Evidence based on numerous post-disaster descriptive studies indicates that women tend to be more likely to be displaced from hurricane events than men. This tendency is associated usually with a more acute perception of risk and responsibility for children and households on the part of women, but is this actually borne out by the available data? New datasets on the post-disaster mobility of Facebook users, disaggregated by gender, sheds important new light on the behaviors of women and men during crises. Preliminary research, conducted by Direct Relief and partners at Harvard School of Public Health during the recent impacts of Hurricanes Laura, Sally, and Delta in Texas, Louisiana, Mississippi, Alabama, and Florida, indicates that the rate of return for women was somewhat faster than for men. Potential reasons for the disparity include a lower capacity to be away from home for longer periods of time, in part based upon income differences and household responsibilities, particularly for single-parent households with children. Linking the displacement analysis to improved baseline understanding of gender variance in the total population as well as co-variance based on income levels, which may play a significant related role in these rates of movement post-disaster, may improve our understanding of the causes, impacts, and consequences of gendered displacement patterns.
- Tuesday — April 27, 2021
5:00PM - 6:00PM
Research Methods and Data Science Meetup
This Research Methods and Data Science (RMDS) meetup group is devoted to make big data & AI useful, and to promote big data technologies. It is a meet-up group bringing together people from research methodology, data science, data professional services, AI and computing in the greater Los Angeles area.
The focus of the group is about utilizing big data & AI technologies to improve data analytics & research for social good, and to promote research methods and data science innovation. On a regular base, we run workshops on big data analytics that can be applied to model and visualize open data and other complicated big data sets. Also, we will organize special meetings to discuss special data science projects, as well as data science automation and augmentation.