Proceedings Article | 15 June 2023
KEYWORDS: Visualization, Scanning electron microscopy, Data modeling, Visible radiation, Natural disasters, Infrared imaging, Defense and security, Radiometry, Climatology, Algorithm development
As cities grow, the risks of local crises grow with them. Researchers and practitioners alike seek to better understand how dense urban communities differentially prepare for, respond to, and recover from natural and anthropogenic shocks and stresses. Community resilience is a function of physical infrastructure, like levees and hospitals, and social capital, the valuable networks of human relationships that allow communities to thrive. In times of crisis, disparate access to these resources means that even adjacent neighborhoods can experience radically different outcomes. Unfortunately, while highly granular data about physical infrastructure is readily available, most research on social capital is limited to coarser, sparser survey data. To address this limitation, we present RESIDENT (Resilience and Stability in Dense Urban Terrain), a web application and data analysis framework that combines open-source and remote-sensed geographic data to characterize the resilience of urban neighborhoods. The user specifies a city, and RESIDENT identifies relevant infrastructure to calculate potential for social capital, visualizing this data with neighborhood- and city-level heat maps and histograms. To validate our approach, we compared RESIDENT’s social capital estimates to Nighttime Lights (NTL) data from the Visible and Infrared Imaging Suite, an established indicator of economic activity and disaster recovery. We found that increased potential for social capital predicted brighter NTL. Our results show that RESIDENT produces reliable estimates of social capital and may be used by social scientists as well as industry, government, and defense agencies to analyze, identify, and support vulnerable neighborhoods in dense urban areas.
