Geoscience Reference
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property damage. Yet no one was killed or even injured; citizen response, at
a time of day when many were likely to have been in their homes having din-
ner, was outstanding. The Washtenaw County website credits fine county-level
planning of an emergency services network that warned the public to take
shelter. Evidently, there had also been sufficient education of the public, in
advance, to create an appropriate response (Washtenaw County website, 2012).
Following the tornado that hit Dexter, Sheriff Jerry Clayton with
Washtenaw County credited the preparedness of the Dexter community
for saving the lives of everyone hit by the tornado. Sheriff Clayton was
quoted saying, “We think that's a testament to the emergency warning
system, a testament to public education as to how you respond, and a
testament to the Dexter residents.” As with all severe weather events,
being prepared is key to keeping our communities and residents safe.
The problem of creating an emergency services and warning network is
rooted in spatial and mathematical concepts. Human beings can be con-
sidered scattered as “data points” across a region. The pattern is irregular
and, to some extent, unknown. At best, it is imprecise: We may have street
addresses associated with individuals but we do not know where an indi-
vidual is at any given time, and even if at home, where he or she is located
within the home. What can be pinpointed is the location of warning stations
to distribute information to the scattered population. The problem is how to
locate the siren towers so as to effectively communicate to all in the coverage
target with no gaps in communication.
In the material below, we consider these problems in the context of the tor-
nado theme. However, we encourage the reader to think more broadly about
distributions of data—what might one wish to know about scattered data,
independent of context? To select the concepts and activities from the vast
array available, we let the tornado study help to guide our selection. Here are
a few ideas; we hope the reader will add more.
• Is the pattern of data regular, as in the case of the model of an urban
hierarchy in the last chapter, or is it irregular? Why does it matter? Is
irregularity less predictable than regularity?
• How are the data points clustered? Are they clustered together in
space—trailer parks have tighter clusters of homes than do estates
on large parcels. Think about the importance of clusters of hous-
ing, or their lack of clustering, in terms of tornado damage or other
phenomena.
• How might clusters of data be captured for analysis purposes? All
data within an ellipse of a certain size? All data within a certain dis-
tance of a central point?
• Are clusters, or lines (tracks), or other patterns of data constant over
time? Are there seasonal variations in regularity or irregularity of data
patterns?
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