Analysis of Local and Remote Mappers’ Open Geographic Data Contribution to Oil Spill Disaster Response in Niger Delta Region, Nigeria
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Number of Parts | 351 | |
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License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
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00:00
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
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Addressing modeAreaDescriptive statisticsLocal ringVulnerability (computing)Observational studyState of matterComputer animation
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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
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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
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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
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Range (statistics)ResultantLevel (video gaming)Archaeological field surveySystem administratorCategory of beingInstance (computer science)Mobile WebDiagram
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Task (computing)Mathematical analysisAddressing modeLine (geometry)Category of beingMappingLocal ringDifferent (Kate Ryan album)Level (video gaming)Observational studyRemote procedure callTotal S.A.BuildingComputer animation
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MathematicsCategory of beingMappingLocal ringLevel (video gaming)Remote procedure callComputer animation
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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)
00:01
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.
00:20
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.
00:55
And I work with Geography and Environmental Management, University of Port Harcourt, Nigeria.
01:04
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.
01:27
Yeah. And to start with, we were looking at open mapping as a crowdsource tool.
01:42
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
02:06
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.
02:25
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.
02:46
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.
03:06
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
03:24
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.
03:47
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.
04:02
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.
04:22
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.
04:46
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.
05:01
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.
05:23
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
05:43
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.
06:11
And so these are the, yeah, that's part of the mobilization and training activities that were carried out for each of these
06:22
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
06:45
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.
07:04
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
07:24
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.
07:44
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.
08:05
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.
08:29
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.
08:54
That's for Gokarna, for Thai and Gokarna local government. That's for the remote mappers and for the local mappers.
09:04
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.
09:26
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
09:42
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
10:08
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.
10:25
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.
10:42
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,
11:00
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
11:28
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
11:46
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.
12:08
You can see the timeline, the changes that were done for each category, the remote mappers for Thai local government, and then the Gokarna.
12:20
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.
12:51
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
13:06
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
13:21
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,
13:46
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.
14:06
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.
14:25
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
14:42
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.
15:07
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
15:24
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.
15:52
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.
16:04
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.
16:28
Yeah, thank you very much for that. I hope I kept the time. Thank you, Victor.