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Computational Urban
Science
Mapping thespatial patterns ofethnic
segregation andits implications tourban policy
inNairobi city
Nthiwa Alex Ngolanye1* , Kisovi Leornard1, Kibutu Thomas1 and Muiruri Philomena1
Abstract
In modern times, cities around the world have grappled with the challenges of racial and ethnic segregation. In
Nairobi city, with its diverse ethnic makeup, there is widening inequalities and emerging patterns of ethnic segrega-
tion, where the five main ethnic groups - Kamba, Luo, Kikuyu, Luhyia, and Kisii - experience varying levels of spatial
concentration. This study analysed the spatial patterns of ethnic segregation in Nairobi, using geocoded question-
naire data from the 2019 Kenya population and housing census data. We used the Index of Dissimilarity in STATA soft-
ware and Geo-segregation Analyzer and Anselin’s Local Moran I method in GIS to map ethnic segregation patterns.
Our findings uncovered a striking socio-spatial divide based on ethnicity. Anselin Local Moran’s I indicators further
pinpointed areas with the highest levels of segregation and spatial clustering of specific ethnic groups. These findings
offer crucial insights for urban planners and policymakers. By pinpointing areas experiencing the most severe spatial
segregation, our research could inform spatially targeted interventions and resource allocation. This could inform poli-
cies that foster inclusivity, reduce spatial inequalities, and build a more equitable and socially cohesive city.
Keywords Ethnic segregation, Spatial inequality, GIS, Index of dissimilarity, Urban policy
1 Introduction
Ethnic segregation remains a persistent challenge in
urban centers worldwide (Bansal, 2021; Hussain & Imi-
tiyaz, 2018; Aggarwal, 2014). A substantial body of
research has documented the negative impacts of segre-
gation on the integration of ethnic groups within cities
worldwide (Nijman & Wei, 2020; Pacione, 2009; Mar-
tinez-Martin, 2005). In Hamburg for instance, Friedrichs
(2013) found that, segregation led to discrimination
based on socioeconomic status and educational attain-
ment. In US cities, white middle-class families concen-
trated in middle-class neighborhoods and schools, while
Black and Hispanic middle-class families were more
likely to reside in disadvantaged areas and send their chil-
dren to high-poverty schools (Quillian, 2012; Charles,
2003). In addition, a study in Michigan demonstrated that
segregation resulted in whites across income levels resid-
ing in better neighborhoods than blacks of similar eco-
nomic standing (Darden etal., 2018). A study of Brussels,
Copenhagen, Amsterdam, Oslo, and Stockholm revealed
a positive correlation between high levels of ethnic/racial
segregation and increased deprivation within those seg-
regated areas (Haandrikman etal., 2023; Harsman, 2006).
In South Africa, persistent occupational segregation is
evident, with blacks disproportionately concentrated
in low-paying jobs compared to Whites (Gradín, 2019).
Similar observations were made in Kenya, where ethnic
divisions during British rule, marginalized Africans and
concentrated minorities in neglected areas (Jones, 2020).
is trapped residents in deprived neighborhoods with
limited opportunities (Costa & De Valk, 2018; Obudho,
1997).
*Correspondence:
Nthiwa Alex Ngolanye
ngolanyen@gmail.com
1 Department of Geography, School of Law, Arts and Social Sciences,
Kenyatta University, P.O. Box 43844-00100, Nairobi, Kenya
Page 2 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
e negative consequences of segregation are often
exacerbated by weaknesses in formal urban planning sys-
tems and ineffective urban governance (Halfani, 1997).
Besides, the most critical challenge lies in the absence of
consensus on the most effective urban policies to target
spatial inequalities, especially those arising in multi-eth-
nic segregation (Bolt, 2009). Some attribute this variation
to the power dynamics faced by authorities, while others
point to differing perspectives on the root causes of eth-
nic segregation. is disagreement makes it more diffi-
cult for policymakers to formulate effective and practical
solutions.
e results of these weaknesses are undeniable. Wid-
ened wealth gaps, unequal distribution of resources
and the formation of ethnically and socio-economically
homogenous suburbs emerge (Nijman & Wei, 2020). On
the other hand, developed nations witness the expan-
sion of isolated ethnic or racial enclaves, characterized
by unequal access to essential services like employment,
healthcare, and basic amenities (UN-Habitat, 2022; Van
Ham etal., 2018). Conversely, developing countries expe-
rience segregation compounded by the rapid prolifera-
tion of slums and impoverished areas juxtaposed against
affluent neighborhoods (Messner, 2019).
e emergence of multi-ethnic segregation presents
distinct social and economic challenges for cities (Benassi
et al., 2023). is hinders progress towards sustainable
development (Bansal, 2021). is is particularly concern-
ing given the observed rise in both ethnic and wealth
divides within cities (Gradín, 2019). Although the threat
of spatial segregation is intensifying, viable solutions to
counteract it are scarce.
e spatial patterns of ethnic segregation within cities
takes diverse forms globally. In Dublin, Ireland, for exam-
ple, segregation is driven largely by socio-economic fac-
tors (Fahey & Bryan, 2010), while Amsterdam confronts
a situation where ethnicity and race are more prominent
determinants of residential patterns (Deurloo & Mus-
terd, 2001). In the United States, many downtown areas
are characterized by concentrated Black and Hispanic
populations, often referred to as “ghettos” (Zapatka etal.,
2021; Archer, 2019; Massey & Denton, 1987). While Aus-
tralian cities also exhibit ethnic dimensions in housing
patterns, segregation is generally less severe compared
to US ghettos (Pacione, 2009). Conversely, research on
Canadian cities suggests a distinct trend, with rising
income inequality emerging as a key driver of residential
segregation (Townsend & Walker, 2002).
e experience of segregation in African cities is dis-
tinct, being strongly tied to their colonial past. During
this era, residential and racial segregation policies were
employed as a tool to restrict the freedom of indige-
nous Africans to choose their place of residence (Jimmy
et al., 2020; Ren et al., 2020; Mwaniki, 2017; K’Akumu
& Olima, 2007). Colonial powers used segregation to
enhance political control and enforce social hierar-
chies. For instance, in Cape Town, South Africa, the city
was racially divided between white and black residents,
with wealthy, well-maintained districts having desirable
amenities reserved for whites. ese areas were juxta-
posed against harsh and uninviting residential areas lack-
ing green spaces and amenities, primarily designated for
black residents (Turok etal., 2021).
In Nairobi, Kenya segregation stretches back to the
early 1900s, well before Kenya gained independence.
Colonial policies laid the foundation for this spatial divi-
sion, creating a racially segregated city (Ren etal., 2020;
K’Akumu & Olima, 2007). Founded in 1899 as a railway
depot (Naji & Schildknecht, 2024), Nairobi’s transforma-
tion into the capital of British East Africa in 1905 was
accompanied by the imposition of racial segregation
through land-use policies. e racial segregation can
be attributed to the influence of early European settlers
(Jimmy etal., 2020; Mwaniki, 2017). As a result, Nairobi
experienced an ethnic tripartition, with Europeans pre-
dominantly inhabiting the high-end northwestern and
western areas, Asians settling in the northeastern parts,
and indigenous Africans being confined to densely popu-
lated regions in the eastern and southern areas of the city
(Wanjiru-Mwita & Giraut, 2020; Obudho, 1997). Euro-
pean settlers were attracted by the fertile red soil of the
hills where they established their exclusive enclaves in
the highlands (van Oostrum, 2023; Achola, 2001).
e colonial spatial organization, structured along
racial/ethnic lines, has had a lasting impact on Nairobi’s
development. While African settlements eventually grew
towards the city center, the initial separation remained
deeply ingrained, and the deliberate segregation extended
beyond neighborhoods. Wealthy, low-density European
enclaves in places like Gigiri, Westlands, and Nyari stood
in clear contrast to the densely populated and flood-
prone areas inhabited by Asian and African communities
in Parklands, Highridge, and Ngara (Achola, 2001). Fur-
thermore, Europeans seeking complete isolation estab-
lished exclusive gated communities in Karen, Muthaiga,
Upper Parklands, Westlands, Loresho, Kileleshwa, and
Kilimani (Naji & Schildknecht, 2024; K’Akumu & Olima,
2007). In present-day, ethnic segregation based on the
city’s largest ethnic groups continues to persist in vari-
ous forms. Additionally, residential areas continue to be
segregated based on income status, creating disparities
in the quality of place and quality of life for residents
depending on their residential choices.
