ABSTRACT
Infectious disease diffusion is inherently a complex spatiotemporal phenomenon. Simplifying this complexity to discover the associated structure of the city is of great importance. However, existing approaches mainly focus on distance property in geographic space to examine randomness, dispersion, or clustered structure of the disease distribution. While, the outbreak continuously changes its properties, shapes, or locations. Regardless of this adjacency-based structure, there may be associated spatial units that exhibit similar behaviors towards the outbreak fluctuations in a city. To reveal these characteristics, this research proposes a novel event-based spatiotemporal model, mining associated areas in space and time simultaneously. This model was applied to the cases rate of COVID-19 at the ZIP Code level in New York City. The results showed that the proposed approach could sufficiently address the spatiotemporal association relationships. To better understand the discovered associations, a map visualization approach is introduced, allowing recognition of these association relations at a glance. This approach develops a deep understanding of the spatiotemporal structure of the outbreak and better manifests the association and cause-and-effect relations between ZIP Code areas. The results provide good assets for the construction of healthy resilient cities with the function of preventing epidemic crises in the future.