| dc.description.abstract | The twenty-first century has seen an increase in conflict-induced refugees (UNHCR, 2021). Understanding the refugee movements that result from these conflicts could aid policy makers and humanitarian organizations in providing aid to and hosting these forcibly displaced peoples (Suleimenova et al., 2017). To this end, Flee was developed (Groen, 2016). Flee is an agent-based social simulation framework for forecasting population displacements in an armed conflict setting (Anastasiadis et al., 2021; Suleimenova & Groen, 2020). Currently the flee-model does not take into account the possibility of refugees taking off-road routes. The goal of this study is therefore to implement into Flee the possibility for off-road driving routes, and test the effectiveness.
The off-road routes are determined by selecting features of the physical environment relevant to refugee movement, and representing these in raster data per season. Values are assigned to the selected features, to represent the degree of resistance that these features offer. The modelled resistance is changed to a cost raster, which allows for the plotting of routes of least cumulative resistance between two points. The resulting routes are used as input for Flee.
Overall, the model’s refugee allocation error has decreased by 16.5% as a result of the changes in routes. However, most of this change is caused by an improvement of allocation in one camp, that influences the total through its relatively large size. When comparing the impact of the routes on the difference in error per location, while weighting the camps equally, the error increases by roughly 7%. Moreover, on a temporal level, the first and last season’s error are lower due to border closure and camp capacity mechanics in Flee. The error in season two and three (April – Oct 2012) are thus the most reliable for testing the differences in model accuracy. These seasons show overall a negligible difference in error.
In conclusion, the addition of routes based on the physical environment does improve the overall accuracy of Flee’s refugee allocation in the Mali case study, but the results are too inconsistent to determine whether this will be the case in other case studies as well. Other factors are the root cause of current differences between the model and reality. These root causes include for example political factors, such as border restriction policies, and decision-making based on emotional factors, such as the attractiveness of cities over refugee camps. | |
