Transcript Fishermen & Internet Data Usability Evaluation

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
Conducted by members of the
COSEE NOW team for MACOORA
September 15, 2008
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 MACOORA’s own data and model
forecast displays.
• To train COSEE NOW team members on the
fundamentals of usability testing.
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
it’s useful to the data users
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.
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
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
Test Objects
1. Website Interfaces
–
–
–
theCOOLroom.org web site
Draft redesign of the
COOLroom web site
Rutgers SST data web site
2. Data Visualizations
–
–
–
–
Sea Surface Temperature
Map
Underwater Profiles from a
Glider
CODAR Velocity Map
CODAR Velocity Animation
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
Finding Temperature with SST
• Then they look for
water temperature
breaks:
areas where there
are dramatic sideby-side
temperature
differences
• Then they want to
locate those break
areas
• They’d also like to
see the bottom
depth and
topography at
break locales
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
Subsurface Glider Data
•2/7 expressed concern that if
the data was not in the Hudson
Canyon area it was not useful to
them
• 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 it’s importance
in offshore fishing
• None of the participants
matched up the transect
plots with their location on
the map
Real-time Surface Currents
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.
• 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).
Surface Current 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
• 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
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
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, we’re 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.