# Socio-spatial segregation and human mobility: A review of empirical evidence

Yuan Liao
Department of Space, Earth and Environment
Chalmers University of Technology
Gothenburg, Sweden
<EMAIL_ADDRESS>
Jorge Gil
Department of Architecture and Civil Engineering
Chalmers University of Technology
Gothenburg, Sweden
<EMAIL_ADDRESS>
Sonia Yeh
Department of Space, Earth and Environment
Chalmers University of Technology
Gothenburg, Sweden
<EMAIL_ADDRESS>
Rafael H. M. Pereira
Institute for Applied Economic Research (Ipea) - Brazil
Data Science Division, Brazil
<EMAIL_ADDRESS>
Laura Alessandretti
Department of Applied Mathematics and Computer Science
Technical University of Denmark
Lyngby, Denmark
<EMAIL_ADDRESS>
Corresponding author. Also affiliated with Department of Applied Mathematics
and Computer Science, Technical University of Denmark, Lyngby, Denmark.

###### Abstract

Social segregation, the spatial and social separation between individuals from
different backgrounds, can affect sustainable urban development and social
cohesion. The literature has traditionally focused on residential segregation,
examining how individuals’ residential locations are distributed differently
across neighborhoods based on income, ethnicity, and education. However, this
approach overlooks the complexity of spatial segregation because daily
activities often extend far beyond residential areas. Over the past one to two
decades, emerging mobility data sources have enabled a new understanding of
socio-spatial segregation by considering daily activities such as work,
school, shopping, and leisure visits. From traditional surveys to GPS
trajectories, diverse data sources reveal that day-to-day movements can impact
segregation by reducing or amplifying segregation levels obtained when
considering residential aspects alone. This literature review focuses on three
critical questions: (a) to what extent do individual mobility patterns
contribute to segregation? (b) Which factors explain the role played by
mobility in segregation? and (c) What insights are gained by incorporating
extensive mobility data into segregation research? Our literature review
contributes to an improved understanding of socio-spatial segregation at the
individual level and offers actionable insights into reducing segregation and
addressing research gaps in the field.

_Keywords_ Spatial segregation $\cdot$ Social integration $\cdot$ Individual
mobility $\cdot$ Transport $\cdot$ Activity space $\cdot$ Urban space

##  1 Introduction

Social segregation refers to the spatial and social separation between
individuals from different socioeconomic backgrounds. Understanding
segregation holds significant importance in our increasingly urbanized planet.
Sustainable urban development fosters diverse populations and promotes social
cohesion by facilitating access to vital resources, public services (Joelsson
and Ekman Ladru, 2022), educational (Zhang et al., 2022a), and employment
opportunities (Silm and Ahas, 2014a) to all socioeconomic groups. However,
segregated cities can lead to differentiated access to such services and
opportunities, perpetuating disparities in economic, social, and health
outcomes (Li et al., 2022a; Hu et al., 2022; Xu, 2023). Furthermore, high
levels of segregation mean fewer opportunities for individuals from different
socioeconomic backgrounds to come into contact with each other, resulting in
limited opportunities for group interaction and exposure (Moro et al., 2021).

Quantitative studies on socio-spatial segregation focus on the geographic
separation or clustering of social groups within physical spaces (Li et al.,
2022a) and share a fundamental assumption that the co-presence of individuals
serves as the precursor of social interaction (Rokem and Vaughan, 2018).
Traditionally, scholars have measured segregation from a static standpoint by
looking exclusively at how individuals’ residential locations are sorted into
different neighborhoods based on income, race, and education (Kwan, 2013;
Feitosa et al., 2007). However, a large body of recent work shows that
residential segregation alone cannot fully capture the complexity of spatial
segregation in urban areas. Thus, understanding spatial segregation requires
considering individual mobility patterns beyond residential spaces (Silm and
Ahas, 2014a). These include locations visited for work (Zhou et al., 2021),
school, shopping, and leisure (Toger et al., 2023) both indoor and outdoor, as
well as locations visited when en route, i.e., in the process of reaching
these destinations.

Over the past one to two decades, many studies have advanced a dynamic
understanding of segregation by incorporating activity spaces (Sampson and
Levy, 2020; Candipan et al., 2021), considering the geography of individuals’
daily activity and mobility patterns beyond their residential area, thus
providing a more comprehensive understanding of socio-spatial segregation.
This enhanced understanding largely stems from the widespread availability of
extensive human mobility data, shedding light on how individuals allocate
their time among various activity locations. The activity space approaches are
driven by empirical mobility data from traditional data sources, such as
travel surveys (Li and Wang, 2017), and emerging sources of big geolocation
data covering large populations and geographical extent, e.g., GPS tracking
devices (Roulston and Young, 2013), mobile phone data (Silm and Ahas, 2014a;
Xu et al., 2019), or social media platforms (Wang et al., 2018; Candipan et
al., 2021).

Despite increasing studies considering activities beyond residential areas,
there is a lack of a comprehensive review of the empirical findings on how and
why individual mobility shapes socio-spatial segregation in everyday life. The
role of individual mobility in socio-spatial segregation remains partly
unclear, given that the literature has shown contradictory findings: time
spent outside the residence can reinforce or alleviate segregation (Kwan,
2013). In exploring the role of individual mobility in socio-spatial
segregation patterns based on the existing literature, we attempt to answer
three questions:

  * •

To what extent do individual mobility patterns contribute to segregation?

  * •

Which factors explain the role played by mobility in segregation?

  * •

What insights are gained by incorporating extensive mobility data into
segregation research?

Answering these questions can contribute to providing actionable insights for
reducing segregation and social inequalities.

In this paper, we review segregation research through the prism of individual
mobility, drawing empirical evidence from the activity space perspective and
built environment research, including transport and urban science. Based on
the literature, we first define socio-spatial segregation and its
quantification (Section 2). We then review studies based on activity space
approaches, including evidence on the effect of mobility on segregation
(Section 3) and the factors driving these effects (Section 4). In Section 5,
we draw findings from the built environment research, including urban science
and transport, to discuss critical factors explaining socio-spatial
segregation experienced by varying socioeconomic groups. Finally, we
synthesize these findings, highlight research gaps, and suggest directions for
future research (Section 6).

##  2 Measuring spatial segregation

Socio-spatial segregation reflects the degree of physical separation among
different groups. The process of estimating segregation requires first
identifying individuals who are in the same location and subsequently
assessing the mix of populations within each location using quantitative
metrics (Yao et al., 2019; Li et al., 2022a; Müürisepp et al., 2022).

Figure 1 illustrates three primary approaches to measuring socio-spatial
segregation. The first focuses on static information about individuals’ places
of residence (Duncan and Duncan, 1955). Analyses of social and demographic mix
in residential areas to calculate residential segregation are by far the most
common approach to study socio-spatial segregation (e.g., Andersson and Bråmå,
2004).

Two other common approaches focus on mixing social groups in non-residential
contexts. One approach focuses on analyzing the built environment and
inferring the potential co-presence of different groups by looking at the
spatial configuration of urban spaces, including transportation networks
(e.g., Yunitsyna and Shtepani, 2023). Another approach focuses on segregation
from an activity space perspective (Müürisepp et al., 2022). There has been a
rapidly growing number of studies leveraging observed mobility patterns of
individuals to quantify the levels of co-presence / spatial segregation
empirically. The empirical mobility data in this literature varies, including
“small” traditional travel surveys (Park and Kwan, 2018) and “big” mobile
phone GPS records (Moro et al., 2021).

Figure 1: Conceptual framework of measuring spatial segregation. (1)
Residential segregation. (2) Analysis of transport networks and urban spaces
to evaluate the built environment’s potential in bringing people together. (3)
Activity space approaches driven by empirical mobility data. This study
includes (2-3) for the evidence synthesis.

This section first introduces the three main approaches to measuring socio-
spatial segregation (Section 2.1), followed by a brief introduction of
different metrics applied in each approach (Section 2.2).

###  2.1 Approaches and their spatiotemporal scale

In this section, we first discuss the residential approach. Then, we discuss
the built environment approach. Finally, the activity space approach focuses
on two aspects: “visiting segregation” and “experienced segregation.”

Socio-spatial segregation has long been investigated as a static area-based
phenomenon, either in terms of residential segregation (Duncan and Duncan,
1955), or at other places such as workplaces and schools (Boterman and
Musterd, 2016). It focuses on interactions among individuals with different
socioeconomic backgrounds in the area, often an administrative or statistical
subdivision, of their home (or another key) location (e.g., Andersson et al.,
2010).

Built environment analysis focuses on the transport networks and urban spaces,
which can bring different populations together (Netto et al., 2015; Carpio-
Pinedo, 2021). Most of the studies using this approach quantify the
opportunities for interaction between different socioeconomic groups through
street network centrality analysis developed in the field of space syntax
(Rokem and Vaughan, 2018; Carpio-Pinedo, 2021; Yunitsyna and Shtepani, 2023).
This type of analysis focuses on potential interactions in locations
individuals can reach from home through walk, car, or transit networks (e.g.,
Rokem and Vaughan, 2018), which we can call network segregation. This
perspective considers that individual mobility facilitates social encounters,
but the analysis methods are not based on empirical mobility data.

