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Roads access analysisTechnical material for Leave no one behind report
Catherine Simonet, Senior Research Officer, ODI
December 2016
Data sources Road network :
Center for International Earth Science Information Network - CIESIN - Columbia University, and Information Technology Outreach Services - ITOS - University of Georgia. 2013. Global Roads Open Access Data Set, Version 1 (gROADSv1). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Accessed May 2016.
Village and settlements: This is a point coverage showing the villages in Kenya according to Almanac Characterization tool (ACT) database ILRI
Population : 2008 and 2014 DHS databases for Kenya Reference report : Kenya National Bureau of Statistics, Ministry of Health/Kenya, National AIDS Control Council/Kenya, Kenya Medical Research Institute, National Council for Population and Development/Kenya, and ICF International. 2015. Kenya Demographic and Health Survey 2014. Rockville, MD, USA: Kenya National Bureau of Statistics. Raw data: http://www.dhsprogram.com/Data/
PresenterPresentation NotesData sources shoulbe cited as mentioned in the slide
http://www.dhsprogram.com/Data/
Road Network(primary data)
The Road database provide information on
- road categories- surface types - on road lengths
Road Category LENGTH_KM CumulativedistributionHighway 3480.27 5.30%Primary 2910.02 9.72%Secondary 15603.88 33.47%Tertiary 43506.15 99.67%Local 95.33 99.81%Unspecified 121.58 100.00%Grand Total 65717.23
Road Surface LENGTH_KM CumulativedistributionPaved 6655.31 10.13%Graved 25832.98 49.44%Dirt/Sand 29979.41 95.06%Unspecified 3249.54 100.00%
!
!!
!
!
NakuruKisumu
Mombasa
NAIROBI
Eldoret
Road CategoriesHighway
Secondary
Tertiary
Tertiary and Local
Unclassified
PresenterPresentation NotesThe Road Network database provides information on the types and the categories of the road.
Here are the the categories of each variables : Road Classes : 1 "Highway" 2 "Primary" 3 "Secondary" 4 "Tertiary" 5 "Local/Urban" 6 "Trail" 0 "Unspecified" Road Surfaces :1 "Paved" 2 "Graved" 3 "Dirt/Sand" 4 "Steel" 5 "Wood" 6 "Grass" 0 "Unspecified"
Village Locations(primary data)
Information on- Lat/Lon of village locations- Name of villages/settlements- County/District location
PresenterPresentation NotesEach dot represents one village settlement
Distance calculation (1) VillagesWe identified the nearest roads for each village and the distance to this roads using a buffer analysis.
We reproduced the analysis by roads category and surface classes and by county
Key hypothesis: the centroid of the village settlement is considered as the village location. The
heterogeneity of access to road within a village/sublocation is not taking into account.
The villages are not weighted by population so this analysis only look at physical settlements/ administrative units access to roads.
Cumulative share of settlements by near distance (in meters)
0.2
.4.6
.81
Shar
e vi
llage
(pct
)
0 20000 40000 60000NEAR_DIST (in meters)
50% of the administrative settlements of Kenya are far from less than 1km to a road
1100m
0.5
An unequal distribution at the county level
Variable Nbvillages MeanStd. Dev. Min Max
Near distance (meters) 43956 3294 5800 0 62450
National distribution
PresenterPresentation NotesIn yellow is the national average
Please note that these results can be biased by the unequal number of administrative settlements by county. In county with more locations the variance of distance could be more important
An unequal access quality (1)0
.2.4
.6.8
1Sh
are
of S
ettle
men
ts in
%
0 100000 200000 300000Near distance in meters
Surface Type I Surface Type IISurface Type III
S f I i d d S f II i d d Cl III i di t/ d
Distance of settlements to Roads
Surface type I is paved roads, Surface II is graved roads, Surface III is dirt/sand and unspecified surface type.
Only 20% of the settlements are far from less than 10km to improved surface roads
PresenterPresentation NotesSurface type I is paved roads, Surface II is graved roads, Surface III is dirt/sand and unspecified surface type.
