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Analysis of Local and Remote Mappers’ Open Geographic Data Contribution to Oil Spill Disaster Response in Niger Delta Region, Nigeria

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Analysis of Local and Remote Mappers’ Open Geographic Data Contribution to Oil Spill Disaster Response in Niger Delta Region, Nigeria
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Analysis of Local and Remote Mappers’ Open Geographic Data Contribution to Oil Spill Disaster Response in Niger Delta Region, Nigeria Open mapping leverages on volunteer mappers mobilized and engaged from the public. volunteers most often are trained and coordinated virtually to carry out dedicated mapping task, irrespective of their geographic location, professional and academic background. In this study volunteer mappers engaged are categorized into two namely: the Local Volunteer Mappers (LVM) comprising of all the potential and actual mappers resident in Nigeria and the Remote Volunteer Mappers (RVM) comprising of all potential and actual mappers not resident in Nigeria. The study sampled 2 Local Government Areas (LGAs) of River State from the 4 vulnerable oil spill disaster LGAs of Ogoni land communities. Ogoni land is a major oil spill disaster vulnerable area of Nigeria, being the major host communities of crude oil exploitation in the Niger Region of Nigeria. Following the hazardous impact and damage of Ogoni land by oil Spill disaster over the years of oil exploitation in Niger Delta, UNEP assessed that the environmental restoration of Ogoni land would require coordinated efforts on the part of government agencies at all levels, industry operators and communities. UNEP also presented its recommendations as a major opportunity to bring new investment, employment opportunities and a culture of cooperation to Ogoni land in addition to driving improvements in the environmental and health situation on the ground. To effectively implement the UNEP recommendations for restoration of Ogoni land, there is a need for a geographic data that provides critical building footprint in the area, especially, to identify and access the vulnerable oil spill communities. Maps produced would be used by government agencies and other stakeholders working to implement UNEP report on Ogoni land restoration as well sustainable development. Consequently, the study engaged volunteer mappers to respond to sampled Oil spill communities viz-viz 3 LGAs in Rivers State, Niger Delta Region of Nigeria. To assess the level of participation of Local (mappers in Nigeria) and Remote Mappers (Not Resident in Nigeria), two mapping projects were created in HOT tasking manager for local and remote mappers respectively. For the purpose of campaigning for Volunteer Mappers the 2 project tasks were tagged ‘’ Mapathon Battle for Vulnerable Oil Spill Disaster Communities in Niger Delta’’ respectively. Project task 6358 was created exclusively for remote mappers outside Nigeria to map Tai LGA, while, project task 6359 was created exclusively local mappers resident Nigeria to map Gokana LGA in a Mapathon battle challenge. Project task 6358 had a total grided cells of 825 mapping tasks for online engagement of mappers while project task 6359 had an automated grided cells of 706 mapping tasks due to differences in the size of the area. The Mapathon unveiled the following research results. Engagement of remote mappers for project task 6358-Tai LGA shows that out of the 583 tasks completely mapped, only 13 were yet to be validated after 2 years of creating the project. This is as a result of archiving the project and diversion of attention to urgent tasks. The project recorded a total of about 16,416 edits comprising of 13,552 buildings and 858km of roads mapped in Tai LGA within the timeline of the study. Demographic characteristics of the contributors to project 6358 on the basis of HOT Tasking Manager users by experience and level shows that 50% were advance mappers and 100 % has more than 1 year mapping experience .The project engaged a total of 56 contribtors by mapping and validation. All mappers and validators by experience has used the tasking manager for more than 1 year while their mapping levels ranges between 40% for beginner mapper, 10 % for intermediate and 50 % for advanced mappers. The project timeline as illustrated by the graph shows that mapping and validation of the Tai LGA task commenced on the same date: 6th August,2019 at the rate of 12% mapping and 2 % validation. Mapping progressively ascended to 64% on the 4th day and got to its peak on the 9th day being 15th August