Nairobi’s residential landscape has undergone signifi-
cant transformation, characterized by increasing ethnic
diversity and complexity. While ethnicity is intertwined
Page 3 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
with politics and development, the spatial dimensions
of ethnic segregation remain understudied. Existing
research primarily focuses on historical racial segregation
(Greenwood & Topiwala, 2020; Murunga, 2012), neglect-
ing contemporary ethnic patterns in the city. Conse-
quently, there is a limited understanding of the spatial
distribution of ethnic groups, the emergence of ethnic
segregation, and its associated implications.
ese contrasting perspectives highlight the complex
nature of segregation and the need for context-specific
research. A notable gap exists in the literature on spatial
segregation in both pre- and post-colonial Nairobi (van
Oostrum, 2023; K’Akumu & Olima, 2007; Achola, 2001;
Obudho, 1997; Ngau, 1979). To address this, this study
aims to spatially map multi-ethnic segregation in Nairobi,
focusing on the Kikuyu, Luo, Kamba, Kisii, and Luhya
ethnic groups. By identifying patterns of segregation, the
study seeks to inform the development of targeted inter-
ventions for intra-urban desegregation and provide a
foundation for future research on the evolution of ethnic
residential patterns in the city.
2 Study area
Nairobi City, the capital and largest city of Kenya by
both population and area, spans an administrative area
of approximately 696.1km² (Nairobi City County, 2014).
Over the past century, it has experienced exponential
population growth, with its population rising from 8,000
in 1901 to 118,579 by 1948, and further increasing to an
estimated 350,000 by 1963 (Obudho, 1997). Currently,
the city’s population is estimated at 4,397,073, with a
density of 6,247 people per square kilometer (Kenya
National Bureau of Statistics, 2019a), and it is projected
to surpass 6,180,029 by 2045 (Kenya National Bureau of
Statistics, 2019b). Although this rate of increase is still
above the average national urban population growth rate
of 3.7% p.a (UN-Habitat, 2023), Nairobi is likely to con-
tinue leading in terms of absolute population size. It is
a culturally diverse and cosmopolitan metropolis, with
its population primarily comprising Africans (95%), fol-
lowed by Asians (4%), and Europeans making up around
1% (Nzau & Trillo, 2020). Among the African population,
five major ethnic groups—Kikuyu, Luo, Kamba, Kisii,
and Luhya—account for over 79% of Nairobi’s residents
(KNBS, 2019; Owuor & Mbatia, 2008).
Founded by the British in 1899 as a railway depot on
the Mombasa-Uganda railway (Kimari & Ernstson, 2020),
Nairobi lies at 1°17’31.44” S, 36°49’19.01” E, and about
140km south of the equator (Nairobi City County, 2023).
It borders Machakos, Kiambu, and Kajiado counties,
and is divided into 17 constituencies and 85 wards (see
Fig.1). Since independence, Nairobi has become a key
economic hub in East and Central Africa, contributing
50% of Kenya’s formal employment and GDP (Nairobi
City County, 2014). It hosts numerous Kenyan businesses
and over 100 international organizations, including
UNEP and UN-Habitat. e city also houses the Nairobi
Stock Exchange, Africa’s fourth-largest by trading volume
(Okiro etal., 2019).
Over half of Nairobi’s population reside in slums, which
occupy 5% of the city’s residential area (Kamau & Njiru,
2018; Archambault etal., 2012). ese areas face severe
economic inequalities, inadequate access to water, sani-
tation, and waste disposal (UN-Habitat, 2016). Nairobi’s
monetary poverty rate is 16.6%, while its multidimen-
sional poverty rate is 12.6% (Nairobi City County, 2023).
Nairobi presents a compelling case study due to its
vibrant yet segregated cityscape, making it crucial to
understand the spatial patterns of ethnic groups within
its boundaries. Like many other cities, Nairobi faces the
challenges of ethnic segregation, with a distinct “East-
West division” highlighting affluent and impoverished
areas amongst different ethnicities (Nyamai & Schramm,
2023). e city’s large multi-ethnic population necessi-
tates an empirical understanding of the spatial patterns
of ethnic segregation (Jones, 2020). erefore, the study
employed a case study approach, focusing on Nairobi
City.
3 Data andmethodology
3.1 Data
is study focused on the five largest ethnic groups in
Nairobi—Kikuyu, Luhya, Kisii, Luo, and Kamba—which
collectively constitute approximately 79% of the city’s
population (KNBS, 2019). To examine the spatial pat-
terns of segregation among these groups, we utilized a
10% sample from the 2019 Kenya Population and Hous-
ing Census (KNBS). is sample, comprising 435,388
households, provides a robust dataset for our analysis.
While the census offers comprehensive demographic
data, the study concentrates on the five primary ethnic
groups, accounting for 346,793 individuals. It’s important
to note that unlike traditional notions of race, ethnicity
remains a significant factor influencing residential pat-
terns in Nairobi. e 10% sample size strikes a balance
between data representativeness and computational effi-
ciency, enabling rigorous spatial and statistical analysis.
3.2 Method
3.2.1 Measuring segregation using theindex ofdissimilarity
e Index of Dissimilarity (ID) is a widely used metric
for quantifying the spatial separation of different social
groups within a defined geographic area, such as cities,
neighborhoods, or regions (Massey & Denton, 1987;
Pacione, 2009). Essentially, it quantifies the level of seg-
regation by comparing the actual distribution of groups
Page 4 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
with an ideal scenario of complete integration.(Pacione,
2009; Huie, 2000; Massey & Denton, 1988). Calculating
the ID involves a three-step process: step one quanti-
fies proportions whereby the proportion of each group
within each geographic unit (e.g., ward, census block) is
determined. In step 2, the disparity is measured whereby
the difference between the actual group proportions
and the expected proportions under perfect integration
is calculated for each unit. Lastly, these disparities are
summed across all units, resulting in the overall ID. A
higher ID value indicates a greater level of segregation,
reflecting a larger hypothetical relocation of members
from one group to achieve integration. Pacione (2009)
describes the ID as a measure of “the net fraction of one
population who would have to move” to create a blended
community. e ID, as a measure of segregation has been
widely used by researchers because of its consistency
when analysed with available census data over a certain
period of time; and for the ease at which it can be used to
compare by analyzing segregation levels across different
ethnicities (Popescu etal., 2018; Boustan, 2013). e ID
was chosen as the primary measure of spatial evenness
in this study due to its well-established methodological
strengths. e ID has a long history of application in seg-
regation research, supported by extensive research, and
offers advantages in both ease of interpretation and com-
putational simplicity (Massey & Denton, 1987, 1988).
e ID, like many metrics, has limitations. One key
concern is its sensitivity to scale. Segregation levels
measured by the ID can vary depending on the chosen
geographic unit of analysis (Pacione, 2009). is high-
lights the importance of carefully selecting the spatial
unit to minimize the impact of the Modifiable Areal Unit
Problem (MAUP). MAUP refers to the well-documented
phenomenon where the choice of geographic units of
analysis (e.g., census tracts, neighborhoods) can signifi-
cantly influence the observed patterns and interpreta-
tions of spatial data (Javanmard etal., 2023; Chen etal.,
2022; Duque et al., 2018; Nthiwa, 2011). To mitigate
MAUP, this study employed location-level data analysis.
