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explicitly incorporate time they are (potentially) better suited to this task and cartographers
have long-sought to exploit the potential of animated maps over the past 50 years (Campbell
and Egbert, 1990; Harrower, 2004).
In the foreword to
Interactive and Animated Cartography
, Mark Monmonier extols: 'In
rescuing both makers and users of maps from the straitjacket of ossified single-map solu-
tions, interactive mapping promises a cartographic revolution as sweeping in its effects as
the replacement of copyists by printers in the late fifteenth and early sixteenth centuries'
(Peterson, 1995, p. ix). Although the 'promises' of this revolution have been at times slow to
materialize, there is justifiable enthusiasm for animated and interactive mapping systems.
Moreover, the concurrent and rapid maturation in the last 15 years of (i) animated mapping
software, (ii) widespread and powerful computers, (iii) fast Internet connections and (iv)
an explosion of rich temporal-spatio data - and tools for viewing those data - has allowed
animated mapping to flourish (e.g. NASA World Wind, Google Earth). Unfortunately, aca-
demic theories and field-validated best practice have not kept pace with these technological
changes, and as a research community we are still learning how to get the most out of our
animated maps and, quite simply, know when or how to best use them. Animated maps do
not replace static maps, nor are they are not intrinsically better or worse than static maps;
they are simply different. Like any form of representation (words, images, numerical for-
mulas), animated maps are better suited to some knowledge construction tasks than others.
Understanding what those tasks are is one of the key research challenges for geovisualization
(MacEachren and Kraak, 2001; Slocum
et al
., 2001).
Research into the cognitive aspects of map animations would help to shed light on how
users understand and process information from these dynamic representations. The con-
struction of sound theoretical foundations for the effective representation of spatio-temporal
phenomena and the adequate depiction of fundamental spatial concepts and relationships,
including people's understanding thereof, is not new to GIScience and geovisualization, but
due to wider usage of GIS tools outside of geography, it has gained new importance in the
past few years.
Preliminary research has shown that animation can reveal subtle space-time patterns that
are not evident in static representations, even to expert users who are highly familiar with the
data (MacEachren
et al
., 1998). A good example of the power of animated maps to stimulate
new knowledge is provided by Dorling and Openshaw (1992). In their investigation of
leukaemia rates in northern England, previously unrecognized hotspots (localized in both
space and time) emerged from an animated map of these data. The cancer hotspot in a specific
area lasted only a few years and had been missed in previous (i.e. static) analysis because
the temporal component of the data had been collapsed, thus 'grossly diluting situations
such as these by amalgamating years of low incidence with a pocket of activity' (Dorling
and Openshaw, 1992, p. 647). The animation also revealed a second unexpected process,
which they described as a 'peculiar oscillation' between leukemia cases in Manchester and
Newcastle with an approximately five-year periodicity. Fresh insights such as these provide
a useful starting point for more formal spatial analysis.
Wood (1992) chastises cartographers for trying to distil time out of the map and states
that 'time remains the hidden dimension' in cartography.
But the map
does
encode time, and
to the same degree
that it encodes space; and
it invokes a temporal code that empowers it to signify in the temporal dimension
(Wood, 1992, p. 126).
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