Keywords
COVID-19, pipeline vandalism, restriction on movement, NNPC pipelines, pipes
This article is included in the Sociology of Health gateway.
This article is included in the Coronavirus collection.
COVID-19, pipeline vandalism, restriction on movement, NNPC pipelines, pipes
The following are some of the modifications made between version 1 and version 2: the inclusion of further background data on pipeline vandalism's prevalence in Nigeria as well as the study's value and importance. The majority of queries from the reviewers focused on the data source and the validity of the analysis approach. As stated in Version 1, the NNPC is the government organization that provides the data. Since the NNPC is the government agency in charge of the production, exploitation, management, and protection of Nigeria's petroleum riches, all data comes from them. As a result, the NNPC is the best place to turn for any information on pipeline vandalism in Nigeria. This source is the origin of all data.
A second statistical examination of the data was conducted and included in the article to highlight its appropriateness in the second version in order to assess the tool's dependability. One-way ANOVA was employed in the original study's analysis; as a result, version 2 includes the verification of one-way ANOVA assumptions with respect to the underlying data sets. Figures 1a–7 display this. As more figurines were added, the positions of the initial figures from version 1 were changed.
See the authors' detailed response to the review by Alessandro Rovetta
Product theft and vandalism of national pipelines are recurring challenges faced by the Nigeria National Petroleum Corporation (NNPC).1,2 Oil spillage is associated with oil pipeline destruction. The destruction of pipelines leads to several environmental problems; fresh and seawater pollution, air pollution, chemical pollution, soil and land pollution.1 It also makes most agricultural practices unsustainable with an associated decline in fish population in polluted waters, biodiversity depletion,3 loss of habitat, and loss of ecological and security systems.3–5
Despite the punishment of 21 years to life imprisonment for pipeline vandalism6 [Section 2 of the Petroleum Production and Distribution (Anti-Sabotage) Act Cap], the practice continues. There are three main aspects of vandalism; that must be acknowledged and addressed if any meaningful sustainable gain against pipeline vandalism can be achieved. One, Nigeria is losing over 300,000 barrels per day (BPD) as a result of crude oil pipeline vandalism.7 Where do these crudes go? Two, there are billions of dollars in losses to the national revenue, environmental degradation, and in some cases loss of human lives.8,9 Where is this money? Thirdly, Pipeline vandalism because of the nature of the criminality, occurs at remote locations.10–12 Which “class” of Nigerians are involved in this act? What is the role of accessibility in pipeline vandalism?
In the global fight against the COVID-19, several liberties, such as our freedom of movement, and association were suspended. The pandemics offer a unique opportunity to study the effect of movement restriction on pipeline vandalism. A relationship between access to pipelines and physical vandalism (which involves the destruction of pipes materially) is assumed. This type of destruction is not remote; it implies “accessibility”. Accessibility by its very nature involves two principal factors; access to the pipeline physically and opportunity. These two aspects of the equation were removed during the lockdown.
“Access” involves proximity to pipelines with ample time to destroy or compromised its structures. Legally, at this point, the miscreant is termed a Vandal.
“Opportunity”, on the other hand, entails a longer time duration to enable the fluid to be scooped/removed and carted away by the (Vandals, now termed) thieves. This project aims to provide information on the role of movement in pipeline vandalism. Verification of this, during “peacetime” is somewhat limited in a democracy. The constitution and economic considerations would not permit such complete, absolute prohibition on movement and assembly.
The pipelines pass through vast expanses of land. From mangrove swamps, tropical rain forest, Savanah, and arid deserts. To effectively, physically lock down all routes and passes would be practically impossible during peacetime. However, during the pandemic, much of the considerations (economically, politically, and constitutionally) were removed therefore, the condition (for vandalism) theoretically should be diminished. This project aims to address this hypothesis and assumptions using statistical methods.
In this light, the COVID-19 pandemic and consequent lockdown could be seen as an experiment. The study would therefore reveal the role “opportunity” and “access to pipeline” play in vandalism collectively. The Researchers could test if a relationship exists between observed variables and their underlying latent constructs. To accomplish this, the researchers use empirical research, to postulate the relationship pattern and test it statistically.
This paper examines the nexus between oil pipeline vandalism and public accessibility in Nigeria.
Given the adverse impact of pipeline vandalism as exemplified in loss of life, economic losses, environmental degradation, and pipeline explosions, the paper submits that an evaluation of the impact of anti-pandemic restrictions on the phenomenon is very relevant as pipeline vandalism poses a danger for economic wellbeing and national security. Thus, a study of the effects of anti-pandemic restrictions on pipeline vandalism is relevant.
