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Research Article |

Predicting Conflict Zones in Kenya Using a Point Process Model

In the past decade, Kenya has continued to experience high levels of conflict, which has affected the country in various ways. This study presents a method for analyzing and predicting conflict zones in Kenya using a Point Process Model. Data utilized in the analysis was obtained from the Armed Conflict Location & Event Data (ACLED) Project and Open Data for Africa. The focus was to develop a point process model, test its predictive capability, and predict conflict zones in Kenya for a specified period of time. The study highlights the framework of the model, focusing on the intensity, and effects of covariates such as population density and spatial coordinates. Spatial data analysis was carried out using the spatstat package of the R Statistical Software, mapping the distribution of the conflict events and further developing the model using the Berman-Turner algorithm. Parameter estimates required for the prediction were obtained from the algorithm. For the 18-year period considered (2004 - 2021), the number of conflict events increased significantly as the election period drew near, during and after the election period. Geographically, the Central and Western parts of Kenya exhibited greater intensity of conflict events, spreading to their surroundings. The spike in the number of conflict events during the electioneering period can be explained by the political differences seen in the country which fuel violence among citizens. Furthermore, population density played a major role in the high cases of conflict as is evident from the many cases of conflict recorded in Nairobi County. Other high cases of conflict during some years in the study period were associated with counties with pastoral communities, such as Mandera. Evaluating the trend of the past conflict events, the model prediction indicates that the capital city of Kenya (Nairobi) and its environs would be more prone to conflict during elections.

Spatial Analysis, Armed Conflict, Poisson Point Process, Berman-Turner Algorithm

APA Style

Ogira, C., Kamau, R., Kamau, S., Bwoma, B. K., Komora, B. K., et al. (2024). Predicting Conflict Zones in Kenya Using a Point Process Model. International Journal of Data Science and Analysis, 10(1), 1-10. https://doi.org/10.11648/ijdsa.20241001.11

ACS Style

Ogira, C.; Kamau, R.; Kamau, S.; Bwoma, B. K.; Komora, B. K., et al. Predicting Conflict Zones in Kenya Using a Point Process Model. Int. J. Data Sci. Anal. 2024, 10(1), 1-10. doi: 10.11648/ijdsa.20241001.11

AMA Style

Ogira C, Kamau R, Kamau S, Bwoma BK, Komora BK, et al. Predicting Conflict Zones in Kenya Using a Point Process Model. Int J Data Sci Anal. 2024;10(1):1-10. doi: 10.11648/ijdsa.20241001.11

Copyright © 2024 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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