Towards a Better Response: Combining Pareto Ranking and Geostatistics to Model Gender-Based Vulnerability in Rohingya Refugee Settlements in Bangladesh

Abstract

Introduction:

The Rohingya refugee crisis in Bangladesh continues to overburden humanitarian resources and undermine the health and security of over 900,000 people. Spatial, sector-specific information is required to better understand the needs of vulnerable populations, such as women and girls, and to target interventions with improved efficiency and effectiveness.

Aim:

The aim of this study was to create a gender-based vulnerability index and explore the geospatial and thematic variations in the gender-based vulnerability of Rohingya refugees residing in Bangladesh by utilizing pre-existing, open-source data.

Methods:

Data sources included remotely-sensed REACH data on humanitarian infrastructure, UN Population Fund resource availability data, and the Needs and Population Monitoring Survey conducted by the International Organization for Migration in October 2017. Gaps in data were addressed through probabilistic interpolation. A vulnerability index was designed through a process of literature review, variable selection and thematic grouping, normalization, and scorecard creation. Pareto ranking was employed to rank sites based on vulnerability scoring. Spatial autocorrelation of vulnerability was analyzed with the Global and Anselin Local Moran’s I applied to both combined vulnerability index rank and disaggregated thematic ranking.

Results:

Twenty-four percent of settlements were ranked as most vulnerable, with 30 highly vulnerable clusters identified predominantly in the Upazila of Sadar. Five settlements in Dhokkin, Somitipara, and Pahartoli were categorized as less vulnerable outliers amongst highly vulnerable neighboring sites. Security- and health-related variables appear to be the largest drivers of gender-specific vulnerability in Cox’s Bazar. Clusters of low security and education vulnerability measures are shown near the refugee ingress point near Gundum.

Discussion:

The humanitarian space produces tremendous amounts of data that can be analyzed with spatial statistics to better target research and programmatic intervention. The critical utilization of these data and validation of vulnerability indexes is required to improve the international response to the global refugee crisis.