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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.
Highlights
On September 19, 2017, a 7.1 magnitude (M) earthquake occurred in Puebla, Mexico [1]
Home and work locations were randomized within census blocks, allowing for the estimation of demographics without revealing the locations of the users; sensitive Points of Interest (POIs), such as primary schools, sexual/reproductive health clinics, places of worship, etc., were removed from the dataset completely; whitelisted POIs were unchanged; and no-match POIs had a noise of 20–100 m based on the density of data points within the area
The spatial distribution and market penetration of the 2017 data posed a challenge when working with the Cuebiq Personal Electronic Device (PED) data; it was difficult to strike a balance between examining localities with a tighter, more reliable geofence and ones that had enough residents to analyze longitudinally
Summary
On September 19, 2017, a 7.1 magnitude (M) earthquake occurred in Puebla, Mexico [1]. Mexico City and the states of Puebla and Morelos sustained significant damage to the infrastructure and population due to the densely populated region [1]. The United States Agency for International Development (USAID) and the Pan American Health Organization (PAHO) estimated that at least 43,000 buildings were destroyed or sustained significant damage, 6100 people were injured, 366 people were killed, and hundreds missing [2]. Traditional methodologies like witnesses’ interviews or satellite imagery are commonly used among relief organizations to determine the post-destruction and estimate the number of mortalities or missing people [3]. They are often slow, potentially biased, and unreliable [3]
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