Fig. 1 Study area
Page 5 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
is approach is crucial for ensuring robust and reliable
research findings, allowing for accurate interpretation
and comparison of spatial and statistical data.
Furthermore, the ID itself does not provide insights
into the root causes or specific spatial patterns of segre-
gation within complex urban environments (Huie, 2000).
While the ID is a valuable tool for quantifying segrega-
tion, its effective use necessitates careful interpretation
and awareness of its limitations. Critical consideration of
scale, MAUP, and the need for additional contextual data
is paramount to drawing accurate and informed conclu-
sions about spatial inequalities.
In this study, we calculated the ID to assess the spatial
segregation between the five main ethnic grouping in
Nairobi at the city and location level. e ID was com-
puted using Geo-Segregation analyzer and statistical
software STATA version 16.1.
We used the Eq.1 below to calculate the ID (Massey &
Denton, 1988).
Equation1: For calculating ID Where ti and pi are the
total population and minority proportion of areal unit I,
and T and P are the population size and minority propor-
tion of the whole city, which is subdivided into n areal
units. e ID measures departure from evenness by using
the weighted mean absolute deviation of every units
minority proportion from the city’s minority proportion
and expressing this quantity as a proportion of its theo-
retical maximum (James and Taeuber,1985 in Massey &
Denton, 1988). Values for the ID ranges from 0 to 1. A
measure of 1 indicates that the city is completely segre-
gated (i.e., maximum dissimilarity) and neighbourhoods
are inhabited exclusively by one group. On the other
hand, a measure of 0 would show that the two groups
being studied are evenly distributed or no dissimilarity.
3.2.2 Mapping thespatial pattern ofsegregation using GIS
anselin local Moran’s I
We leveraged data from the 2019 KNBS population
and housing census to define neighborhood boundaries
within the study area. Socio-economic and spatial data
(1)
D
=
n
i
−1
[ti|Pi−P|/2TP(1−P)
]
were then disaggregated to the location level, enabling an
analysis of spatial segregation patterns. We used Cluster
and Outlier Analysis (Anselin Local Moran’s I) in ArcGIS
10.8’s Spatial Statistics toolbox to identify statistically
significant clusters of neighborhoods with similar ethnic
characteristics. e method revealed not only the pres-
ence of clusters but also their dispersion patterns, allow-
ing for the grouping of neighborhoods based on shared
attributes. Importantly, this analysis helped determine
the optimal scale for further spatial analysis and explora-
tion of spatial relationships.
To assess spatial patterns of ethnic segregation, we
employed Anselin Local Moran’s I (Mitchell, 2005).
Unlike traditional non-spatial statistics, Anselin Local
Moran’s I was employed due to its ability to explicitly
account for spatial relationships such as proximity and
area. As a specialized tool for analyzing patterns within
spatial and socio-economic data (ESRI, 2020), Anselin
Local Moran’s I was pivotal in identifying statistically sig-
nificant clusters or dispersions of similar ethnic popula-
tions across neighboring geographic units. It does this
while accounting for potential bias caused by variations
in neighborhood sizes (using row standardization). Anse-
lin Local Moran’s I is a spatial geostatistical method that
is used to assess the presence of localized spatial autocor-
relation and to map spatial clusters or outliers. It does
this by classifying the statistical significance of Z-values
and p-values into hot spots of High-High clusters, High-
Low clusters, cold spots of Low-High clusters, Low-Low
clusters or statistically non-significant areas at p < 0.01
or p < 0.05. e analysis yields Z-scores (LMiZScore)
and p-values (LMiPValue), which indicate the statistical
significance of the observed patterns (Mathenge et al.,
2022; Nkamwesiga etal., 2022). LMiIndex provides infor-
mation about the intensity of clustering, while COType
highlights the type of spatial pattern observed. Positive
Moran’s I value signal clustering of similar ethnic groups,
whereas negative values suggest dispersion.
4 Results
4.1 Results oftheextent ofmulti‑ethnic spatial
segregation inNairobi City
e statistics presented in Table1 offers a breakdown of
Nairobi’s ethnic composition based on the tabulation of
Table 1 Nairobi County’s ethnicity crosstabulation
Source: KNBS, 2019 Housing and Population census
Ethnicity Total
Kamba Luo Kisii Kikuyu Luhya
Nairobi City Population 64,512 66,445 28,980 117,555 69,301 346,793
% 18.6% 19.2% 8.4% 33.9% 20% 100%
Page 6 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
the five largest ethnic groups. e Kikuyu ethnicity forms
the largest segment, comprising 33.9% of the total popu-
lation. e Luhyia ethnic group comes next, represent-
ing 20% of residents. e Luo follows closely, making up
19.2% of Nairobi’s population, while the Kamba ethnicity
constitutes 18.6%. e Kisii ethnic group comprises the
smallest percentage at 8.4%, but still contributes signifi-
cantly to Nairobi’s overall diversity.
e ID (Table2; Fig.2) between Kamba and Luo ethnic
groups (0.3610), suggests that a low level of dissimilar-
ity exists between them. In other words, approximately
36.10% of individuals from either group would need to
relocate in order to achieve perfect integration. ese
findings highlight a relative degree of residential segre-
gation between the Kamba and Luo communities, indi-
cating that they tend to reside in separate geographic
areas in Nairobi. Conversely, the dissimilarity between
the Kamba and Kisii ethnic groups (0.2679) is compara-
tively lower. is suggests the presence of a low level of
spatial mixing or integration between these two groups.
About 26.79% of individuals would need to relocate for
the groups to be perfectly integrated. is suggests that
Kamba and Kisii communities are relatively more inter-
mingled across Nairobi City compared to the Kamba-
Luo dynamic.
e ID between Kamba and Kikuyu (0.3978), indicates
a low level of dissimilarity. Roughly, 39.78% of individuals
would need to relocate for perfect integration, highlight-
ing a significant level of residential segregation between
these groups. Similarly, the Kamba and Luhyia ethnic
groups show a moderate level of dissimilarity (0.3109).
Around 31.09% of individuals from either group would
need to move for perfect integration, suggesting a low
level of residential segregation between them, which is
similar to the Kamba-Luo dissimilarity. ere are dis-
tinct areas in Nairobi City where each group is more
concentrated.
e findings from ID between Luo and Kisii (0.3850)
shows a moderate low level of dissimilarity between
them. Approximately 38.50% of individuals from either
group would need to relocate to achieve perfect similar-
ity. is suggests that while the Luo and Kisii communi-
ties are not completely segregated, there are noticeable
differences in their residential patterns within the region.
e moderate dissimilarity value might stem from his-
torical or geographical factors that have influenced their
settlement patterns.
At the same time, the ID between Luo and Kikuyu
(0.4162) ethnic groups, shows the highest level of dis-
similarity observed compared to all other IDs for all eth-
nic crosstabulations. is means that, about 41.62% of
individuals from either group would need to move for
perfect integration to be achieved in Nairobi City. is
higher level of dissimilarity indicates that the Luo and
Kikuyu communities are more spatially separated from
Table 2 ID between Kamba, Luo, Kisii, Kikuyu and Luhyia at City
level
NB, Those in bold show a higher level of dissimilarity
Name Kamba Luo Kisii Kikuyu Luhyia
Kamba 0.3610 0.2679 0.3978 0.3109
Luo 0.3610 0.3850 0.4162 0.2778
Kisii 0.2679 0.3850 0.4075 0.2834
Kikuyu 0.3978 0.4162 0.4075 0.4034
Luhyia 0.3109 0.2778 0.2834 0.4034
Fig. 2 ID between Kamba, Lu o, Kisii, Kikuyu and Luhyia at City level
Page 7 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
each other within Nairobi City. e political landscape
in Kenya could explain these dynamics, according to an
expert:
“is might be due to a variety of factors, such as
cultural differences or localized settlement patterns
due to the prevailing political affiliations between
the Kikuyu and the Luo nations that have contrib-
uted to their distinct residential areas” (KII).
