The government was required to publish an
environmental impact statement for this LNG terminal,
outlining potential impacts to local fishermen.
How would you find a copy?
Designing a specialized search platform
Finding the Needle: How UX research helped make a large complex technical database searchable by anyone
The challenge
A team of scientists and developers at the University of Arizona aimed to revolutionize environmental policy decisions by creating a specialized search engine for 50 years of environmental planning documents, a large, complex dataset.
Tasks: translate systems thinking concepts into a user-friendly interface easily understandable by a range from professional to average citizens.
The outcome
Guided by UX research, the team successfully developed a specialized search platform tailored for a complex dataset, addressing the unique needs of a range of user types who found existing search options inadequate.
Key Achievements were: deep user understanding guided development to a high success rate and user satisfaction.
Project summary
Project name
NEPAccess.org
Organization
The Udall Center for Studies in Public Policy at the University of Arizona.
Funded by
Seed funding from the National Science Foundation. New funding needed for version 2
What we did
Use data science and design to create a searchable platform to make millions of pages of complex and unstructured government technical documents generated by the National Environmental Policy Act (NEPA) easily findable by both professionals and average citizens.
2020-2024
My role
Sole UX researcher and UI designer
Design goals
Make interface usable by 6 user groups from professional consultants to common citizens.
I worked with
Developers and an interdisciplinary team of data scientists, machine-learning experts, environmental and social scientists, public policy experts, lawyers, and students.
Tools
- In-person user testing / jobs to be done interviews
- Pencil & paper to sketch ideas
- Balsamic and Draw.io for diagraming and wireframes,
- Figma for mockups and prototypes
- Photoshop
Technology
ReactJS, SQL database, Lucene search engine, Zoom.us, WordPress
Why we built this
A series of high-profile environmental disasters in the 1960s led to the unanimous passage of the National Environmental Policy Act (NEPA) in 1969.
NEPA has generated over 40,000 environmental impact statements, containing millions of pages of critical scientific analyses. The documents are valuable for solving “wicked” social and environmental problems.
However, this data has been largely inaccessible due to data silos, lack of structure, and ineffective retrieval systems. Existing search options like Google and government websites were inadequate for research needs.
Making science usable for public policy decisions
To address this challenge, our team developed NEPAccess, a free knowledge-discovery-and-engagement platform that centralizes NEPA data and makes it easily searchable for various user groups, from professionals to private citizens.
Phase 1: Discovery–understanding the problem space
Stakeholder interviews
After joining the project in its second year, I conducted interviews with project leaders to understand the complex problem space. Our assumptions included the belief that making science available to government decision-making serves the public good, and that data science could solve the problem of finding scattered documents.
Stakeholder insights
Why are you building this product?
For research
“NEPA is an important environmental law and, we know almost nothing about how it works in practice because we can’t gather enough data.”
A public good
“There’s a lot of valuable data for public decision-making contained within the documents that’s completely inaccessible.”
What problem(s) are you trying to solve for users?
“In the past, you just didn’t know if you’re going to be able to find enough data to do your research. You didn’t know where to go to look for it. And it’s really just like throwing darts at a dartboard.”
What are the main pain points
“Just finding documents. Just the fact that they’re so huge…It can be really challenging to navigate them and get to the information that you need”
“NEPA users may not even know that they have those pain points, because people can’t really imagine it being any different.”
What do you need to learn about users in order to move forward?
- “Right now the biggest challenge is figuring out how people will want to use it at this point in time.
- Once we have that, then the biggest challenge will be: can we do what people want?”
What does success look like for this project?
“All these multiple user types should be able to sit down–and come away feeling that they’ve either got what they wanted to know, or with a bit more probing they will get what they wanted. I think that’s the big challenge”
Phase 1: Discovery–understanding the problem space
Competition review
A competition review revealed that NEPAccess would fill a unique gap in the market for comprehensive, unbiased environmental data access.
The explainer video
To communicate the problem to administrators and funders, we created an explainer video illustrating how documents were scattered among multiple silos and lacked common structure.
Watch the video
Personas and user journey
Anticipated user groups include: lawyers, government agencies, industry consultants, scientists, academic researchers, and engaged citizens
How people find NEPA documents now
To experience a NEPA search first-hand, I put myself in the scenario of a citizen wanting to read the science behind a controversial mine whose name they heard in the local news.
My hypothesis: Designing for the common citizen would also help make the site usable for professional users.
User journey: search for a single project
Phase 2: empathy–How would people use this
User testing
Initially, developers placed all the database information on the search page due to uncertainty about user needs. My role involved translating developer concepts into user-friendly models.
To gain user insights, I conducted a combination of usability testing sessions with interviews to understand how people used the data. I recorded users and logged issues while people searched on the live prototype site.