Both residential and network segregation measures revolve around where people
live and their surrounding built environment. The timescale for observed
social segregation can span years, reflecting the long-term nature of
residential mobility in contrast to daily mobility and stays at various
activity locations, which often operate on a scale of minutes to hours.

Recent research underscores activity space approaches that conceptualize
socio-spatial segregation dynamically, considering individuals’ travel
behavior and daily visited locations. They are driven by empirical mobility
data from traditional sources such as travel surveys (Ravalet, 2006; Wang et
al., 2012; Le Roux et al., 2017; Park and Kwan, 2018; Landis, 2022; Lin and
Ta, 2023) or emerging ones such as mobile phone GPS records and social media
geolocation data (Östh et al., 2018; Wang et al., 2018; Candipan et al., 2021;
Moro et al., 2021; Huang et al., 2022; Wu et al., 2023). The commonly used
mobility data from large populations include high-resolution location data
from mobile phone applications (Moro et al., 2021), telecommunications
companies (Östh et al., 2018), and geotagged tweets (Netto et al., 2018; Wang
et al., 2018; Candipan et al., 2021). They can cover geolocations of millions
of individuals over months and years, at the resolution of meters and seconds
(Barbosa et al., 2018). Although co-presence (being in the same place) does
not always lead to social interactions, co-presence is a precursor to social
interaction (Collins, 2004; Netto et al., 2015). Utilizing large-scale digital
data, it was shown that there is a significant correlation between spatial and
social segregation (Blumenstock and Fratamico, 2013), demonstrating that human
mobility data offers a realistic approximation of group interactions
(Nilforoshan et al., 2023).

The activity space approach describes dynamic segregation building on big
empirical mobility data and focusing on two aspects: urban spaces (visiting
segregation) and individuals (experienced segregation). Some recent studies
combine the two perspectives (Xu et al., 2019; Moro et al., 2021). The stream
on visiting segregation focuses on urban spaces. It seeks to understand how
segregated a given location is, given how mixed its visitors are (Netto et
al., 2015; Phillips et al., 2021). These studies examine the composition of
the visitors of urban spaces based on characteristics such as income,
ethnicity, nativity, etc. (e.g., Netto et al., 2015; Moro et al., 2021). In
contrast, the stream on experienced segregation focuses on individuals. It
captures how much a person is exposed to diverse individuals as they go about
their daily lives (Wu et al., 2023). In other words, it looks at the activity
spaces of individuals, i.e., time spent in different locations at different
times of the day. It considers the average segregation they experience across
these activity visits (Zhang et al., 2019; Ta et al., 2021).

Due to data availability, the experienced segregation has been commonly
approximated by measuring the stays at various places, excluding interactions
happening while individuals are moving (e.g., on public transport) (e.g., Moro
et al., 2021). However, some studies particularly investigate en route
segregation, pointing to equitable transport systems (Shen, 2019; Abbasi et
al., 2021; Zhou et al., 2023).

In summary, there are different facets of measuring segregation (Table 1). The
literature has witnessed a clear paradigm shift from a traditional static view
toward a mobility perspective that is dynamic and data-driven (Li et al.,
2022a). Big geolocation data are widely used to better understand socio-
spatial segregation in the urban landscape, focusing more on individuals
(Müürisepp et al., 2022).

Table 1: Three approaches to socio-spatial segregation by their spatiotemporal scale of (potential) co-present individuals. Approach | Segregation type | Perspective | Subject | Time scale
---|---|---|---|---
Residential | Residential | Urban space | Residents | Static (year)
Built Environment | Network
Activity space | Visiting | Urban space | Visitors | Dynamic (minutes–hours)
Experienced | Individual | Travels and activities

###  2.2 Segregation metrics

This section briefly overviews commonly used metrics in quantifying socio-
spatial segregation. The aspects that are more widely considered to measure
segregation include race/ethnicity (e.g., Vachuska, 2023), nativity (e.g.,
Bertoli et al., 2021), income (e.g., Moro et al., 2021), education (e.g.,
Zhang et al., 2022a), housing (e.g., Zhang et al., 2019), etc. They cover
different aspects of the phenomenon: evenness, exposure, concentration,
centralization, and clustering (Massey and Denton, 1988), where evenness and
exposure remain the most widely used aspects (Silm and Ahas, 2014b).

In Table 2, we provide an overview of the most widely used segregation
metrics, categorized into three types. The first class (Type 1) consists of
traditional segregation metrics developed for residential segregation, which
can integrate the co-presence outside homes in activity space approaches. The
second class (Type 2) comprises metrics commonly seen in studies using
activity space approaches. Socio-Spatial Isolation Indices, as one example in
Type 2, Table 2, look at individual isolation at various locations (Athey et
al., 2021), while the Extended Original Exposure Index and the Getis-Ord Local
G-based Exposure Index incorporate the concept of activity space to assess
exposure to different groups (Yao et al., 2019). The third class (Type 3)
considers network centrality metrics to quantify potential co-presence, e.g.,
the likelihood of different population groups occupying or traversing the same
spaces within a spatial network (Legeby et al., 2015).

Table 2: Selection of key segregation metrics. Type 1 = Classic, developed for quantifying residential segregation. Type 2 = Activity space, applied or developed in the context of activity space approaches, often along with a large amount of geolocation data on human mobility. Type 3 = Network analysis, applied in built environment research in approximating the potential co-presence of individuals. Type | Index/Model |  Description
---|---|---
1 | Evenness or Dissimilarity |  How evenly groups are distributed across a geographical space (Reardon and O’Sullivan, 2004).
Exposure Indices |  Potential contact between groups (Silm and Ahas, 2014b).
Concentration Indices |  The extent to which minority populations are situated in specific regions (Johnston et al., 2007).
Spatial Distribution Indices |  Broader spatial patterns (Demoraes et al., 2021).
Spatial Clustering Indices |  The degree of clustering of high-density group areas, e.g., Moran’s I (O’Sullivan and Wong, 2007).
2 | Socio-Spatial Isolation Indices |  Individual isolation within activity locations (Athey et al., 2021).
Social Interaction Potential |  Metric of exposure based on time-geography for metropolitan scale (Farber et al., 2015).
i-STP index |  Individual-level segregation index considering different times of the day (Park and Kwan, 2018).
Flow-based spatial interaction model |  It can capture the impact of specified commuting routes on segregation experiences (Shen, 2019).
Segregation hotspots |  Cluster multiscalar fingerprint to identify hotspots of segregation in urban spaces (Olteanu and Lamirel, 2020).
Segregated Mobility Index (SMI) |  Racial segregation in how neighborhoods of varying racial compositions are connected (Candipan et al., 2021).
Income Unevenness |  Quantifies income unevenness in US cities using population income quartiles (Moro et al., 2021).
Graph Embedding |  Income segregation that combines residential and mobility perspectives (Zhang et al., 2021).
Index of Concentration at the Extremes (ICE) |  How population concentrates at certain groups in a given area (Iyer et al., 2023).
Spatial Segregation Index |  This index integrates distance-decay functions to measure individual experienced segregation (Wu et al., 2023).
3 | Integration Analysis/Closeness |  Connectedness of each space highlighting areas likely to be frequented by diverse groups (Yunitsyna and Shtepani, 2023).
Choice Analysis/Betweenness |  It quantifies a space’s role in facilitating encounters (Rokem and Vaughan, 2018).
Visibility Graph Analysis |  Which spaces are visually connected, providing insight into interaction opportunities (Turner et al., 2001).
Angular Segment Analysis |  Spaces accessible with fewer angular changes often being busier and thus prime for co-presence (Turner, 2001).
Combined integration and choice values |  They identify the network’s most accessible areas (Rokem and Vaughan, 2018).
Multi-accessibility |  Simultaneous ease of access to a place by multiple transport modes (Carpio-Pinedo, 2021).

##  3 Individual segregation experiences

In this section, we critically assess the existing literature on how daily
mobility influences individual segregation experiences, examining whether it
mitigates or reinforces what is shown at the residential level (Sections 3.1,
3.2, and 3.3), followed by a discussion on the methodological limitations of
the cited literature (Section 3.4). We also explore how mobility affects
experienced segregation amid and after crises, e.g., hurricanes and the
COVID-19 pandemic (Section 3.5).

An important question is whether experienced segregation is lower or higher
than residential segregation. On the one hand, mobility could enable one to
encounter diverse populations, especially beyond one’s residential
neighborhood. Hence, mobility can reduce the experience of segregation at
residence (Alfeo et al., 2019; Hedman et al., 2021). On the other hand,
mobility could strengthen segregation patterns depending on the nature of
these encounters, the individuals’ openness to engagement, and the broader
societal and systemic factors that influence social interactions and
integration (Shdema et al., 2018). Furthermore, mobility is often influenced
by economic and social factors, which means that not everyone has the same
ability to travel outside their residential area (Tiznado-Aitken et al.,
2023). The results revealed by the literature are contradictory, partly
indicating that the potential of mobility to reduce segregation can vary
widely among different individuals and groups.