During the COVID-19 pandemic, movements were restricted in a manner unparalleled in modern living memory. It is desirable to determine if a significant difference exists in the incidence of pipeline vandalism of Nigerian oil pipelines during the COVID-19 pandemic. This dataset provides that information.
Findings from this research would enable a greater understanding of the diverse players involved in these practices. A greater understanding of the nature of the vice would be achieved. This could lead to better decisions to checkmate the vice of pipeline vandalism.
Several methods exist to determine the significance and relationship between groups of data; for example, t-test, ANOVA, etc. In recent times, some scholars have challenged the use of a threshold to declare the statistical significance of the p-value.13–17 Two main arguments exist. One; research data contain more meaning than is summarized in a P-value and its statistical significance. Two, the concepts are frequently misunderstood and consequently inappropriately interpreted. The abolishment of p-values has been echoed in some articles; an example is Ref. 18.
Traditionally, Researchers examine differences between groups using t-test, ANOVA.17,19-21 In this study the one-way ANOVA was used; and, percentage differences were added as quantifiers. A one-way ANOVA is a statistical test used to determine whether or not there is a significant difference between the means of three or more independent groups.
The assumptions in the one-way ANOVA are:
Normality - This can check visually or, by the histogram. Normality checks were carried out on all subgroups datasets (Figure 1), prior-COVID, COVID and post-COVID datasets (Figure 2) and on historical datset (Figure 3). Before data can be analysed statistiscally, it must be shown to be “normally distributed”. “Normal” data are data that are drawn from a population that has a normal distribution. For the subgroups these are:
Normality check on the group data:
Variance
This can be checked with a boxplot. For the subgroups these are:
The check on the group data:
Independence
There is no formal test to verify that the observations in each group are independent.
The data
The study uses easily accessible and verifiable data. Primary data was collected by the Researchers from first-hand sources. All data used in this study reside in public domains. This is in line with the Authors’ aim to allow ease to the methods, materials, and protocols. It also allows replication. Primary data of the number of cases of pipeline vandalism each month from January 2015 to January 2021were collected and grouped based on date. The names of each group are self-explanatory. The groups are:
1. Historical data –1 January 2015 to 31 July 2019.
2. Prior COVID-19 data – 1 August 2019 to 31 January 2020.
3. COVID-19 data – 1 February 2020 to 31 July 2020.
4. Post COVID-19 data – 1 August 2020 to 31 January 2021.
Under the land use decree, the oil wealth of the country (Nigeria) resides with the Federal Government. All aspect is controlled by the Nigeria National Petroleum Cooperation (NNPC) or its subsidiaries. Incidences of vandalism of pipeline are ascribed in the Monthly Financial and Operations Reports (MFOR) of the Nigeria National Petroleum Corporation (NNPC).22 Thus, the integrity and veracity of data used cannot be in doubt. These monthly reports are available for free download by the public from the NNPC website link (NNPC; https://www.nnpcgroup.com).22 This information can be accessed by clicking “NNPC Business” and selecting “Business Information”, then “Monthly Performance Data” on their website (https://www.nnpcgroup.com) or through (https://www.nnpcgroup.com/NNPC-Business/Business-Information/Pages/Monthly-Performance-Data.aspx). The underlying data is available for free download by the public, thus; reproducibility of the dataset is facilitated. The information abstracted from the NNPC MFOR was the number of cases of pipeline vandalism per month, the month of vandalism, and the year of vandalism.
Furthermore, we obtained information and dates of major National and International events that may be additional external stimuli in this analysis. This information was collected from national and regional newspapers and web-based publications, and web pages.
These are:
• May 2016, incorporation and deployment of sophisticated weapons, use of satellite images and geographical information system (GIS) into the security apparatus to ensure vandalism is contained, the setting up of a pipeline security force to stamp out the menace, and the formation of the Trans-National Organized Crime (TNOC) with regional allies to fight against the proliferation of Small Arms and Light Weapons.23 This was a welcome development as the area under physical patrol was massive.
• The onset of COVID-19 in December 2019 and the declaration of COVID-19, on 30th January 2020, as a Public Health Emergency of International Concern by WHO (World Health Organization), and the upgrade to a pandemic by the 11 March 2020
• In Nigeria, the pre-lockdown commenced from 28 February – 29 March 2020; 31 days duration. The lockdown was 35 days; from 30 March to 3 May 2020. And an ‘easing up’ of 73 days, 5 May – 15 July 2020.
