Meso-scale modeling of residential and business locations

Worldwide, cities are growing and consequently becoming more complex. It is difficult to understand the possible impacts of policy, planning and design options to urban systems. We present a modeling framework that enables fast feedback on proposed interventions. The proposed framework models residential and business agglomerations in the meso-scale and allows full control on any stage of the modeling and simulation process. At its core is a spatial distance network sub-model that captures distances between sub-areas. Each sub-area is represented through a maximum likelihood equilibrium model of residences and firms. In practical terms this supports exploring many possibilities within the short time frames typically available in planning processes. We have implemented the framework and demonstrate our implementation with both abstract data and data from the city of Zürich.