Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria.

A real-time understanding of the distribution and duration of power outages after a major disaster is a precursor to minimizing their harmful consequences. Here, we develop an approach for using daily satellite nighttime lights data to create spatially disaggregated power outage estimates, tracking electricity restoration efforts after disasters strike. In contrast to existing utility data, these estimates are independent, open, and publicly-available, consistently measured across regions that may be serviced by several different power companies, and inclusive of distributed power supply (off-grid systems). We apply the methodology in Puerto Rico following Hurricane Maria, which caused the longest blackout in US history. Within all of the island's settlements, we track outages and recovery times, and link these measures to census-based demographic characteristics of residents. Our results show an 80% decrease in lights, in total, immediately after Hurricane Maria. During the recovery, a disproportionate share of long-duration power failures (> 120 days) occurred in rural municipalities (41% of rural municipalities vs. 29% of urban municipalities), and in the northern and eastern districts. Unexpectedly, we also identify large disparities in electricity recovery between neighborhoods within the same urban area, based primarily on the density of housing. For many urban areas, poor residents, the most vulnerable to increased mortality and morbidity risks from power losses, shouldered the longest outages because they lived in less dense, detached housing where electricity restoration lagged. The approach developed in this study demonstrates the potential of satellite-based estimates of power recovery to improve the real-time monitoring of disaster impacts, globally, at a spatial resolution that is actionable for the disaster response community.

Food Security Monitoring via Mobile Data Collection and Remote Sensing: Results from the Central African Republic.

The Central African Republic is one of the world's most vulnerable countries, suffering from chronic poverty, violent conflicts and weak disaster resilience. In collaboration with Doctors without Borders/Médecins Sans Frontières (MSF), this study presents a novel approach to collect information about socio-economic vulnerabilities related to malnutrition, access to resources and coping capacities. The first technical test was carried out in the North of the country (sub-prefecture Kabo) in May 2015. All activities were aimed at the investigation of technical feasibility, not at operational data collection, which requires a random sampling strategy. At the core of the study is an open-source Android application named SATIDA COLLECT that facilitates rapid and simple data collection. All assessments were carried out by local MSF staff after they had been trained for one day. Once a mobile network is available, all assessments can easily be uploaded to a database for further processing and trend analysis via MSF in-house software. On one hand, regularly updated food security assessments can complement traditional large-scale surveys, whose completion can take up to eight months. Ideally, this leads to a gain in time for disaster logistics. On the other hand, recording the location of every assessment via the smart phones' GPS receiver helps to analyze and display the coupling between drought risk and impacts over many years. Although the current situation in the Central African Republic is mostly related to violent conflict it is necessary to consider information about drought risk, because climatic shocks can further disrupt the already vulnerable system. SATIDA COLLECT can easily be adapted to local conditions or other applications, such as the evaluation of vaccination campaigns. Most importantly, it facilitates the standardized collection of information without pen and paper, as well as straightforward sharing of collected data with the MSF headquarters or other aid organizations.