Simple Agents, Complex Emergent City: Agent-Based Modeling of Intraurban Migration - Computational Approaches for Urban Environments - page 135

The Syrjala test of intraurban migration in the Twin Cities sheds light on complex
patterns. Key measures are the percentage of subdivisions (not the number of
households) that are n ot statistically different fr om the actual distribution ( H 1 ), the
mean Syrjala statistic S , and the mean p value p.S/. First, both decision-making
models score well on H 1 , where 71 % of subdivisions for the distance-only strategy
and 69 % for the distance-plus-direction strategy match reality. Second, the distance-
only strategy fares slightly better than distance-plus-direction strategy in recreating
real migration patterns given lower average Syrjala statistics (0.696 vs. 0.771) and
higher average p value (0.212 vs. 0.182). Overall, these two strategies are similarly
successful in how they replicate real-world migration destinations.
The minimum spanning tree (MST) method focuses more on the relative position
among intraurban migration destinations than the other two methods. Besides
providi ng trees for visual inspection, the approach generates simple mean path
length d and variance ( d ), where a short path length indicates that points are
close to each other and a small variance means the points are evenly distributed.
An MST is network structure that connects all nodes with a minimum total distance
(Zahn 1971 ;West 2001 ). MSTs treat individual locations as nodes of a network
in which each is connected to neighboring locations, which preserves information
about the adjacency of nodes (Fig. 6.6 ). Importantly, an MST minimizes the length
of the path connecting location while guaranteeing that every location linked to
another one (Guo 2008 ). This approach preserves both absolute and topological
spatial characteristics in a way that heightens sensitivity and accuracy (Wallet and
Dussert 1998 ), as well as offering the benefit of identifying spatial hierarchies of
migration.
Two specific examples illustrate how MST analysis compares the spatial distribu-
tion of simulated and actual migration destinations. In the exurban city of Norwood,
migration extends toward the Minneapolis downtown (Fig. 6.7 ), which mirrors the
simulated results. However, both decision-making models also produce two extra
spurs on the MST that trend south and north, which is not consistent with the true
situation.
For the inner-ring suburban city of Robbinsdale, simulated results have a more
concentrated pattern than the real situation, implying that the average path length of
simulated move destinations is shorter than the reality (Fig. 6.8 ).
A comprehensive comparison using MST features provides insights into the
predictive powers of the two different de ci sion-making strategies (Table 6.1 ). In
terms of the mean shortest path length d , the distance-only strategy produces
the smallest minimum root of mean squared errors (RMSE) compared to actual
migration. Both methods generate a smaller average path length than the real
migration data, which means more compact patterning of moves. The lower value
of the direction-plus-direction method compared with the distance-only method is
expected because the directional bias compresses the migration destinations into a
smaller region. The significantly shorter average path length of the distance-only
method, together with the lower variance, implies that the distance-based methods
tend to generate a more compact pattern than found in reality. In other words, they
will underestimate urban growth and sprawl.