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

Provided by Research Papers in Economics

42^nd ERSA Congress - Dortmund August 27^th-31^st 2002

The urban sprawl dynamics: does a Neural Network understand the spatial logic
better than a Cellular Automata?

Lidia Diappi, Paola Bolchi, Lorena Franzini *

Massimo Buscema, Marco Intraligi**

* Dep. Architecture and Planning- Polytechnic of Milan-Milan

** Semeion Research Center- Rome

Cellular Automata (CA) are usually considered the most efficient technology to
understand the spatial logic of urban dynamics: they are inherently spatial, they are
simple and computationally efficient and are able to represent a wide range of pattern
and situations.

Nevertheless the implementation of a CA requires the formulation of explicit spatial
rules which represents the greatest limit of this approach. Whatever rich and complex
the rules are, they aren’t able to capture satisfactorily the variety of the real processes.
Recent developments in natural algorithms, and particularly in Artificial Neural
Networks (ANN), allow to reverse the approach by learning the rules and the
behaviours in urban land use dynamics directly from the Data Base, following a bottom-
up process.

The basic problem is to discover how and to what extent the land use change of each
cell i at time t+1 is determined by the neighbouring conditions (CA assumptions) or by
other social, environmental, territorial features (i.e. political maps, planning rules)
which where holding at the previous time t. Once the NN has learned the rules, it is able
to predict the changes at time t+2 and following.

In this paper we show and discuss the prediction capability of different architectures of
supervised and ANN.

The Case study and Data Base concern the land use dynamics, between two temporal
thresholds, in the South metropolitan area of Milan.

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