Only 20% of the settlements are far from less than 10km to improved surface roads
0.2
.4.6
.81
Shar
e of
Set
tlem
ents
(in
%)
0 50000 100000 150000 200000Near Distance (in meters)
Road Class I Road Class IIRoad Class III
An unequal access quality (2)
Class I includes highway and primary roads; Class II includes secondary roads; Class III includes tertiary local roads and unspecified classes
10 % of the settlementsare far from less than20km to primary and secondary road networks
Distribution Road access by countyvisualization to be improved
0.5
10
.51
0.5
10
.51
0.5
10
.51
0.5
1
0 20000 40000 60000 0 20000 40000 60000
0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000
Baringo Bomet Bungoma Busia Embu Garissa Homa Bay
Isiolo Kajiado Kakamega Keiyo-Marakwet Kericho Kiambu Kilifi
Kirinyaga Kisii Kisumu Kitui Kwale Laikipia Lamu
Machakos Makueni Mandera Marsabit Meru Migori Mombasa
Murang'a Nairobi Nakuru Nandi Narok Nyamira Nyandarua
Nyeri Samburu Siaya Taita Taveta Tana River Tharaka Trans Nzoia
Turkana Uasin Gishu Vihiga Wajir West Pokot
Shar
e vi
llage
(in
pct)
NEAR_DIST (in meters)Graphs by COUNTY,C,20
0.5
1
0 20000 40000 60000
Turkana
Population Distribution(primary data DHS database 2014)
Information on
- Lat/Lon on cluster of sampling (1585 clusters, we removed 4 which were not georeferenced)
- 36224 Households- Region of sampling (5 regions)- Weight of each hh of the survey- Representative sample at national and regional level
PresenterPresentation NotesHere is the map for Road categories
Distance calculation (2) DHS We identified the nearest roads for each of the 400 clusters of the survey, using buffer analysis.We weight each of the household to have a national representative picture (we use the weight produced by DHS)We reproduced the analysis by roads category and surface classes and by region
Hypothesis: The centroid of the cluster is considered as the cluster location. The heterogeneity
of access to road within each cluster is not taking into account. The each cluster is weighted by population. Using DHS data, we follow DHS sampling strategy (stratification and weighting) Centroid of cluster is displaced up to 2km in urban areas and 5km in rural with 1 %
of 10km displacements. Clusters are maintain in the same regions and counties. For these reasons we strongly suggest to use with cautions all statistics using information of distance below 5km.
0
.2
.4
.6
.8
1
Shar
e of
pop
in p
ct
0 5000 10000 15000 20000 25000 30000 35000distance in meters
Source: DHS 2014 and CEISIN databases-author's calculation
Distribution of population
Cumulative share of population by near distance (in meter)
An unequal distribution at the regional level (1)
Max of Near distance (in meters)Countymaxdis
1018 - 3174
3175 - 6195
6196 - 11233
11234 - 21569
21570 - 32481
Min of Near distance (in meters)0.153509 - 1.997720
1.997721 - 12.561800
12.561801 - 39.213700
39.213701 - 79.207500
79.207501 - 159.582000
An unequal distribution at the regional level (2)
Mean of Near distance (in meters)361 - 656
657 - 941
942 - 1445
1446 - 3374
3375 - 6868
An unequal access quality (1)
Note: Dirt/Sand category encompasses other surface
0
.2
.4
.6
.8
1
Shar
e of
pop
in p
ct
0 100000 200000 300000 400000distance in meters
Dirt/Sand Roads Paved RoadsGraved Roads
Source: DHS 2014 and CEISIN databases-author's calculation
Distribution of population
An unequal access quality (2)
Note: Tertiary category encompasses others categories
0
.2
.4
.6
.8
1
Shar
e of
pop
in p
ct
0 100000 200000 300000 400000distance in meters
Dirt/Sand Roads Paved RoadsGraved Roads
Source: DHS 2014 and CEISIN databases-author's calculation
Distribution of population
0.2.4.6.81
0.2.4.6.81
0.2.4.6.81
0.2.4.6.81
0.2.4.6.81
0.2.4.6.81
0.2.4.6.81
0 50000 100000 150000 0 50000 100000 150000
0 50000 100000 150000 0 50000 100000 150000 0 50000 100000 150000 0 50000 100000 150000 0 50000 100000 150000
Baringo Bomet Bungoma Busia Elgeyo Marakwet Embu Garissa
Homa Bay Isiolo Kajiado Kakamega Kericho Kiambu Kilifi
Kirinyaga Kisii Kisumu Kitui Kwale Laikipia Lamu
Machakos Makueni Mandera Marsabit Meru Migori Mombasa
Murang'a Nairobi Nakuru Nandi Narok Nyamira Nyandarua
Nyeri Samburu Siaya Taita Taveta Tana River Tharaka-Nithi Trans-Nzoia
Turkana Uasin Gishu Vihiga Wajir West Pokot
Highway and Primary Roads Secondary RoadsTertiary Roads
Shar
e of
pop
in p
c