Data in Migration: Exploring the predictability of the 2023 Messenia migrant boat disaster
Introduction:
The tragic incident known as the 2023 Messenia migrant boat disaster, that happened on 14–06–2023, drew global attention to the perilous journeys undertaken by migrants seeking refuge. While boat disasters involving migrants are regrettably not unprecedented, they underscore the need for a comprehensive understanding of these events. Organizations such as the International Organization for Migration (IOM) systematically gather and compile data on the movement of vulnerable migrant groups. Such data can shed light on seasonal patterns and provide valuable insights for policymakers.
This study leverages the IOM Missing Migrants Project (MMP), a prominent dataset encompassing the years from 2014 to 2023. The MMP dataset offers a wealth of information, including Incident ID, Incident Type, Region of Incident, Reported Date, Incident Year, Reported Month, Number of Fatalities, Estimated Number of Missing, Total Number of Fatalities and Missing, Number of Survivors, Gender Breakdown, Age Group Breakdown, Region of Origin, Cause of Death, Country of Origin, Migration Route, Location of Death, Information Source, Geographic Coordinates, UNSD Geographical Grouping, Article Title, Source Quality, and URL.
Analysis:
To initiate our examination, we begin by investigating the primary causes of migrant deaths on a monthly basis over the available years, focusing on the Mediterranean region — the context of interest in our analysis, particularly with respect to the Messenia incident.
Our analysis reveals that drowning consistently emerges as the leading cause of migrant deaths throughout the year. However, a noteworthy spike in drowning incidents occurs in April and persists until October. Subsequently, we delve deeper into the Mediterranean region to identify the most perilous migration routes, again examining the data on a monthly basis.
The bar chart unequivocally illustrates that the Central Mediterranean route is the most treacherous within the region. These findings align with the characteristics of the incident that took place on 14–06–2023, as indicated by the MMP dataset.
It is essential to acknowledge that these initial metrics are insufficient for drawing definitive conclusions or constructing predictive models. Nonetheless, they serve as valuable indicators for policymakers to conduct more extensive investigations and formulate policies aimed at safeguarding vulnerable migrants on these hazardous routes.
In pursuit of evaluating the feasibility of developing a predictive system, we investigate whether discernible patterns emerge from the coordinates of incidents provided in the dataset. Specifically, we examine the “Number of Deaths Within a 450km Radius of Target Coordinates by Month.”
Our analysis reveals that June is one of the months in which most deaths occurred within a 450km radius of the Messenia incident over the past 10 years.
A closer look at the deaths in June over the years reveals no incremental pattern but definitely shows us that there were warning signs hidden in the data.
These observations suggest the potential for predictions that could aid policymakers in testing preventive measures for similar incidents in the future. The extensive data collected by the IOM MMP presents a robust foundation upon which local policymakers can base life-saving interventions.
Furthermore, it is worth noting that the dataset contains information on another migrant boat disaster that occurred on 2016–06–03, where a vessel departing from Libya resulted in the tragic loss of nearly 300 lives. This incident shares striking similarities with 14–06–2023, including the location of the incident, departure point, month of occurrence, and cause of death. A comprehensive study of this event could have served as a crucial reference during the turbulent periods experienced in the region.
Conclusion:
While this study does not yield concrete findings, its primary objective is to ascertain whether datasets such as the MMP offer a substantial basis for more comprehensive research. The analyses conducted herein provide the impetus to explore the feasibility of developing predictive models and conducting in-depth investigations in the future.
As we embark on this journey of exploration, I am pleased to announce that this is the inaugural instalment of the “Data in Migration” series. Stay tuned for more in-depth analyses and insights in subsequent episodes.