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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Sustain. Cities</journal-id>
<journal-title>Frontiers in Sustainable Cities</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Sustain. Cities</abbrev-journal-title>
<issn pub-type="epub">2624-9634</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/frsc.2025.1612980</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Sustainable Cities</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Ethnic residential patterns in the inner-city core of Riga, Latvia using scalable individualized neighborhoods</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Balode</surname> <given-names>Sindija</given-names></name>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
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<contrib contrib-type="author">
<name><surname>B&#x00113;rzi&#x00146;&#x00161;</surname> <given-names>M&#x00101;ris</given-names></name>
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<aff><institution>Department of Geography, University of Latvia</institution>, <addr-line>Riga</addr-line>, <country>Latvia</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Remus Cretan, West University of Timi&#x0015F;oara, Romania</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Carlos Aguirre-Nu&#x000F1;ez, San Sebasti&#x000E1;n University, Chile</p>
<p>Martin &#x00160;imon, Institute of Sociology Czech Academy of Sciences, Czechia</p>
<p>Francesca Bitonti, University of Catania, Italy</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Sindija Balode <email>sindija.balode&#x00040;lu.lv</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>07</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>7</volume>
<elocation-id>1612980</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>20</day>
<month>06</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2025 Balode and B&#x00113;rzi&#x00146;&#x00161;.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Balode and B&#x00113;rzi&#x00146;&#x00161;</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>Urban residential segregation of immigrant populations is a key research area. Riga, Latvia, offers a unique context due to its Soviet-era migration legacy and increasing diversity from new immigration flows amidst demographic decline of established minorities. Analyzing the spatial patterns of emerging ethnic groups, who often exhibit residential clustering, is key to understanding potential spatial inequalities. This study investigated the residential concentration, isolation, and segregation of emerging ethnic groups in Riga&#x00027;s inner-city core using a multi-scalar geographic approach. Analyzing anonymized individual-level 2021 census data processed with EquiPop, we found that residential distribution and concentration varies significantly by spatial scale. Individualized neighborhoods effectively revealed multi-scalar patterns and intra-neighborhood heterogeneity. Results identified a concentration of new ethnic groups in the southern inner-city core, functioning as an &#x0201C;arrival space&#x0201D;, alongside an influence of existing ethnic infrastructure on shaping residential patterns. This research provides crucial insights into the fine-scale spatial organization of new immigrant communities, informing the understanding and addressing of spatial inequalities, particularly those faced by non-European groups within the complex European context.</p></abstract>
<kwd-group>
<kwd>ethnic segregation</kwd>
<kwd>urban geography</kwd>
<kwd>k-nearest neighbor</kwd>
<kwd>immigration</kwd>
<kwd>Riga</kwd>
</kwd-group>
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<fig-count count="4"/>
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<equation-count count="3"/>
<ref-count count="66"/>
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<word-count count="9088"/>
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<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Social Inclusion in Cities</meta-value>
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</front>
<body>
<sec sec-type="intro" id="s1">
<title>1 Introduction</title>