###  3.1 Studies observing that mobility reinforces segregation

Using traditional data sources such as surveys and interviews, a small body of
activity space literature concludes that mobility outside residential areas
reinforces segregation. Aksyonov (2011) suggest that while residential spatial
segregation in St. Petersburg, Russia, is relatively weak, segregation in
personal activity spaces is much more prominent. Similarly, in Milwaukee,
Wisconsin, the US, Gordon (2018) reports that racial segregation is
pronounced, with distinct patterns of daily mobility among different racial
groups. This notion of persistent segregation patterns is further supported by
Le Roux et al. (2017), who found that the most segregated group during the
night in Paris remained the most segregated during the day, indicating a
strong correlation between night-time and day-time segregation. Consistent
with these findings, studies on ethnic groups in urban China reveal that a
residents’ home neighborhood continues to be a strong predictor of segregation
in their daily activity locations (Tan et al., 2017, 2019).

These findings are corroborated by studies informed by large-scale data
(Roulston and Young, 2013; Hedman et al., 2021; Hilman et al., 2022). GPS data
from the Ivory Coast show that tribal affiliations significantly influence
social connections within tribes, potentially reinforcing social segregation
(Amini et al., 2014). This tendency of individuals to associate with similar
groups is echoed in the US, where geotagged tweets reveal that people
frequently visit areas dominated by their racial group, thus mirroring macro-
level racial segregation (Bora et al., 2014), despite the different racial
groups traveling similar distances to these destinations (Wang et al., 2018).
A similar segregation pattern is observed in North Belfast, as daily movement
patterns on tertiary streets show that individuals primarily travel within
areas occupied by their community, actively avoiding areas associated with
opposing communities (Davies et al., 2019). This relationship between
residential segregation and segregated mobility patterns is further
underscored by Candipan et al. (2021), who identify black-white residential
segregation as a critical determinant of segregated urban mobility.
Complementing these findings, Zhang et al. (2022a) establish a significant and
strong correlation ($r=0.646$, $p<0.001$) between residential segregation and
activity space-based segregation concerning education in Beijing.

###  3.2 Studies finding that mobility reduces segregation

In contrast, several studies have found that accounting for daily mobility
patterns significantly reduces individuals’ levels of experienced segregation
compared to approaches that measure segregation solely at residential
locations. Wong and Shaw (2011) reveal that high levels of segregation in
residential spaces might be moderated by lower levels of segregation at
activity locations. In fact, activity places are substantially more
heterogeneous regarding key social characteristics than residential
neighborhoods (Jones and Pebley, 2014; Pinchak et al., 2021). A lack of
exposure to diversity (higher segregation) in the residential neighborhood may
be compensated by greater workplace exposure or transport exposure (Boterman
and Musterd, 2016; Wang and Li, 2016; Lin and Ta, 2023). Therefore, studies
have uncovered reduced segregation levels considering daytime activities (Le
Roux et al., 2017; Li and Wang, 2017; Park and Kwan, 2018; Garlick et al.,
2022; Fuentes et al., 2022).

Emerging data sources such as geotagged tweets and mobile phone data also
reveal a similar reducing effect of mobility on segregation (Grujić et al.,
2019; Athey et al., 2021; Moya-Gómez et al., 2021; Shin, 2021; Silm et al.,
2021; Xian et al., 2022; Xu, 2022). Research indicates that individuals from
poor and black neighborhoods in the city are more mobile within the metro area
than previously thought, often traveling outside their neighborhoods for work
or other activities (Shelton et al., 2015). This trend is also seen in
Sweden’s metropolitan areas, where daily mobility, especially among those who
frequent central places, can lessen segregation levels (Östh et al., 2018).
However, this reduction in segregation is less noticeable in areas with low
accessibility and mobility levels, typically located on the city outskirts
(Östh et al., 2018). In Seoul, South Korea, a significant decrease in daytime
segregation of 20% was observed when compared to residential segregation
levels (Hong, 2020), a trend similarly reflected in Turkey in the context of
segregation between Syrian refugees and the native population (Bertoli et al.,
2021).

###  3.3 Beyond the reinforcing-reducing dichotomy

Studies using both small and big data reveal that the impact of mobility on
individual segregation experiences is not as simple as reinforcing or reducing
segregation. The relationships between residential (home vicinity and sub-
neighborhood), spatial or territorial (daily mobility), and social
(interactive) segregation are found to be complex (Schnell and Haj-Yahya,
2014; Selim, 2015). For instance, Goldhaber and Schnell (2007) found weak
correlations among these segregation types, indicating that factors
influencing each type of spatial segregation vary. It turns out that sharing
residential neighborhoods does not necessarily translate into shared routines,
particularly across different socioeconomic statuses (Browning et al., 2017).
Interestingly, Lin and Ta (2023) reveal that people with higher migrant
exposure in their residential areas often experience lower migrant exposure in
their activity locations, and vice versa, suggesting a negative correlation
between residential and experienced segregation levels.

Whether mobility reduces or reinforces the total experienced segregation
depends largely on individuals’ lifestyle, i.e., which kind of locations they
visit and when during the day (Zhang et al., 2023). Using mobile phone data,
Silm et al. (2018) find that places of daily activities outside home and work
are less segregated, noting a surprising trend of higher workplace segregation
than residential segregation, particularly in age groups 30 – 39 and above 60.
However, Silm et al. (2021) observed higher segregation in residential areas
than workplaces, with segregation levels during leisure activities fluctuating
based on the specific activity. Zhang et al. (2022b) further contribute to
this understanding by using location-based service data in Beijing, revealing
more pronounced segregation at workplaces than residences and a general
decrease in segregation outside these typical environments. Additionally, the
city structure and distribution of amenities emerge as a critical factor for
socioeconomic mixing, where diverse and unique amenities, particularly in city
centers, tend to attract a mix of socioeconomic groups (Dannemann et al.,
2018; Juhász et al., 2023). A low segregation level is generally associated
with being in the middle of the day, away from home, such as in places like
workplaces, restaurants, commercial areas, and outdoor spaces, supported by
extensive geolocation data on human mobility (Sampson and Levy, 2020; Abbasi
et al., 2021; Athey et al., 2021; Qiao et al., 2021; Moro et al., 2021).
However, not everyone can equally access these places to fulfill their daily
activity demand.

###  3.4 Limits of existing approaches

A limitation of the existing work is that spatial co-presence does not
necessarily capture meaningful social interactions. Schnell and Haj-Yahya
(2014) utilize GPS tracking and interviews to explore how daily commutes
between segregated Arab towns and Jewish-Israeli spaces shape the socio-
spatial experiences of Arab-Palestinian citizens in Israel, suggesting that
while mobility influences aspects of segregation and integration, it cannot
fully counteract societal structural stratification. Zhou and Cheng (2019)
also note that physical proximity alone does not guarantee a social mix.
Dorman et al. (2020) even find that economic and cultural factors influence
co-presence, often leading to increased segregation, especially among
wealthier and younger groups. In addition, there is no one-way relationship
between residential and activity space-based segregation. Zhang et al. (2019)
argue that segregation based on where people live influences where they
conduct their daily activities. In turn, the differential usage of urban space
- where certain groups are privileged to access specific locations and others
are not - reinforces residential segregation in the long run.

The conclusions drawn from studies on social segregation, which utilize
empirical mobility data, are influenced by the daily activity locations and to
what extent co-presence in these locations approximates social interactions.
However, methodological discrepancies across the reviewed studies may also
contribute to the contradictory findings on whether mobility reinforces or
diminishes segregation.

In particular, the contradictory findings may stem from the spatial resolution
considered and how co-presence is computed. It was shown, for example, that
two restaurants located next to each other can cater to distinct populations
(Moro et al., 2021). Hence, studies that consider interactions over relatively
large areas (e.g., Silm and Ahas, 2014a) due to the low spatiotemporal
resolution of the data, e.g., call detail records (CDR) or social media data,
may hinder detailed segregation experiences in a specific location. GPS
records, instead, capture visits with higher granularity and thus offer a more
nuanced and accurate description of segregation. Moreover, focusing on the
exposure level to an area’s residential population (e.g., Wang et al., 2018)
can result in different segregation levels than those considering the mixing
of its visitors (e.g., Nilforoshan et al., 2023). These methodological
inconsistencies partly contribute to varying findings on mobility’s role in
segregation, as these approaches capture social mixing at different levels
(Moro et al., 2021; Athey et al., 2021).

###  3.5 Amid and post-crisis segregation experiences

Socio-spatial segregation can change dramatically during and after crises.
This section reviews the results in the area, focusing on COVID-19 and natural
disasters.