However, the dissimilarity between the Luo and Luhyia
ethnic groups is low, with an ID of 0.2778. is suggests a
less segregated residential pattern between these groups,
with approximately 27.78% of individuals needing to
move for perfect spatial mix. ese two groups are rela-
tively close to each other but not to the extent of com-
plete similarity. An expert observed that:
“Culturally, the Luo and the Luhyia have shared
common cultural practices, names and geographical
borders and historically have been affiliated to the
same political persuasions for a long time” (KII).
e calculated ID value (0.4075) between the Kisii and
Kikuyu ethnic groups suggests a high level of dissimilar-
ity between them. Roughly 40.75% of individuals from
either the Kisii or Kikuyu group would need to relocate
to avert segregation. is finding indicates a signifi-
cant degree of residential segregation between the two
groups. e higher ID value underlines the distinct geo-
graphic areas where the Kisii and Kikuyu communities
tend to reside, with limited overlap. Equally, the ID value
(0.4034) between the Kikuyu and Luhyia ethnic groups
reflects a higher level of dissimilarity, similar to the Kisii-
Kikuyu dissimilarity. Around 40.34% of individuals from
either the Kikuyu or Luhyia group would need to move
for perfect integration to be achieved between them. is
indicates that the Kikuyu and Luhyia populations are also
more segregated from each other within the studied area.
Just like with the Kisii-Kikuyu patterns, the higher ID
value underscores the distinct residential patterns of the
Kikuyu and Luhyia communities, pointing to historical,
cultural, or socio-economic factors that may have con-
tributed to their spatial separation.
Lastly, the dissimilarity between the Kisii and Luhyia
ethnic groups is relatively lower, with an ID of 0.2834.
is suggests a less segregated residential pattern
between these groups, with approximately 28.34% of
individuals needing to relocate for perfect integra-
tion. is implies some degree of mixing or integration
between these groups. ese findings provide evidence of
varying levels of residential segregation between differ-
ent ethnic groups at the city scale in Nairobi. e results
highlight the concentration of individuals from specific
ethnic backgrounds in particular areas and suggest the
presence of social, cultural, and historical factors that
contribute to these segregation patterns.
4.2 The spatial patterns ofethnic segregation inNairobi
City
e results of the cluster and outlier analysis using Anse-
lin’s Local Moran’s I shown in Fig. 3a-e below demon-
strated significant spatial patterns of the five main ethnic
groupings in Nairobi. Positive Z-scores greater than 1.96
(at 0.05, 0.01 and 0.1 confidence levels) and negative
Z-scores less than − 1.96 indicated statistically significant
spatial clustering or dispersion for each ethnicity. Based
on this, we therefore rejected our null hypothesis as the
observed spatial patterns could not be attributed to ran-
domness in the data.
e occurrence of High-High (HH) Clusters demon-
strated that there was significant positive spatial auto-
correlation for Kamba in Embakasi, Tassia, Kware,
Mukuru Kwa Njenga, Imara Daima, Kwa Reuben, Don-
holm, Umoja 2, Viwandani and Hazina. is implies that
some neighbourhoods with a high presence of Kamba
tended to be surrounded by neighborhoods with simi-
larly high concentrations of Kamba and hence the exist-
ence of clusters dominated by the Kamba in Nairobi
City. Low-low (LL) clusters suggests that Ngando, Gitiba,
Mutuini, Kagira, Ruthimitu, Kiuru, Uthiru, Mukarara,
Waithaka, Majengo, Makina, Jamhuri, Woodley, Karen,
Lenana, Lang’ata, Kamukunji, Ngara East, Ngara West,
City Centre, Racecourse, Pangani,
Highridge, Karura, Muthaiga, Spring Valley, Upper
parklands, Kitisuru, Kilimani and City Square are neigh-
borhoods with minimal Kamba presence. On the other
hand, a significant negative spatial autocorrelation (LL
cluster) indicated that neighbourhoods with a low pres-
ence of Kamba were surrounded by neighborhoods with
similarly low concentrations of Kamba. is signifies the
presence of clusters or locations where Kamba are less
prevalent in Nairobi City.
All the areas that were shown as non-significant areas
and with values close to zero e.g. Hamza for Kisii (LMi-
Index = 0.020, Z Score = 0.087 and p > 0.05) or Jericho/
Lumumba for Kikuyu (Index = −0.0090, Z Score =
−0.0536 and p > 0.5) were not statistically significant and
indicated a lack of significant spatial autocorrelation. is
implied a more random distribution of the specific eth-
nicity across the city.
Spatial cluster and outlier analysis identified regions
where the concentration of a specific ethnicity was sig-
nificantly higher or lower compared to neighboring loca-
tions (Fig. 3: a-e). is served as an indicator of areas
deviating from the norm in terms of ethnic composition.
Page 8 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
For instance, High-Low Outliers (HL) in Kangemi Cen-
tral (Index = −0.180, Z Score = −0.994, at a significance
level of, p < 0.05) for Kamba shows that Kangemi Cen-
tral has a significantly higher concentration of Kamba
compared to their neighboring locations. is implies
that Kangemi Central is characterized by unique socio-
cultural factors, historical developments, migration
patterns, or economic opportunities that have led to a
Fig. 3 a‑e: Anselin Local Moran I for Kamba, Luo, Kisii, Kikuyu and Luhyia
Page 9 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
higher concentration of the Kamba in the neighbour-
hoods within.
Likewise, Low-High Outliers (LH) for Luhyia in
Maziwa (LMi Index = −0.342, Z Score = −2.048 and
p < 0.05), Kikuyu in Utawala (Index =−0.3873, Z Score
= −3.1042 and p < 0.05), and Luo in Komarock North
(Index = −0.353, Z Score = −2.94 and p = 0.01) (Fig. 3)
represents locations with a low value surrounded by high
values of a particular ethnicity. is indicates instances
of integration or assimilation where members of Luhyia,
Kikuyu and Luo respectively are living within areas pre-
dominantly occupied by other ethnicities.
5 Discussion
5.1 Interpretation ofresults
By employing Anselin’s Local Moran’s I analysis to exam-
ine the spatial patterns of the Kamba, Kikuyu, Luo, Luhya,
and Kisii ethnicities in Nairobi, we gained insights into
the spatial clusters and outliers within the city. We were
able to understand the spatial distribution of the differ-
ent ethnic groups within the city and the extent of their
spatial segregation. Kamba were seen to cluster in areas
such as Embakasi, Tassia, Mukuru Nyayo and Umoja
and Kikuyu in Kahawa West, Zimmerman, Roysambu,
Kasarani and Mwiki. e Luo showed significant cluster-
ing in areas such as Kariobangi, Lucky Summer, Kayole,
Komarock, Kibera, Mathare, and Korogocho while Luhya
exhibited concentrations in locations like Kangemi and
Kawangware with Kisii dominating Utawala, Savannah
and Viwandani among others.
is study demonstrates the spatial clustering of spe-
cific ethnic groups within Nairobi. is finding aligns
with other research documenting similar patterns of eth-
nic concentration in other urban areas. As Sarwari and
Ono (2023) observed in Kabul City, new city residents
often gravitate towards neighborhoods with established
co-ethnic communities. is preference can be attributed
to shared cultural heritage, language, and social connec-
tions that foster a sense of belonging.
is observed pattern likely arises from a complex
interplay of historical legacies, cultural dynamics, and
economic opportunities associated with each ethnic-
ity (Bocquier et al., 2009). Achola (2001) notes that
ethnic concentration often intertwines with socio-
economic disparities in Nairobi. Historical settlement
patterns and resource allocation during the colonial
era (Obudho, 1997) contribute to this spatial distri-
bution. is resonates with Greenwood and Topi-
wala (2020) where the spatial distribution of different
races/ethnicities in Nairobi was a function of colonial
segregationist policies. Furthermore, contemporary
economic inequalities lead to affluent areas attracting
specific ethnicities, while poorer areas remain more
diverse (Jimmy etal., 2020). Additionally, the desire for
co-ethnic residence fosters clustering, as shared tradi-
tions, language, and social networks creates a sense of
belonging and community. is phenomenon, docu-
mented among Black households in the US (Boustan,
2013; Charles, 2003; Massey, 1990), persists in Nairobi
despite post-colonial policy changes.
