Geoscience Reference
In-Depth Information
6.3.2.4
Step 4: Model Validation
Model validation involves measuring how well the model duplicates real-world
phenomena. Model validation in the absolute or predictive sense is theoretically
infeasible as no single model can reproduce every aspect of a complex open system
(Oreskes
1998
). That said, statistical measures can provide a useful benchmark for
assessing how well different complex model configurations perform (Windrum et al.
2007
;Manson
2007
). Validation requires comparing model results to empirical
data, to which end we used three different metrics: inner-migration rates, Syrjala
tests, and minimum spanning trees. As model migration rates are calculated from
actual relocation data, the number of modeled movers equals the actual number
of migrations and values of in Eq.
6.1
and for Eq.
6.2
for direction. The main
difference is therefore the spatial distribution of these migrant households. The three
approaches employed to assess spatial fit are well suited to the problem at hand given
that point pattern methods vary in their sensitivity and accuracy, as determined by
their capacity to discriminate between point patterns, remain stable over different
samples, and deal a range of underlying distributions (Wallet and Dussert
1998
).
The three model validation approaches offer specific advantages while com-
plementing one another. First, inner-migration rates compare the percentage of
households that move within housing submarkets (i.e., those that stay within a given
area or neighborhood). We use a multiscalar model specification across several
submarket specifications to develop a strong measure of comparison between
modeled and actual migration. Second, Syrjala tests compare the spatial distribution
patterns of simulated and actual destinations of migrant households, offering the
advantage over many standard point pattern analyses in assessing not just locations
but also quantities across several scales of aggregation via a modified procedure
that apportions simulated and actual destination points. Third, use of minimum
spanning trees (MST) offers an optimized nearest-neighbor distance analysis that,
instead focusing on local nearest neighbors, describes the shortest, noncircular path
connecting all points. Each of these three approaches offers distinct advantages as
well as overlaps in validating how well simulated and actual migration match.
6.4
Results
In order to compare the simulated results with the actual distribution of migration
destinations, we employ inner-migration rate comparison alongside Syrjala tests
and MST to compare the similarity between the spatial distributions of actual and
simulated migration destinations. Both distance and distance-and-direction yield
realistic moves across scales of aggregation. Inner-migration rates at various scales
indicate that the model recreates realistic aggregate spatial patterns of intraurban
migration. Inner-migration rates measure the percentage of migrants who remain in
the originating spatial unit and indicate the extent to which simulated moves match
real relationships among vacant housing supply, move distance distribution, and
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