Evidence-based policy and practice create a major opportunity for donors and agencies to make programs more effective and efficient (Miliband & Gurumurthy, 2015). The most common measures of people affected by disasters are direct deaths, injured, and disaster affected. Among these indicators, the number of those affected is one of the most challenging to define and measure. The extent to which populations are affected by disasters, including those who are evacuated and relocated from their homes to nearby city slums and rural areas, has not been well defined.

In the disaster context, a range of evidence is needed as it is challenging to prioritize key questions that could provide necessary information for decision makers and responders (Evidence Aid Priority Setting Group, 2013) (Van den Homberg, Monné, & Spruit, 2018).

Like in other domains traditional data such as census and survey data are widely used for understanding the situation of people in the disaster context for a long time. The data are built on rigorous and standardized methodologies, and provide detailed information on the characteristics and situation of the population under the study. These are reliable resources for understanding affected populations (Gray & Mueller, 2012) (Plyer, Bonaguro, & Hodges, 2010) (Fussell, Curtis, & DeWaard, 2014) (Myers, Slack, & Singelmann, 2008). However, they have limitations in measuring large-scale population and its movements due to logistical constraints in data collection (Brown et al. 2001). For instance, traditional surveys are not designed to collect detailed mobility information over a range of temporal and spatial scales while panel survey data can provide detailed information on the attributes of migrated families and individuals (Smith & Mccarty, 1996). Also, preparation and implementation of surveys require certain coordination and time so it is difficult to start data collection at the onset of disasters where migrating populations could easily spread across large areas (Hori, Schafer, & Bowman, 2009) (Fussell et al., 2014). It has been increasingly acknowledged that there are data gaps in examining the impact of disasters (Ager et al. 2014) (CRED, 2015).

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