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Craig McCabe
 Craig
McCabe (cam509 [at] psu.edu) graduated this summer with his
Master's Degree in Geography, having worked under Dr. Alan
M. MacEachren. McCabe's thesis looked at the effects of data
complexity and map abstraction on the perception of spatio-temporal
patterns in animated maps. Using a 10-year dataset of weekly
measles infections in Niger, Craig administered an experiment
that employed temporal aggregation and moving-window averaging
approaches in combination with geographic and schematic map
representations to measure participants' abilities to complete
a series of map-reading tasks. During his 2 years as a research
assistant at Penn State, Craig collaborated closely with the
Center
for Infectious Disease Dynamics to create novel methods
of exploring patterns and possible environmental drivers of
measles epidemics in sub-Saharan Africa.
Craig's Thesis Work:
Effects of Data Complexity and Map Abstraction on the
Perception of Patterns in Infectious Disease Animations
Geographic map animations have become an increasingly popular
method for exploring spatio-temporal datasets. While some
have questioned the effectiveness of animations compared to
the static alternatives, little research has been done on
our ability to retain and recall data presented in animated
maps. This study uses a dataset of weekly measles infections
in Niger from 1995-2004 in a controlled experiment to test
the ability of participants to complete a series of map-reading
tasks using choropleth and schematic map representations.
Infectious disease data is often imperfectly sampled and inherently
noisy. As a result, an important question for map animation
designers is whether to represent the raw data directly or
to transform the data to dampen the impact of noise. To address
this question, the experiment compares animations using the
raw weekly data to transformed data sets using temporal averaging
and aggregation. These approaches were used to determine what
effects data complexity and spatial abstraction have on our
ability to perceive spatio-temporal patterns in map animations.
Experiment participants (N = 96) were recruited from undergraduate
geography classes at Penn State University, then divided into
groups according to three data smoothing approaches: raw (weekly)
data, 5-week moving-window average, and 2-week aggregation.
Participants viewed a series of animations of yearly measles
epidemics using three different map representations, then
provided quantitative and qualitative assessments of the spatial
and temporal characteristics of the infection patterns. The
results showed that overall, map representation had a larger
impact on task performance than data complexity, due primarily
to the perceptual salience of large, but low-population districts
in Niger. It was also found that temporal aggregation, an
approach used by infectious disease researchers, did not result
in any significant interpretation errors overall, despite
expectations to the contrary. In some tasks, such as determining
when the peak of a yearly epidemic occurred, aggregation proved
to be most effective, enabling participants to identify the
correct peak week 50% more often, compared to those who used
raw data. The results also support the idea that animated
maps are best used for simple map-reading tasks or gaining
a broad overview of a dataset, and less effective for making
quantitative comparisons.
Example
animations & tasks from Craig's thesis work | Craig's
full thesis
More about Craig's Research
As a research assistant, I spent my first year as a graduate
student collaborating with the Center
for Infectious Disease Dynamics (CIDD), working on an
11-year dataset of measles incidence in Niger. In the process
of exploring spatio-temporal patterns in this highly complex
dataset, I began to use animations. One of the first animation
efforts looked at the apparent relationship between seasonal
vegetative change and the onset of measles epidemics. CIDD
researchers had hypothesized that subsistence agriculture
was driving seasonal migration in and out of cities, resulting
in increased population density in the cities and a corresponding
increase in measles transmissions during the dry season. The
anti-phase relationship between vegetative "greenness"
and measles incidence can be seen in the simple Flash animation
linked below. A Gates Foundation grant awarded to CIDD 2008
involved additional resources to the effort, with a focus
on transportation pathways and population connectivity - to
better understand and improve vaccine supply chain. An animation
of a composite year in Niger (11 years of data from 1995-2005
combined, linked below) was used to look for consistencies
in outbreak patterns for all years. My interest in map animations
and my thesis research topic came about as a direct result
of my still ongoing, successful collaboration with CIDD.
Animation
of Vegetation and Measles 2001-2005 | Composite
Year of Measles Incidence 1995-2005
In my two years in the Master's program, I have also had
the opportunity to contribute to a number other research projects.
Drawing from my experience working as a geologist at the USGS
prior to returning to graduate school, one of my first papers
for Dr. Cindy Brewer's cartography seminar outlined vector
generalization approaches for creating multi-scale geologic
maps, earning an E. Willard Miller award in Geography. In
my 2nd year RA, I also assisted with data and map design on
the CalFloraViz web-map (with Tom Auer and Scott Pezanowski)
which provides an intuitive means of exploring a large database
of plant observations in California, and database, map and
poster design for the DC Crime Viz web-map, which won 3rd
place at the 2009 DHS Summit student poster competition in
Washington D.C. (with Kevin Ross and Robert Roth). Links for
these are available below. In addition, I am currently assisting
researchers at the GeoVISTA Center with an investigation into
the use of the ANSI point symbology standard for emergency
management among Department of Homeland Security agencies.
Geologic
Map Generalization Paper | CalFloraViz
Web-Map | 2009
DHS Summit Poster
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Tom Auer
 Tom
Auer (mta138 [at] psu.edu) graduated in the summer of 2009
with his Master's Degree in Geography, having worked under
Dr. Alan M. MacEachren. Auer's thesis sought to understand
whether explicitly symbolizing time-series change in map animations
would help users recognize patterns in those animations, showing
that for animated map reading tasks explicitly about change,
symbolizations encoding change were most successful. As a
corollary, Auer developed a task typology on movement patterns
found in aggregated point data. Working as a research assistant,
Auer helped develop a web-map, CalFloraViz,
which allows the quick and easy spatiotemporal exploration
of a large California plant sample collection.
Read more about this grad student . . .
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