Additionally, the ID proved to be an effective tool in
identifying spatial patterns of segregation among Nairo-
bi’s five major ethnic groups: Kikuyu, Luo, Kamba, Kisii,
and Luhya. is approach pinpointed the most segrega-
tion locations, creating opportunities for targeted inter-
ventions and area-based policy measures. is targeted
approach is crucial to prevent further ethnic-based social
divisions. As highlighted by Ajulu (2002), ethnic segrega-
tion among these groups can reinforce social divisions
and limit integration. is can potentially lead to social
unrest (Haandrikman etal., 2023; Van Stapele, 2015).
Our findings reveal pronounced ethnic segregation
among major ethnic groups in Nairobi, with consequen-
tial disparities in access to services and neighborhood
quality for affected populations. is pattern is ech-
oed in other global cities. For instance, Dakar, Senegal,
experienced deliberate ethnic segregation under French
colonial rule (Njoh, 2017). While US cities have histori-
cally exhibited extreme spatial segregation of marginal-
ized ethnic minorities in urban ghettos, recent trends
suggest a shift. Zapatka et al. (2021) reported a decline
in white households in cities like New York and Newark.
However, although racial and ethnic disparities in hous-
ing exist in places like Brazil and Australia, they are less
pronounced than the stark segregation observed in US
cities (Carvalho & Netto, 2023; Azpitarte et al., 2021;
Leibbrand etal., 2020). At the same time, research con-
sistently links ethnic segregation to access to services
and neighborhood selection. Studies from Brazil, South
Africa, Estonia, Belgium, and England and Wales corrob-
orate this association (Carvalho & Netto, 2023; Järv etal.,
2021; Gradín, 2019; Costa & De Valk, 2018; Harris etal.,
2017).
e ID’s findings underscore the need for policymak-
ers and researchers to design strategies that promote
inter-ethnic integration, foster coexistence, and build
social cohesion within Nairobi’s diverse communities.
ese findings resonate with Fung-Loy and Van Rompaey
(2021), research in Suriname, where the ID identified
Maroons and Javanese as the most segregated ethnicities.
eir work underscores the importance of desegregation
policies that aim to increase ethnic mixing and reduce
socio-economic disparities. However, research by Tan
(2023) in Singapore suggests that ethnic desegregation
policies, while successful in some contexts, may not uni-
versally address rising socio-economic segregation.
Page 10 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
e spatial distribution of ethnicities in Fig.3a-e show
segregation patterns. ese mixed-ethnicity neighbor-
hoods might exhibit distinct demographic characteris-
tics and cultural dynamics compared to homogenous
areas. is finding aligns with Bocquier etal. (2009) who
established a correlation between ethnicity, language,
and assimilation patterns within Nairobi. Furthermore,
K’Akumu and Olima (2007) highlight the historical
underpinnings of this segregation, potentially rooted in
colonial land policies. eir work suggests opportuni-
ties for intervention through robust and transformative
policies aimed at promoting integration. Building on this,
Ponzo (2010) emphasizes the value of learning from suc-
cessful models like England’s strong community cohesion
policies and anti-racist/ethnic equality legislation. How-
ever, Baud etal.,(2009) offer a crucial caveat, stressing the
importance of understanding the historical and spatial
distribution of these inequalities for policymakers and
urban planners to design effective interventions.
5.2 Consequences ofethnic segregation
e consequences of ethnic segregation are detrimen-
tal, fostering societal divisions and limiting access to
resources and quality of life for marginalized communi-
ties (Jimmy etal., 2020; Costa & De Valk, 2018). Our find-
ings reveal a stark ethnic divide in Nairobi City, rooted in
its colonial legacy. British colonial authorities instituted a
system of ethnic tripartition through urban planning and
spatial control (Jimmy etal., 2020; Mwaniki, 2017), mar-
ginalizing the African population despite their crucial
role in the economy. Post-independence, rapid urbaniza-
tion exacerbated this divide, forcing Africans into under-
serviced areas that evolved into slums (Wanjiru-Mwita &
Giraut, 2020; Obudho, 1997).
is study confirms the persistence of heavily segre-
gated neighborhoods in Nairobi, leading to significant
disparities in quality of life. As Jones (2020) argues, the
enduring legacy of colonial spatial planning perpetu-
ates inequality and marginalization. Coupled with grow-
ing inequality, ethnic segregation traps minorities and
migrants in deprived areas, eroding social cohesion and
limiting opportunities (Costa & De Valk, 2018).
Our research demonstrates that Nairobi’s five largest
ethnic groups—Kikuyu, Luo, Luhya, Kamba, and Kisii—
exhibit concentrated residential patterns stemming from
historical discrimination and economic disparities (Ajulu,
2002). is segregation fosters ethnic enclaves, hindering
social integration and cultural exchange (Charles, 2003).
e clustering of disadvantaged populations in specific
neighborhoods exacerbates social and economic chal-
lenges, hindering civic participation, employment, and
education, and potentially fueling social unrest (Haan-
drikman etal., 2023). e 2007/8 post-election violence
in Kibera and Mathare slums underscores the danger-
ous consequences of these deep-rooted divisions (Van
Stapele, 2015). Additionally, as noted by K’Akumu and
Olima (2007), segregation presents significant challenges
for effective urban planning and governance. erefore,
context-specific strategies are needed to promote inte-
gration and social cohesion within Nairobi (Mwaniki,
2017). is is critical considering the potential risks to
national identity, unity, and development, as outlined
in Kenya’s national goals (Republic of Kenya, 2008). As
such, utilizing ID and cluster analysis to grasp the nature
and extent of segregation can aid policy makers to iden-
tify areas that require area-based spatial interventions for
integrated, equitable communities.
5.3 Policy implications
Policy solutions for segregation can be categorized as:
place-based (enhancing minority neighborhoods or man-
dating affordable housing in affluent areas), people-based
(supporting homeowners/renters through fair housing
enforcement or improved mortgage access), or indirect
(addressing segregation symptoms like improving public
transportation in isolated suburbs (Van Ham etal., 2018;
Boustan, 2013). erefore, for Nairobi, spatial integra-
tion policies promoting mixed-use developments (Smets
& Salman, 2008) are needed. ese developments, inte-
grating residences, businesses, and schools, foster social
cohesion and economic opportunity across socioeco-
nomic groups. However, Van Ham etal. (2018) empha-
size education as the most impactful tool for reducing
inequality. Additionally, slum rehabilitation schemes
(Nijman, 2008), affordable housing programs, skills
development initiatives, and social infrastructure pro-
jects could mitigate segregation. A similar observation
was made in Bengaluru, by Roy etal. (2018) that success-
ful slum interventions for each of the segregated groups
improved their living standards.
