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.
| Published in | International Journal of Data Science and Analysis (Volume 10, Issue 1) |
| DOI | 10.11648/ijdsa.20241001.11 |
| Page(s) | 1-10 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Spatial Analysis, Armed Conflict, Poisson Point Process, Berman-Turner Algorithm
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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
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Download@article{10.11648/ijdsa.20241001.11,
author = {Carol Ogira and Roselynn Kamau and Shallom Kamau and Bridgette Kerubo Bwoma and Bonaya Kiinywi Komora and Henry Athiany},
title = {Predicting Conflict Zones in Kenya Using a Point Process Model},
journal = {International Journal of Data Science and Analysis},
volume = {10},
number = {1},
pages = {1-10},
doi = {10.11648/ijdsa.20241001.11},
url = {https://doi.org/10.11648/ijdsa.20241001.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.ijdsa.20241001.11},
abstract = {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.
},
year = {2024}
}
TY - JOUR T1 - Predicting Conflict Zones in Kenya Using a Point Process Model AU - Carol Ogira AU - Roselynn Kamau AU - Shallom Kamau AU - Bridgette Kerubo Bwoma AU - Bonaya Kiinywi Komora AU - Henry Athiany Y1 - 2024/02/05 PY - 2024 N1 - https://doi.org/10.11648/ijdsa.20241001.11 DO - 10.11648/ijdsa.20241001.11 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 1 EP - 10 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/ijdsa.20241001.11 AB - 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. VL - 10 IS - 1 ER -
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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