<p>The escalating ethnic diversification of urban populations, driven by international migration, is a defining global trend of the twenty-first century. Within the European Union, this phenomenon has been particularly pronounced over the past decade, prompting urgent scholarly and policy attention toward understanding the complex relationship between immigration, shifting demographics, and spatial inequalities related to residential segregation. As K&#x00159;&#x000ED;&#x0017E;kov&#x000E1; and &#x00160;imon (<xref ref-type="bibr" rid="B32">2022</xref>) highlight, European nations display considerable heterogeneity in their ethnic neighborhood structures and associated inequalities, shaped by distinct welfare regimes, economic pathways, historical trajectories, and migration histories (Marci&#x00144;czak et al., <xref ref-type="bibr" rid="B39">2015</xref>). This inherent variability underscores the necessity for context-specific research into the dynamics of urban ethnic residential segregation.</p>
<p>Historically, research on ethnic segregation has been heavily influenced by the North American experience, which focused on deprived inner-city areas shaped by a sharp Black-White racial dichotomy and fueled by discriminatory federal policies (Massey et al., <xref ref-type="bibr" rid="B41">2009</xref>). Although this paradigm is evolving with the rapid diversification of US suburbs (Walker, <xref ref-type="bibr" rid="B64">2018</xref>), it provides a key point of contrast to Europe. The manifestation of ethnic spatial inequalities across European cities is contingent upon varied migration histories (e.g., post-colonial, Soviet-era, intra-European) and different urban-political dynamics, including policies regarding social housing and privatization. In these European contexts, socioeconomic class has often played a more dominant role than race in shaping settlement patterns, resulting in different spatial patterns and lower levels of ethnic segregation (Arbaci, <xref ref-type="bibr" rid="B7">2007</xref>).</p>
<p>Nevertheless, many European cities still exhibit significant ethnic minority concentrations within their central cores. Cities such as Berlin, Barcelona, Leeds, Brussels, and Rome exemplify this trend, with high levels of immigrant settlement and increasing multi-ethnicity in inner-city districts (Stillwell and Phillips, <xref ref-type="bibr" rid="B57">2006</xref>; Martori and Apparicio, <xref ref-type="bibr" rid="B40">2011</xref>; Salvati, <xref ref-type="bibr" rid="B51">2016</xref>), alongside neighborhood polarization (Marci&#x00144;czak and Bernt, <xref ref-type="bibr" rid="B37">2021</xref>). Although European inner cities typically present more mixed socioeconomic profiles and more recent migrant populations than their American counterparts (Marci&#x00144;czak et al., <xref ref-type="bibr" rid="B39">2015</xref>; Stonawski et al., <xref ref-type="bibr" rid="B59">2022b</xref>), they are not immune to pockets of deprivation where vulnerable immigrant groups in lower-quality housing face heightened risks of spatial isolation (Costa and de Valk, <xref ref-type="bibr" rid="B15">2018</xref>).</p>
<p>Compounding the substantive analysis of these trends is the growing recognition of the methodological complexities inherent in measuring and comparing segregation. Residential segregation is a fundamentally multi-scalar phenomenon, contingent on context, the specific groups being analyzed, and the spatial scale of observation (Sleutjes et al., <xref ref-type="bibr" rid="B55">2018</xref>; Lichter et al., <xref ref-type="bibr" rid="B33">2020</xref>; Rogne et al., <xref ref-type="bibr" rid="B50">2020</xref>). Consequently, conventional approaches relying on fixed, often arbitrarily defined administrative boundaries present significant challenges for comparative analysis and fail to capture the granular, dynamic nature of residential concentration. Addressing these methodological limitations is crucial for advancing our understanding of contemporary urban ethnic geographies.</p>
<p>This study directs its focus toward Riga, the capital of Latvia, situated on the eastern periphery of the European Union. Riga offers a unique socio-historical context for examining ethnic residential dynamics. Shaped significantly by Soviet-era migration policies, Latvia historically maintained a high proportion of foreign-born residents (14.0% in 2012, compared to the OECD average of 8.9%). However, recent demographic shifts indicate a decline in the long-established ethnic minority population (down to 11.9% by 2022) (<xref ref-type="bibr" rid="B56">Society at a Glance 2024</xref>, <xref ref-type="bibr" rid="B56">2024</xref>), concurrent with new migration flows stimulated by globalization, geopolitical realignments, and European Union accession in 2004. These emerging flows, encompassing a greater diversity of origin countries and migration motivations and including a notable increase in immigrants from outside Europe, are reshaping Latvia&#x00027;s migration landscape.</p>
