Using Multidimensional Networks to Better Understand Constraints and Possibilities of Urban Development

Urban and regional systems are subject to both intentional and unintentional transitions, during which the system’s economic structure is altered. To better understand, guide, or respond to these transitions, planners must move beyond traditional ambiguous notions of economic structure. A recent trend emerging from the nexus of complexity science and regional science has been to operationalize urban economic structures as networks of interacting economic components. However, these networks have previously only been constructed for single types of economic entities such as occupations or industries. Here, for the first time, multiple types of entities are merged into a single network, offering a granularity not possible using a single type of economic entity. In this study, occupations, industries, college degrees, and patent technology codes are merged into one multidimensional or multimodal network. As in previous studies, a dense core of highly connected entities emerges in this network. The network locations of individual cities are contrasted, and community detection algorithms are used to identify clusters of highly connected economic entities, showing that the densely connected network core is associated with science and technology and business-related economic entities. Proximities between individual cities within the network are also measured revealing that many cities that are close to each other in the network are also close to each other in physical space. Finally, several potential applications and future directions for research are outlined