Studies based on large populations’ mobility data have shown that the COVID-19
pandemic amplified socio-spatial segregation. Results are similar across
several Western countries and contexts. In the US, it was found that racial
segregation shapes urban spatial patterns and behaviors during public health
crises like the COVID-19 pandemic, as noted by (Hu et al., 2022). This is
echoed in Sweden, where the pandemic highlighted existing socio-economic and
ethnic segregations, with low-income, multi-ethnic neighborhoods experiencing
high mortality rates, as reported by Joelsson and Ekman Ladru (2022).
Investigating mobility inequality across U.S. cities, Marlow et al. (2021)
found that factors like population size, public transportation usage, and
neighborhood isolation intensify racial segregation, particularly during 2020.
These findings align with Li et al. (2022b), who observed increased social
segregation in the US during the pandemic, with mobile phone location data
showing the most and least vulnerable census tracts, considering socioeconomic
status, experienced the highest segregation. This trend is particularly stark
for low-income and ethnic minority populations, who become more isolated from
higher-income and white groups due to decreased mobility, as highlighted by Lu
and Giuliano (2023). While Yabe et al. (2023) apply counterfactual analysis to
show that, two years after the first COVID-19 wave, changes in experienced
segregation were mainly due to lifestyle changes, i.e., less willingness to
explore new places.

The literature highlights that changes in mobility patterns due to the
COVID-19 pandemic, such as reduced public transport usage and increased
reliance on cars, have deepened existing inequalities (Joelsson and Ekman
Ladru, 2022; Shin, 2021). This impact is particularly pronounced among young
and vulnerable groups in socially disadvantaged neighborhoods, who often
depend on public transport for their jobs with irregular hours (Joelsson and
Ekman Ladru, 2022). In Italy’s economically distressed regions, a slow
U-shaped recovery was observed, with mobility levels still about 20% below
pre-lockdown levels by late October 2020 (Bonaccorsi et al., 2021), and areas
with lower income and higher income inequality were the most affected,
indicating increased segregation. Similarly, in cities like New York, Los
Angeles, and Sao Paulo, poor peripheries with high population density and
limited access to individual transportation options faced heightened virus
transmission risks (Sathler and Leiva, 2022).

Evacuations during and after disasters displace large populations,
underscoring social inequalities and intensifying segregation. Analyzing
large-scale mobility data from over 1.7 million mobile phone users and income
census data, Yabe and Ukkusuri (2020) found that higher-income individuals in
Florida, US, were more likely to evacuate disaster-affected areas and relocate
to less damaged areas, both within and outside mandatory evacuation zones.
This disparity in mobility resulted in prolonged spatial income segregation
post-disaster, with higher-income individuals avoiding severely damaged areas
while lower-income individuals remained, exacerbating segregation. Similar
patterns were observed during Hurricane Harvey’s evacuation, where people from
disadvantaged minority populations were notably less likely to evacuate than
wealthier white residents despite similar evacuation distance (Deng et al.,
2021).

In summary, crises tend to magnify existing segregation, mainly through
individual mobility. Given the critical role of mobility in disaster
evacuation and disease transmission, it is imperative to closely examine the
mobility disparities across socioeconomic groups and formulate targeted
policies to enhance social integration and mitigate the adverse effects of
disasters.

##  4 Disparity in individual segregation experiences

Different social groups have different daily-life activity spaces, depending
on age, race/ethnicity, and income level (Moro et al., 2021). These
differences can exacerbate socio-spatial segregation (Wang et al., 2012).
Here, we first present the effect of subjective factors such as homophily and
lifestyle (Section 4.1). Then, we discuss the effect of other individual
factors such as socioeconomic status (Section 4.2), ethnicity and nativity
(Section 4.3), and other aspects (Section 4.4).

###  4.1 Homophily and lifestyle

Individual differences concerning lifestyle and choices of places to visit can
lead to different experienced segregation levels.

On the one hand, several studies suggest that individuals are more likely to
interact with others from similar socio-economic backgrounds due to homophily
(Xu et al., 2022). For example, mobile activity tracking data also suggest
that people from poor neighborhoods tend to participate in activities in other
poor neighborhoods (Yip et al., 2016; Wu and Huang, 2022). As observed in the
US, African Americans (Gordon, 2018), Whites (Gordon, 2018), and Latinos tend
to frequent areas predominantly occupied by their respective racial groups
(Jones and Pebley, 2014). Similarly, in Sweden, Hedman et al. (2021) find that
individuals from areas with a higher proportion of non-Europeans generally
stay within or move towards immigrant-dense areas. Social network data in
Stockholm, Sweden, reveals the tendency of individuals to move through
neighborhoods of similar income, education levels, and immigrant makeup, even
when other factors, such as transit time and opportunities for interaction,
are controlled (Heine et al., 2021). In short, apart from geographically
distant areas, people are less likely to travel between socioeconomically
distant areas (Park et al., 2021; Chen et al., 2024).

On the other hand, studies based on GPS data have revealed that experienced
segregation is partly influenced by individuals’ lifestyles, as captured by
the kind of places that they visit in their daily life (Moro et al., 2021).
For example, a study found that lifestyles with more socializing, shopping
weekends, and coffee shop visits are associated with higher social integration
(Yang et al., 2023; Nilforoshan et al., 2023). Instead, individuals visiting
entertainment venues and restaurants may experience higher levels of
segregation because those locations cater to specific income groups, and their
cost or cultural context may exclude lower-income individuals (Moro et al.,
2021).

###  4.2 Socioeconomic status

The wealthiest and the poorest groups demonstrate contrasting mobility
behaviors and activity demand (Aksyonov, 2011; Farber et al., 2012; Östh et
al., 2018), resulting in systematically different activity spaces (Wang et
al., 2012). Heringa et al. (2014) reveal that social status (in terms of
education and income), as well as the opportunity to perform leisure
activities, influences the extent of inter-ethnic contact more than
neighborhood attributes. In turn, performing leisure activities is strongly
influenced by economic factors, according to large-scale mobile phone data
collected in Stockholm (Toger et al., 2023).

Table 3 summarizes the characteristics of individual mobility and activity
spaces by income level observed in various studies. Data from developed
countries shows that wealthier individuals use various types of urban areas
(Wang et al., 2022) and travel longer distances (Xu et al., 2022). A study
suggests that the highest classes in society are more likely to form
connections with all classes (Farber et al., 2012). In contrast, the less
wealthy experience limited activity spaces, leading to a more localized life.
These may explain the higher segregation levels experienced by less wealthy
populations (Wu and Huang, 2022). In Hong Kong, the mobility gap between high-
and low-income is widening between 2002 and 2011 (Tao et al., 2020a).

Table 3: Characteristics of individual mobility and activity space by income level and ethnicity/nativity observed in studies. Aspect |  Group |  Mobility patterns |  Activity space
---|---|---|---
Income |  Wealthy |  Longer trip distance (Xu et al., 2022; Farber et al., 2012) |  Diverse and widely spread (Wang et al., 2022)
|  |  Sports, leisure (Zambon et al., 2017), business (Wang et al., 2012; Heringa et al., 2014), shopping (Aksyonov, 2011)
|  Less wealthy |  Shorter trip distance (Wu and Huang, 2022) |  Constrained and localized (Netto et al., 2015; Zhou et al., 2015)
|  |  Less frequently travel outside city (Aksyonov, 2011) |  Convenience stores (Aksyonov, 2011)
Ethnicity/nativity |  Majority |  Longer commuting (Garlick et al., 2022) |  Social, recreational, errand (Shirgaokar and Nobler, 2021)
Minority |  Shorter travel distance (Silm and Ahas, 2014a) |  Exercise, education (Shirgaokar and Nobler, 2021)

In some contexts, however, a different effect was observed, with the wealthy
living relatively more segregated lives (Shelton et al., 2015; Xu et al.,
2019). Atkinson (2016) illustrate that the super-rich in London creates a
“cloud space” or “flowing enclave,” engaging with the city’s diversity in a
limited way. This phenomenon of social segregation among the upper classes is
also observed in the Paris region, where Le Roux et al. (2017) note they
remain the most segregated group both in residence and during daytime
activities.

In developing countries, the less wealthy population does not always travel
less or have more limited activity spaces than the rest. Using mobile phone
data, Moya-Gómez et al. (2021) reveal that in Medellin, Colombia, the low-
income population is more likely to leave their neighborhood during the day
and travel longer distances to the most diverse destinations than the wealthy.
Furthermore, the diversity of activities among the less wealthy is not
necessarily inferior to that of the wealthy. For example, in Bangkok,
Thailand, Wissink and Hazelzet (2016) find a contrasting pattern, where
residents in informal settlements and lower-middle-income neighborhoods
exhibit a more active social life than those in affluent areas.

