Understanding how spatial configurations of economic activity emerge is important when formulating spatial planning and economic policy. Not only microsimulation and agent-based model such as UrbanSim, ILUMAS and SIMFIRMS, but also Simon's model of hierarchical concentration have widely applied, for this purpose. These models, however, have limitations with respect to simulating structural changes in spatial economic systems and the impact of proximity. The present paper proposes a model of firm development that is based on behavioural rules such as growth, closure, spin-off and relocation. An important aspect of the model is that locational preferences of firms are based on agglomeration advantages, accessibility of markets and congestion, allowing for a proper description of concentration and deconcentration tendencies. By comparing the outcomes of the proposed model with real world data, we will calibrate the parameters and assess how well the model predicts existing spatial configurations and decide. The model is implemented as an agent-based simulation model describing firm development in the Netherlands in 21 industrial sectors from 1930 to 2004.

A surprising aspect of the agglomeration economy is the lack of attention to the impact on the physical environment. Even in the field of spatial planning, road infrastructure has been built in situations where the consideration of the agglomeration economy is insufficient. The urban scaling proposed by theoretical physicists is an excellent tool to solve this problem but is only at the level of conceptually comparing the index values extracted by individually scaling socioeconomic indicators and urban infrastructure with the population. Accordingly, the frame model scales the urban infrastructure with the number of workers by industry sector and includes a density externality structure so that the agglomeration economy and urban infrastructure can be linked directly. Three experiments were conducted to verify the frame model: first, the Zipf distribution of economic activity found straight lines in large cities, peaks in medium cities and hills in small cities; the cities were categorised by urban size. The second experiment verified that linearisation was due to Jacobs externalities, while the third confirmed that the peak was due to Marshall–Arrow–Romer externalities. Moreover, in distinguishing traditional and modern industries, thresholds of 0.6 in agglomeration and 1.0 in economic interaction were found.

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