THE WAY TO SCHOOL
Transportation Inequality and School Access in Nairobi
RAISSAH KOUAME + CLAIRE YANG
TABLE OF CONTENTS
01 INTRODUCTION 02 RESEARCH QUESTIONS 03 METHODOLOGY 04 ACTIONS 05 APPENDIX "The learners who complete their education will have more opportunities to obtain high-wage jobs, reducing the likelihood that they will experience extreme poverty." - TOOLKIT FOR CHILD HEALTH AND MOBILITY IN AFRICA
INTRO DUC TIO N Spatial inequality is at the heart of Kenya's social stratification. Nowhere else in Kenya is this inequality more conspicuous than in Kenya's capital city Nairobi. Nairobi is home to the largest slum in
DEFINITIONS DEFINITIONS DEFINITIONS
Africa -- one million urban poor live in Kibera. Meanwhile, “government workers and other upper-class Kenyans live in gated-
Low Income xbz
housing complexes”(Kuo,2016).
Consumption below poverty line (cost of basic basket of goods Low Income estimated below poverty at rateKSh etec . 2,913 ).
A new Nairobi Mass Rapid Transit System (MRTS) is proposed and hailed as long-sought solution to mobility problems, however, scholars observed that “new infrastructure being built in Nairobi overwhelmingly benefits the richer classes rather than the poorer classes” (Miller, 2016).
Transportation Access Whether residents live within Transportation Access distance to transit certain distances from public transportation stations. Disparity
Disparity difference
Transportation inequality is most impactful in the case of the most vulnerable: low income school age children. Heifer International’s report identified both income and education inequalities as the most salient types of inequalities in Kenya (Heifer International, 2012). The intersectional inequality of both income and education in Nairobi as manifested in transportation is the topic of this study. The study intends to inform policy makers and nonprofits to equalize the way to school.
table of definitions Difference between overall population's percentage near transit and low income 's Access to School percentage near transit. xyc
Access to School Percentage of public primary schools located in areas with more than half population living below poverty line.
FIRST RESEARC H Q UESTIO N: Public Primary school and Transit in Nairobi, Kenya
Are public primary schools located near public transit?
We geocoded all but three of the street addresses of public primary schools in Nairobi using Google Fusion Table and integrated the formal and informal transit in Nairobi. It seems that most schools are served by either formal transportation Bus Rapid Transit (BRT) or informal transportation Matatus.
170 schools within city boundary (0.2 per sqkm) 133 within 500 m radius around transit lines (0.4 per sqkm)
The preliminary analysis suggests that there is a concentration of public primary schools in the center of Nairobi and a sparse amount on the outskirts of the center.
Our analysis yields the result that 170 public primary schools are within Nairobi city boundary with a search distance of 1 km. 133 of the 170 are completely within 500 m radius around transit lines. As a result, 78 % public primary schools are within 500 m of transit. The majority of public primary schools are located near transit, making public transit an important element in access to schools. The density of schools within city boundary is 0.2 per square km while the density of schools in areas within 500 m radius around transit lines is 0.4 per square km. More schools are close to rather than far from transit lines.
Inside Nairobi's Matatu and Students on sidewalk taken by co-author Claire Yang
SEC O ND RESEARC H Q UESTIO N: Do low income people have less access to formal transit? Population within 1 km road distance of BRT line 1 stations
% low income within 1 km road distance of BRT line 1 stations
Overall PNT 7.3% 3,759,014 people live within city boundary Below poverty PNT: 6.3% 275,911 within 1 km road distance of BRT line 1 stations Above poverty PNT: 8.1%
: tion sit a l u p Tran l po l r a a r e Ove le N p o Pe % 3 7.
1,625,566 people live below poverty line within city boundary 102,576 within 1 km road distance of BRT line 1 stations
: line y t r e sit pov n a w r belo Near T e l p Peo People % 6.3
METHO DO LO GY: TRANSIT ZO NE SC ENARIO
1 km radius around BRT line 1 stations
1 km road distance of BRT line 1 stations
10 min walk around BRT line 1 stations
Method behind Population Data Used Population data comes from Facebook Connectivity Lab and Center for International Earth Science Information Network Columbia University (2016). It is an estimates from recent census and high-resolution satellite image of settlement.