Socioeconomic status often interacts with housing type, which affects
individual mobility and segregation experiences. Li and Wang (2017) discovered
a “reinforcing effect” of income and housing type, indicating that poor
families in public housing face heightened isolation due to income and housing
segregation. Zhang et al. (2019) complemented this by noting that individual
activity spaces vary significantly among social groups by housing type,
potentially intensifying segregation due to limited interaction opportunities.
Extending this understanding, Demoraes et al. (2021) found that in Santiago de
Chile, young working tenants with lower education and purchasing power and
older working property owners experience longer commutes, highlighting a
spatial displacement between their residential areas and activity locations,
and the interdependence between housing type (or tenure) and residential
location.

###  4.3 Ethnicity and nativity

Ethnic groups exhibit distinct segregation experiences (Bora et al., 2014;
Raanan and Shoval, 2014). These segregation experiences by ethnicity or
nativity can be ascribed to their distinct mobility and activity space
patterns (Table 3). For example, the study by Järv et al. (2015) suggests that
ethnic differences in spatial behavior become more pronounced in leisure-
related activities and other non-routine behaviors (Silm and Ahas, 2014a).
Ethnicity also affects transport choices. Living in co-ethnic neighborhoods
increases the likelihood of inter-household carpooling for Asian and Hispanic
groups, while this is not the case for African Americans (Shin, 2017).

Intersectionality has received attention in a few studies, as ethnicity or
nativity often intertwines with factors like income and education. High-income
natives (Boterman and Musterd, 2016) and low-income minorities (Tan et al.,
2019) can experience higher segregation levels than the other populations.
Most racial-ethnic groups’ ethnicity/nativity segregation increases along with
higher economic status (Wu et al., 2023). However, Asian groups show the most
diverse interactions, regardless of economic status, possibly because of their
high presence in Downtown Commercial contexts, revealed by extensive
geolocation data on human mobility (Salgado et al., 2021).

###  4.4 Other individual aspects

Besides socioeconomic status and ethnicity/nativity, other aspects, such as
individual values, fears, trust, lifestyles, and social networks, affect
individual segregation experiences.

Individuals with different values and preferences may experience different
levels of segregation in their daily lives, driven by their distinct lifestyle
choices. Järv et al. (2021) identify the subjective self-estimated social
status as a critical factor affecting the extent of activity spaces and
experiences of segregation, suggesting a complex relationship between socio-
economic status, ethnicity, and segregation. People’s inclination towards
ascriptive (traditional roles) or achievable (personal accomplishments) status
affects their spatial segregation (Goldhaber and Schnell, 2007). Those
focusing on achievable status are more likely to move beyond their ethnic
enclaves, while ascriptive-oriented individuals stay within them. Moro et al.
(2021) suggest that people who actively explore different places tend to be
more economically integrated, experiencing less income segregation.

Security barriers and fear also affect the ability to move, interact, and use
specific spaces, perpetuating the cycle of segregation (Selim, 2015). GPS
tracking shows that Catholic and Protestant students in Northern Ireland
generally move within different areas, though they do share spaces,
particularly in shopping and entertainment venues (Roulston and Young, 2013).
Complementing this, Dixon et al. (2020) suggest that religious segregation
results not only from socioeconomic and institutional forces but also from
individual mobility choices influenced by perceived intergroup threats and
contact experiences. This perceived fear often restricts residents’
willingness to travel, leading to geographical isolation and limited access to
work, education, and other activities (Hernandez and Titheridge, 2016).
Furthermore, trust plays a crucial role in segregation experiences, with
Browning et al. (2017) finding that high levels of neighborhood trust can
mitigate the effects of socioeconomic inequality on spatial segregation in
daily routines.

Besides cultural differences or socio-economic factors, social networks within
ethnic groups strongly affect segregation patterns (Silm and Ahas, 2014a).
Segregation experiences can vary significantly between individuals; they show
a strong correlation at a broader social level between social network
segregation and spatial segregation (Xu et al., 2019).

###  4.5 Key takeaways

Activity space studies on segregation using mobility data often quantify the
segregation levels experienced in daily lives and between different
socioeconomic and demographic groups.

  * •

Socioeconomic status

Variances in wealth lead to distinct mobility patterns. In developed
countries, those with higher socioeconomic status often engage in more diverse
activities, accessing various locations, which dilutes their experienced
segregation levels. Conversely, lower-income individuals typically have more
localized mobility, intensifying their experienced segregation levels. In
developing countries, the opposite effect has been observed.

  * •

Ethnicity and nativity

Different ethnic groups display unique mobility patterns, often gravitating
towards or remaining within areas predominantly occupied by their communities.
This tendency is influenced by various factors such as social comfort,
security perceptions, and targeted policing practices.

  * •

Lifestyle, homophily and other subjective factors

Individual perceptions of social status, personal values, and security
concerns significantly influence lifestyles and mobility choices, thus
affecting segregation experiences. People’s preferences, whether leaning
towards traditional or achievement-oriented values, also shape their activity
spaces.

##  5 Explaining segregation experiences

Using activity space approaches on empirical mobility data, studies have
revealed persistent segregation in individuals’ daily mobility (Section 3),
and how different populations experience various levels of segregation in
their daily lives (Section 4).

In this section, we draw insights from the transport and urban science
research literature to systematize key factors that help explain observed
disparities in individual segregation experiences (Figure 2). Individuals’
experienced segregation is partly affected by their preferences on certain
activities (Moro et al., 2021) and unequal access to diverse social settings
(Netto et al., 2015), such as housing, transport access, etc. These unequal
accesses result in different levels of mobility, translating into distinct
movement networks in urban space, leading to different encounter
opportunities. These daily experiences eventually create homophilic personal
networks, therefore perpetuating segregated class networks. These elements are
interlinked in shaping how individuals are exposed to different socioeconomic
groups.

Covering various components of how spatial segregation is produced, we
investigate four aspects of explaining individual segregation experiences in
this section: housing regarding individuals’ residence and its environment
(Section 5.1), urban sprawl characterizing the relative spatial relationship
between residence and other activity locations (Section 5.2), transport access
describing how easy one can reach various resources and opportunities (Section
5.3), and urban design characterizing the built environment of activity spaces
(Section 5.4).

Figure 2: Conceptual framework explaining socio-spatial segregation.

###  5.1 Housing

Housing locations and types primarily affect individual mobility and
segregation experiences (Table 4). Minorities often live in disadvantaged
neighborhoods, which makes it hard to mix with the other groups (Tao et al.,
2020b), with the built environment’s slow evolution locking urban communities
into persistent settlement patterns and inequalities to resources (Patias et
al., 2023).

Table 4: Housing effects on segregation experience. Aspect |  Impact
---|---
Rural vs. urban |  Rural migrants experience higher nativity segregation in both residential and activity spaces than urban migrants (Lin and Ta, 2023; Shen and Luo, 2023).
Land-use diversity |  Areas with high land-use diversity shows less daytime segregation, despite high levels of nighttime segregation (Fuentes et al., 2022).
High land values |  Lead to segregation both day and night, attracting high-class residents and contributing to segregation (Fuentes et al., 2022).
Disadvantaged neighborhoods |  Face difficulties mixing with other groups due to persistent settlement patterns, with urban evolution fostering segregation into "ghettos" (Power, 2012).
Peripheral and disconnected areas |  Poorer populations residing in these areas face socio-economic disadvantages due to limited access to services and job opportunities (Kronenberger and De Saboya, 2017; Atuesta et al., 2018).

###  5.2 Urban sprawl

Urban sprawl is the uncontrolled expansion of low-density urban areas into the
surrounding rural land. Escalating segregation levels usually accompany such
increasing size of cities (Monkkonen et al., 2018; Nilforoshan et al., 2023),
observed in several countries, such as Brazil (Bittencourt et al., 2021),
China (Zhao, 2013), Iran (Azhdari et al., 2018), and Chile (Figueroa et al.,
2019). Increasing urban compactness counteracts urban sprawl, significantly
enhancing upward mobility through better job accessibility and indirectly
mitigating poverty segregation (Ewing et al., 2016). Conversely, urban sprawl
contributes to disparities in public transportation and job opportunities
(Bittencourt et al., 2021; Zhao, 2013), a decline in street network
accessibility (Figueroa et al., 2019), and the isolation of peripheral areas
(Azhdari et al., 2018; Figueroa et al., 2019). These aspects intensify
segregation through housing aspects discussed in Section 5.1.

In explaining residential segregation, the concept of “spatial mismatch” was
proposed in 1968, referring to the geographical separation between low-income
communities, often inhabited by racial or ethnic minorities, and employment
opportunities, typically located in areas located far from these communities
(Kain, 1968). Spatial mismatch creates a complex interplay between where
people live and where they can work or access services. Minority groups tend
to experience higher spatial mismatch levels than their white counterparts
living in the same Metropolitan Statistical Areas in the US (Easley, 2018).
The commuting distance of workers with the lowest wages might be associated
with a spatial mismatch between their residential locations and places of
employment, leading to longer commuting times (Blázquez et al., 2010; Wang et
al., 2022). Although urban development in Chinese cities has not resulted in
clear residential segregation akin to Western cities, the low-income
population faces a trade-off between service accessibility and the floor area
of their residences (Chen and Yeh, 2019). In China, the availability of low-
rent housing within urban villages, coupled with short commuting times,
alleviates the spatial mismatch for disadvantaged groups in specific regions
(Chen et al., 2021).

