These successful applications also motivated the leading social media platforms to develop their official crisis response tools. Studies and actual practices have been carried out using the geotagged Tweets and Flickr picture uploads to track and reveal the change and distribution of population during natural disasters and public safety crises. For the most popular social media platforms like Twitter and Facebook, their crowdsourced geolocation data can serve as proxies of population distribution to answer the most basic yet important question for any crisis response task: 'Where are the people?' Crowdsourced geolocation data through location-based services are sensitive to the fluctuation of users' position change and respond promptly to the spatial movement and formation of clusters. Therefore, many researchers and practitioners have turned to geolocation data that are crowdsourced through leading social media platforms to assimilate population movements in a timely manner and assist disaster relief. The accessibility of cellular tower transmission data with geospatial signature has been greatly constrained amid the concern of data abuse and privacy preservation. Evaluating the effectiveness of this strategy and obtaining useful information for disaster relief during a large population displacement is of great interest to both policy-makers and researchers. As applications and data generation mature, FBDM will harness crowdsourced data and aid first responder decision-making.Ībrupt population displacement is a common emergency response strategy applied before or during a natural disaster.

Integrated with an assessment on underrepresented communities, FBDM data and the derivatives can provide much needed information of near real-time population displacement for crisis response and disaster relief. In sparsely populated fire-prone areas, resources should be prioritized to move people to shelters as the displaced residents do not have many alternative options, while their counterparts in densely populated areas can utilize their social connections to seek temporary stay at nearby locations during an evacuation. A comparison between the Mendocino Complex and Woolsey fires indicates that a densely populated region can be evacuated faster than a scarcely populated one, possibly due to better access to transportation. Coupled with monotonic trends, the fall and rise of cold and hot spots of population revealed the areas with the greatest population drop and potential places to house the displaced residents. During the two mega-fires in California, FBDM data effectively captured the temporal change of population arising from the placing and lifting of evacuation orders. Due to usage habits, the elder population is underrepresented in FBDM data. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. 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 agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices.

Cal fire map of malibu fire