In addition, effective urban planning, supported by a
robust housing policy framework is required (Sylvie &
Frouillou, 2023; Abolghasem Rasouli, 2021; Smets & Sal-
man, 2008; Obudho, 1997). is is considered key for a
fair distribution of resources and services. However, Bolt
(2009), argue that housing policies exhibit limited effi-
cacy in influencing ethnic concentration due to their fre-
quent contradictions and failure to address the primary
causes of segregation.
e Nairobi City Government should prioritize these
policy solutions by integrating robust planning frame-
works into its transformation agenda. However, acknowl-
edging the complexity is essential. Mathenge (2022)
highlights challenges in implementing a multitude of
national and county-level urban planning and devel-
opment laws and policies due to conflicting timelines,
Page 11 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
diverse spatial and sectoral focuses, and fragmented
legal frameworks. By acknowledging these complexities
and adopting a multi-faceted approach, policymakers
can create a more inclusive and cohesive Nairobi for all
residents.
5.4 Limitations andfuture research directions
is study has provided valuable insights into the spatial
aspects of ethnic segregation in Nairobi City, utilizing
the ID to analyze patterns among the five largest ethnic
groups. While these methods were appropriate to achieve
our research objectives, future research should expand
beyond residential segregation to fully grasp the multi-
faceted nature of ethnic dynamics. Further, in order to
deepen our understanding of the factors influencing
segregation and integration patterns, future research
should consider methodological approaches, such as
multilevel modeling, that incorporate hierarchical data
structures to analyze the interplay between individual-
level patterns and ecological factors. is should also
explore the underlying social, economic, and historical
forces in depth. is approach would not only improve
spatial models but also provide a richer understanding
of the complex dimensions of ethnic segregation, mov-
ing beyond quantitative (ID) and spatial autocorrelation
metrics (Anselin Local Moran’s I) to reveal the personal
experiences of those affected. Additionally, due to the
unavailability of 2009 ethnicity data from the Kenya
National Bureau of Statistics, this study adopted a cross-
sectional approach, examining Nairobi City neighbor-
hoods using the most recently available data linked to
GIS at the location level (2019). Given the absence of
comparable ethnicity and spatial data for previous years
(1988, 1999, and 2009), this study was not premised on
assessing temporal changes in ethnic segregation or
neighborhood characteristics. Such analyses will be the
focus of future research, building upon the foundational
insights of this study. It is anticipated that subsequent
studies will measure evolving patterns of ethnic segrega-
tion among the five largest ethnic groups for comparison
with the present findings.
6 Conclusion
is study has revealed the intricate patterns of ethnic
segregation within Nairobi. By mapping the spatial dis-
tribution of the city’s five largest ethnic groups – Kamba,
Luo, Kisii, Kikuyu, and Luhyia – we have shown the reali-
ties of ethnic segregation and offered crucial insights for
policymakers and urban planners. Our analysis, employ-
ing the ID and Anselin’s Local Moran’s I, has proven its
effectiveness in pinpointing areas of concentrated segrega-
tion and highlighting potential sources of social exclusion.
is understanding enables targeted spatial interventions,
tailoring policy and planning initiatives to specific neigh-
borhoods and communities experiencing the most acute
disunity. e identified hotspots of segregation necessitate
proactive measures to foster integration and coexistence.
Urban policy interventions like fair housing laws, afford-
able housing programs, and initiatives addressing systemic
racism and ethnicity can pave the way for more equitable
neighborhoods (Baud et al.,(2009). As Boustan (2013),
Darden etal. (2018), and Popescu etal. (2018) emphasize,
effectively addressing the multifaceted consequences of
segregation necessitates a comprehensive approach. is
approach should prioritize fostering social cohesion and
inclusivity within communities.
However, navigating Nairobi’s segregated landscape
requires acknowledging its complex historical roots.
Pacione (2009) noted that urban segregation often origi-
nates from power imbalances within the real estate mar-
ket, highlighting the need for policies sensitive to unique
historical, political, and social dynamics. Moving for-
ward, sound urban policies, especially spatially targeted
interventions aimed at empowering marginalized com-
munities, become paramount. In conclusion, this study
transcends mere mapping; it highlights a roadmap for
transforming Nairobi’s segregated landscape. By embrac-
ing the interplay of historical, social, political, and geo-
graphic factors shaping the city’s ethnic landscape, urban
policymakers can pave the way for a more integrated and
equitable future for all Nairobians. Achieving cohesive
cities demands a collaborative, multi-disciplinary and
collective commitment to addressing segregation and
fostering a sense of shared belonging.
Acknowledgements
We express our gratitude to the Kenya National Bureau of Statistics for sup-
plying us with the data from the 2019 Kenya Population and Housing Census.
Additionally, we extend our thanks to Rose Kairu, Peter Gikubu, and Douglas
Kobowen for their assistance as research assistants during the qualitative data
collection process.
Authors’ contributions
Alex Nthiwa developed the proposed concept, conducted a review of the
existing literature, performed computations and GIS analysis, validated the
analytical methods, and discussed the results. Leonard Kisovi, Thomas Kibutu,
and Philomena Muiruri supervised the data collection, findings of this research
and reviewed the final manuscript.
Funding
The authors did not receive any financial support for the research, authorship,
and/or publication of this article.
Data availability
Datasets and analysis results tables are accessible via Zenodo at DOI https://
doi. org/ 10. 5281/ zenodo. 11080 309.
Declarations
Ethics approval and consent to participate
This work was approved by the National Commission for Science, Tech-
nology and Innovation of the Republic of Kenya vide letter reference
NACOSTI/P/16/71188/8986.
Page 12 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests or conflicts of interest with
respect to the research, authorship or publication of this article.
Received: 9 May 2024 Revised: 18 November 2024 Accepted: 24 Novem-
ber 2024
References
Abolghasem Rasouli, S. (2021). Urban segregation in Malmö: Discourse policy
analysis at the local level and the emergence of New actors. [Master of Arts,
University of Malmo].
Achola, M. A. (2001). Colonial policy and urban health: The case of colonial Nai-
robi. AZANIA: Journal of the British Institute in Eastern Africa, 36(1), 119–137.
Aggarwal, S. (2014). Emerging global urban order and challenges to harmonious
urban development IGC Cologne 2012. Down to Earth.
Ajulu, R. (2002). Politicised ethnicity, competitive politics and conflict in Kenya:
A historical perspective. African Studies, 61(2), 251–268.
Archambault, C. S., de Laat, J., & Zulu, E. M. (2012). Urban services and child
migration to the slums of Nairobi. World Development, 40(9), 1854–1869.
Archer, D. N. (2019). The housing segregation: The jim crow effects of crime-
free housing ordinances. Mich L Rev, 118, 173.
Azpitarte, F., Alonso-Villar, O., & Hugo-Rojas, F. (2021). Socio-economic groups
moving apart: An analysis of recent trends in residential segregation in
Australia’s main capital cities. Population Space and Place, 27(3), e2399.
Bansal, B. (2021). Intra-urban inequalities during rapid development: Space
egalitarianism in Tokyo between 1955–1975. International Journal of
Urban Sustainable Development, 13(2), 368–382. https:// doi. org/ 10. 1080/
19463 138. 2021. 19077 49
Baud, I. S., Pfeffer, K., Sridharan, N., & Nainan, N. (2009). Matching deprivation
mapping to urban governance in three Indian mega-cities. Habitat
International, 33(4), 365–377. https:// doi. org/ 10. 1016/j. habit atint. 2008. 10.
024 http:// www. scien cedir ect. com/ scien ce/ artic le/ B6V9H- 4VP12 8K-1/ 2/
5c50c 56636 568d3 caccc dae3c a2695 28.
Benassi, F., Naccarato, A., Iglesias-Pascual, R., Salvati, L., & Strozza, S. (2023).
Measuring residential segregation in multi‐ethnic and unequal European
cities. International Migration, 61(2), 341–361.
