Urban and Hinterland Evolution Under Growing Population Pressure

The design makes use of the general modeling procedure of “Sociodynamics” which is exhibited in detail in Weidlich (2002). It consists of the following steps: 1. Search for a “window of perception”. This search leads to the choice of a model-specific set of appropriate order parameters or key variables. It is anticipated that these variables satisfy an approximately self-contained dynamics under general boundary conditions. 2. The elementary steps of dynamics. The elementary steps lead to small changes of the order parameters. They are provided by motivation-driven probabilistic transition rates composed of mobility and utility terms. These establish the link between the microlevel of order parameters. 3. The equations of evolution. As a consequence of the elementary dynamic steps equations of motion for the order parameters can now be set up. They can be derived on the stochastic level (master equation) or on the deterministic level (quasi mean value equations) for the mean evolution of stochastic trajectories. 4. Simulation of Characteristic Scenarios. The transition rates entering the dynamic equations contain certain control- and trend- parameters. After their calibration characteristic (realistic or virtual) scenarios can be simulated by solving the equations. These can be compared with empirical data. 5. What can be learned from the model?. First, the urban evolution depends decisively on the trend parameters in the transition rates. They are measures of the influence of conditions of the landscape, of socio-economic preferences, of neighbourhood relations between building plots, and of migratory trends in the population. Secondly, the model simulations of urban dynamics not only include the imitation of the real evolution but also virtual evolutions and forecasts resulting from the choice of different trend parameter sets. Thirdly, the urban evolution is path-dependent. Even if the trend parameters coincide, small deviations of the initial conditions may lead (at instable situations) to diverging further evolution paths, due to inherent nonlinearities. Simulations help to detect the situations where bifurcations occur. Fourthly, the migration of population and the development of city and hinterland are interrelated processes. In particular, migratory phase-transitions, e.g. a population rush from hinterland to city can occur and are analyzable in terms of the model.