with 99% of the entire task mapped. However, validation of the mapping task had a straight curve with the highest peak of validation being the 12th of September with 95% of the task being validated. By 8th January ,2020, being 6th months of the project,100% of the tasks were completely mapped while 13 of the 596 tasks were yet to be validated. The timeline statistics also shows that an average of 20mintes 46 seconds was the time spent per task to map a total of 583 tasks of 16,416 edits. Also, an average of 6minutes 16seconds was spent for validation per task leaving about 1hour 21minutes 29seconds to finish up the validation of 13 tasks left unvalidated due to a shift to other project tasks and less passion for the project under study. However, the analysis of local mappers engaged in HOT Project Task 6359 Gokana LGA also unveiled the following: The study shows that 706 (100%) of the tasks were completely mapped except for validation of 473(67%) tasks which requires further coordination of mappers. There is no record of bad imagery and tasks left unmapped. The project also recorded a total of about 2064 changesets for mapping a total of about 18,367 edits, comprising of 14,983 buildings and 521 km of roads. The project also recoded a total of 173 contributors comprising of 169 mappers and 8 validators. These mappers (100%) had more than 1year experience in online mapping with OpenStreetMap and are categorized into beginner mappers (72%), intermediate (6%) and advance mappers (21%). The entire project timeline by mapping and validation took a period of about 2years 4months(28months) from 6th August 2019 to 27th December ,2021 as at the time of writing this report. Conclusively, there is a lacuna worthy of research investigation in the mapping response level and capability of remote mappers from other countries and local mappers from Nigeria in crowdsourced rapid response mapping using OpenStreetMap.
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Dependent and independent variablesOpen setLaceTexture mappingAreaMereologyInformationTexture mappingCategory of beingPartial derivativeIntegrated development environmentPerturbation theoryLevel (video gaming)Term (mathematics)Observational studyOpen setMappingAreaMetropolitan area networkNeuroinformatikUniqueness quantificationSource codeComputer animation
Addressing modeAreaDescriptive statisticsLocal ringVulnerability (computing)Observational studyState of matterComputer animation
Perturbation theoryAnalytic continuationCache (computing)Coordinate systemSuite (music)MappingData managementVulnerability (computing)Sampling (statistics)Category of beingWave packetAreaPerturbation theoryObservational studyLevel (video gaming)Process (computing)Morley's categoricity theoremEndliche ModelltheorieComputer animationSource code
Multiplication signCoordinate systemLocal ringDifferent (Kate Ryan album)Letterpress printingMobile WebWave packetMereologyUniverse (mathematics)Direction (geometry)Category of beingOpen setMessage passingUniformer RaumTexture mappingUniqueness quantificationUniform resource locatorArchaeological field surveyComputer animation
Task (computing)Client (computing)Addressing modeTexture mappingDependent and independent variablesAreaLocal ringComputer fontMappingData managementSystem administratorSampling (statistics)Remote procedure callTexture mappingLink (knot theory)Open setSource codeComputer animation
Range (statistics)ResultantLevel (video gaming)Archaeological field surveySystem administratorCategory of beingInstance (computer science)Mobile WebDiagram
Task (computing)Mathematical analysisAddressing modeLine (geometry)Category of beingMappingLocal ringDifferent (Kate Ryan album)Level (video gaming)Observational studyRemote procedure callTotal S.A.BuildingComputer animation
MathematicsCategory of beingMappingLocal ringLevel (video gaming)Remote procedure callComputer animation
Task (computing)SineData typeTotal S.A.Information managementCharacteristic polynomialLevel (video gaming)Category of beingRemote procedure callDependent and independent variablesMappingContent (media)Uniform resource locatorTerm (mathematics)Local ringVapor barrierInternetworkingObservational studyDigitizingMultiplication signVulnerability (computing)Texture mappingInstance (computer science)Open setMusical ensembleContext awarenessPay televisionComputer animationSource code
Transcript: English(auto-generated)
Okay, the speaker is Victor Sande, who is a lecturer in just partial information science at the University of Port Harcourt, Nigeria. And he is also a member of HOT and a PhD researcher at the University of Nigeria.