<p>Responding to these developments, this paper investigates the multi-scalar residential geographies of emerging ethnic groups within Riga&#x00027;s inner-city core. We seek to understand how spatial patterns of ethnic concentration, isolation, and segregation manifest and transform across different spatial scales. Specifically, we pose two central research questions: (1) What are the levels of residential concentration for emerging ethnic groups in Riga&#x00027;s inner-city core, and how do they vary with the scale of analysis? (2) How do indices of spatial isolation and segregation for these groups change across scales, and what do these variations reveal about the evolving dynamics of intra-urban ethnic diversity?</p>
<p>This research offers several distinct contributions. Methodologically, it introduces a novel multi-scalar approach to the Latvian context using fine-grained spatial units. It represents an early application of the 2021 census data to map the distribution of emerging immigrant communities in Riga&#x00027;s inner-city core across micro-, meso-, and macro-scales. Empirically, the study illuminates the changing migration dynamics within a post-socialist urban environment, leveraging Riga&#x00027;s distinctive demographic profile. By providing a fine-grained, scale-sensitive perspective on contemporary ethnic residential patterns, this analysis addresses a significant gap in the literature, particularly concerning newer immigrant populations in post-socialist cities. Furthermore, the findings contribute to a more nuanced understanding of ethnic community spatial organization&#x02014;a subject of increasing public and political concern in Latvia&#x02014;and hold important policy implications for anticipating and mitigating spatial inequalities as these emerging communities grow.</p>
<p>This paper is structured as follows. Section 2 reviews the literature on immigration and ethnic residential segregation. Section 3 introduces the study area, providing its historical and contemporary context. Section 4 details our methodology. Section 5 presents the empirical results. Finally, Section 6 discusses the implications of these findings, and Section 7 concludes the paper.</p>
</sec>
<sec id="s2">
<title>2 Immigration and ethnic residential segregation</title>
<p>As a persistent form of spatial inequality with profound social implications, ethnic residential segregation remains a central theme in urban studies. Residential segregation, broadly defined as the spatial separation of social groups along dimensions such as ethnicity, socioeconomic status, and age (Andersson et al., <xref ref-type="bibr" rid="B3">2018</xref>), is intrinsically linked to shifts in urban residential configurations driven by international migration. Consequently, there is a link between a city&#x00027;s level of immigration and its degree of ethnic segregation (Skifter Andersen et al., <xref ref-type="bibr" rid="B54">2016</xref>).</p>
<p>Although the discourse on ethnic residential segregation in European contexts is scarce, a key debate concerns the relative weight of its causal factors. Evidence points toward the significance of residential preferences and voluntary self-segregation, where individuals choose to live near co-ethnics for social support, cultural familiarity, or perceived safety (Bolt, <xref ref-type="bibr" rid="B9">2009</xref>; Malmberg and Clark, <xref ref-type="bibr" rid="B36">2021</xref>). However, structural and economic constraints, including housing market discrimination, affordability issues, and information barriers (Bolt et al., <xref ref-type="bibr" rid="B10">2010</xref>), are also acknowledged as significant contributing factors, particularly shaping the residential outcomes of more vulnerable populations.</p>