“Historical data” span from January 2015 to July 2019, these data represent pipeline vandalism data before the advent of COVID-19 and its restrictions. These data were collected before the outbreak. Consequently, it could be assumed that COVID-19 did not influence the incidences of pipeline vandalism during this time. These data can therefore be used as a “baseline”; a such of “norm”. For in-depth study, this group (spanning approximately 4 years was further divided into subgroups of a year durations each).
The sub-groups are:
• Sub-group A (one-year before deployment) - May 2015-April 2016
• Sub-group B (the year of deployment) - May 2016-April 2017
• Sub-group C (one-year after deployment) - May 2017-April 2018
• And, sub-group D (two years after deployment) - May 2018-April 2019.
An analysis of the sub-groups would reveal if the use of GIS had any impact on cases of pipeline vandalism.
Data from groups 2-4 were arbitrarily set within a duration of 6-months each. Pipeline vandalism during the time frames could be imparted by COVID-19. A comparative analysis of data six months before lockdown (group 2, Table 1) and six months after lockdown (group 4, Table 1) would reveal if COVID-19 had any impact on cases of pipeline vandalism.
To minimize/remove seasonal variations due to the weather (wet and dry season) data were compared only with data from the corresponding seasonal frame. This is logical, in temperate zones, data of pipeline vandalism in the summer should be compared against summer data; winter against winter in the colder zones; similarly data of pipeline vandalism in the rainy season should be compared only against data of pipeline vandalism in another rainy season in the tropic.
For ease of accessibility, the software used for analysis was the MS office Excel 2013 with the Analysis ToolPak add-in.
The reproducibility of data determines if similar results or conclusions could be attained by a different research team, using the same methods. The results in this study, are not artifacts of the unique setup, therefore any researcher using any statistical tool should lead to the same/similar results.
Replication, on the other hand, refers to the repetition of a research study, usually with different situations and different subjects. This determines if the basic findings of the original study can be applied to other participants and circumstances. It can be considered as a “re-run” study; aimed to confirm results. The severe acute respiratory syndrome (novel coronavirus COVID-19 or SARA-CoV-2) and its associated lockdown offered a unique opportunity that may not be replicable on the same scale.
In all statistical analyses in this project, an alpha = 0.05 as the significance threshold was set. In line with best practice for transparency in data analysis, our research hypotheses were clearly articulated; and null and alternative hypotheses were established. This means that the null hypothesis would be rejected if the p-value is less than or equal to 0.05 and the alternative hypothesis would be accepted.
The programmed MS Excel spreadsheet was used in the calculation of the time series analysis using a moving average.
The programmed Excel spreadsheet consist of rows and column. Each column was given a unique identifier ranging from 1 to 10 (Figure 7) and a column heading which is the formula used for the calculation in the column (Figure 7).
The third and fourth columns are the date and cases of pipeline vandalism for the period. The fifth column is the moving average. In the excel spreadsheet, the moving average, MA was calculated by:
The moving average,
Where Dt = is the cases of pipeline vandalism at time, t
And
D(t+1) = is the cases of pipeline vandalism at time, t+1
In columns 6-9 the seasonal and irregularity components are handled and the data deseasonalized.
For the group data, the total incidences during the time frame covered by the group, average, and standard deviation were established. The grouped data were subjected to an ANOVA analysis, A null and alternative hypothesis were set as followed:
For the sub-groups, the total cases in each subgroup, the mean, and the standard deviation were calculated. A null and an alternative hypothesis were set.
• Null hypothesis: There is no significant difference between the mean case of pipeline vandalism incidences prior, during, and post the deployment.
• Alternative hypothesis: There is a significant difference between the mean case of pipeline vandalism incidences prior, during, and post-deployment.
The sub-grouped data were also subjected to an ANOVA analysis, and a time series analysis (after the data were smoothened by moving average).
Seasonal confounds
There are two principal seasons, the wet rainy season and the hot dry season in Nigeria. Pipeline vandalism takes place in remote locations near isolated, rural roads and footpaths; not readily accessible during adverse weather conditions. We, therefore, assume that the rainfall affects the number of cases of pipeline vandalism. However, rainfall patterns are fairly predictable and torrential rainfall occurs in the mid of the rainy seasons. Seasonal confounds were eliminated by comparing data for the same months in each group. This implies that the rainfall season data (in one group or year) were compared only with the rainfall season data (in another group or year); with similar arguments for the dry season data.
Data points in each group or sub-group
For all analyses, the number of data points was of uniform length to reduce any possible bias due to unparalleled data points.
In each group (prior, during, and post COVID-19 lockdown groups) the number of data points was six. In the four sub-groups of the historical data (i.e., sub-group “A”, sub-group “B”, sub-group “C”, and sub-group “D”) each sub-group had 12 data points.
The data was assembled over an even interval and ordered chronologically with equal time-frequency.
Exclusion of data
All cases/incidences of vandalism of pipelines that fall before or after the time frame under review (1 January 2015 to 31 January 2021) as ascribed in the MFOR were removed from the analysis.
Other assumptions made
The destruction of these pipelines has been a scourge on the national petroleum industry in Nigeria since time immemorial,1 two groups of people disrupt pipelines in Nigeria. One; the activists, radicals, and militants, to make political statements, and two, the thieves. Thieves, solely for monetary consideration via illegal possession of the fluids therein.
The former, make political statements before any attempted disruptions, often to inform the government and allow negotiation for the fulfillment of their demands; the latter does not. During the lockdown, no activists, radicals, or militants made any political statement; so, we can assume they also heeded the order to “isolate and social distance”. We, therefore, attributed all pipeline vandalism during the COVID-19 lockdown period to thieves.
The number of cases of vandalism of pipeline as ascribed in the Monthly Financial and Operations Reports (MFOR) of the Nigeria National Petroleum Corporation (NNPC),24 for the years under study is shown on Table 2.
Other information considered in the interpretation of the data plot of monthly cases of pipeline vandalism vs. time in month/year (Figure 8) include;
i. The date of deployment (May 2016) of sophisticated weapons, satellite imagery, and geographical information system into the security apparatus to checkmate pipeline vandalism. Before the deployment, the pipeline security method involved the active patrol in pipeline installation by security agents using patrol vehicles. Another method adopted by past administration was the involvement of local militia leaders in delicate but dangerous and remote locations. After the deployment a combination of the active patrol of pipeline installation by security agents and GIS are used; in addition to a reversal of the policy on the use of local militia.25
ii. The date of declaration of public health emergency of international concern.
iii. The date of the upgrade of the COVID-19 to “pandemic: status.26,27
iv. And the dates of the COVID-19 lockdown narratives in Nigeria.28,29
The May 2016 event (from a cursory glance of Figure 8) had a great impact on cases of pipeline vandalism.21
A comparative analysis of data six months before lockdown (group 2, Table 1) and six months after lockdown (group 4, Table 1) would reveal if COVID-19 had any impact on cases of pipeline vandalism.
Figure 11 compares the cases of pipeline vandalism 6 months after the deployment of GIS and other security apparatus and in the months of lockdown. The total lockdown was observed to yield better results. It was observed that a blanket restriction lowered the cases of vandalism the most; to an all-time low of 19 cases in March 2020 after a start of 32 cases in Febuary 2020 it was observed that cases started to rise (Figure 11), although it never reach pre-lockdown levels. The blanket restriction on movement was most effective (Table 3).
A “lag” is a fixed amount of time. In the lag analysis in this paper, two key observations (number of cases of vandalism) are plotted lagged. These are the time periods after the implementation of the GIS into the security apparatus to checkmate pipeline vandalism before changes could be observed. The six-month lag may be the “learning/training and implementation phase” after the media announcements and deployment.
Notice the declining incidences of pipeline vandalism from August after the installation in May with an all-time low in December (Figure 9). It was noted that after the periods of renovations of the methodologies used to checkmate the activities of vandals.
The uncompromising movement restrictions also favored a reduction in cases of pipeline vandalism, as a similar shift was observed in the groups’ data. These data span from six months before lockdown, the COVID-19 pandemics lockdown group and six months after. The lockdown period and the periods immediately after, presented the fewest cases of pipeline vandalism (Figure 10).
As observed from Figures 11 and 12, short term benefits were observed. The restriction of movement led to a reduction of pipeline vandalism when the number of cases of vandalism for any month are compared. However by December, initial ripples were observed.
The implementation of a different security protocol in May 2016 was found to be followed by a reduction in cases of pipeline vandalism. The restriction during the pandemic was found to be followed by a reduction in cases of pipeline vandalism. These methods could be said to be also effective.
From a security viewpoint, it was therefore desirous to determine if greater success would be attributed to either the use of GIS or blanket restriction. This would enable the design of a more winning approach to vandalism. For this, the cases of pipeline vandalism 6 months after the incorporation and implementation of the GIS systems and the 6 months of total and comprehensive lockdown were compared (Table 4).
To determine this, the rate of effectiveness was considered. This may be defined as:
Rate of effectiveness,
Where:
Rate of effectiveness = Re
Number of cases at start of observation period = A
Number of cases at end of observation period = B
Time duration of observation period = t
A time series analysis of the data was undertaken to determine the effect of time vandalism on pipeline and allow forecasting using a moving average (MA) model.
In time series analysis a sequence of data points recorded over an interval of time and collected at consistent intervals over the set period of time at consistent intervals is analysised. the data points are not intermittently or randomly selected. The time series analysis of subgroups A, B, C and D are shown in Tables 5, 6, 7, and 8 respectively. In Figures 13, 14, 15, and 16 dataset from each subgroups and generated moving average models are plotted. This shows the degree of fitness of the moving average models in each incidence.
Harvard Dataverse. Effects of COVID-19 on pipeline vandalism in Nigeria, West Africa. DOI: https://doi.org/10.7910/DVN/8X5KKB.30
This project contains the following underlying data:
Dataset Data for Effects of COVID-19 on pipeline vandalism ingested files:
• Original data.tab. (Contains the unfiltered data from the NNPC reports, with cases of pipeline vandalism tabulated by month and year.).
• ANOVA-Historical subgroups.tab. (Two sheets. One; (MasterDataSheet) contains the original data divided into the groups and a second (Historical sub-groups) preliminary analysis on the sub-groups).
• ANOVA-COVID-19 groups.tab. (ANOVA analysis of COVID-19 group (prior, during, and post COVID-19 lockdown groups)).
• Graph-subgrpB-and-6-months lockdown.tab. (Comparative analysis of key periods – 1 February-30 July 2017 and 1 February-30 July 2020).
• Time series analysis -COVID-19 groups.tab. (Time series analysis of COVID-19 group (prior, during, and post COVID-19 lockdown groups) smoothening with moving average).
• Time-series analysis-Historical subgroups.tab. (Time series analysis of historical subgroups with smoothening by moving average).
Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Views | Downloads | |
---|---|---|
F1000Research | - | - |
PubMed Central
Data from PMC are received and updated monthly.
|
- | - |
Is the rationale for creating the dataset(s) clearly described?
Yes
Are the protocols appropriate and is the work technically sound?
No
Are sufficient details of methods and materials provided to allow replication by others?
Partly
Are the datasets clearly presented in a useable and accessible format?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Risk, reliability, resilience, pipeline integrity, probabilistic modeling, statistical analysis, structural integrity
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Infodemiology, Infoveillance, Public Health, Statistics
Is the rationale for creating the dataset(s) clearly described?
Yes
Are the protocols appropriate and is the work technically sound?
No
Are sufficient details of methods and materials provided to allow replication by others?
Yes
Are the datasets clearly presented in a useable and accessible format?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Safety Engineering; Cascading effects
Is the rationale for creating the dataset(s) clearly described?
Partly
Are the protocols appropriate and is the work technically sound?
Partly
Are sufficient details of methods and materials provided to allow replication by others?
No
Are the datasets clearly presented in a useable and accessible format?
Partly
References
1. Amrhein V, Korner-Nievergelt F, Roth T: The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research.PeerJ. 2017; 5: e3544 PubMed Abstract | Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: Infodemiology, Infoveillance, Public Health, Statistics
Alongside their report, reviewers assign a status to the article:
Invited Reviewers | |||
---|---|---|---|
1 | 2 | 3 | |
Version 2 (revision) 20 Dec 22 |
read | read | |
Version 1 19 Jul 21 |
read | read |
Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
Sign up for content alerts and receive a weekly or monthly email with all newly published articles
Already registered? Sign in
The email address should be the one you originally registered with F1000.
You registered with F1000 via Google, so we cannot reset your password.
To sign in, please click here.
If you still need help with your Google account password, please click here.
You registered with F1000 via Facebook, so we cannot reset your password.
To sign in, please click here.
If you still need help with your Facebook account password, please click here.
If your email address is registered with us, we will email you instructions to reset your password.
If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance.
Comments on this article Comments (0)