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When asking questions through the lens of Geography one of the first issues to grapple with are the issues of scale. When seeking to move beyond the country level and their neighbors (small scale - Gapminder is good tool for making comparisons between countries) and comparisons and working with information that is at a larger scale (one country); we may need for information about the internal physical, and natural structure of a country. This activity will focus specifically on the general geographic trends in the country of Nigeria.
In the following example, the author is proposing the following question:
To what extent do the poor reside in rural or remote provinces or low-income provinces or that are poorly connected to economic growth?
The focus here is on understanding the spatial characteristics of poverty households by focusing on a non-monetary indicator of poverty: under-nutrition.
The Key spatial aspect: More than three quarters of under-nutrition related poverty is to be found in rural areas.
Key Social aspect: a third of under-nutrition related poverty households have a head of household engaged in agriculture.
~Global Poverty, Andrew Sumner, pages 50 - 52
Other social aspects to consider include lack of critical mental health medical care:
"According to the Nigerian Ministry of Health, at least three in 10 Nigerians suffer from a mental illness that often goes untreated due to Nigeria's lack of mental health care systems. The WHO's 2018 Global Health Observatory data repository records an estimated 17 suicides per 100,000 people across Nigeria, ranking seventh in Africa just behind Zimbabwe with 19 suicides per 100,000 people. "
The Northeast region of Nigeria in particular is of special concern:
"The nonprofit NEEM Foundation was established in direct response to growing insecurity in northeastern Nigeria and their mission is clear: prevent violent extremism."
Source: How Twitter therapists are helping bring mental health care to Nigeria.
Oluwatosin Adeshokanhttps://theweek.com/articles/854701/how-twitter-therapists-are-helping-bring-mental-health-care-nigeriaBelow are visualizations created to help support the argument that the spatial characteristics of poor communities within a country tend to be rural and isolated.
Country: Nigeria.
The map here was produced using ArcOnline software: https://hws.maps.arcgis.com/
The big question:
How does the country’s geography impede economic growth; how does it keep the country in the low-income or lower-middle-income category?
Formal Writing Assignment: Logically Persuasive Essay with Evidence for Geography, Professor Jenny Tessendorf, Fall 2019
Analyze the geography
Analyze the geography
Using the map app below; analyze the geography of Nigeria to help identify supporting map-based information that will strengthen the argument and compliment the chart.
Using the layers tool included on the map; perform a visual overlay analysis of Nigeria. This will require you to:
- Search for Nigeria
- Click on the layers button
- Examine each layer and determine what the information on the map is communicate
- Turn all of the layers off. And then add them one at a time in order to see how the pieces of information in each layer overlay and intersect with each other.
Using the zones for Nigeria outlined in the chart, examine the map and the layers to discover any patterns that may or may not re-enforce the argument made here.
Use the drawing tools to mark regions of the country that would be important areas to perhaps to include as a map in this analysis.
Key question to consider:
- For each layer, what are some general spatial trends about Nigeria?
- What spatial patterns do see that support the spatial and social characteristics of poverty in Nigeria?
- What can you determine about the isolation of those in poverty in Nigeria?
- What other spatial/social characteristics might Nigeria be grappling with?
- What further information or other spatial information may be of help support the spatial and social characteristics of this analysis?
Map Layers Meta Data:
- Urban Access Layer: "This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. " This map (layer) was produced through a collaboration between the University of Oxford Malaria Atlas Project (MAP), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. This data layer references the 'World Wide Night Time Lights' layer posted to the World Bank Data Portal: https://datacatalog.worldbank.org/dataset/worldwide-night-time-lights
- GlobCover: Global Land Cover Map: "The global land cover map counts 22 land cover classes defined with the United Nations (UN) Land Cover Classification System (LCCS). " This is the same layer referenced on the World Bank Data Portal: https://datacatalog.worldbank.org/dataset/global-land-cover-2009
- GPWv411: Population Density: These population density grids contain estimates of the number of persons per square kilometer consistent with national censuses and population registers. There is one image for each modeled year. Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW.
This app is designed to allows users to perform a visual spatial analysis of countries around the world in terms of access to urban areas and specific land cover types.
Cropland Legend:
Mapping resources to explore and consider:
- World Bank Data Portal: https://data.worldbank.org/
- The Malaria Map Project: https://map.ox.ac.uk/explorer/#/
- IPUMS Data Base project - Project Terra: https://terra.ipums.org/
References:
Sumner, A. (2016). Global poverty: deprivation, distribution, and development since the Cold War. Oxford, United Kingdom: Oxford University Press.Citations:Accessibility Layer: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature (2018). doi:10.1038/nature25181
Global Land Cover:ESA 2010 and UCLouvain. http://due.esrin.esa.int/page_globcover.php
Population Density: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. Accessed DAY MONTH YEAR.
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