###  5.3 Transport accessibility

This section first reviews evidence on how transport accessibility differs by
income and ethnicity/nativity (Section 5.3.1) and brings attention to
transport equity and how it affects socio-spatial segregation (Section 5.3.2).

Transport accessibility plays a crucial role in shaping experienced
segregation patterns via mobility. Because spatial accessibility reflects how
easy it is to reach locations and activities, it ultimately determines the
range of places and social environments people can visit. Moreover, urban
transport plays a crucial role in promoting social inclusion or deepening
exclusion, as limited transportation opportunities are closely linked to an
increased risk of social exclusion for various individuals or groups (Lucas et
al., 2016; Luz and Portugal, 2022). This relationship is further accentuated
in a pre- and post-pandemic analysis by Gallego Méndez et al. (2023), which
reveals growing disparities in transit access among socioeconomic groups.
Given these challenges, to foster better social integration, Rokem and Vaughan
(2019) argues for substantially reevaluating transport infrastructure
accessibility, which is central to measuring the equity impacts of transport
investments (Pereira et al., 2017).

####  5.3.1 Factors of income and ethnicity/nativity

Transport access disparity between low and high-income groups may explain
their distinct mobility patterns, as reviewed in Section 4.2. High-income
groups generally have better accessibility than the other groups (Jang and Yi,
2021; Arellana et al., 2021). Material constraints among lower-income
households limit where they go due to low affordability and poor accessibility
(Logiodice et al., 2015; Hernandez and Titheridge, 2016; Martínez et al.,
2018; Cortés, 2021), leading to socioeconomic segregation in their activity
spaces (Hu et al., 2017; Peña et al., 2022; Cromley and Lin, 2023).

How transport access affects mobility also depends on ethnicity and nativity.
Racial residential segregation is associated with lower equitable travel
across neighborhoods (Haque, 2016) and fewer visits to common hubs Sampson and
Levy (2020). In the US, living in racially segregated metropolitan areas leads
to longer travel times for Black individuals when compared to White
individuals, particularly when driving (Landis, 2022). Although homophily in
activity spaces holds across races, for White residents, it is more a
preference rather than accessibility Vachuska (2023). While for Black and
Hispanic residents, this is mainly due to lower accessibility of various
transport modes.

Ethnicity and nativity often interact with socioeconomic status, resulting in
different accessibility patterns (Xiao et al., 2021). Rokem and Vaughan (2019)
reveal two distinct migrant groups - economic migrants and refugees, where the
economic migrants have activity spaces closer to the city center, indicating
better mobility opportunities, while refugees are found to be more spatially
segregated.

The poor transport access where low-income and minority groups live tends to
create a vicious circle of segregation and inequalities (Bittencourt et al.,
2021). Extended travel times and restricted access to resources expose the
low-income to a greater risk of long-term unemployment (Korsu and Wenglenski,
2010), diminishing their opportunities for upward mobility. In Turkey, Syrian
refugees struggle with the lack of language skills, which often limits their
accessibility to various transportation methods and activities, further
compounding their problems of poverty and social exclusion (Ozkazanc, 2021).
In contrast, efficient transportation networks can offset the adverse effects
of segregation and narrow the income disparity between them and White
populations (Galaskiewicz et al., 2021), and job accessibility is positively
associated with earnings for the Black population living in the suburbs (Eom,
2022).

####  5.3.2 Transport equity

Transport access discrimination against certain social groups partly stems
from planning stages being intentionally or unintentionally biased towards
privileged groups and might perpetuate existing social segregation (Govender
and Maharaj, 2012). The study by Golub et al. (2013) highlights “environmental
racism,” emphasizing how historical racial segregation in urban development
has left a lasting imprint on the metropolitan area of Oakland, US, which
continues to perpetuate discrimination due to race-neutral decisions that
ignore this racialized legacy. Naranjo Gomez (2016) highlights that while
expanding Spain’s road and high-speed rail network aims to enhance social
cohesion, the plan could negatively impact socio-economic development for a
quarter of the population, thus potentially hindering social cohesion. Road
infrastructure enhancements in peri-urban communities often improve
connectivity and attract middle-to-high-income individuals seeking enhanced
services (Adugbila et al., 2023). However, this influx often displaces low-
income residents, pushing them into hinterlands and leading to fragmentation
within these peri-urban areas.

Elevated highways and urban freeways deepen socio-spatial divisions by
favoring affluent commuters and exposing marginalized groups to environmental
risks and poor services (Graham, 2018; Mahajan, 2023). However, even public
transport infrastructure, primarily intended for promoting social integration,
can have discriminatory consequences against disadvantaged groups. High-income
neighborhoods seem to benefit more from public transport investments than low-
income ones, reinforcing residential segregation (Heilmann, 2018; Pereira et
al., 2019). Higher-income homeowners can better take advantage of transit-
induced price capitalization effects on their property values and upgrade to
more affluent neighborhoods (Nilsson and Delmelle, 2020). However, lower-
income homeowners do not reciprocate this benefit.

###  5.4 Urban design

Socio-spatial segregation is influenced not only by current socioeconomic
variables but also by historical patterns of urbanization and transformation
(Zhou et al., 2015). This section reviews how urban spaces affect individuals’
segregation experiences, where specific forms of urban areas are better than
others in facilitating individual mobility and fostering social inclusion
(Goldblatt and Omer, 2015), as illustrated in Table 5.

Architecture, urban design, and planning are vital in mitigating inequalities
(Legeby and Feng, 2022). Consequently, the built environment’s impact on
segregation variation demands careful consideration. To effectively address
segregation dynamics, it is essential to examine the co-presence of social
groups in public spaces, utilizing urban form to foster positive change
(Miranda, 2020).

Table 5: Urban design effects on segregation experiences. Aspect |  Impact
---|---
Hierarchical urban structures |  Limit public space use and social inclusion, particularly affecting immigrants (Legeby and Marcus, 2011).
Distribution of consumption spaces |  Drives segregation in public space usage, indicating urban exclusion (Bolzoni, 2016).
Streets with slower traffic and good environment |  Enhance social integration and contribute to vibrant public life (Sauter and Huettenmoser, 2008).
High spatial integration |  Leads to enhanced access to services, reducing segregation (Van Nes and Aghabeik, 2015; Garnica-Monroy and Alvanides, 2019; Legeby and Feng, 2022).
High pedestrian density, walkability, and bikability |  Aligns with lower segregation through synergistic urban patterns with the surrounding cities (Alghatam, 2019; van den Berg et al., 2017; Mouratidis and Poortinga, 2020; Wang and Liu, 2022).
Multi-scalar spatial configurations |  Facilitates urban encounters and improves social integration (Şevik and Çalişkan, 2022).
Mixed-use design and linear parks |  Enhances activity interactions and socioeconomic integration (Gao and Lim, 2023).

As a crucial public space, urban parks enhance social integration by providing
inclusive, shared spaces for interaction, cultural activities, and community
engagement, strengthening communal bonds and cohesion Samson (2017).
Abdelmonem and McWhinney (2015) recommend that urban park design includes
inclusive and accessible features, considers the socio-cultural dynamics of
local communities (Xiao et al., 2019), offers neutral spaces that do not
exacerbate territorial claims, and encourages shared activities to foster
interaction among diverse community members. However, in some areas, the best
parks and access to such parks are found in high-social-status areas, while
those with low social status lack access to public green spaces (Van Nes and
Aghabeik, 2015; Miller, 2019).

###  5.5 Facilitating mobility to promote integration

This section reviews practices and policies from various regions that affect
socio-spatial segregation, focusing on housing, public transport, and urban
design. Evidence in transport and urban science research highlights the
essential role of mobility in promoting integration (Mooses et al., 2020).
Social integration benefits from a greater understanding of highly segregated
communities, optimizing accessibility-based planning and policies to maintain
community diversity (Huang et al., 2022; Camarero and Oliva, 2019).

Table 6-1 suggests that accounting for housing affordability and lack of
transport access faced by minorities, housing policies could facilitate
interactions across socioeconomic groups and promote social integration
(Utzig, 2017).

