GIScience 2016 Short Paper Proceedings Understanding the Mapping Sequence of Online Volunteers in Disaster Response Yingjie Hu and Krzysztof Janowicz {yingjiehu,jano}@geog.ucsb.edu STKO Lab, Department of Geography, University of California, Santa Barbara, USA Abstract In recent years, online volunteers have been actively participating in disaster response, thanks to the advancement of information technologies and the support from humanitarian organizations. One important way in which online volunteers contribute to disaster response is by mapping the a↵ected area based on remote sensing imagery. Such online mapping generates up-to-date geographic informa- tion which can provide valuable support for the decision making of emergency responders. Typically, the area a↵ected by an disaster is divided into a number of cells using a grid-based tessellation and each volunteer can select one cell to start the mapping. While this approach coordinates the e↵orts from many online volunteers, it is unclear in which sequence these grid cells have been mapped. This sequence is important because it determines when the geographic information within a particular cell will become available to emergency responders, which in turn can directly influence the efficiency of rescue tasks and other relief e↵orts. In this work, we study three online mapping projects which were deployed and utilized in 2015 Nepal, 2016 Ecuador, and 2016 Japan earthquakes to gain insights into the mapping sequences performed by online volunteers. Keywords: Disaster response, crisis mapping, volunteered geographic information Introduction In recent years, online volunteers have been actively involved in disaster response. On the one side, infor- mation and communication technologies allow volunteers to contribute to disaster relief without having to be physically present at the a↵ected areas. On the other side, humanitarian communities, such as Standby Task Force (Meier, 2012a) and Crisis Mapper (Shanley et al., 2013), play an important role in bringing together online volunteers and coordinating their e↵orts. With the support from technologies and hu- manitarian organizations, volunteers have made important contributions to 2010 Haiti earthquake (Zook et al., 2010), 2012 Hurricane Sandy in the U.S. (Meier, 2012b), 2013 Typhoon Haiyan in the Philippines (Humanitarian OpenStreetMap Team, 2013), and the 2015 Nepal Earthquake (Hu and Janowicz, 2015). One important way in which volunteers contribute to disaster response is by mapping the a↵ected areas based on remote sensing images. During the online mapping process, volunteers digitize geographic features which may be missing from the previous maps, as well as update the existing geographic data to reflect the current status (e.g., a road may be blocked after an earthquake). This process generates up- to-date geographic information which can provide valuable support for the decision making of emergency responders. While these volunteer-contributed data may not be of highest quality, they generally satisfy the needs of disaster response (Goodchild and Glennon, 2010). Since many volunteers may be participating in online mapping at the same time, humanitarian orga- nizations often divide the a↵ected area into cells using a grid-based tessellation. Each online volunteer can then select a grid cell to start the mapping task. While this approach helps avoid editing conflicts and duplications, there is a lack of understanding on the sequence in which online volunteers map the grid cells. Such a sequence is important because it directly determines the time when the geographic information within a particular cell will become available. From our observation, the mapping time di↵er- ence between two neighboring grid cells can be 4 days and sometimes even longer. In disaster response, the first 72 hours after a disaster have been widely considered as the critical period for rescue tasks. After this period, the survival rate drops dramatically (Fiedrich et al., 2000; Comfort et al., 2004; Ochoa and Santos, 2015). Thus, if the grid cells that contain the critical information for disaster response are mapped first, more people can be potentially saved. Intuitively, selecting cells at random is not an ideal solution, since population, transportation infrastructure, and potential rescue routes should be taken into account.
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Apr 19, 2024
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