Large-scale humanitarian disasters often disproportionately damage poor communities. This effect is compounded when communities are remote with limited connectivity and response is slow. While humanitarian response organizations are increasingly using a wide range of satellites to detect damaged areas, these images can be delayed days or weeks and may not tell the story of how many or where people are affected. In order to address the need of identifying severely damaged communities due to humanitarian disasters, we present an algorithmic approach to leverage pseudonymization locational data collected from personal cell phones to detect the depopulation of localities severely affected by the 2017 Puebla earthquake in Mexico. This algorithm capitalizes on building a pattern of life for these localities, first establishing which pseudonymous IDs are a resident of the locality and then establishing what percent of those residents leave those localities after the earthquake. Using a study of 15 localities severely damaged and 15 control localities unaffected by the earthquake, this approach successfully identified 73% of severely damaged localities. This individual-focused system provides a promising approach for organizations to understand the size and severity of a humanitarian disaster, detect which localities are most severely damaged, and aid them in prioritizing response and reconstruction efforts.
Among the many striking features of the COVID 19 pandemic is the geographic heterogeneity of its incidence and its disproportionate effects on low income people. We examine links between individual risk and COVID 19 outcomes in the federal context in Mexico characterized by high socioeconomic and political heterogeneity. Using highly detailed individual mobility data for five Mexican cities, we document the relationship between local income and education factors and the behaviors associated with COVID 19 risk after the national lockdown: staying home, going to work, and going other places. While low income people are disproportionately likely to contract COVID 19 and die from illnesses associated with COVID 19 in Mexico, we find very mixed evidence that people living in low income urban census blocs are engaging in observably riskier behaviors. Both before and after the national lockdown, people in low income locations spend more time at home and less time going other places, suggesting a lower overall risk of contracting the virus based on voluntary movement. However, people in low income and less educated places appear to shift their movement less in response to Mexico’s national lockdown. Less educated people, in particular, show much less change in their movement patterns in response to the lockdown. At the same time, we find enormous variance between cities and in some cities such as Mexico City and Ecatepec people in low income places changed their behavior more after the lockdown. Understanding the reasons for these income and education differences in outcomes is crucial for policy responses–whether the government should focus on educating individuals about their behavior, or whether the response requires a much more difficult overhaul of societal protections.
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