Spatial patterns and demographic indicators of effective social media content during theHorsethief Canyon fire of 2012

A significant challenge during any emergency response scenario is the timely compilation of actionable data from disparate sources. Although numerous strategies exist, crisis mapping techniques have emerged as effective tools for synthesizing and visualizing event specific data when establishing situational awareness. User generated content (UGC) voluntarily submitted through social networking outlets (e.g., Twitter, Instagram, Flickr, and others) are increasingly being consumed and mined for actionable data (e.g., Goodchild 2007 Goodchild, M. F. 2007. Citizens as Sensors: The World of Volunteered Geography.”. GeoJournal, 69(4): 211–221. doi:10.1080/00045608.2011.595657.Crossref , Google Scholar; Elwood, Goodchild, and Sui 2012 Elwood, S., Goodchild, M. F. and Sui., D. Z. 2012. Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice.”. Annals of the Association of American Geographers, 102: 571–590. (3) doi:10.1080/00045608.2011.595657.Taylor & Francis Online, Web of Science ® , Google Scholar). Despite its apparent value as a rich and instantaneous source of actionable content, little research has emerged examining the underlying demographic characteristics that are likely to produce effective crowdsourced content during a crisis. Accordingly, this study collected and synthesized user generated data extracted from multiple social networks during the Horsethief Canyon Fire that occurred near Jackson, Wyoming, in September 2012. The spatial distribution of wildfire and non-wildfire specific UGC were compared to confirm the presence of a social networking user base that contributed to situational awareness. Regression analysis was used to identify relevant demographic characteristics that reflect the portion of the impacted community that will voluntarily contribute meaningful data about the fire. The preliminary findings suggest that emergency responders can use these techniques to quickly and efficiently assess the efficacy of mining actionable data from social media in a given community. Recommendations for additional research are discussed and provided.