research portfolio

projects, products, and designs

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Research-to-Operations Framework

1. Diagnose

2. Redesigns or Modifications

3. Empirical Testing

   • Primary data collection and analysis

   • A / B Tests

   • User testing

   • Control / Treatment Tests

   • Mixed method designs (surveys, experiments, focus groups, interviews, think-out-louds, etc.)

4. Synthesizing and Contextualizing Results

5. Making Data and Evidence-backed Recommendations

Framework in action

research-driven projects and products

Step 1: DIAGNOSING hydrological forecast visualizations for the National Weather Service

Using social science research and best practices in visualization design, communication and decision sciences, and cognitive psychology to diagnose usability and understandability challenges with decision support tools such as forecast visualizations showing high flow magnitude in instances of flooding.

The images below are only for representation purposes as these product designs are yet to be finalized.

Most people generally associate the color red with the ‘worst case’ or most extreme scenario indicative of the highest danger. Using ‘red’ to denote ‘moderate flooding’, therefore, can be easily misinterpreted and can draw attention away from the purple being used to denote major flooding.

Using green as a warning color to show flood warnings is counterintuitive and may not get users to perform the behaviors expected during a hazardous event such as a flood. Design choices, therefore, must be simple, intuitive, and deliberate.

SAMPLES OF OTHER DECISION SUPPORT PRODUCTS DIAGNOSED:

Step 2: REDESIGNING / MODIFYING hydrological forecast visualizations for NOAA

National Water Prediction Service

NOAA - NWS are developing a hydrologic decision support mapping tool called the National Water Prediction Service which is intended to be a substantial upgrade to an existing tool. I was fortunate enough to work with an incredible team of hydrologists and scientists at the Office of Water Prediction and NWS.

Situation: NOAA NWS needs a new interface. The image below was their original landing page for this under-development interactive map.

Task: They needed us to run a social science diagnostic on this and identify potential pitfalls and pain points that could impede the usability of the product and hamper accurate interpretations of the rich information in contains.

Action: I worked with my colleagues to diagnose design elements that can be potential pain points for users - such as the use of non-intuitive defaults to auto-populate the thousands of green dots that show gauges with no flooding; and using deliberate design choices to draw users’ attention to the priority information such as the data points that indicate major flooding or other severe forecasted hazardous conditions.

Result: I designed a toolkit to clearly and efficiently communicate the findings from our diagnostic evaluation alongside science-based recommended modifications. The second image below shows a cleaner UI, a de-cluttered landing page, and a clever use of defaults to highlight the most important information while still allowing users to activate layers that reveal other data that supplements decision-making.

U.S. Drought Outlooks

Step 3: EMPIRICAL TESTING of forecast visualizations for NOAA Climate Prediction Center (CPC)

Situation: The NOAA Climate Prediction Center (CPC) publishes monthly and seasonal drought forecast visualizations which support decisions across multiple sectors such as agriculture, shipping, transportation, resource management, emergency management, etc.

Task: CPC wants to know how they can improve the design of their US Drought Outlooks so that they can be made more intuitive and have an updated look to them.

Action: I led a workshop for our CPC partners at the NOAA office in Maryland in 2023. My first action was to get them to identify what the key message they want to communicate through the Drought Outlook forecasts was. From focus groups and interviews, we had already run a diagnostic evaluation on these products to reveal that some of the primary pain points were the colors and legend categories being used. My control / treatment or A/B testing therefore focused on designing a survey-experiment such that the survey could measure user preferences while the experiment could specifically identify the precise design modifications that are causing statistically significant improvements in product usability and understandability.

Each of the three designs above represents an experimental graphic modification we will be testing before synthesizing results and providing evidence-based recommendations to NOAA CPC and NIDIS on which design modifications they should operationalize and why.

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