
pmid: 31607276
ABSTRACTIn August 2017, Hurricane Harvey struck the US Gulf Coast and caused more than US $125 billion in damages in Texas. The loss of lives and the economic damages resulted in an outpouring of support for the recovery efforts in the form of federal assistance and private donations. The latter has supported more creative approaches to recovery. Organizations that normally would not receive funding were able to obtain resources to use in novel manners. Using the framework of Dynes typology to identify groups and their respective structures and tasks, this report from the field analyzes Hurricane Harvey and the financial support mechanisms used to support recovery efforts in Texas, what organizations were funded to do, and where they fit into Dynes typology. The authors close by noting the importance of these emerging organizations and the need to support diversity in funding disaster response and recovery efforts beyond large nonprofit organizations.
330, Cyclonic Storms, Charities, Hurricane Harvey (2017), Disaster relief--Finance, Humans, Disaster Planning, Fund Raising, Gulf of America, Emergency management, United States
330, Cyclonic Storms, Charities, Hurricane Harvey (2017), Disaster relief--Finance, Humans, Disaster Planning, Fund Raising, Gulf of America, Emergency management, United States
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
