While the world’s total urban population continues to grow, not all cities are witnessing such growth—some are actually shrinking. This shrinkage has caused several problems to emerge, including population loss, economic depression, vacant properties and the contraction of housing markets. Such issues challenge efforts to make cities sustainable. While there is a growing body of work on studying shrinking cities, few explore such a phenomenon from the bottom-up using dynamic computational models. To fill this gap, this paper presents a spatially explicit agent-based model stylized on the Detroit Tri-County area, an area witnessing shrinkage. Specifically, the model demonstrates how the buying and selling of houses can lead to urban shrinkage through a bottom-up approach. The results of the model indicate that, along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also denoted (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation for exploring urban shrinkage and potentially offers a means to test policies to achieve urban sustainability.
Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in Urban Science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of disease. These use cases not only demonstrate how our geographically-explicit synthetic population can be easily utilized for initializing agent populations which can explore a variety of urban problems, but also show how social networks can be integrated into such populations and large-scale simulations.
Social connections between people influence how they behave and where they go; however, such networks are rarely incorporated in agent-based models of disaster. To address this, we introduce a novel synthetic population method which specifically creates social relationships. This synthetic population is then used to instantiate a geographically explicit agent-based model for the New York megacity region which captures pre-and post-disaster behaviors. We demonstrate not only how social networks can be incorporated into models of disaster but also how such networks can impact decision making, opening up a variety of new application areas where network structures matter in urban settings. CCS CONCEPTS • Computing methodologies → Modeling and simulation; Agent /discrete models.
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