Anastasia Marshak
Assighnment 5: International Census Discovery and Documentation
PART A
I am looking for Kenyan census data to add to my final project. I used the WGS_1984_UTM_Zone_37
projection.
1. The resources that I found were the UN agency Statistics Division: Demographic and Social
Statistics, Kenya National Bureau of Statistics, plus this fantastic website called Kenya Open Data
(http://opendata.go.ke) which has online data from a ton of Kenyan surveys, including the most
recent census, in easily exportable format – beautiful!
2. The most current completed census was done in August of 2009.
3. The previous census was completed in August 1999, and August 1989 prior to that.
4. The next census should take place in August of 2019.
5. The administrative units are as follows:
Level
Name
1
Province
2
District
3
Division
4
Location
5
Sub location
6. I was able to find quite a bit on Kenya Open Data by district (level 2) and some as far down as
sub-location (level 5). I found data on employment, urbanization, assets, livestock, wall material
and school attendance by district. By sub-location, I found population numbers and water
access. It appears as though you could request the entire Kenyan census dataset, but I have not
had luck in the past.
7. I was able to acquire shape files of Kenya for all five administrative boundaries. However, those
shape files are from the 90s and have 40+ districts, the census has 158, and the sub locations are
very difficult to match up because boundaries have been redistricted so much in the past 20
years in Kenya. I could not find the shape files specifically designed for the new administrative
boundaries in 2009, but I did find a district level shape file from the UN funded Second
Administrative Level Boundaries (SALB) project from 2001, which has 69 districts that better
correspond to the census districts. I found the SALB administrative data files on the World
Resource Institute website (http://www.wri.org/publication/content/9291). I also pulled the
cities in Kenya and agricultural cover data from there. So I had to regroup my 159 districts into
the 69 districts for which shape files were available, which is a shame because I lose a lot of
precision and cannot take advantage of the data I have at level 5 (sub-location), instead I have to
use level 2 data. For the final project, I think I will have to either sacrifice more precise/higher
level administrative boundaries or using the latest census data.
PART B
Pastoralism, the raising and caring of livestock, particularly cattle, is one of the main forms of livelihoods
available to households living in the northern and bordering Somalia eastern stretches of Kenya. These
areas are significantly drier, prone to cyclical drought, and host a much smaller proportion of Kenya’s
population, as partly evident by the smaller number of ‘major towns.’ The populations residing here
tend to be more nomadic and generally rely on livestock rearing for their livelihood. On the other hand,
areas that can support agriculture (as visible by the agricultural cover) tend to have a smaller number,
on average, of cattle per household (the white spot in the middle is Nairobi), partly because the
population density is greater and partly because pastoralism requires large tracts of land that are not
occupied or utilized for growing crops. Interestingly the major towns are mostly located in areas with
some agricultural cover, even amidst the arid northern and eastern areas.