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Analysis of Relief Materials Distribution Based on Road Network Connectivity in the North East Nigeria

Adebambo. O. Somuyiwa, Boye. B. Ayantoyinbo, Olusola. J. Kolawole, Babaji. A. Pindiga

Abstract


The connectivity of roads networks plays an important role in ensuring quick delivery of relief materials to the affected population and improves the satisfaction rate. However, unconnected transportation network increases the distances at which relief materials traveled to reach disaster victims. This study analysed relief materials distribution based on road network connectivity in the North East Nigeria. Google map analysis was done on road network using geographical images that actually represent series of vertices (nodes) and set of edges (links). The degree of network connectivity in the North East was done by counting the numbers of nodes and links in the google map. Gamma index was used to calculate the level of the connectivity leading to the identified IDP camps. The result of road network connectivity over which relief materials were been transported to various IDP camps in the zone showed that only the routes from Bauchi to Rindibin Camp and Maiduguri to Biu Camp reflect an average degree of connectivity, while the others show a poor level of network connectivity. Therefore, the study recommended that NEMA should be proactive in its planning to deliver relief materials to the designated location by evaluating several alternative links and utilising various tactics by consulting transportation service providers and other public and private organisations that can help with humanitarian efforts. Lastly, Government should also adopt the use of cashless policy while providing aids instead of distribution of food and material items.


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References


Abbas, A. M., & Hashidu, R. B. (2019). Transportation network analysis, connectivity and accessibility indices in North East, Nigeria. J. Res. Hum. Soc. Sci, 7, 60-66.

Akbari, V., & Salman, F. S. (2017). Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity. European Journal of Operational Research, 257(2), 625-640.

Akbari, V., Sadati, M. E. H., & Kian, R. (2021). A decomposition-based heuristic for a multicrew coordinated road restoration problem. Transportation Research Part D: Transport and Environment, 95, 102854.

Altay, N., & Green III, W. G. (2006). OR/MS research in disaster operations management. European journal of operational research, 175(1), 475-493.

Anyamele, O. D. (2020). Disparities and Inequality in Infant and Child Mortality among the 36 states and Federal Capital Territory (FCT, Abuja), Nigeria. Journal of Health Care for the Poor and Underserved, 31(3), 1166-1190.

Below, R., Guha-Sapir, D., Vos, F., & Ponserre, S. (2011). Annual disaster statistical review 2010. Centre for Research on the Epidemiology of Disasters.

Cain, D. S., & Barthelemy, J. (2008). Tangible and spiritual relief after the storm: The religious community responds to Katrina. Journal of Social Service Research, 34(3), 29-42.

Centre for Research on the Epidemiology of Disasters-CRED, EM-DAT (2021): The international disaster database. https://public.emdat.be/data. Accessed on: 10 Feb. 2020.

Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, S. G., & Bian, L. (2017). Drones for disaster response and relief operations: A continuous approximation model. International Journal of Production Economics, 188, 167-184. Hope Bridge (2021). Relief kits and goods. https://relief.or.kr/eng/business/relief.php

Displacement Tracking Matrix (2023). Nigeria — Displacement Report 43 (February 2023) | Displacement Tracking Matrix (iom.int)


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