<p>The intersection of socioeconomic status and ethnicity is crucial in reinforcing spatial segregation, as a lower socioeconomic status can significantly limit housing choices (Imeraj et al., <xref ref-type="bibr" rid="B26">2018</xref>). This dynamic is particularly relevant in contexts with rising socioeconomic disparities, as observed across parts of Europe, including Eastern Europe (Marci&#x00144;czak et al., <xref ref-type="bibr" rid="B39">2015</xref>; Lichter et al., <xref ref-type="bibr" rid="B33">2020</xref>), even where overall urban ethnic residential segregation was comparatively low in the early 21st century (Musterd, <xref ref-type="bibr" rid="B43">2005</xref>). The complexity of this intersection is evident in Southern European cities, where low spatial segregation often paradoxically reflects high social segregation due to an exclusionary, owner-dominated housing system (Arbaci, <xref ref-type="bibr" rid="B7">2007</xref>). Consequently, the degree of socioeconomic integration represents an important set of constraints and opportunities that influence the initial and subsequent residential patterns of emerging immigrant populations.</p>
<p>Furthermore, empirical research consistently reveals differentiated segregation experiences across migrant groups. Notably, migrants of non-European origin often exhibit higher levels of spatial concentration in European cities compared to their European counterparts (Malmberg et al., <xref ref-type="bibr" rid="B35">2018</xref>; Lichter et al., <xref ref-type="bibr" rid="B33">2020</xref>), a trend also observed among smaller non-European groups in Krakow (Stonawski et al., <xref ref-type="bibr" rid="B58">2022a</xref>). This is frequently attributed to initial settlement patterns, where newcomers facing precarious socioeconomic circumstances settle in established areas with existing co-ethnic support networks (Imeraj et al., <xref ref-type="bibr" rid="B26">2018</xref>). The combination of socioeconomic vulnerability and greater cultural distance can thus foster distinct concentration patterns. Understanding these dynamics is vital for analyzing the residential geographies of diverse migration flows arriving in cities like Riga, where they interact with a demographic landscape shaped by a Latvian majority and large, established post-Soviet minority populations.</p>
<p>High levels of residential concentration, as a result of preference and constraint across socioeconomic and cultural dimensions, can lead to the formation of ethnic enclaves. These spaces can function ambivalently (Catney, <xref ref-type="bibr" rid="B12">2016</xref>): offering initial vital resources, social capital, and a buffer against discrimination for newcomers (Harris, <xref ref-type="bibr" rid="B22">2023</xref>), while potentially limiting wider social networks and long-term socioeconomic mobility (Hack-Polay, <xref ref-type="bibr" rid="B20">2019</xref>). Strong concentrations of specific ethnic groups may also influence the residential decisions of other populations. While the term &#x0201C;white flight&#x0201D; carries specific historical connotations, broader processes of majority population avoidance or relocation from diversifying, predominantly non-European neighborhoods, driven by factors ranging from prejudice to preferences for social homogeneity or perceived changes in neighborhood status, have been observed in various European contexts (Bolt, <xref ref-type="bibr" rid="B9">2009</xref>; H&#x000E5;rsman, <xref ref-type="bibr" rid="B18">2006</xref>; Malmberg and Clark, <xref ref-type="bibr" rid="B36">2021</xref>; Stonawski et al., <xref ref-type="bibr" rid="B59">2022b</xref>). While this study&#x00027;s focus on a single time point cannot track such dynamics longitudinally, mapping the current multi-scalar concentrations of emerging non-European groups in Riga&#x00027;s inner-city core provides a crucial baseline for understanding potential future shifts in the residential distribution of all population segments within the area.</p>
<p>Ultimately, the spatial organization of ethnic groups has far-reaching consequences for migrants&#x00027; individual opportunity structures and the overall trajectory of urban development. Residential location significantly shapes access to opportunities (employment, education, healthcare), exposure to risks (crime), and potential for social cohesion, or tension, with segregation amplifying these effects (Kaplan and Douzet, <xref ref-type="bibr" rid="B28">2011</xref>). Furthermore, segregation reduces inter-ethnic contact and increases the risk of neighborhood stigmatization (Malmberg et al., <xref ref-type="bibr" rid="B35">2018</xref>), contrasting with the benefits of ethnic mixing and diversification. Understanding how patterns of concentration and isolation for emerging ethnic groups vary across different spatial scales within Riga&#x00027;s rapidly diversifying inner-city core is therefore paramount for developing informed urban policies aimed at fostering equitable integration and mitigating potential spatial inequalities.</p>
