PREDICTIVE ANALYTICS FOR FORECASTING IRREGULAR MIGRATION PATTERNS IN WEST AFRICA Article Swipe
Ayanyemi, Hammed Abayomi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17784870
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17784870
This article examines the strategic relevance of Predictive Analytics (PA) in forecasting irregular migration patterns within West Africa. Drawing on the author’s dual expertiseover seven years as an Immigration Officer with the Nigeria Immigration Service (NIS) and current postgraduate program in Information Technology, the paper argues that current reactive border management strategies based on lagging indicators are no longer sufficient. By integrating Machine Learning (ML) models with multi-dimensional data sources—including socio-economic indicators, operational border records, conflict data, and environmental metrics. PA provides a proactive forecasting framework. The analysis demonstrates that PA enables dynamic resource allocation, early policy warning systems, and actionable intelligence for disrupting human smuggling networks. The paper concludes that PA is not optional but a strategic imperative for transforming West Africa’s border security architecture from reactive response to future-driven migration intelligence.
Related Topics
Concepts
Lagging
Analytics
Predictive analytics
Immigration
Service (business)
Relevance (law)
Computer science
Business
Warning system
Officer
Data science
Investment (military)
Credibility
Regional science
Human resources
Business intelligence
Operations research
Geography
Political science
Economics
Decision support system
Resource (disambiguation)
Marketing
Economic indicator
Market intelligence
Dual (grammatical number)
Human capital
Operations management
Process management
Human resource management
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.5281/zenodo.17784870
- OA Status
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https://openalex.org/W7108337350Canonical identifier for this work in OpenAlex
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https://doi.org/10.5281/zenodo.17784870Digital Object Identifier
- Title
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PREDICTIVE ANALYTICS FOR FORECASTING IRREGULAR MIGRATION PATTERNS IN WEST AFRICAWork title
- Type
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articleOpenAlex work type
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2025Year of publication
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2025-12-02Full publication date if available
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Ayanyemi, Hammed AbayomiList of authors in order
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https://doi.org/10.5281/zenodo.17784870Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doi.org/10.5281/zenodo.17784870Direct OA link when available
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Lagging, Analytics, Predictive analytics, Immigration, Service (business), Relevance (law), Computer science, Business, Warning system, Officer, Data science, Investment (military), Credibility, Regional science, Human resources, Business intelligence, Operations research, Geography, Political science, Economics, Decision support system, Resource (disambiguation), Marketing, Economic indicator, Market intelligence, Dual (grammatical number), Human capital, Operations management, Process management, Human resource managementTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.actionable | 100 |
| abstract_inverted_index.author’s | 21 |
| abstract_inverted_index.disrupting | 103 |
| abstract_inverted_index.framework. | 85 |
| abstract_inverted_index.imperative | 118 |
| abstract_inverted_index.indicators | 55 |
| abstract_inverted_index.management | 50 |
| abstract_inverted_index.strategies | 51 |
| abstract_inverted_index.Immigration | 28, 33 |
| abstract_inverted_index.Information | 41 |
| abstract_inverted_index.Technology, | 42 |
| abstract_inverted_index.allocation, | 94 |
| abstract_inverted_index.forecasting | 11, 84 |
| abstract_inverted_index.indicators, | 71 |
| abstract_inverted_index.integrating | 61 |
| abstract_inverted_index.operational | 72 |
| abstract_inverted_index.sufficient. | 59 |
| abstract_inverted_index.architecture | 125 |
| abstract_inverted_index.demonstrates | 88 |
| abstract_inverted_index.intelligence | 101 |
| abstract_inverted_index.postgraduate | 38 |
| abstract_inverted_index.transforming | 120 |
| abstract_inverted_index.environmental | 78 |
| abstract_inverted_index.expertiseover | 23 |
| abstract_inverted_index.future-driven | 130 |
| abstract_inverted_index.intelligence. | 132 |
| abstract_inverted_index.socio-economic | 70 |
| abstract_inverted_index.multi-dimensional | 67 |
| abstract_inverted_index.sources—including | 69 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.910082 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |