Disaster-induced population displacement causes fragmentation and weakening of existing bonding social capital (Aldrich & Meyer, 2015; Sadri et al., 2018) . Although, well connected communities with strong social capital are associated with quicker economic recovery and higher resilience (Aldrich, 2012; Doerfel et al., 2020) In this project we leverage network science methods to test if post-disaster population displacement induces socio-spatial segregation. We find that in the aftermatch of disaster communities restructure their social ties, but this effect was not as expected under observed evacuation but reflected systemic adaptability and self organization. This selective recovery shows that disasters can increase inequalities in social capital, highlighting the need for targeted support to prevent vulnerable communities from becoming further isolated.

In this project we use large-scale data of anonymized GPS traces and online social connections from the Marshall Fires in Colorado, USA, to unravel the associations between social and behavioral factors and evacuation destinations and long-term displacement decisions. We find that behavioral characteristics and social homophily play a significant role in post-disaster mobility decisions. Conditioned on the same evacuation distance, evacuees chose locations that have a high sociodemographic homophily with their home locations, and locations that have more friendship connections, compared to the level of homophily they are spatially exposed to. The social homophily effect varies across demographics and is a significant predictor of displacement and return decisions. Our findings highlight the importance of incorporating the social, behavioral, and economic characteristics to better understand and predict evacuation and displacement dynamics after disasters.

Affordable housing is an integral aspect that determines the level of equity, for it to function sustainably, it is important to ensure that these units are placed in high-opportunity neighborhoods, that provide good proximity and accessibility to essential social determinants. This research leverages spatial analysis approach to assess the fairness of accessibility with public transit for the affordable housing stock of New York City. In particular, transit-based accessibility statistics are calculated by developing catchment analyses from each grid cell representing housing and affordable housing. These results are used to devise a set of indicators to measure the potential to reach important social determinants, such as health and education facilities, and the number of available jobs. We observe variability in access scores across five boroughs. This methodology presented here can prove very useful for conducting the feasibility analysis while planning the location of affordable housing in the city,