In the wake of a disaster, Disaster Maps share real-time information with response teams, helping them determine things like whether communities have access to power and cellular networks, if they have evacuated, and what services and supplies they need most.
Disaster Maps use statistical techniques to maintain individuals' privacy. For example, we only share privacy-protected information and also add up data points in a given area (called a map 'cell' or a 'tile') to prevent re-identification. If there are only a few individuals in an area, we also smooth populations across tiles, meaning that we average the number of people in a given area with nearby areas, making it even harder to re-identify anyone.
Disaster Maps can be generated within 24 hours of a natural disaster — much faster than comparable tools— and update daily as the situation on the ground unfolds. This allows agencies to respond to changing circumstances in evacuations, connectivity, or supply needs.
Since the launch of Disaster Maps in June 2017, we have generated data for 100+ natural disasters, helping to guide response efforts around the world. During the 2018 hurricane season, our maps informed active disaster recovery in India, Guatemala, Indonesia, the Philippines, California, North Carolina, Florida, and other disaster-affected locations around the world.
Who Uses Disaster Maps
International agencies and UN organizations like UNICEF and the World Food Programme use Disaster Maps data to guide their local deployments to disaster-affected areas and support local governments in their response efforts.
Domestic organizations like the American Red Cross, SEEDS India, and Humanity Road use Disaster Maps to support their local communities, track evacuations, and route supplies to the areas that need them most.
Universities and researchers
Universities and researchers use Disaster Maps to analyze how disaster-affected populations are using social services, whether they evacuate based on official orders, and how social ties affect their resilience after a disaster.
Displacement, Gender Disparities, and Shelter Utilization after Hurricane Laura
Data from Facebook and Camber Systems highlights patterns of movement as people moved to shelter from the hurricanes that landed on the Gulf Coast in the early part of September. First, we can see the estimated change in the population of devices from Facebook data. The difference in densities between counties can be seen by comparing the percentage change of the population with an estimated absolute change extrapolated using the American Community Survey (ACS) data from the US Census.
Where People Went Before, During, and After Hurricane Laura, Amid Pandemic
Direct Relief, as in past natural disasters, is working with Facebook’s Data for Good team to analyze the movement of populations prior to and following the storm’s landfall. Comparing mobility patterns to a baseline figure allows Direct Relief and other disaster response agencies to glean useful information, including whether evacuation orders were heeded, which areas received higher concentrations of evacuees, and which communities saw more people sheltering in place.
Using social media to measure demographic responses to natural disaster: Insights from a large-scale Facebook survey following the 2019 Australia Bushfires
In this paper we explore a novel method for collecting survey data following a natural disaster and then combine this data with device-derived mobility information to explore demographic outcomes. Using social media as a survey platform for measuring demographic outcomes, especially those that are challenging or expensive to field for, is increasingly of interest to the demographic community.
Patterns of population displacement during mega-fires in California detected using Facebook Disaster Maps
Facebook Disaster Maps (FBDM) is the first platform providing analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglomeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA.
In the News
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