</sec>
<sec id="s3">
<title>3 Study area: Riga&#x00027;s inner-city core</title>
<p>This study focused on the inner-city core of Riga, the capital of Latvia. This area, encompassing the historic Old Town and six adjacent neighborhoods (Centrs, Avoti, Grizinkalns, Brasa, Skanste, Petersala-Andrejsala), forms a distinct geographical and social unit within the city. The inner-city core of Riga is characterized by a predominantly regular street network, with residential neighborhoods that trace their origins to the early 20th century, prior to the onset of the First World War. The delineation of these neighborhoods largely aligns with the street network and the prevailing pattern of urban development (<xref ref-type="fig" rid="F1">Figure 1</xref>). On the outskirts of the inner-city core, to the south and east lie former working-class residential districts and erstwhile manufacturing sites. In contrast, the northern areas comprise the former port, industrial, and railway territories.</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Study area: a grid of populated 100 m x 100 m cells and administrative neighborhoods of Riga&#x00027;s inner-city core.</p></caption>
<alt-text>Map showing the districts of a city with areas labeled as Petersala-Andrejsala, Skanste, Brasa, Grizinkalns, Centrs, Avoti, and Old Town. Grid lines, shaded areas, and a north arrow are present. A scale bar indicates distances in kilometers. Legend with symbols representing different elements: a grid cell labeled &#x0201C;1-ha grid cell,&#x0201D; a darker grid cell labeled &#x0201C;populated grid cell,&#x0201D; a line labeled &#x0201C;River Daugava,&#x0201D; and an irregular shape labeled &#x0201C;urban neighborhood.&#x0201D;</alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frsc-07-1612980-g0001.tif"/>
</fig>
<p>Our focus on this area was based on three primary rationales. First, as demonstrated in previous research (Balode and B&#x00113;rzin&#x00161;, <xref ref-type="bibr" rid="B8">2025</xref>) and corroborated by recent census data, Riga&#x00027;s inner-city core exhibits the most significant concentration and growth of newly emerging non-European ethnic minority groups compared to the rest of the city and country. Second, the area&#x00027;s unique urban morphology, which is largely characterized by 19<sup>th</sup> and early 20<sup>th</sup>-century architecture, creates a distinct built environment that contrasts sharply with the Soviet-era mass housing estates in surrounding neighborhoods, making it potentially more appealing to immigrant populations. Third, the inner core is geographically delineated by major railway lines and the Daugava River, creating a degree of spatial enclosure that limits social interactions and reinforces its suitability as a discrete analytical unit.</p>
<p>The administrative neighborhoods constituting the inner-city core display considerable heterogeneity in spatial extent (ranging from 94 to 373 hectares) and population size (1,900 to 30,700 residents in 2021). This results in substantial variations in population density (8.7 to 98.4 inhabitants per hectare), highlighting the potential pitfalls of using administrative boundaries for spatial analysis due to the Modifiable Areal Unit Problem (MAUP), encompassing both scale and zoning effects (Openshaw, <xref ref-type="bibr" rid="B45">1996</xref>). This internal variability necessitates methods robust to these challenges.</p>
<sec>
<title>3.1 Riga&#x00027;s inner-city core: from post-socialist legacies to fragmented gentrification</title>
<p>This subsection provides essential background on Riga&#x00027;s inner-city core, outlining the historical legacies, contemporary migration and socioeconomic trends, and housing characteristics that shape the context for analyzing the residential patterns of emerging ethnic groups.</p>
