Predicting Conflict Zones in Kenya Using a Point Process Model

Carol Ogira; Roselynn Kamau; Shallom Kamau; Bridgette Kerubo Bwoma; Bonaya Kiinywi Komora; Henry Athiany

doi:doi:10.11648/ijdsa.20241001.11

Research Article |

| Peer-Reviewed

Predicting Conflict Zones in Kenya Using a Point Process Model

Carol Ogira^*, Roselynn Kamau, Shallom Kamau, Bridgette Kerubo Bwoma, Bonaya Kiinywi Komora, Henry Athiany

Published in International Journal of Data Science and Analysis (Volume 10, Issue 1)

Received: 5 January 2024 Accepted: 23 January 2024 Published: 5 February 2024

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

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

Keywords

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

References

[1]	B. Rohwerder, “Conflict analysis of Kenya,” Birmingham, UK: GSDRC, University of Birmingham, pp. 1–33, 2015.
[2]	C. Dowd and C. Raleigh, “Regional Report : Kenya December 2013,” ETH Zürich, vol. 2013, no. December, pp. 1–7, 2013.
[3]	J. Lind, P. Mutahi, and M. Oosterom, “Tangled ties: Al-Shabaab and political volatility in Kenya,” 2015. [Online]. Available: https://opendocs.ids.ac.uk/opendocs/bitstream/handle/20.500.12413/6018/ER130_TangledTiesAlShabaabandPoliticalVolatilityinKenya.pdf
[4]	ACLED, “Armed Conflict Location & Event Data Project (ACLED) Codebook,” Armed Conflict Location & Event Data Project, pp. 1–36, 2019, [Online]. Available: https://acleddata.com/resources/general-guides/
[5]	P. Kimani, C. Mugo, and H. Athiany, “Trends of Armed Conflict in Kenya from 1997 to 2021: An Exploratory Data Analysis,” International Journal of Data Science and Analysis, vol. 7, no. 6, p. 161, 2021, doi: 10.11648/j.ijdsa.20210706.14.
[6]	O. A. Makinde, E. Olamijuwon, I. Mgbachi, and R. Sato, “Childhood exposure to armed conflict and nutritional health outcomes in Nigeria,” Confl Health, vol. 17, no. 1, pp. 1–12, 2023, doi: 10.1186/s13031-023-00513-0.
[7]	D. K. Kinyoki et al., “Conflict in Somalia: impact on child undernutrition,” BMJ Glob Health, vol. 2, no. 2, 2017, doi: 10.1136/bmjgh-2016-000262.
[8]	E. Velázquez, I. Martínez, S. Getzin, K. A. Moloney, and T. Wiegand, “An evaluation of the state of spatial point pattern analysis in ecology,” Ecography, vol. 39, no. 11, pp. 1042–1055, Nov. 2016, doi: 10.1111/ECOG.01579.
[9]	J. Lancaster and B. J. Downes, “Spatial point pattern analysis of available and exploited resources,” Ecography, vol. 27, no. 1, pp. 94–102, 2004, doi: 10.1111/j.0906-7590.2004.03694.x.
[10]	S. Schutte, “Regions at risk: predicting conflict zones in African insurgencies,” Political Sci Res Methods, vol. 5, no. 3, pp. 447–465, 2017.
[11]	H. Hegre, J. Karlsen, H. M. Nygård, H. Strand, and H. Urdal, “Predicting armed conflict, 2010–2050,” International Studies Quarterly, vol. 57, no. 2, pp. 250–270, 2013, [Online]. Available: https://www.jstor.org/stable/24016137
[12]	B. C. Boekestein, “A predictive logistic regression model of world conflict using open source data,” AFIT Scholar, pp. 2–97, 2015, [Online]. Available: https://scholar.afit.edu/etd/101
[13]	J. K. Nyiri, “The impact of ethnic conflict on economic development: the case of post-election violence in Kenya, 2007-2008.,” University of Nairobi, 2014. [Online]. Available: http://erepository.uonbi.ac.ke/handle/11295/75407
[14]	V. Owuor and S. Wisor, “The Role of Kenya’s Private sector in peacebuilding: the case of the 2013 election cycle,” Broomfield, CO: One Earth Future Foundation, pp. 1–36, 2014, doi: 10.18289/OEF.2014.003.
[15]	C. Raleigh, A. Linke, H. Hegre, and J. Karlsen, “Introducing ACLED: An armed conflict location and event dataset,” J Peace Res, vol. 47, no. 5, pp. 651–660, 2010, doi: 10.1177/0022343310378914.
[16]	A. Zammit-Mangion, M. Dewar, V. Kadirkamanathan, A. Flesken, and G. Sanguinetti, Modeling Conflict Dynamics with Spatio-temporal Data. Springer, 2013.
[17]	Open Data for Africa, “openAFRICA,” Kenya Data Portal. [Online]. Available: https://africaopendata.org/
[18]	A. Baddeley and others, “Analysing spatial point patterns in R,” 2008.
[19]	A. Baddeley, E. Rubak, and R. Turner, Spatial point patterns: methodology and applications with R. CRC press, 2015.
[20]	R Core Team, “R: A Language and Environment for Statistical Computing.” Vienna, Austria, 2022. [Online]. Available: https://www.r-project.org/
[21]	F. P. Schoenberg, “Introduction to point processes,” Wiley Encyclopedia of Operations Research and Management Science, p. 1, 2010, doi: 10.1002/9780470400531.
[22]	N. Weidmann and M. D. Ward, “Spatial–temporal modeling of civil war: The example of Bosnia,” in GROW-Net Conference, Zurich, 2008, pp. 1–10.
[23]	C. Raleigh and C. Wafula, “Kenya’s Political Violence Landscape in the Lead-Up to the 2022 Elections,” 2022. [Online]. Available: https://acleddata.com/2022/08/09/kenyas-political-violence-landscape-in-the-lead-up-to-the-2022-elections/
[24]	P. Kimani, H. Athiany, and C. Mugo, “Spatial dynamics of armed conflict in Kenya during the electioneering periods of 1997, 2002, 2007, 2013 and 2017 general elections,” Strathmore University, pp. 1–7, 2019, [Online]. Available: https://su- plus.strathmore.edu/handle/11071/10501.
[25]	L. Zhu, S. J. Cook, and M. Jun, “The promise and perils of point process models of political events,” arXiv preprint arXiv:2108.12566, pp. 1–25, 2021.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | 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}
}

Copy | Download

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 -

Copy | Download

Author Information

Carol Ogira

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email
Roselynn Kamau

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email
Shallom Kamau

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email
Bridgette Kerubo Bwoma

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email
Bonaya Kiinywi Komora

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email
Henry Athiany

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | 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}
}

Copy | Download

VL - 10
IS - 1
ER -

Copy | Download