9.5% people live within BRT line 1 stations using 1 km radius transit zone (Highest) 7.3% people live within BRT line 1 stations using 1 km road distance transit zone (Medium) 6.0% people live within BRT line 1 stations using 10 min walk distance transit zone (Lowest)
METHO DO LO GY: TRANSIT ZO NE SC ENARIO 1 km radius around BRT line 1 stations
1 km road distance of BRT line 1 stations
10 min walk around BRT line 1 stations
Logic behind scenario chosen For the purpose of our study: representing access, not comparing to other cities or going into details of how walking time differs for different groups. we chose road distance transit zone.
METHO DO LO GY: DIMENSIO N O F LO W INC O ME Social-Economic-Political Context Kenya is divided in counties, sub-counties, divisions, districts, provinces, constituencies, wards, locations, sub-locations and villages. From the book Spatial dimensions of well-being in Kenya : where are the poor? : from counties to wards, we know that local level poverty data helps programs to target poverty better and decentralize decisions.
Location and sub-location level percentage blow poverty line map
The multiple dimensions of low income data, however, creates a problem for us when
The dark brown to light yellow categorical color scheme we chose
mapping and calculating the amount of people living below poverty line overall and in transit zone. We consulted multiple low income data and choose location level data because it's the only subdivision boundary that is available in GIS format online. We used a color scheme different from sources for readability The poverty maps from the book are mapped using a categorical 7color scheme that ranges from dark green
Constituency level percentage blow poverty line map
indicating relatively wealthier areas to dark brown for relatively poorer areas. We chose to use dark brown to light yellow.
The overlay of population data and classified poverty map illustrates that these people live in locations with similar percentage of people living below poverty line
AC TIO NS: WHERE SHOULD WE BUILD NEW SCHOOLS? Public Primary School and Poverty in Nairobi
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Based onLow theIncome map below, the location of the next school should be a below poverty rate etec .
place where there is high building density (represented by the big arrow). as this may mean a large number of people. However,building density Transportation Accessmay not always mean population density transit especiallydistance whentoinformal living is common in the context of Nairobi.
On the other hand, our analysis leads us to believe that placing a school where there is a lack of building density (represented by the Disparity difference
small arrow) as well as a lack of major roads may incentivize public officials to build infrastructure such as roads as well as housing/residential areas so as to facilitate access to that school. Access to School xyc
In order for public primary school children to access education in an unfettered way, more schools need to be built in low - income areas as only 25 % of public primary schools are in low - income areas. In addition, modes of transit in Nairobi need to be more varied whether it be in the domain of formal or informal transportation modes. We think that city official should work with people on the ground to understand how they move around the city and what works for their everyday lives. Finally, our policy recommendation is that school buses be implemented in Nairobi in order to get children safely to school everyday and back home.
table of definitions
APPENDIX : Digital Matatus (2014, Jan 29). Nairobi Matatus [Data file]. Retrieved from http://www.digitalmatatus.com/map.html Victor Matara (2018, May 25). List Of All Public Primary Schools In Nairobi County 2018. Retrieved from https://victormatara.com/list-of-all-public-primary-schools-in-nairobi-county-2018/ Facebook Connectivity Lab and Center for International Earth Science Information Network Columbia University (2016). High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Retrieved from https://ciesin.columbia.edu/data/hrsl/ ITDP Africa (2018). BRT Line 1 Station And Routes [Data file]. Retrieved from Internal Email Columbia University Center for Sustainable Urban Development (2010). Roads Data-Major Roads Only [Data file]. Retrieved from http://nairobigismaps.wikischolars.columbia.edu/ Columbia University Center for Sustainable Urban Development (2010). Roads Data-Major Roads Only [Data file]. Retrieved from http://nairobigismaps.wikischolars.columbia.edu/ Columbia University Center for Sustainable Urban Development (2010). Land Use Data - Building Density [Data file]. Retrieved from http://nairobigismaps.wikischolars.columbia.edu/ North Arrow retrieved from https://gis.stackexchange.com/tags/north-arrow/info Map Kibera http://www.mapkibera.org/blog/2010/09/05/kiberas-census-population-politics-precision/ Spatial dimensions of well-being in Kenya : where are the poor? : from counties to wards
Geographic Information System Professor Leah Meisterlin Fall 2018 Graduate School of Architecture, Preservation and Planning Columbia University Master of Urban Planning Candidates Raissah Kouame (2020) + Claire Yang (2020)