Space Syntax vs Agent-Based Modelling in the Maze of Urban Complexity: A Critical Comparison Between Top-Down and Bottom-Up Approaches and Applications

By their very nature, cities are complex systems in which relationships and phenomena occurring in them are difficult to understand by adopting a cause-effect logic. This, as a consequence, challenges city Governance in developing effective plans which could properly take into account many interconnected aspects influencing people's well-being. In this general picture, spatial modelling is crucial to understand complex urban dynamics. In particular, it looks necessary - for both academics and practitioners - to develop a wide awareness of the urban planning 'toolbox' and a solid knowledge of the inner development logic and potentials of each tool. In fact, the clearer the tools' rationale, the more appropriate the tool choice which, in turn, is made accordingly to the desired analysis and answers. In this way for any specific investigation a proper model could be chosen and developed, thus representing an optimized decision support tool for urban planning policies. As such, this paper proposes a comparative analysis of two antithetical approaches to spatial and urban modelling: the top-down versus the bottom-up approaches, with a threefold objective. First, this paper is going to investigate the differences and similarities between the two approaches with a brief excursus of their fields of application and main tools. The focus will shift, then, to the analysis of Space Syntax and Agent-based Modelling as relevant expressions of the two approaches, retracing significant uses of these techniques in the understanding of urban dynamics. Third, their potential integration in the field of crime prevention will be discussed.