Transit-oriented development (TOD) often raises land values and can promote gentrification and the displacement in low-income communities. Little research, however, has shown how communities have organized to fight for more equitable TOD processes and outcomes within particular metropolitan contexts and dynamics of neighborhood change. This case study examines the role of neighborhood-based advocacy and organizing in fighting for equitable TOD and tackling key political and planning challenges in a predominantly Latinx immigrant inner-ring suburb. Their successes show the strengths of community-based, cross-sector coalitions in generating more equitable and inclusive TOD processes, plans, and policies that target conditions of place-based precarity.

No abstract

Understanding human movements in the face of natural disasters is critical for disaster evacuation planning, management, and relief. Despite the clear need for such work, these studies are rare in the literature due to the lack of available data measuring spatiotemporal mobility patterns during actual disasters. This study explores the spatiotemporal patterns of evacuation travels by leveraging users’ location information from millions of tweets posted in the hours prior and concurrent to Hurricane Matthew. Our analysis yields several practical insights, including the following: (1) We identified trajectories of Twitter users moving out of evacuation zones once the evacuation was ordered and then returning home after the hurricane passed. (2) Evacuation zone residents produced an unusually large number of tweets outside evacuation zones during the evacuation order period. (3) It took several days for the evacuees in both South Carolina and Georgia to leave their residential areas after the mandatory evacuation was ordered, but Georgia residents typically took more time to return home. (4) Evacuees are more likely to choose larger cities farther away as their destinations for safety instead of nearby small cities. (5) Human movements during the evacuation follow a log-normal distribution.

No abstract

No abstract