Bocquier, P., Otieno, A. T., Khasakhala, A. A., & Owuor, S. (2009). Urban integra-
tion in Africa: A socio-demographic survey of Nairobi. CODESRIA.
Bolt, G. (2009). Combating residential segregation of ethnic minorities in Euro-
pean cities. Journal of Housing and the Built Environment, 24(4), 397–405.
Boustan, L. P. (2013). Racial residential segregation in American cities. In N.
Brooks, K. Donaghy, & G. Knaap (Eds.), The handbook of urban economics
and planning. National Bureau of Economic Research. https:// doi. org/ 10.
3386/ w19045
Carvalho, C., & Netto, V. M. (2023). Segregation within segregation: Informal
settlements beyond socially homogenous areas. Cities, 134, 104152.
https:// doi. org/ 10. 1016/j. cities. 2022. 104152
Charles, C. Z. (2003). The dynamics of racial residential segregation. Annual
Review of Sociology, 29(1), 167–207.
Chen, X., Ye, X., Widener, M. J., Delmelle, E., Kwan, M. P., Shannon, J., Racine, E. F.,
Adams, A., Liang, L., & Jia, P. (2022). A systematic review of the modifiable
areal unit problem (MAUP) in community food environmental research.
Urban Informatics, 1(1), 22–33.
Costa, R., & De Valk, H. A. (2018). Ethnic and socioeconomic segregation in
Belgium: A multiscalar approach using individualised neighbourhoods.
European Journal of Population, 34(1), 225–250.
Darden, J., Malega, R., & Stallings, R. (2018). Social and economic consequences
of black residential segregation by neighbourhood socioeconomic char-
acteristics: The case of Metropolitan Detroit. Urban Studies, 56(1), 115–130.
https:// doi. org/ 10. 1177/ 00420 98018 779493
Deurloo, R., & Musterd, S. (2001). Residential profiles of Surinamese and moroc-
cans in Amsterdam. Urban Studies, 38(3), 467–485.
Duque, J. C., Laniado, H., & Polo, A. (2018). S-maup: Statistical test to measure
the sensitivity to the modifiable areal unit problem. PLoS One, 13(11),
207–227.
ESRI. (2020). An overview of the spatial statistics toolbox. ESRI. Retrieved 30th
June from https:// pro. arcgis. com/ en/ pro- app/ latest/ tool- refer ence/ spati
al- stati stics/ an- overv iew- of- the- spati al- stati stics- toolb ox. htm
Fahey, T., & Bryan, F. (2010). Immigration and Socio-spatial segregation in
Dublin, 1996–2006. Urban Studies, 47(8), 1625–1642.
Friedrichs, J. (2013). Social inequality, segregation and urban conflict: The
case of Hamburg. Urban Segregation and the Welfare State (pp. 168–190).
Routledge.
Fung-Loy, K., & Van Rompaey, A. (2021). Socio-economic and ethnic segrega-
tion in the Greater Paramaribo Region, Suriname. In Maarten van Ham
et al. (Ed.), Urban socio-economic segregation and income inequality (pp.
491–505). https:// doi. org/ 10. 1007/ 978-3- 030- 64569-4_ 25
Gradín, C. (2019). Occupational segregation by race in South Africa after apart-
heid. Review of Development Economics, 23(2), 553–576.
Greenwood, A., & Topiwala, H. (2020). Visions of colonial Nairobi: William Simp-
son, health, segregation and the problems of ordering a plural society,
1907–1921. Social History of Medicine, 33(1), 57–78.
Haandrikman, K., Costa, R., Malmberg, B., Rogne, A. F., & Sleutjes, B. (2023).
Socio-economic segregation in European cities. A comparative study of
Brussels, Copenhagen, Amsterdam, Oslo and Stockholm. Urban Geogra-
phy, 44(1), 1–36. https:// doi. org/ 10. 1080/ 02723 638. 2021. 19597 78
Halfani, M. (1997). Governance of urban development in East Africa. In S. Mark
(Ed.), Governing African cities (pp. 115–159). Witwatersrand University.
Harris, R., Johnston, R., & Manley, D. (2017). The changing interaction of ethnic
and socio-economic segregation in England and Wales, 1991–2011.
Ethnicities, 17(3), 320–349.
Harsman, B. (2006). Ethnic diversity and spatial segregation in the Stockholm
Region. Urban Studies, 43(8), 1341–1364. https:// doi. org/ 10. 1080/ 00420
98060 07764 34
Huie, S. A. B. (2000). The components of density and the dimensions of resi-
dential segregation. Population Research and Policy Review, 19(6), 505–524.
Hussain, M., & Imitiyaz, I. (2018). Urbanization concepts, dimensions and fac-
tors. International Journal of Recent Scientific Research, 9(1), 23513–23523.
James, D. R., & Taeuber, K. E. (1985). Measures of Segregation. In N. Tuma
(Ed.), Sociological Methodology (pp. 1–32): Jossey-Bass.
Järv, O., Masso, A., Silm, S., & Ahas, R. (2021). The link between ethnic segrega-
tion and socio-economic status: An activity space approach. Tijdschrift
Voor economische en sociale geografie, 112(3), 319–335.
Javanmard, R., Lee, J., Kim, J., Liu, L., & Diab, E. (2023). The impacts of the modifi-
able areal unit problem (MAUP) on social equity analysis of public transit
reliability. Journal of Transport Geography, 106, 103500.
Jimmy, E. N., Martinez, J., & Verplanke, J. (2020). Spatial patterns of residential
fragmentation and quality of life in Nairobi City, Kenya. Applied Research in
Quality of life, 15, 1493–1517.
Jones, P. S. (2020). Nairobi: The politics of the capital. In N. Cheeseman, K.
Kanyinga, & G. Lynch (Eds.), The Oxford handbook of Kenyan politics (p. 0).
Oxford University Press. https:// doi. org/ 10. 1093/ oxfor dhb/ 97801 98815
693. 013. 44
K’Akumu, O. A., & Olima, W. H. (2007). The dynamics and implications of resi-
dential segregation in Nairobi. Habitat International, 31(1), 87–99.
Kamau, N., & Njiru, H. (2018). Water, sanitation and hygiene situation in Kenya’s
urban slums. Journal of Health care for the poor and Underserved, 29(1),
321–336.
Kenya National Bureau of Statistics. (2019a). 2019 Kenya Population and Housing
Census. KNBS.
Kenya National Bureau of Statistics. (2019b). Summary Report on Kenya’s Popula-
tion projections – 2019 KPHC. KNBS.
Kimari, W., & Ernstson, H. (2020). Imperial remains and imperial invitations:
Centering race within the contemporary large-scale infrastructures of
East Africa. Antipode, 52(3), 825–846.
KNBS. (2019). Kenya population and housing census volume IV: distribution of
population by socio-economic characteristics 2019. KNBS.
Leibbrand, C., Massey, C., Alexander, J. T., Genadek, K. R., & Tolnay, S. (2020). The
great migration and residential segregation in American cities during the
twentieth century. Social Science History, 44(1), 19–55.
Martinez-Martin, J. (2005). Monitoring intra-urban inequalities with GIS-based
indicators. [Doctoral Dissertation, Utrecht University].
Page 13 of 13
Ngolanyeetal. Computational Urban Science (2024) 4:41
Massey, D. S. (1990). American apartheid: Segregation and the making of the
underclass. American Journal of Sociology, 96(2), 329–357. https:// doi. org/
10. 1086/ 229532
Massey, D. S., & Denton, N. A. (1987). Trends in the residential segregation of
blacks, hispanics, and asians: 1970–1980. American Sociological Review,
52(6), 802–825.
Massey, D. S., & Denton, N. A. (1988). The dimensions of residential segregation.
Social Forces, 67(2), 281–315.
Mathenge, M. (2022). The spatial dimension of agriculture and food security: a
GIS-based spatially explicit approach for integration of smallholder agricul-
ture into Agribusiness Vrije University].
