The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

high highways
∕∖∕ main roads
/V other roads
∕∖∕ railroads

M -100 --80

M -80 - -60

M -60 - -40

M -40 - -20

M -20 - 0

M 0 -10

M 10 -20

Figure 7 - the differences between observed and calculated values, in blue the underestimations, in
red the overestimations

8. The prediction capabilities of the SANN

Once the learning and testing phase has been concluded, the averaged weight matrix of
the SANNs is processed with the Data set of cells “potentially” in urbanisation in the
next time lag (1994-2008). The prediction concerns “green” cells with urbanized
neighbourhood at 1994.

Figure 8 shows the estimated surfaces for each land use. As expected the trend is linear,
given the availability of only two temporal thresholds.

The resulted pattern shows a probable scenario (fig. 9 - Residence prediction) where
prevailing urbanisation process takes place at the boundaries of the cities and villages

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