Detecting Destroyed Communities in Remote Areas with Personal Electronic Device Data: A Case Study of the 2017 Puebla Earthquake

Abstract

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.

Human mobility data and analysis for urban resilience: A systematic review

“…Using mobile phone GPS data (Safegraph), Yabe et al (2020c) detected the effects of hurricanes on the performances of 635 businesses in Puerto Rico after Hurricane Maria. Meanwhile, international aid agencies such as UNICEF and the World Bank use human mobility data for real-time humanitarian purposes (Marx et al, 2020). The location information associated with these various applications, combined with user demographic information, can be valuable for analyzing human mobility, but privacy considerations must be considered, as discussed in the section Challenges.…”

“…Vulnerability assessment. Although the application of HMDA in vulnerability assessment is still in its infancy, it is gaining increasing attention in the field of disasters due to natural hazards (Marx et al, 2020;Yabe et al, 2020c;Yin et al, 2021) and the COVID-19 pandemic (Huang et al, 2021). Yabe et al (2020c) utilized location data collected from mobile phones to analyze the causal impacts of hurricanes on the economic vulnerability of the business sector.…”

“…Using foot traffic data provided by a location information company (Safe-Graph), Yin et al (2021) proposed a heat vulnerability index to monitor vulnerability to heat at finer timescales at more vulnerable locations in the urban environment. Marx et al (2020) proposed an algorithm that uses pseudonymized location data collected from individual cell phones to detect community depopulation caused by a massive earthquake in Puebla, Mexico, in 2017. This approach can provide humanitarian response organizations with an inexpensive, accurate, and automated method of detecting the communities most severely affected by a major disaster, such as those that have no other means of reporting or are reluctant to share information with local authorities or the international community.…”