First developer’s screen
We started from square one. This black screen below was the result of a year’s work by developers before I arrived. This was what I started with. Developers were unsure about user needs and decided to use this as a starting point to develop a live prototype.
Research Questions
Context
What are users trying to accomplish with these documents?
Analyze workflow
How do they currently find this information?
Find opportunities
What could be better about how they currently do this?
How will people use it?
What advanced search features will people use?
Search Behavior
Will users search for single known documents or explore related document sets?
Results of user testing
Initial user tests revealed a stark contrast between developer-centric design and user expectations. While developers favored complex search options and table formats, users craved simplicity and intuitive navigation. Key insights included:- Users strongly preferred a simple, Google-like search functionality.
- A card format was favored over tables for improved readability and visual grouping.
- Users wanted to find related documents within projects and browse other projects for solutions.
- We simplified the interface to a single smart search box
- moved options to a familiar sidebar filter
- replaced advanced search with keyboard modifiers
- implemented a card-within-card format for better project visualization
Early prototypes
To simplify the visual clutter, I made a prototype that revealed the advanced features as a dropdown triggered by a checkbox.
Make it open and close?
This approach tried to balance simplicity for novice users with powerful features for experts. This was an improvement, yet, this concept failed in user testing.
From developer thinking to user experience
User test A
Objective: Evaluate the effectiveness of multiple search options, advanced search and filters.
Visual confusion: The multiple search options created a negative user experience by overcomplicating what users expected to be a straightforward search process.
Cognitive overload: The presence of too many options near the search box (including title-only search) caused users to spend excessive time trying to understand the interface rather than actually performing searches.
Six kinds of search for multiple words = too many options!
Annotations on screen above: 1 “AND” boolean search 2 “OR” boolean search 3 exact phrase search 4 full document text search 5 excludes certain words. 6 searches various metadata.
User test B
People didn’t want to distinguish between “Advanced search”, “title search” and “full text search.” That was developer thinking. Users wanted just one thing: “Search.”
Recognition over recall
This high level lawyer sent us an email to get technical support. But when he came in for the user testing, he had forgotten the solution. It was just a poor labeling choice by the developer.
“To find the full text search button…I had to send you guys an email to tell me where to find it because I began by being very frustrated. I couldn’t find anything.”
User quotes
(numbers relate to image above)
1. Google-like search: “If it were possible to do a Google-like search where I could go into a database that was all EISs from the dawn of time and do a Google search for the same way I would do any other kind of Google search just against a database fro EISs, that’s what I’ve been hoping would happen…”
Insights: Use a simple search box like Google. This lawyer is “tech-savvy” and already using keyboard search term modifiers in the search box that are common in legal databases. Show a table of keyboard modifiers for advanced users.
2. Toggle between title search and full-text search: “To find the full text search button…I had to send you guys an email to tell me where to find it because I began by being very frustrated. I couldn’t find anything.”
Insight: Nobody used title-only search–but just in case add a checkbox for that.
3. Deciding on relevance using text snippets: “I don’t know whether this is going to be a real discussion of extreme heat or whether this is just a random reference to public health. And I’m not interested in that.”
Insight: Text snippets are hard to read in table format. They also don’t give enough context for an energy-consuming decision to download the document and read it. Future feature: PDF reader to preview the document.
Replacing advanced search with a table of search word modifiers
To help those who understand advanced search, I designed a table incorporating search-word modifiers from various systems they might be used to.
The pivot:
bridging developer logic and user needs
Initial user tests revealed a contrast between developer-centric design and user expectations: while developers favored complex search options and table formats, users craved simplicity and intuitive navigation.
During a crucial all-team meeting, I proposed what might look like a radical shift. But it was supported by the research above.
- Simplify to a single smart search box
- Moved search options to a familiar sidebar filter and…
- Replaced advanced search with keyboard modifiers
- a card-within-card format for scanning project data and grouping relted documents
Addressing Team Concerns
After my presentation, the meeting initially went silent. Either they were thoughtful, or about to veto my ideas. The team worried about:- making major design decisions based on a limited number of user interviews.
- whether they could afford the time and developer costs for continuous changes and testing.
- an assumption that most of their users were fairly tech-savvy, potentially not needing simplified interfaces.
- qualitative insights did not need large data sets
- the investment in technology is improved with UX
- designing for accessibility has been shown to help everyone.
The Resolution
The moderator tabled the questions so the team could discuss the new ideas off-line for the next all-team meeting. At that time, I showed this sketch and mockup in my presentation:
UX and developers reach agreement
At the next meeting two weeks later, the team unanimously agreed to these design pivots:
- The card-within-a-card pattern allowed users to see search results as whole projects, with process documents set in a time sequence.
- Developers implemented a single search box, with the data team making it smarter, similar to a Google-style search box.