Table 6: Policies and actions in housing, public transport, and urban design and their impact on segregation. 1 = Housing, 2 = Public transport, and 3 = Urban design. # |  Policy/Action |  Description |  Segregation implications
---|---|---|---
1 |  Urban village placement for rural migrant workers (China) |  Good job accessibility for rural workers in urban villages. |  Lowers workplace segregation (Zhu et al., 2017; Zhou et al., 2021).
Urban renewal (China) |  Urban village demolition due to renewal policies. |  Risks increasing segregation due to displacement (Zhu et al., 2022).
Housing Choice Voucher program (US) |  Housing affordability programs. |  Effectiveness limited by entrenched socio-demographic barriers (Garboden, 2021).
2 |  Minibus taxi system (South Africa) |  Integrated transport systems for diverse populations. |  Could decrease segregation by serving actual transport needs (Nelson, 2023).
Public transport fare reform (Portugal) |  Enhancing commuter accessibility. |  May reduce social inequalities and segregation (Silver et al., 2023).
Bus and metro systems (Colombia) |  Public transport accessible to the poor. |  Reduces segregation by improving transport access for the poor (Valenzuela-Levi, 2023).
Light-rail transportation (US) |  Light-rail construction in neighborhoods. |  Gentrification risk with potential for demographic shifts (Hess, 2020).
3 |  Bike share program (US) |  Equitable bike station planning. |  Equitable transport access can lower segregation (Bhuyan et al., 2019).
Cycle hire scheme (UK) |  Wider distribution of city cycling schemes. |  Enhanced inclusiveness and reduced income segregation (Lovelace et al., 2020).
Inclusive sidewalk design (Saudi Arabia) |  Gender-responsive public space design. |  Targets gender-based segregation reduction (Almahmood et al., 2018).
Urban transformation (Turkey) |  Social inclusion efforts for refugees. |  Fosters integration and combats segregation in mixed-use areas (Altaema and Hatipoğlu, 2023).
Carpooling (US) |  Carpooling algorithms for diverse groups. |  Encourages social integration via shared transport (Librino et al., 2020).

Car-centric urban development is often associated with increased segregation
(Sanchez et al., 2004). These policies and specific land use practices have
restricted transport access to essential opportunities like jobs, education,
and healthcare due to direct and indirect effects, including inequitable costs
and residential segregation (Sanchez et al., 2004; McDonagh, 2006). Better
public transportation may enhance exposure levels between population groups
and reduce the overall levels of segregation experienced by individuals (Wong
and Shaw, 2011; Kryvobokov and Bouzouina, 2014; Landis, 2022; Power, 2012;
Utzig, 2017; Carpio-Pinedo, 2021; Athey et al., 2021; Huang et al., 2022).
This is also demonstrated in Table 6-2. Factors beyond public transport are
worth investigating simultaneously (Arellana et al., 2021). In transit-
oriented development planning, we also need strategic methods to counter
spatial inequality to fully unleash its potential in promoting both
environmental and social sustainability in modern cities. In this process,
Mueller et al. (2018) emphasize the integration of affordable housing
preservation into city planning, particularly near transit corridors, and
proposes a tool to assess, compare, and prioritize these corridors as
potential sites for maintaining existing affordable rental housing.

More activities and interactions do not automatically lead to stronger
communities (Mouratidis and Poortinga, 2020). Targeted urban design
initiatives in walkability, public space, and housing design can enhance urban
vitality and social cohesion (Table 6-3). In implementing these urban design
initiatives, Unceta et al. (2020) highlight the importance of considering
local context and potential in space and society rather than solely relying on
land regularization and imported solutions. One example is a measuring tool by
Alipour and Galal Ahmed (2021) for assessing social sustainability indicators,
covering factors like density, land use, mobility options, street layouts,
etc., potentially revealing segregation.

###  5.6 Key takeaways

The studies within transport and urban science research provide detailed
explanations for the observed mobility differences among various groups,
shedding light on how these variations contribute to corresponding disparities
in individual segregation experiences.

  * •

Urban design and housing

The location and type of housing have profound impacts on individual mobility.
Urban sprawl, a feature of many modern cities, often results in increased
social segregation due to the geographic spread and the nature of residential
development.

  * •

Transport accessibility

The availability and quality of transport options and proximity to activities
crucially affect individual mobility patterns. Limited access to
transportation and opportunities often results in social exclusion, further
entrenching segregation experiences for certain groups.

##  6 Discussion

Our analysis of over 170 research works reveals that increased mobility
enables individuals to interact with more diverse populations, particularly
outside their residential neighborhoods, offering the potential to reduce the
levels of segregation experienced by individuals and groups. However, the
extent to which mobility reduces segregation depends on various factors. These
include the nature of the encounters, the individuals’ willingness to engage
with others, and the broader societal and systemic influences at play. The
effectiveness of mobility in lessening segregation varies significantly across
different groups and individuals, mainly due to economic and social factors
influencing their travel capacity. This interplay between residential
segregation and mobility-related segregation patterns is intricate, as
economic, cultural, physical, and spatial factors not only shape individual
mobility but also, in turn, influence individual segregation experiences. Such
complexity underscores the multifaceted nature of socio-spatial segregation
and its challenges.

This section synthesizes the literature review to answer the research
questions proposed in this study regarding the role of mobility in individual
segregation experiences. Initially, we shift our focus from a static
residential viewpoint to a dynamic activity-space paradigm, exploring what
this perspective shift reveals (Section 6.1). We then examine the factors
contributing to disparities in individual segregation experiences (Section
6.2). Subsequently, we evaluate the burgeoning literature utilizing emerging
data sources, assessing its potential and challenges in understanding spatial
segregation (Section 6.3). Finally, we pinpoint existing research gaps and
propose corresponding future directions.

###  6.1 Mobility in shaping individual segregation experiences

This study investigated the literature using the activity space approach and
empirical mobility data in quantifying socio-spatial segregation to examine
whether individual mobility contributes lower to segregation levels than
traditional residential segregation. The literature using either traditional
or emerging data sources reveals no single answer.

Several studies underscore the correlation between residential segregation and
experienced segregation, drawing attention to the phenomenon of mobility
homophily. These studies indicate that despite increased mobility, segregation
patterns often persist or become more pronounced, with mobility sometimes
restricted to within certain community bounds or along racial, ethnic, or
tribal lines. Conversely, other research focuses on how mobility can mitigate
segregation experiences, revealing disparities among different socioeconomic
groups. They highlight instances where we see lower levels of segregation in
activity spaces than the ones of residential segregation. This reduction is
due to greater exposure to diverse racial-ethnic groups in nonresidential
environments, workplaces, and other activity spaces.

Overall, the literature highlights the segregation-reducing effect of mobility
compared to the other side of the coin, i.e., its segregation-reinforcing
effect. However, these two aspects are not contradictory to each other. Some
studies highlight both sides (e.g., Silm et al., 2018; Zhang et al., 2022b;
Lin and Ta, 2023), drawing attention to the complexity of the relationship
between residential and experienced segregation levels.

###  6.2 Mechanisms in segregation experiences

Synthesizing the findings from the literature in a broader context, we
summarize the mechanisms of spatial segregation.

  * •

Economic and cultural influences

Job opportunities, leisure options, and cultural preferences based on
ethnicity, nativity, and income level significantly shape individual mobility
and, consequently, segregation experiences.

  * •

Social networks and interactions

The nature of social networks within ethnic communities, neighborhood trust
levels, and preferences for social interaction contribute to forming
segregation patterns. Mobility homophily, or the tendency to interact with
similar socioeconomic groups, further reinforces these patterns.

  * •

Physical and spatial factors

The design of urban spaces, including the layout of neighborhoods, proximity
to amenities, and public transportation systems, influences how individuals
experience segregation in their daily lives. Therefore, housing policies,
urban planning decisions, and transport infrastructure can potentially
mitigate or exacerbate segregation experiences. These elements determine where
people can reside, work, and participate in daily activities, significantly
influencing their segregation experiences.

###  6.3 Emerging data sources: nuanced understanding at scale

We have found over 70 reference studies using geolocation information from
emerging data sources such as GPS tracking devices, mobile phone GPS, Call
Detail Records, and geotagged tweets to quantify socio-spatial segregation.
Studies using emerging data sources, compared to those relying on traditional
data, provide evidence based on actual behavioral data on mobility rather than
stated preferences and offer broader spatial and population coverage with high
spatiotemporal granularity (Nilforoshan et al., 2023). These emerging-data
studies typically quantify multi-dimensional aspects of socio-spatial
segregation, simultaneously examining race/ethnicity, nativity, income, and
other factors (e.g., Heine et al., 2021). In contrast, traditional data-driven
studies often focus on a single aspect of segregation, e.g., housing (Wang et
al., 2012), linking it to other relevant and readily available explanatory
variables in the applied data. With data from a wide range of users, these
sources offer diverse demographic insights, capturing the experiences and
behaviors of different social groups and revealing interacting effects between
various dimensions, e.g., income and nativity (Gao et al., 2021).

Emerging data sources often provide large-scale insights into segregation
levels while preserving nuanced understandings to zoom in at the block level
(Moro et al., 2021; Nilforoshan et al., 2023). Most studies examining the
entire country (Vachuska, 2023) or major metropolitan areas (Moro et al.,
2021; Wu and Huang, 2022; Huang et al., 2022) use data collected from the US.
Leveraging these widely available data sources allows for a detailed
examination of various points of interest (Moro et al., 2021) and regions,
uncovering segregation patterns at the block level (Wu and Huang, 2022). They
also enable near real-time analysis with high temporal granularity, providing
insights into dynamic segregation patterns and trends as they change over
different times of the day (Shen and Luo, 2023) and seasons of the year
(Mooses et al., 2016).

