Tuna Anglers in the Online OOS World: A pilot study of how usability testing can guide the development of OOS data products and web portals

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Tuna Anglers in the Online OOS WorldA pilot
study of how usability testing can guide the
development of OOS data products and web portals

• Conducted by members of the COSEE NOW team for
  MACOORA
• September 15, 2008

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Project Goals

• To gather input from fishermen to understand how
  ocean observatory data can best be communicated
  via Web-based visualization displays, such as
  maps, charts, etc.
• To use the results to inform the design of data
  displays for the COSEE NOW website, as well as
  MACOORAs own data and model forecast displays.
• To train COSEE NOW team members on the
  fundamentals of usability testing.

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Method Usability Testing

• An digital media evaluation method that measures
  the effectiveness of your digital Web product
  with members of your target audience.

• Why this Approach?
• Verify appeal of current design
• Verify effectiveness of current design
• Modernize existing design
• Determine usefulness of content
• Determine how best to display data so that its
  useful to the data users

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Usability Testing

• A typical usability test involves
• recruiting a group of users representative of
  your target audience
• meeting with them at a neutral location
• observing and interviewing them individually as
  each performs common tasks on a Web site
• audio video recording the conversation and
  tracking the viewing of the Web site in real time.

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Test Subjects

• Fishermen/woman n 7
• Ages all 45
• Professions varied
• Fish at least weekly, mostly offshore for tuna
  and shark
• Use of Internet 5 of 7 daily

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Participants Use of Online Data

• Do you use the Internet to search for information
  before going fishing? Yes (5 of 7), Sometimes
  (1/7), No (1/7)
• What type of information do you look for?
  weather (3), water temperature (4), water
  conditions (2), tides (2), wind, turbidity,
  chlorophyll, solunar tables (1 each)
• Role of online ocean data to locate conditions
  where fish are likely to be

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Test Objects

• Website Interfaces
• theCOOLroom.org web site
• Draft redesign of the COOLroom web site
• Rutgers SST data web site
• Data Visualizations
• Sea Surface Temperature Map
• Underwater Profiles from a Glider
• CODAR Velocity Map
• CODAR Velocity Animation

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Finding Temperature Data
During this task, users were asked to find water
temperatures that would help them fish today.

• Subjects start with location the general area
  where they plan to fish
• When find sea temperature images, they look for
  the most recent image with the most data (least
  cloud cover) for their fishing location

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Finding Temperature with SST

• Then they look for water temperature breaks
  areas where there are dramatic side-by-side
  temperature differences
• Then they want to locate those break areas
• Theyd also like to see the bottom depth and
  topography at break locales

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Data Visualization Findings

• Location can be via a list (4) or map (3)
• Map would like zoom feature, plus grab and move
  feature
• Water temperature images preferably in
  Fahrenheit
• Date and time preferably local
• Water temperature breaks indicated by colors or
  well-defined lines
• Break area locations longitude/latitude
• Bottom depth and topography need major features
  and detailed (5 degree) lines

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Subsurface Glider Data

• 6/7 recognized the display as temperature and
  knew how to interpret the colors (preferred
  Fahrenheit to Celsius)
• 2/7 recognized the thermocline being represented
  in the temperature graph and expressed its
  importance in offshore fishing
• None of the participants matched up the transect
  plots with their location on the map

• 2/7 expressed concern that if the data was not in
  the Hudson Canyon area it was not useful to them

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Real-time Surface Currents

• 5/7 users recognized the arrows as the flow of
  water, but many were not sure. 3 did not
  realize this until the animation.
• 3/7 users identified the colors as temperatures,
  even after noting the colorbar axis showing
  velocity
• Some users wanted to click on the image to zoom
  in to see smaller regions
• Users did not like the units (cm/s). Because of
  the range of values (0-50), one user mistook them
  as wind speeds (i.e. mi/hr).

Expressed interest in seeing where the hotspots
they identify in the temperature fields will
move. Especially useful if the clouds cover the
more recent SST data or as a forecast for future
movements.
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Surface Current Animation

• 6/7 were able to recognize the depiction of
  currents and how they change over time
• 4/7 users said they thought the colors were
  showing temperatures and assumed the animation
  was showing directions of temperatures or
  currents of temperatures
• Because of the usefulness of combining temp and
  current data, many users were interested in
  exploring further.
• 2/7 users wanted to determine if the animation
  was showing hourly or seasonal changes but did
  not recognize the timeframe of the animation

Several users wanted additional information about
the animation, including - What does the map
show? - How frequently it was updated? - What
time period does it cover? - an example
animation with a description of the features it
depicts
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Recommendations

• Provide detailed SST imagery
• Provide links to other relevant data sources on
  the web
• Develop new data displays and tutorials to
  explain their use
• Improve data legends and displays

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Acknowledgements

• We would like to thank David Chapman for the
  funding to conduct this study. We are grateful
  for the partnership of MACOORA partners. A
  special thanks to Cia Romano and Kyle Kulakowski
  at Interface Guru for their knowledge and
  guidance and Jeff Yapalater, from the Freeport
  Tuna Club for recruiting the participants and his
  gracious assistance in logistical planning for
  the test. In addition, were grateful to the
  COSEE NOW team Chris Parsons (Word Craft) for
  her assistance in data tabulation, Janice
  McDonnell and Dr. Rebecca Jordan (Rutgers
  University) for moderating the tests, Stephen
  Gray (graduate student at Rutgers University) for
  assistance with working with the test subjects,
  Sage Lichtenwalner (Rutgers University) for his
  technical expertise, Corinne Dalelio (graduate
  student Rutgers University) for assistance with
  data tabulation, and Igor Heifetz and Lisa
  Ojanen (Rutgers University) for attending the
  training.