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Strategic Retail Location
Gravity Modeling (or Spatial Interaction Modeling)
A Spatial Interaction Model
(SIM) considers the likelihood that a shopper in a given location (a)
has
a need for a good or service, and (b) chooses to acquire it from a
particular
store. That likelihood depends on attributes of the customer
(e.g.
age, income), attributes of the store (e.g. floor space, availability
of
parking and public transit, advertising, pricing), and its
accessibility
to the customer. Thus all stores get at least a few patrons from
even the most distant locations, given enough time; but the majority of
shoppers are those who reside or work nearby. This is
consistent
with reality—for example a Los Angeles boutique gets the odd customer
from Tulsa, Toronto, or even Tokyo; but for the most part its
clientele
is drawn from the neighbourhood and nearby towns.
The SIM is
mathematically
analogous to gravitational laws regarding forces that keep planets in
their
orbits, hence the original term, gravity model. Some researchers took
the concept to extremes and proposed bizarre hypotheses of social
physics. For this reason, and other technical reasons, the term
“spatial interaction model” is preferred.
 A
key feature of the SIM is the Distance Decay (DD) profile. This
is a curve that summarizes how patronization of the store declines over
distance. For the first 1–2 km the store dominates the
market.
As distance from the store increases, customers are more likely to
consider
other options, and the likelihood of patronization of that store
declines.
There are differences between the DD profiles of businesses. A
shopper
may drive 20 km for a good deal on a major appliance, up to 10 km for
weekly
groceries, but for just a carton of milk the limit is probably 3
km.
Therefore a convenience store has a completely different profile from
an
appliance store. The compactness and degree of urbanization of
the
community, distribution of competing and complementary opportunities,
shopping
traditions, etc, make a difference. Even for a single retail
chain,
some locations have profiles significantly different from others, even
within the same city. The model specification itself may differ,
depending on the nature of competitive forces.
The SIM doesn't rely on boundaries,
and it does not cast behaviour in black and white. The
destination-choice
process is modelled in terms of probabilities, allowing residents of a
given area to distribute their patronage over a selection of stores,
near
and far.
Each business needs a set
of SIMs specified and calibrated to suit its practice. The SIM is the
foundation of location evaluation and
sales
forecasting. Moreover, the intensive local study required to
calibrate
the models, and to understand the individual deviations from it, are
critical
in interpreting the results of subsequent numerical analyses.
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