Yeah, thank you very much. Yeah, this work was done with the passion to drive an open street map community in Nigeria as a crowdsource mapping tool. And so, it was also part of my PhD research work, which Professor Maria Antonia Brevele and Rafael Ikendoku supervised.
And I work with Geography and Environmental Management, University of Port Harcourt, Nigeria.
And I do my research, PhD work at the Geoinformatics and Surveying Department, University of Nigeria, Inugu Campus, Nigeria. And then the community that emerged out of this work is Unique Mappers Network Nigeria, which is currently the open street map community in Nigeria.
Yeah. And to start with, we were looking at open mapping as a crowdsource tool.
And so we know that open mapping leverages some volunteer mappers that are mobilized from the public. And most often they are trained and coordinated to drive a definite mapping tax. And so for this study, we engaged two categories of mappers, which we call the local
mappers that are resident in Nigeria, and then the remote mappers that are resident outside Nigeria. So we wanted to look at their level of participation in terms of contribution to open street map.
And so we decided to take a study area, which is basically in the Niger Delta, specifically the Ogoni land. And in Ogoni land, we have about four local governments that are highly vulnerable to oil spill disaster.
And so in that area, we have the UNEP coming in to assess the environmental situation in Ogoni land. And the recommendation is that there is need for environmental restoration in the area.
And so there is also need for critical open geospatial data for those vulnerable communities. And so that gives us the opportunity to actually contribute to the ongoing projects by
providing open geospatial data that will help address the oil spill vulnerable communities in that area. And in Ogoni land, we have four local governments, like I mentioned, that are critically vulnerable to oil spill.
That's Thai local government, Gokarna, and LMA local government. And so out of these four, we selected two local governments, which is Thai and Gokarna local government.
And so that's the study area. Gokarna, the two local governments are situated in River State and River State in Niger Delta and Niger Delta in Nigeria. So that's the description of the study area.
And so in our methodology, we adopted a sample survey to mobilize and capture the population of volunteer mappers that were engaged in the project. And thereafter, we also sent out questionnaire to actual mappers via the hot taxing project for each local government.
And at the end of the day, we use that data to analyze the demographics of the remote mappers and then the local mappers. We also collected data from the hot taxing manager.
And then we also moved ahead to organize a mapper ton for each of these categories of mappers. There's the remote mappers and the local mappers. And so we made use of both in-person and virtual facilities to provide training for the mappers.
We targeted a population of 200 for each of these categories of mappers. And so we had online mapping activities that were categorized into three stages, which we
tagged the mapper ton battle for vulnerable communities, season one, season two, and then season three. And so by this approach, we were able to use the gamification technique that was applied to coordinate the mapping process so that we can trigger and motivate a crowdsourced online mapping activity based on the motive incentive activation behavior model for crowdsourcing.
And so these are the, yeah, that's part of the mobilization and training activities that were carried out for each of these
local mappers because for local mappers, we were so much interested in making sure that we have a direct contact with them, know who they are, know the category of persons they are, and then we provided an in-person mapper ton
during which we provided refreshments, data access, and every other thing to help them and then give them direct training. And then for the online mappers, that's the remote mappers, we had a virtual mapper ton.
As you can see from the picture, you see the OYO mappers team, the UNIZIC, the OYO mappers team is the team we created at the Federal School of Survey, OYO, and then the UNIZIC mappers team. That's at the University of, Namdia Zikwe University, and so on, you can see
the UNIC mappers team at the University of Portacote, and then the APSU mappers team. So we traveled to those different locations, universities, campus, and then mobilized them and introduced them for the first time because most of them are new to what we talk about in OpenStreetMap.
So we take time to, you know, provide a campaign, print handbills, flyers, publicity, and we're also able to get coordinators for each of them so that we had a date. So, okay, season one, season two, this is the date we are going to have for you.
