Geography Reference
In-Depth Information
In their suggestions for improving animated maps, Johnson and Nelson (1998) suggested
giving the subjects the freedom to have direct control over the animated sequence. Moreover,
Andrienko, Andrienko and Gatalsky (2000) claim that the greatest understanding may be
achieved when the animation is under user control and the geospatial data can be explored
in a variety of ways.
Temporal tense is an issue explored by Wood (1992, p. 126): '[t]ense is the direction in
which the map points, the direction of its reference in time'. Building on basic linguistic
concepts of present, past and future, Wood demonstrates that
all
maps possess 'temporal
codes'. These temporal codes allow us to construct a temporal topology for geographic
data. Like its spatial counterpart, the concept of temporal topology allows the relationship
between temporal entities to be understood and encoded. The development of temporal
analytical capabilities in GIS such as temporal queries requires basic topological structures
in both time and space (e.g. an object is both
to the left of
and
older than
another object;
Peuquet, 1994).
4.4 Potential pitfalls of map animation
While there are some representational tasks for which animation seems especially well-suited
(e.g. showing motion), equally there are some representational tasks for which animation is
poorly suited. For example, animating changes in property ownership of a neighbourhood
over the past 10 years is unwise because these changes are discrete events that can be concep-
tualized as happening without time. Although buying property is a complex human process,
the actual cadastral change is applied at an instant of time (e.g. noon on 1 January 2007).
Creating a linear temporal animation of this event might be ineffective (Goldsberry, 2004),
and certainly would be boring since the animation would depict long periods of no change,
punctuated by periods of instantaneous change that might easily be missed (
Nothing. Noth-
ing. Nothing. Something. Nothing.
). Put another way, important changes often occur over
very short time intervals, and thus a static map with ownership names and dates, or even a
simple data table, is likely to be a better choice for the tasking of retrieving dates.
Choropleth maps are seldom created with more than 10 data classes, seven data classes
being the often-cited upper limit for good map design (Slocum
et al
., 2005). These limits
derive from psychological studies performed a half century ago (Miller, 1956) that revealed
that most individuals can process seven (plus or minus 2) 'chunks' of information at once.
Probably class limits are even lower for animated maps considering the increased human
memory load required to remember earlier map frames when looking at later ones in the
animation sequence (Goldsberry, Fabrikant and Kyriakidis, 2004; Harrower, 2003). Does
this mean that animated maps should contain no more than seven frames? Clearly not, as
people are capable of working with and understanding animations composed of thousands
of individual frames, but the question remains: what are the cognitive limits to the complexity
of animated maps? In other words, at what point do animated maps become too data-rich
for the user? What forms the basic mental chunks of an animated map? How can the size
of these chunks be increased? Although answers to these questions are few, we suspect it
is driven in part by the length of the animation (i.e. running time), the complexity of the
spatial patterns depicted (i.e. spatial heterogeneity) and the complexity of the patterns of
change (i.e. temporal heterogeneity).
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