Mathenge, M., Sonneveld, B. G., & Broerse, J. E. (2022). Application of GIS
in Agriculture in promoting evidence-informed decision making for
improving Agriculture sustainability: A systematic review. Sustainability,
14(16), 9974.
Messner, D. (2019). The Century of cities: Pathways towards sustainability.
Concilium: International Journal for Theology, 1(1), 13–23.
Mitchell, A. (2005). The Esri guide to GIS analysis, volume 2: Spatial measure-
ments and statistics. Esri Press (Vol. 2). Spatial-measurements-and-statis-
tics. https:// www. esri. com/ enus/ esri- press/ browse/ the- esri- guide- toGIS
analy sis
Murunga, G. R. (2012). The cosmopolitan tradition and fissures in segregation-
ist town planning in Nairobi, 1915–23. Journal of Eastern African Studies,
6(3), 463–486.
Mwaniki, D. (2017). Smart City Foundation, the core pillar for smart economic
development in Nairobi. In T. M. Vinod Kumar (Ed.), Smart economy in
smart cities: international collaborative research: Ottawa, St.Louis, Stuttgart,
Bologna, Cape Town, Nairobi, Dakar, Lagos, New Delhi, Varanasi, Vijayawada,
Kozhikode, Hong Kong (pp. 657–685). Springer Singapore. https:// doi. org/
10. 1007/ 978- 981- 10- 1610-3_ 24
Nairobi City County. (2014). The project on Integrated Urban Development Master
Plan for the City of Nairobi in the Republic of Kenya. Nairobi City County.
Nairobi City County. (2023). County Integrated Development Plan 2023–2027.
Nairobi City County.
Naji, N., & Schildknecht, D. (2024). Containing the ‘Suspect’other: Perpetuating
Colonial spaces through a global Counterterrorism Regime in Nairobi.
Geopolitics, 1, 1–24.
Ngau, P. M. (1979). The internal structure of residential areas in Nairobi. [Masters
of Arts Thesis, University of Nairobi].
Nijman, J. (2008). Against the odds: Slum rehabilitation in neoliberal Mumbai.
Cities, 25(2), 73–85.
Nijman, J., & Wei, Y. D. (2020). Urban inequalities in the 21st century economy.
Applied Geography, 117, 102188. https:// doi. org/ 10. 1016/j. apgeog. 2020.
102188
Njoh, A. J. (2017). Toponymic inscription as an instrument of power in Africa:
The case of colonial and post-colonial Dakar and Nairobi. Journal of Asian
and African Studies, 52(8), 1174–1192.
Nkamwesiga, J., Korennoy, F., Lumu, P., Nsamba, P., Mwiine, F. N., Roesel, K.,
Wieland, B., Perez, A., Kiara, H., & Muhanguzi, D. (2022). Spatio-temporal
cluster analysis and transmission drivers for Peste Des Petits ruminants in
Uganda. Transboundary and Emerging Diseases, 69(5), e1642–e1658.
Nthiwa, A. (2011). Modeling scale and the effects of the modifiable areal unit prob-
lem on multiple deprivation in Instabul, Turkey. [Master of Science Thesis,
University of Twente (ITC)].
Nyamai, D. N., & Schramm, S. (2023). Accessibility, mobility, and spatial justice
in Nairobi, Kenya. Journal of Urban Affairs, 45(3), 367–389.
Nzau, B., & Trillo, C. (2020). Affordable housing provision in informal settle-
ments through land value capture and inclusionary housing. Sustainabil-
ity, 12(15), 5975.
Obudho, R. A. (1997). Nairobi: National capital and regional hub. In C. Rakodi
(Ed.), The urban challenge in Africa: Growth and management (pp.
292–333). United Nations University Press.
Okiro, K., Omoro, N., & Kamwaro, M. O. (2019). The Effect of Financial Literacy
on Individual Investment decisions at the Nairobi Securities Exchange.
International Journal of Creative Research and Studies, 3(3).
Owuor, S. O., & Mbatia, T. (2008). Post independence development of Nairobi city,
Kenya. Paper presented to the Workshop on African Capital Cities, Dakar,
Senegal.
Pacione, M. (2009). Urban geography: A global perspective (3rd ed.). Routledge.
Ponzo, I. (2010). Immigrant integration policies and housing policies: the hidden
links. Torino: FIERI.
Popescu, I., Duffy, E., Mendelsohn, J., & Escarce, J. J. (2018). Racial residential
segregation, socioeconomic disparities, and the White-Black survival gap.
PLoS One, 13(2), e0193222. https:// doi. org/ 10. 1371/ journ al. pone. 01932 22
Quillian, L. (2012). Segregation and poverty concentration: The role of three
segregations. American Sociological Review, 77(3), 354–379.
Ren, H., Guo, W., Zhang, Z., Kisovi, L. M., & Das, P. (2020). Population density and
spatial patterns of informal settlements in Nairobi, Kenya. Sustainability,
12(18), 7717.
Republic of Kenya. (2008). Nairobi Metro 2030 strategy: A World Class African
Metropolis. Government of Kenya.
Roy, D., Lees, M. H., Pfeffer, K., & Sloot, P. M. (2018). Spatial segregation, inequal-
ity, and opportunity bias in the slums of Bengaluru. Cities, 74, 269–276.
Sarwari, F., & Ono, H. (2023). A study on urban ethnic segmentation in Kabul
City. Afghanistan Sustainability, 15(8), 6589.
Smets, P., & Salman, T. (2008). Countering Urban Segregation: Theoretical
and policy innovations from around the Globe. Urban Studies, 45(7),
1307–1332.
Sylvie, F., & Frouillou, L. (2023). Urban segregation. John Wilery, Sons Inc.
Tan, S. B. (2023). Do ethnic integration policies also improve socio-economic
integration? A study of residential segregation in Singapore. Urban Stud-
ies, 60(4), 696–717.
Townsend, I., & Walker, R. (2002). The structure of income residential segrega-
tion in Canadian metropolitan areas. Canadian Journal of Regional Science,
25(1), 25–52.
Turok, I., Visagie, J., & Scheba, A. (2021). Social inequality and spatial segregation
in Cape Town. Springer.
UN-Habitat. (2016). World cities Report 2016; urbanization and development-
emerging futures. UN.
UN-Habitat. (2022). World cities Report 2022: Envisaging the future of cities.
UN-Habitat.
UN-Habitat. (2023). Kenya 2023 Country brief: A better quality of life for all in an
urbanizing world. UN-Habitat.
Van Ham, M., Tammaru, T., & Janssen, H. (2018). A multi-level model of vicious
circles of socio-economic segregation. In OECD (Ed.), Divided cities: Under-
standing intra-urban disparities. OECD (pp. 135–154). OECD Publishing.
van Oostrum, M. (2023). Informal extension of public housing estates in Nai-
robi – an appraisal of historical typologies and emergent spatial patterns.
Journal of Urban Design, 1–19, 1. https:// doi. org/ 10. 1080/ 13574 809. 2023.
21803 52
Van Stapele, N. (2015). Respectable illegality: gangs, masculinities and belonging
in a Nairobi ghetto. [PhD Thesis, Universiteit van Amsterdam].
Wanjiru-Mwita, M., & Giraut, F. (2020). Toponymy, pioneership, and the politics
of ethnic hierarchies in the spatial organization of British colonial Nairobi.
Urban Science, 4(1), 6.
Zapatka, K., Mollenkopf, J., & Romalewski, S. (2021). Reordering occupation,
race, and place in Metropolitan New York. In van M. Ham, T. Tammaru, R.
Ubarevičienė, & H. Janssen (Eds.), Urban socio-economic segregation and
income inequality: a global perspective (pp. 407–429). Springer.
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