And so that is what we did in the mobilization and training of volunteer mappers. Then for the actual mapping tags, we had a delineation of mapping tags for each, for remote mappers, as well as the local mappers, using the whole taxing manager.
And so the extraction was done using the Nigerian admin data set, which was imported into the taxing manager. And then the area of interest was gridded, and so we had about 596 and 706 grids respectively.
That's for Gokarna, for Thai and Gokarna local government. That's for the remote mappers and for the local mappers.
And so we now published that on OpenStreetMap, and we published that with the taxing manager and shared the link so that the mapper tone started just the same day for local mappers and the remote mappers.
So these are the samples of the tags that were created for each of the local governments. And then in our results, because we are interested in looking at the
level of contribution, performance, the level of participation from both categories of mappers. So we also looked at the demographics of each of the mappers that were mobilized. For instance, about 16%, 16.85% of the mappers fell within the ages of 26 to 35
years, and then followed by 18 to 25 years, accounting for about 15.73% of the participants. So the least age are those below 18 years, which accounted for 4.93.
Then for the male-female ratio, in administration of the survey, we had male, female, and then those who are neutral. And so you can see the percentage for those who contributed.
And then for the level of participation for each of these categories, we discovered that for Project 6358 that was created exclusively for mappers outside Nigeria, that's Thai local government,
we had a total grid of 596 mapping tags, while for 6358, that's for the local mappers, we had about 706 mapping tags due to the differences. And so only for 6358, which is Thai local government, out of the 596 tags that
were completely mapped, only 13 were yet to be validated after two years of creating the project. And so the project recorded a total of about 16,000 edits, which includes 13,000 buildings and then
858 kilometers of roads mapped by the remote mappers in Thai local government within the timeline of the study. And this category of mappers, they were able to map this within six months, within six months.
You can see the timeline, the changes that were done for each category, the remote mappers for Thai local government, and then the Gokarna.
So for Gokarna, which is the local mappers, they mapped the local government within 28 months, while the remote mappers completed their own mapping just within six months. And so that helped us to analyze the contribution, the level of contribution for each category of mappers, as you can see here.
So in conclusion, we discovered that there's a lacuna that needs to be investigated from this study arising from the mapping response level of OpenStreetMap contributors
from other countries, that is remote mappers, and then local mappers are resident in Nigeria. And we feel that this is based on the geographic context, because when you
look at the remote mappers in terms of international community or the global community, and then the local mappers in terms of the indigenous content of the contribution, you find out that there is a kind of need for investigation to know why, you know,
those within outside and those within inside based on geographical location could differ in their level of participation. And then another thing we discovered is the digital citizenry talks about Internet access and so on.
Yeah, for instance, the local mappers, especially in the developing countries, would have a need for the cost of Internet, yeah, to break the barrier for the cost of Internet devices and so on.
Whereas those in the developed countries, they have free access to Internet. You can see just around here, if we have opportunity in the developing countries, Nigeria, where you
could just come with your laptop and sit down and begin to map, it's not like that. So you need to buy them data. You need to subscribe for data and so on. And then the exposure, that now reduces the level of participation because the individual would think about the cost of payment for Internet data subscription.
What about the demographic characterization in terms of age, sex, and other things and so on? Economic disposition of the volunteers, yeah, the volunteers, the remote mappers from other countries, if
they are well positioned and they don't have need of, you know, much need of, they have time to volunteer in as much as their economic disposition are quite comfortable. But local mappers, especially in the developing countries, would need the cost of the basic, you know, the basic needs, taking care of the things.
You can't just engage a youth and at the end of the day, he goes home looking for food. So you rather want to use his time to invest in what will give him money.
So the economic disposition of volunteers matters a lot in the level of participation of volunteer mappers. Yeah, both, especially in responding to a rapid, providing a rapid response mapping for vulnerable communities in Nigeria.
Yeah, thank you very much for that. I hope I kept the time. Thank you, Victor.