<p>As Latvia&#x00027;s capital and primary economic hub, Riga&#x00027;s development has been significantly shaped by historical processes. During the Soviet era (1940&#x02013;1991), the city experienced substantial in-migration, predominantly of Russian speakers, fundamentally altering its demographic composition. Understanding contemporary residential patterns requires acknowledging these legacies, particularly the Soviet-era housing allocation policies, subsequent large-scale privatization processes following independence, and a persistently underdeveloped social housing sector (Kri&#x00161;j&#x00101;ne et al., <xref ref-type="bibr" rid="B31">2019</xref>). Notably, Soviet-era migrants usually possessed relatively high socioeconomic status (Bolt et al., <xref ref-type="bibr" rid="B10">2010</xref>), and, consequently, Riga historically exhibited relatively low levels of both socioeconomic and ethnic residential segregation, lacking the pronounced ethno-social hierarchy similar to other post-socialist cities (Kri&#x00161;j&#x00101;ne et al., <xref ref-type="bibr" rid="B30">2015</xref>; Hess et al., <xref ref-type="bibr" rid="B23">2018</xref>). However, the emergence of new, post-independence migration flows necessitates a critical re-evaluation of ethnic segregation dynamics (Tammaru et al., <xref ref-type="bibr" rid="B60">2016</xref>).</p>
<p>Latvia, like many Central and Eastern European (CEE) nations, fits within migration cycle theory as a country with relatively recent international migration patterns, characterized by a predominance of first-generation migrants (Arango, <xref ref-type="bibr" rid="B6">2018</xref>). Migration policies are increasingly influenced by European Union directives, yet the specific trajectories of CEE countries are unlikely to simply replicate those of established Western European immigration nations due to recency of migrant reception, differing historical contexts, global positioning, and structural economic and social characteristics. Post-socialist cities across the region are witnessing new patterns of immigration (K&#x00159;&#x000ED;&#x0017E;kov&#x000E1; and &#x00160;imon, <xref ref-type="bibr" rid="B32">2022</xref>), raising concerns about the potential for increased ethnic residential segregation as immigration levels rise.</p>
<p>The post-socialist transition to capitalism in Riga has coincided with increasing socio-spatial disparities, particularly evident within the inner city (Kri&#x00161;j&#x00101;ne and B&#x00113;rzin&#x00161;, <xref ref-type="bibr" rid="B29">2014</xref>). Examining the interplay between contemporary immigration, ethnic change, and gentrification processes is therefore crucial, though it remains an understudied nexus. Existing international research often indicates a positive correlation between higher immigration rates and gentrification processes, particularly where less affluent neighborhoods experience ethnic diversification alongside urban renewal (Hwang, <xref ref-type="bibr" rid="B25">2015</xref>). In-migration can stimulate housing demand, potentially leading to price escalation, subsequent in-migration of higher-income residents, and displacement pressures on lower-income populations (Haase et al., <xref ref-type="bibr" rid="B19">2020</xref>; Malmberg and Clark, <xref ref-type="bibr" rid="B36">2021</xref>). This highlights the complex role ethnic minorities can play as both potential victims and sometimes inadvertent agents of neighborhood change (Huse, <xref ref-type="bibr" rid="B24">2018</xref>). Furthermore, fragmented socio-spatial transformations, encompassing marginal gentrification alongside non-EU migrant settlement, may intensify social divisions, especially if gentrifiers exhibit limited cross-ethnic social engagement while immigrants rely more heavily on neighborhood-based social networks (Malheiros et al., <xref ref-type="bibr" rid="B34">2013</xref>).</p>
<p>Illustrating these dynamics, recent socio-demographic data (<xref ref-type="table" rid="T1">Table 1</xref>) revealed growing divergence between Riga&#x00027;s inner-city core and the rest of the city. Between 2011 and 2021, the inner core experienced significantly slower overall population decline, partly because of the smaller share of long-established declining ethnic groups and new in-migration. The inner core also exhibited a younger population profile, which was also influenced by a notable surge among non-European residents in younger age classes&#x02014;particularly males aged 15&#x02013;29, suggesting an influx of international students (Apsite-Berina et al., <xref ref-type="bibr" rid="B5">2023</xref>), and females aged 20&#x02013;39 (Central Statistical Bureau of Latvia, <xref ref-type="bibr" rid="B14">2025</xref>). Critically, markedly elevated median monthly net salaries and higher educational attainment levels in the inner core signaled a widening socioeconomic gap compared to the rest of Riga. This concentration of relative affluence risks exacerbating inequalities through differentiated housing markets, service provision, and urban policy outcomes.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Demographic and socioeconomic characteristics of Riga&#x00027;s inner-city core and rest of the city in 2021, with changes from 2011 to 2021.</p></caption>