- We replaced advanced search features with a chart of keyboard modifiers for advanced users.
- We moved options to a sidebar filter, a familiar pattern from e-commerce and library sites.
This pivot not only improved user satisfaction, but unexpectedly enhanced the platform’s value–users began to search for similar projects and learn how others solved similar problems–very difficult to do previously.
Results
Metrics
User testing Participants: I interviewed over 30 people from 5 different personas or user groups.
816
registered users
5,500
active users
3,013
searches
78,000
page views
8,743
Engaged sessions
12,909
downloads
Estimated success rate: Considering a download as a conversion, there were 4.3 downloads for every search performed, a 430% success rate.
Testimonials from users
These were some responses from a user survey sent out during a funding hiatus when the public site was offline.
How much time or money do you think using NEPAccess saved you? Just a quick estimate, if possible.
It enabled me to find things would would have been impossible to find otherwise.
NEPAccess saved me probably at least 50 hours of time trying to track down all the information!
I am not sure how much time or money, more that NEPAccess provided me with the ability to look at NEPA documents I would never have access to or never be able to find. Its utility is almost invaluable.
Please describe how NEPAccess helped you.
It is nearly impossible to find old NEPA documents online, even the agency with whom you may be currently working doesn’t have access to all their NEPA documents. Examples of what has been done including approach, methods, and wording is a huge time saver and allows for consistency in wording and style within an agency.
I was able to ask a scientific question about how citizen science data are used in EISs (from NEPA) that would not have been made possible any other way.
I was doing research on the scope of NEPA review recently implemented by the Corps, so having EISs and EAs available in a single platform was very, very helpful. The criteria for searches were also very efficient.
Where will you go for the information you are seeking while NEPAccess is unavailable?
Will try Google again (to little avail)
I don’t know. I suspect the research I am curious about just simply won’t get done.
I do not think that what I was looking for is available any other way. It’s not practical to do manual searches of hundreds or thousands of EISs.
Testimonials from team members
Laura shared with me your findings based on the three interviews conducted thus far. Excellent overview and analysis of priority fixes! … First, we learned SO much from these interviews. Thank you so much for organizing them and thinking through the resulting changes that need to be made… With some of these basic items fixed now, we can then learn more about how users are likely to dig more deeply into more complex searches.
–Team member, Professor of Practice in Collaborative Governance at the University of Arizona School of Government and Public Policy
Paul’s UX work has helped to define our project–and has helped it to stand out. The best cocepts, data, and progrsmming in the world can be lost if the people… can’t use it, or stop trying. I see so many important institutions that lack this basic insight.
Project leader, UA law school
Learning
Findings
- User preference for simplicity: Users strongly preferred a simple, Google-like search functionality over complex options. The design was simplified to allow users to focus on their search tasks rather than understanding the UI.
- Card format choice over tables: A card format was adopted instead, offering improved readability, visual grouping, enhanced hierarchy, and clear time sequences.
- Comprehensive project view: Users wanted to find related documents within projects and browse other projects to learn from solutions that others have used. This created an institutional memory.
- Universal Design Approach: Both expert users and common citizens faced similar psychological obstacles when using the interface, emphasizing the need for a universally accessible design.
- Alignment with Government Recommendations: In 2024, the White House Council on Environmental Quality issued a report to Congress that aligned with NEPAccess’s proposals for improving NEPA efficiency through data science and UX design.
These findings resulted in significant design changes showing that improved accessibility and usability of a large data repository, supporting better decision-making in environmental policy and infrastructure planning.
User quotes
Improving usability led to users finding new ways to use the site that we had not anticipated
“What NEPA access offers me is the ability to quickly search to find those critical issues on the project I’m working on, find ways that others have dealt with it. And this is, by the way, the first time that something like this has ever been available, and it’s really a game changer… And I’m really excited to be using this on every project.”
–Consultant, multinational infrastructure consulting firm
Once a person found a document that might be useful, they still had to search within the PDF to find the text that matched their query.
Search is only half of the work. Once they found a candidate and downloaded it, a person had to spend more mind-numbing brain time searching through hundreds of pages within the PDF to find the relevant sectioons.
Future feature: This prompted our research into a Retrieval Augmented Generative (RAG) AI to find and summarize long sections of text.
User research engages early adopters, who build social proof within their networks.
According to the “law of diffusion of innovation,” (E.M. Rogers, 1962) there is a tipping point as new ideas diffuse through enough early adopters before they enter the mainstream. Our participants became early adopters.
Design must translate computer system models to human mental models of how things work
Nobody wanted to read technical language in the interface. One of my tasks was to replace system language with labels that everyone already understood. They didn’t want to think about how the system worked.
Are they tech-savvy?
I decided this was not a useful term.
Future
