Migrants: the pull effects of rural industrial sites as seen from space

Abstract

Accessibility to raw materials, cheap labour and lenient labour laws make rural areas attractive to many industries in West Africa. The set-up of small-scale solid mineral industries is popular in rural West Africa. These industries are labour intensive and require small to large areas of land. This is just one of the examples of industrialization taking place in rural areas. Nigeria is well known for its vast oil reserves, which in turn creates a lot of employment opportunities, especially for low-skilled workers, since many of the reserves are in rural areas. Ghana's southern western region has a wealth of gold, which has caused small-scale industries to spring up and led to an influx of people from more rural areas. In combination with proximity to mineral resources, this has led to rural industrialization. This can be seen in the increase in the number of people in an area which indicates an influx of migrants. When this happens there's an upsurge in migration to rural areas, pressure on land and water resources from agricultural activities, which affects the livelihood of migrants. This study seeks to identify migrants' behaviours to move to rural industrial areas in Ghana and Nigeria using remote sensing proxies. The method will use several remote sensing products such as Landsat, Copernicus datasets, Hansen Global Forest dataset, WorldPop and JRC-Global Human Settlement Layer dataset. The Random Forest classifier will be used to generate a Landcover map of the selected areas with Copernicus and Landsat datasets. The expected result will have the potential to demonstrate that Copernicus data, World Pop and Hansen Forest Cover data can be a useful proxy for population and migration studies. Moreover, the monitored significant changes in land use and land cover in the industrial areas compared over the past 20 years reveal certain trends of the industrialization era in Western Africa. The research has the capabilities of producing effective and accurate methods for identifying the pull effects of industries in rural areas. This is essential for the implementation of policies for improved infrastructure, improved labour laws, good health and decent wages.

Full text

Migrants: the pull effects of rural
industrial sites as seen from space
Itohan-Osa Abua*, Michael Thiela, Marta Sapena-Mollb, Rutendo Mukaratirwa a, Jürgen Rauh a, Hannes Taubenböck a, b
Motivation and Background
•Migration, the permanent shift of a person's
life, influences the African continent's
dynamics. 1
•The availability of migration data in West Africa
is inadequate.2
•We examine whether remote sensing can proxy
migration, particularly on rural industrial
locations in West Africa.
REFERENCES
[1] Steinbrink, Malte, and Hannah Niedenführ. Africa on the Move: Migration, Translocal Livelihoods and Rural Development in Sub-Saharan Africa. Springer International
Publishing, 2020. DOI.org (Crossref), https://doi.org/10.1007/978-3-030-22841-5.
[2] Beauchemin, Cris. ‘Rural-Urban Migration in West Africa: Towards a Reversal? Migration Trends and Economic Situation in Burkina Faso and Cote d’Ivoire’. Population
Space and Place, vol. 17, no. 1, Feb. 2011, pp. 47–72. Web of Science, https://doi.org/10.1002/psp.573
[3] Pesaresi, Martino; Freire, Sergio (2016): GHS Settlement grid following the REGIO model 2014 in application to GHSL Landsat and CIESIN GPW v4-multitemporal (1975-1990-
2000-2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: https://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_r2016a
[4] Marconcini, Mattia, et al. "Outlining where humans live, the World Settlement Footprint 2015." Scientific Data 7.1 (2020): 1-14.
[5] Tatem, Andrew J. "WorldPop, open data for spatial demography." Scientific data 4.1 (2017): 1-4.
ACKNOWLEDGEMENT: This study is part of the MIGRAWARE project is funded by the Federal Ministry of Education and Research
(BMBF) through the German Aerospace Centre e.V. (DLR) under grant number 01LG2082B.
Findings
•Rural industrial towns have expanding populations and
settlements.
•Industrial sites grown faster than control sites 20 km
away.
•Rural industrial sites are pull areas of migration
•Earth observation data is a good proxy for rural African
migration studies.
a Institute for Geography and Geology, Julius-Maximilians-Universität Würzburg, Würzburg 97074, Germany
b German Aerospace Center (DLR), Earth Observation Center (EOC), Oberpfaffenhofen, Germany
* itohan-osa.abu@uni-wuerzburg.de
Figure 1: Newmont gold mine, Ghana
*Photos displayed with participants consent.
Ahafo Kenyasi, host community to
Newmont gold mine at dusk. Ntotroso, Ahafo host community
to Newmont gold mine
Right to Left, Return migrant from Ilorin, capital
city of Kwara state, Nigeria, who currently lives in
Ahafo Kenyasi with his wife, a native of Atebubu,
Bono East region, Ghana
Migrant from Sunyani, the
Bono area's capital, who
now resides in Ntortroso.
Natives of
Ntotroso mining
community
Figure 2: The distribution of rural
industrial locations in Burkina Faso,
Ghana, and Nigeria. The exhibited charts
depict the leading industrial sites with
expanding settlement areas.
Year
Population Relative Growth [%]
Control sites Industrial sites
Settlement Relative Growth [%]
Control sites Industrial sites
Year
Manga Dam
1
0
2
3
5
10
0
15
20
Year
Year
Industrial sitesControl sites
Control sites Industrial sites
Teberebie
Gold Mine
Settlement Relative Growth [%]
1
0
2
3
10
0
20
4
5
-10
Population Relative Growth [%]
Control sites Industrial sites
Control sites Industrial sites
Year
Year
Dangote Gas
Plant
Population Relative Growth [%] Settlement Relative Growth [%]
1
0
0.5
1.5
2
10
0
20
0
30
0
40
0
0
Conceptualization of Study
•Datasets industrial locations were downloaded
from industryabout.com.
•To designate rural industrial locations, the JRC GHSL 3for the year
2000 was employed.
•Control sites were chosen 20 kilometres (km) distant from rural
industrial locations. At this time, the dataset was updated to
include the industries founding dates, types, and ISO codes.
•We established a 6 km buffer to accommodate large-scale
industrial sites based on this. For 2000–2015, we used the World
Settlement Footprint Evolution (WSF-Evo) 4dataset as well as a
built-up growth product using Sentinel-1 and -2 developed by
DLR for 2015–2020.
•We utilised data from the WorldPop5dataset for the years 2000-
2020 to evaluate the number of people living in rural areas.
Agricultural Industry
Energy Industry
Oil & Gas Industry
Solid Mineral Industry
Transportation Industry
Coordinate Reference System: WGS 84
Basemaps: WSF-Evo & Natural Earth Map