GIS Assignment 7
My project will explore how immigration patterns across
villages in the Tahoua department of Niger correlate with proximity
to markets and paved roads. Does access to markets promote local
development and thus dissuade immigration? Or does proximity to
major roads and markets facilitate opportunities to find work
elsewhere? Additionally, using data on Nigerien immigrants
returning from conflict in Libya, I will assess how these geographic
characteristics correlate with forced migration. These maps could
help demonstrate infrastructure projects’ impact on migration flows
in times of peace and crisis.
Human Sciences Research Council. (2006). Migration in south and
southern Africa: dynamics and determinants. Cape Town: HSRC Press.
Migration in south and southern Africa provides a useful appendix on
how GIS can be used as a tool in migration research, both in terms of
mapping the flow and concentration of migrants. In the appendix, the
editors highlight how they applied GIS software to assess whether
social mobility is the dominant reason for inter-provincial migration
in South Africa. Their research found, counter-intuitively, a
surprising proportion South Africans actually migrant out of urban,
more economically successful provinces and into the periphery. This
source was a great primer on the different ways (flow vs.
concentration) to map migration patterns.
Van der Geest, K., Vrieling, A., & Dietz, T. (2010). Migration and
Enviroment in Ghana: A Cross-District Analysis of Human Mobility
and Vegetation Dynamics. Enviroment and Urbanization, 22(107).
This research analyzes how environmental factors (farming
opportunities, environmental degradation) shape internal migration
in Ghana. The authors argue that rural population density and thus
access to resources has a greater influence on migration flows than
the proximity or abundance of that natural resource. This research
aggregated household survey data on to a departmental level. Using
this data, they produced a useful map of Ghana (112) that highlights
both the concentration of migrants per department, as well as arrows
indicating the flow of migrants from one department to other. Not
surprisingly, the capital Accra had the highest share of migrants.
Though my data is on the village level, I liked how they captured net
migration (whether positive or negative) per department using a
darkening color scheme in addition to listing the percentage change
in parenthesis.
Brown, S. (2003). Spatial Analysis of Socioeconomic Issues: Gender
and GIS in Nepal. International Mountain Society, 23(4), 338 – 344.
This study assessed how Nepalese women’s proximity to watersheds,
markets and other geographic variables correlates with levels of
gender inequality and socioeconomic activity in their household. The
authors collected household survey data and mapped socioeconomic
indicators (women’s literacy rates, ect) in relation to geographic
indicators. One of their maps looks into how distance from markets
and roads relates to gender inequality (figures 3 and 4). I am
interested in how those two variables relate to village level migration
activity. These maps did a good job presenting the survey data – a
surveyed house was given a symbol depending on a level of gender
inequality. However, because so many households were surveyed,
they cluttered the maps, making it hard to process any geographic
correlations with household gender inequality.
Aker, J., Boumnijel, R., McClelland, A., & Tierney, N. (2011). Zap It to
Me: The Short-Term Impacts of a Mobile Cash Transfer Program
(Working Paper No. 268). Center for Global Development.
All of my project’s survey data stems from Professor Jenny Aker’s
working paper. Dr. Aker wanted to measure the economic impact of
mobile money platforms on households and villages in the Tahoua
department of Niger. Figure 1 illustrates the location and density of
the surveyed villages. This will be a useful guide when I map these
villages. Aker’s research also provides information on the three
nearest markets to each village. Combined, I can use this survey data
to assess how proximity to markets correlates with migration levels.
Layers,
key
attribute
Surveyed
Villages
Markets
Format
Source
Date
Tabular
Prof. Aker’s survey data
Tabular
Prof. Aker’s survey data
May,
2011
May,
Data
Format
Major
Roads
Nigerien
Departme
nts (Level
II)
2011
Shapefile Michael Bauer Research,
Publish
Environmental Systems
ed
Research Institute, ESRI Data 2011,
Maps,
accurat
Scale: 1:250,000.
e as of
2007
Shapefile GfK Marktforschung,
Publish
http://www.gfked
geomarketing.com/en/digita 2011,
l_maps/niger.html
accurat
e as of
1996
Vector
Vector
4. Data Processing:
I.
II.
III.
IV.
V.
VI.
VII.
Use survey data on excel spreadsheet to aggregate
household data on to the village level.
Calculate a) migrants as percent of village population b)
change in reported Nigerien migrant population in Libya
from time I (January, 2011) to time II ( after NATO’s
intervention in Libya, May, 2011) using Excel functions. This
second calculation represents forced migration. It will be
interesting to see whether Nigerien migrants in Libya
returned to their home villages or travelled elsewhere.
“Oceans” base map will be imported. All labels will be
turned off.
Data frame properties and layers will be projected on to :
WGS_1984_UTM_Zone_31N, with a linear unit of 1 meter.
The Nigerien department layer will be uploaded.
a. Tahoua district will be selected by attributes, and then be
made into a separate “Tahoua” layer.
The Major Roads layer will be uploaded.
a. Clip roads that lie in the Tahoua district layer. This will
create a new layer titled “Tahoua Major Roads.”
Map the surveyed villages and markets using their X – Y
coordinates from the survey data. Create a village layer and
a market layer.
VIII. For the village layer set, apply symbology of graduated
symbols (circles) for two variables:
a. Migrants as a percentage of total household population
b. Percentage change in migrant population in Libya from
time I (January) and time II (May).
c. Once these symbols are mapped, their size and
classification will be adjusted according to what appears
most accurate.
IX. Apply 5, 10 and 15 km buffers to market points on market
layer. This will create a buffer layer that will help categorize
which villages are closest to markets. More research must
be conducted to verify that these 5km buffer intervals are
valid categorizations of accessibility.
X.
Use the Euclidean distance tool to create a raster grid from
the “Tahoua Major Road” layer. I will classify each distance
grid based on five break values with the first break value at
3 km and the last break value at greater than 15 km. The
first break will be given a “1” rank and values above 15 km
will be given a “5” rank. More research will be conducted to
determine whether this classification is valid in determining
the ease of access to major roads. The scale of each raster
cell is yet to be determined.
XI.
If time permits, I could create a “Market” raster grid and
overlay it with the “Tahoua Major Road” raster grid. I will
create the “Tahoa Major Road” raster as the snap raster and
snap the “Market” raster grid on top of it. The “Market”
raster grid would also contain five break values with less
than 3 km given a “1” rank and values above 15 km will be
given a “5” rank. I still need to research an appropriate scale
size for reach raster cell in the “Market” raster grid. I would
then use the raster calculator to add the “Market” and
“Tahoua Major Road” variables together. Locations with
high values would be far from both markets and major
roads. This suitability map will examine how migration
rates differ between villages that have access to both
markets and major roads, villages with access to one of
those resources and villages with access to neither.
XII. In sum, this research could produce up to six maps. Three
maps (market buffer, roads, market - roads combined)
relating to migration levels per village and three maps
pertaining to the change in levels of Nigerien migrants in
Libya.
Products I hope to include on my poster:
I.
Maps:
1. Surveyed villages in the Tahoua department of
Niger
2. Migration Concentration
1. In relation to proximity to markets
2. In relation to proximity to roads
3. In relation to preference grid of markets and
roads.
3. Forced Migration – Niger
1. In relation to proximity to markets
2. In relation to proximity to roads
3. In relation to preference grid of markets and
roads.
II.
Table of Tahoua villages with highest concentration of
migrants
III. Table of Tahoua villages with greatest change in flow
of Nigerien migrants in Libya.