Abstract
ABSTRACTNatural disasters, such as wildfires, earthquakes, landslides, or floods, lead to an increase in topical information shared on social media and in increased mapping activities in volunteered geographic information (VGI) platforms. Using earthquakes in Nepal and Central Italy as case studies, this research analyzes the effects of natural disasters on short-term (weeks) and longer-term (half year) changes in OpenStreetMap (OSM) mapping behavior and tweet activities in the affected regions. An increase of activities in OSM during the events can be partially attributed to those focused OSM mapping campaigns, for example, through the Humanitarian OSM Team (HOT). Using source tags in OSM change-sets, it was found that only a small portion of external mappers actually travels to the affected regions, whereas the majority of external mappers relies on desktop mapping instead. Furthermore, the study analyzes the spatio-temporal sequence of posted tweets together with keyword filters to identify a subset of users who most likely traveled to the affected regions for support and rescue operations. It also explores where, geographically, earthquake information spreads within social networks.
Highlights
Crowd-sourced data, such as volunteered geographic information (VGI) (Goodchild 2007) and social media posts, have been used to manage relief efforts around natural disasters (Zook et al 2010; Haworth and Bruce 2015), to study the formation of user mapping communities after such disasters (Budhathoki and Haythornthwaite 2013), and to analyze human dynamics as a result of such events (Qi and John 2014)
While short-term effects of crisis events on Twitter activity patterns (Goolsby 2010) and OpenStreetMap (OSM) mapping patterns (Zook et al 2010) in response to natural crises have already been analyzed in previous studies, their effects on longer term VGI contribution patterns and social media usage, as well as on human travel patterns toward affected regions are less understood
The analysis provides new insights into OSM data growth patterns (Neis and Zipf 2012) and user loyalty (Napolitano and Mooney 2012), adding on to previously analyzed factors that affect VGI contribution patterns, such as land use type (Arsanjani et al 2015; Alivand and Hochmair 2017), socio-economic factors (Heipke 2010), organized mapping events and campaigns (Dittus, Quattrone, and Capra 2017), and natural disasters, such as earthquakes (Poiani et al 2016)
Summary
Crowd-sourced data, such as volunteered geographic information (VGI) (Goodchild 2007) and social media posts, have been used to manage relief efforts around natural disasters (Zook et al 2010; Haworth and Bruce 2015), to study the formation of user mapping communities after such disasters (Budhathoki and Haythornthwaite 2013), and to analyze human dynamics as a result of such events (Qi and John 2014). It contributes to the large research topic of human movement pattern analysis from crowd-sourced and social media data (Valle et al 2017), especially in response to natural disasters (Goodchild and Glennon 2010). This knowledge will improve planning abilities for crisis management in the future, since it reveals which type of OSM data will get mapped and continuously updated after such an event, and to which extent it contains local contributions, allowing to draw conclusions about its data quality (Zielstra et al 2014). The case study revolves around two crisis events, which are a 2015 earthquake in Nepal and a 2016 earthquake in central Italy
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