Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networks
The Niger Delta region of Nigeria is a major oil-producing area which experiences frequent oil spills that severely impacts the local environment and communities. Effective environmental monitoring and management remain inadequate in this area due to negligence, slow response times following oil spi...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2448218 |
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| author | Seyi Adewale Adebangbe Deborah Dixon Brian Barrett |
| author_facet | Seyi Adewale Adebangbe Deborah Dixon Brian Barrett |
| author_sort | Seyi Adewale Adebangbe |
| collection | DOAJ |
| description | The Niger Delta region of Nigeria is a major oil-producing area which experiences frequent oil spills that severely impacts the local environment and communities. Effective environmental monitoring and management remain inadequate in this area due to negligence, slow response times following oil spills, and difficulties regarding access and safety. This study investigates the spatiotemporal patterns of oil spills along the pipeline network from 2013 to 2021 using geo-computation techniques. Utilising the spNetwork package in R, Network Kernel Density Estimates (NKDE) and its temporal extension, Temporal Network Kernel Density Estimates (TNKDE) were carried out. Pipeline data were transformed into 500-metre lixels (linear pixels) to compute network distances and generate density estimates. NKDE identified oil spill hotspots, while TNKDE illustrated the temporal transitions of spills. These methods surpass traditional approaches (e.g. KDE, cluster analysis, point pattern analysis) by incorporating network constraints and uncovering critical spatial–temporal patterns. The findings offer valuable insights for targeted interventions to reduce future spills and mitigate past impacts. |
| format | Article |
| id | doaj-art-94f76547a07f46f9a4e4e058efb08891 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-94f76547a07f46f9a4e4e058efb088912025-08-25T11:31:40ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2024.2448218Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networksSeyi Adewale Adebangbe0Deborah Dixon1Brian Barrett2School of Geographical and Earth Sciences, University of Glasgow, Glasgow, UKSchool of Geographical and Earth Sciences, University of Glasgow, Glasgow, UKSchool of Geographical and Earth Sciences, University of Glasgow, Glasgow, UKThe Niger Delta region of Nigeria is a major oil-producing area which experiences frequent oil spills that severely impacts the local environment and communities. Effective environmental monitoring and management remain inadequate in this area due to negligence, slow response times following oil spills, and difficulties regarding access and safety. This study investigates the spatiotemporal patterns of oil spills along the pipeline network from 2013 to 2021 using geo-computation techniques. Utilising the spNetwork package in R, Network Kernel Density Estimates (NKDE) and its temporal extension, Temporal Network Kernel Density Estimates (TNKDE) were carried out. Pipeline data were transformed into 500-metre lixels (linear pixels) to compute network distances and generate density estimates. NKDE identified oil spill hotspots, while TNKDE illustrated the temporal transitions of spills. These methods surpass traditional approaches (e.g. KDE, cluster analysis, point pattern analysis) by incorporating network constraints and uncovering critical spatial–temporal patterns. The findings offer valuable insights for targeted interventions to reduce future spills and mitigate past impacts.https://www.tandfonline.com/doi/10.1080/17538947.2024.2448218Oil spillnetwork analysisnetwork kernel density estimatestemporal network kernel density estimates |
| spellingShingle | Seyi Adewale Adebangbe Deborah Dixon Brian Barrett Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networks International Journal of Digital Earth Oil spill network analysis network kernel density estimates temporal network kernel density estimates |
| title | Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networks |
| title_full | Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networks |
| title_fullStr | Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networks |
| title_full_unstemmed | Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networks |
| title_short | Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networks |
| title_sort | geo computation techniques for identifying spatio temporal patterns of reported oil spills along crude oil pipeline networks |
| topic | Oil spill network analysis network kernel density estimates temporal network kernel density estimates |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2448218 |
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