The abundance and intricacy of big data have spurred the development of
innovative analytical methods and tools in spatial segregation research. For
example, the theory of mobility homophily, confirmed across multiple regions
(Xu et al., 2019; Heine et al., 2021; Huang et al., 2022), extends its
relevance beyond residential segregation to include daily segregation
experiences. Leveraging emerging data sources allows for integrating extensive
mobility geolocation data with social network data, unveiling patterns
previously unobservable at such refined scales (Xu et al., 2019; Silm et al.,
2021). Building on this, Moro et al. (2021) have developed a social
exploration and preferential return model using vast human mobility
geolocation data, effectively capturing the dual aspects of economic
integration: social and place explorations. By harnessing extensive mobility
data from large populations, it becomes feasible to construct a large-scale
mobility network (Nilforoshan et al., 2023) and employ network analysis tools,
like community detection, to uncover insights into segregation patterns at a
national level (Huang et al., 2022).

####  6.3.1 Challenges

Emerging data sources, such as mobile phone applications and GPS-enabled
services, face two significant challenges: population bias and uneven sampling
of locations. Population bias arises because the user demographics behind this
big geodata, including age, gender, and ethnicity, often do not accurately
represent the broader population. Therefore, appropriately weighting
individual devices in spatial segregation analysis is crucial to prevent
inaccurate results (Wang et al., 2018). Saxon (2021) show that spatial models
using mobile phone GPS data have systematic bias, notably towards
overestimating the park access of minority populations, which results in
understating inequity.

An uneven sampling of locations brings two types of biases in the collected
geolocation data on human mobility. Firstly, self-reported geolocations such
as geotagged tweets have selective biases, overly representing leisure and
non-routine activities (Liao et al., 2019). Secondly, passively collected
geodata from call detail records or GPS-enabled phone applications are
contingent on user interactions with mobile phones, resulting in data sparsity
and biases, particularly towards activities in the afternoon and nighttime
(Liao, 2021). These factors significantly influence the analysis of
segregation experiences, considering the recorded activity spaces visited
instead of the full range.

Analyzing socio-spatial segregation with anonymized mobile phone data
necessitates implementing home detection methods to infer the sociodemographic
attributes of the device users. Traced by human circadian rhythms, temporal
rules are commonly used to infer individuals’ home and work locations from
mobile phone data (e.g., Gao et al., 2021). There is a notable lack of
validation against ground truth data, mainly due to privacy concerns and the
anonymization of big geolocation data (Verma et al., 2024). However,
accurately identifying home and work locations remains crucial for
understanding daily mobility patterns and capturing individual segregation
experiences. More work is needed to examine and enhance the validity of these
methods for practical applications in socio-spatial segregation research
(Pappalardo et al., 2021).

Another limitation is regarding the collection and analysis of demographic and
household data. The inability to directly ascertain gender, age, or family
composition, alongside reliance on residential location to infer income,
restricts our understanding of socio-economic diversity. Additionally, the
focus on individual mobile devices, without access to household-level
information, limits insights into how household structure affects mobility and
segregation patterns, highlighting a significant gap in analyzing collective
household dynamics.

###  6.4 Future research directions

Numerous studies employing activity space approaches quantify spatial
segregation, utilizing both small and large data sets. These approaches are
predominantly descriptive, focusing on defining and measuring social
segregation through human mobility data. Their principal contribution lies in
transcending traditional residential perspectives. With over a decade since
this paradigm shift in socio-spatial segregation research, moving from a
static residential viewpoint to a dynamic, mobility-based one, the field is
now poised to advance beyond mere descriptive analysis. There is a pressing
need to delve deeper into explaining observed social segregation phenomena
within various spatiotemporal contexts.

As highlighted in this review (Section 5), numerous transport and urban
science studies have provided insights into socio-spatial segregation in
diverse spatial contexts over time. However, these studies often treat socio-
spatial segregation primarily as a static residential phenomenon, seldom
considering how individuals experience it daily. For instance, the concept of
spatial mismatch is typically employed to explain residential segregation, and
in transport equity evaluations, accessibility is primarily calculated based
on residential locations. Despite these efforts, the direct contributions of
these factors to individuals’ daily segregation experiences remain somewhat
ambiguous.

Looking ahead, we advocate for three critical research directions that
emphasize the need for a cross-disciplinary approach at the intersection of
urban social science and transportation. These include exploring how people
experience social segregation daily and devising region-specific explanations.
First, we suggest that activity space approaches fueled by big geodata should
be integrated with additional data sources that quantify transport systems and
urban spaces. This integration would enable a more comprehensive analysis of
the relationships among housing, transport access, urban design, and
individual segregation experiences (Zhang et al., 2022a; Vachuska, 2023;
Nilforoshan et al., 2023), thereby maximizing the potential of big data’s
scale effect. Second, studies employing urban space analysis to foster spatial
integration should incorporate empirical insights into people’s mobility
behaviors. As empirical mobility data reveals, the urban design challenge for
promoting social inclusion may reside in bridging the gap between intended and
observed social mixing between different populations. Third, one shall explore
the causal relationships between land use, transportation infrastructure, and
experienced segregation. Investigating how variations in land use patterns and
accessibility levels influence human mobility could yield valuable insights
into the effectiveness of policy interventions that foster social mixing. This
approach could help identify strategies for mitigating segregation and
enhancing community cohesion through urban planning and policy design.

## Acknowledgements

We thank E. Moro for providing insightful comments on an earlier draft of this
manuscript. This research is funded by the Swedish Research Council (Project
Number 2022-06215).

During the preparation of this work the authors used GPT-4 and Jenni.ai in
order to improve the language use. After using this tool/service, the authors
reviewed and edited the content as needed and take full responsibility for the
content of the preprint.

##  Appendix A Literature selection methods

This literature review is centered around three pivotal concepts: segregation,
activity space, and the role of the built environment in facilitating
mobility. The included studies were extracted from the Scopus database on Oct
18, 2023, covering the original research in English published as journal or
conference articles. Table A.1 shows the search keywords and search query,
which are designed to identify the role of mobility in shaping individual
segregation experiences quantified using activity space approaches and gain
relevant insights in the field of built environment, including transport and
urban science research.

Table A.1: Keywords for literature search. Theme |  Keywords
---|---
(1) Segregation |  segregation, spatial integration, social integration, socio-spatial integration, social cohesion
(2) Activity space |  spatial mobility, human mobility, daily mobility, personal mobility, individual mobility, spatio-temporal mobility, spatiotemporal mobility, socio-spatial mobility, sociospatial mobility, urban mobility, spatial movement, activity space, action space, spatial network, spatial behavior, spatio-temporal behavior, spatiotemporal behavior, use of space, lifeworld, person-based, individual-based
(3) Encounter |  social mix, encountering, encounter, encounter network, social ties, third places, cross-cultural encounters, shared experiences, connectivity, co-presence, co-existence, co-presenting
(4) Built environment |  mobility, access inequality, accessibility, access, social and spatial inequality, transport-related social exclusion, urban sprawl, transport modes, modal split, transit deserts, transport justice, active transportation, transit-oriented development, multi-modality, travel behavior, transport affordability, traffic congestion, public transport subsidies
Search query |  Titles, abstract, and keywords include (1 AND 2) OR (1 AND 3) OR (1 AND 4)

Our study focuses on research that employs empirical data to quantify socio-
spatial segregation from an activity space viewpoint. This approach addresses
our first research question regarding the influence of mobility on individual
segregation experiences, encompassing Themes 1 & 2, which are detailed in
Sections 3 and 4. We categorize the studies into two groups: those relying on
traditional data sources like travel surveys, census, and register data and
those employing emerging data sources, including GPS trackers and mobile phone
data. Additionally, we examine studies that estimate potential interactions
facilitated by the built environment alongside insights from transport and
urban science regarding housing, transport accessibility, and urban design.
These studies, which align with Themes 1 & 3 and Themes 1 & 4, are explored in
Section 5. They contribute to our second research question, aiming to
elucidate segregation experiences and propose potential solutions to socio-
spatial segregation.

Figure A.1 summarizes the included original studies. Figures A.1a-b show the
studies cited under Sections 3-4 on empirical findings of individual
segregation experiences using activity space approaches. Figures A.1c-d
illustrate those references under Section 6 on explaining segregation from a
built environment perspective.

Figure A.1: Referenced papers from activity space and built environment
perspectives. These statistics only include original studies published in
journals or conference proceedings, excluding reviews, opinions, and other
articles cited in this study. (a) Number of activity space papers by studied
country, year, and data source (Themes 1 & 2.) (b) Number of transport and
urban science papers by studied country and year (Themes 1 & 3 and Themes 1 &
4.)

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