How Social Media and Big Data Improve Emergency Relief
Large-scale emergencies and natural disasters are by nature chaotic. Relief efforts must rely on past experience, information available about the situation and, ultimately, a lot of guess work in order to rescue those in danger, supply food and water, and restore the necessary infrastructure. In order to improve response times and better focus rescue efforts where they are needed most, emergency responders have started turning to social media and other forms of big data to gain a better picture of the situation.
During super-storm Sandy, for example, Geeks Without Bounds and Splunk4Good partnered to analyze data from Twitter feeds in order to uncover patterns of activity in certain areas. They inspected hashtags and tweets, looking for specific words about power, food or fuel, as well as indicators of evacuation rates. With that data, the team was able to pinpoint locations where relief was needed the most.
Other sources of big data also played a role in the Sandy relief efforts. For example, FEMA used more than 150,000 geo-tagged photos taken by the Civil Air Patrol in the wake of the storm to get a better assessment of the damage. CAP has flown missions like this before, but during Sandy the images were also made public for the first time through an online app. Users of the app could enter their address to look at photos of that particular area to see if any damage had occurred to their home.
Emergency relief efforts for super-storm Sandy are just one example of how social media and big data can be applied to emergency response. Here are some other ways big data can make a difference in disaster situations.
Predict Disaster to Reduce Fatalities
While certain natural disasters, such as hurricanes, can be tracked well in advance so that those in the storm’s path can be evacuated, other disasters, such as tornadoes, can strike so fast that people only have a matter of minutes to seek safety. In Moore, Oklahoma, for example, residents only had a 14 minute warning before a tornado struck the town. As a result, 24 people ended up dying in the tragedy. If the city had been warned sooner, more than likely this death toll would have been greatly reduced, or even eliminated entirely.
A researcher at the University of Oklahoma is working to do just that. By using models of previous tornado outbreaks, the researcher was able to predict the exact path of two tornadoes that struck later that year, one hour before the tornadoes touched down. The U.S. Geological Survey is also using data from Twitter to create an earthquake detection system.
Predict Community Response
The Victorian Fire Services Commissioner is developing a tool that can predict how communities will respond to warnings of brushfires. The model uses data on brushfire progression and the warnings released to determine if communities will stay put or evacuate, and when and where evacuees will flee to. So far the model has had more than 90 percent accuracy rate in predicting responses.
When a typhoon impacted the central Philippines, the Digital Humanitarian Network and Translators Without Borders teamed up to help first responders analyze tweets and Facebook posts written in the Tagalog and Cebuano. The team used a list of key terms including “flood”, “injured”, and “damaged” to get a better idea of how badly communities were impacted and how relief efforts should be prioritized.
With big data technologies becoming more accessible, organizations can access analytics without needing specific expertise in Hadoop. Now, it’s easier for nonprofit organizations to use data analysis to improve their work. Those wanting to use predictive analytics, for example, can use apache hive to run queries in the cloud without any investment in infrastructure.
While organizations will still need to rely on other resources when gauging response (as data can be unreliable), having other tools in their arsenal will hopefully reduce the impact of disasters and help us to better prepare for emergencies in the future.
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