visual learners and found AR more intuitive for understanding the data. This challenges the assumption that 2D is universally better for structured analysis and highlights the potential for AR in catering to different learning styles.

Based on my class activity results, I evaluated my final water visualization using my evaluation criteria and usability targets table.

Fluid Visualization Criteria and Targets

Justification for each descriptor:

Accuracy to Real-Life Volumetric Data — 5/5, as the scaling to real-world measurements was very high (shown below)

Realism of Water Appearance and Texture — 4/5, as water visualization appeared like water in terms of texture and appearance, but was not transparent

Resemblance to Real Fluid Movement/Dynamics — 5/5, as the visualization mimicked real water movement/flow, and matched water levels to the data

Ability to Compare Water Availability Between Countries — 2/5, as ease of comparison had an average 4.27 score (on a 10 point scale) in class activity

Feasibility of Visualization in Terms of Scale — 3/5, as scale was feasible for 3/4 of all countries, but remaining 1/4 had too much water to show in a room

Ease of Navigation (in this case, switching between countries’ data) — 3/5, as results suggested even split between AR/2D preference for navigation

Ability to Relate to the Information (Data) Provided by the Visualization — 4/5, as relatability of AR version was given average score of 8.1/10

Retention of Information (i.e. ability to recall the data) — 2/5, as many people struggled to recall any of the data at all

Interactivity & User Engagement — 5/5, as almost all participants (except 1) emphasised the engagement of the AR model, preferring it to the 2D

Scale Evaluation:

To ensure that my visualization was accurate in terms of volumetric scale (as one of the primary goals of my project was to make sure water volumes were represented in real life scale), I decided to measure my visualization with a measuring tape. Upon repeating the measurments in different contexts and lightings, I found that 2m in my visualization was represented as 2.0m to 2.02m in real life (being 2 cm off at max) -- this amounts to 99% accuracy in 1 dimension, which would be around a 97% accuracy in 3D (for volumetric data). I also wanted to ensure that the chair in my visualization was appropriately scaled, so I compared its size to other chairs in passthrough as well.

Takeaways + Challenges:

Takeaways

Need to allocate more time to get familiar with new tools (budget significant time for this — Unity took a long while to get used to)
Important to find resources that are up to date when researching (especially for tutorials and documentation)
Be open to pivoting from original ideas / implementation (create a flexible timeline)

Challenges

Configuring Unity hierarchy + settings, and not realizing limitations of Unity's version control
Re-arranging timeline deadlines and deliverables due to prototyping section taking longer than expected
Working with the limitations of the dataset + my implementation of the visualization

Second Project Ideas:

Interactive Groundwater & Aquifer Visualization: visualize underground water reserves in AR, showing depletion/recharge over time. Users could place virtual markers to explore how water moves through soil layers.
AR-Driven Historical vs. Predicted Climate Change: Overlay historical climate data onto real-world locations in AR, with interactive controls to compare past, present, and future projections.
AR & Haptic Earthquake Data Simulation: Visualize seismic activity in AR while using haptics to simulate tremors based on real-world earthquake data.