  Community resilience is a complex and muti-faceted phenomenon that emerges
from complex and nonlinear interactions among different socio-technical systems
and their resilience properties. However, present studies on community
resilience focus primarily on vulnerability assessment and utilize index-based
approaches, with limited ability to capture heterogeneous features within
community socio-technical systems and their nonlinear interactions in shaping
robustness, redundancy, and resourcefulness components of resilience. To
address this gap, this paper presents an integrated three-layer deep learning
model for community resilience rating (called Resili-Net). Twelve measurable
resilience features are specified and computed within community socio-technical
systems (i.e., facilities, infrastructures, and society) related to three
resilience components of robustness, redundancy, and resourcefulness. Using
publicly accessible data from multiple metropolitan statistical areas in the
United States, Resili-Net characterizes the resilience levels of spatial areas
into five distinct levels. The interpretability of the model outcomes enables
feature analysis for specifying the determinants of resilience in areas within
each resilience level, allowing for the identification of specific resilience
enhancement strategies. Changes in community resilience profiles under urban
development patterns are further examined by changing the value of related
socio-technical systems features. Accordingly, the outcomes provide novel
perspectives for community resilience assessment by harnessing machine
intelligence and heterogeneous urban big data.