<table frame="box" rules="all">
<thead>
<tr style="background-color:#919498;color:#ffffff">
<th/>
<th valign="top" align="center"><bold>Inner-city core (2021)</bold></th>
<th valign="top" align="center"><bold>Change 2011&#x02013;2021 (%)</bold></th>
<th valign="top" align="center"><bold>Rest of the city (2021)</bold></th>
<th valign="top" align="center"><bold>Change 2011&#x02013;2021 (%)</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Inhabitants (thousands)</bold></td>
<td valign="top" align="center">82.3</td>
<td valign="top" align="center">&#x02212;1.1</td>
<td valign="top" align="center">534.5</td>
<td valign="top" align="center">&#x02212;7.1</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Average age</bold></td>
<td valign="top" align="center">38.8</td>
<td valign="top" align="center">&#x02212;2.3</td>
<td valign="top" align="center">43.1</td>
<td valign="top" align="center">3.6</td>
</tr>
<tr>
<td valign="top" align="left">% of 15- to 44-year-olds</td>
<td valign="top" align="center">43.3</td>
<td valign="top" align="center">&#x02212;4.6</td>
<td valign="top" align="center">36.1</td>
<td valign="top" align="center">&#x02212;11.4</td>
</tr>
<tr style="background-color:#dee1e1">
<td valign="top" align="left" colspan="5"><bold>Income and education</bold></td>
</tr>
<tr>
<td valign="top" align="left">Median monthly net salary (EUR)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">837.9</td>
<td valign="top" align="center">129.7</td>
<td valign="top" align="center">718.6</td>
<td valign="top" align="center">123.7</td>
</tr>
<tr>
<td valign="top" align="left">Highest education<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">52.7</td>
<td valign="top" align="center">24.9</td>
<td valign="top" align="center">38.7</td>
<td valign="top" align="center">22.1</td>
</tr></tbody>
</table>
<table-wrap-foot>
<fn id="TN1">
<label>&#x0002A;</label>
<p>Reported annually, non-equalized.</p></fn>
<fn id="TN2">
<label>&#x0002A;&#x0002A;</label>
<p>Share of permanent residents aged 18 and over who have highest education or doctorate degree.</p></fn>
<p>Source: Authors&#x00027; calculations based on Central Statistical Bureau of Latvia (<xref ref-type="bibr" rid="B14">2025</xref>).</p>
</table-wrap-foot>
</table-wrap>
<p>The housing landscape within Riga&#x00027;s inner-city core is far from uniform, exhibiting significant heterogeneity at the administrative neighborhood level (<xref ref-type="table" rid="T2">Table 2</xref>). New-build gentrification appeared significant but spatially limited; only two neighborhoods showed a proportion of residents in new-build housing (post-2011) significantly above the city average (50.3% and 8.6% vs. city average of 2.7%). Such new developments can sometimes contribute to increased ethnic segregation (van Gent and Hochstenbach, <xref ref-type="bibr" rid="B61">2020</xref>). The remaining inner-city neighborhoods, while displaying lower levels of new construction, have often experienced fa&#x000E7;ade renovations and other forms of marginal gentrification.</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Housing profiles of Riga inner-city core neighborhoods: new-built (post-2011) and rental housing shares.</p></caption>
<table frame="box" rules="all">
<thead>
<tr style="background-color:#919498;color:#ffffff">
<th valign="top" align="left" colspan="2"></th>
<th valign="top" align="center"><bold>% of residents in new-built housing</bold></th>
<th valign="top" align="center"><bold>% of rental housing</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left" rowspan="7">Inner-city core neighborhoods</td>
<td valign="top" align="left">Avoti</td>
<td valign="top" align="center">0.3</td>
<td valign="top" align="center">48.0</td>
</tr>
 <tr>
<td valign="top" align="left">Brasa</td>
<td valign="top" align="center">2.2</td>
<td valign="top" align="center">27.4</td>
</tr>
 <tr>
<td valign="top" align="left">Centrs</td>
<td valign="top" align="center">1.7</td>
<td valign="top" align="center">37.8</td>
</tr>
 <tr>
<td valign="top" align="left">Grizinkalns</td>
<td valign="top" align="center">0.5</td>
<td valign="top" align="center">44.7</td>
</tr>
 <tr>
<td valign="top" align="left">Petersala-Andrejsala</td>
<td valign="top" align="center">8.6</td>
<td valign="top" align="center">20.8</td>
</tr>
 <tr>
<td valign="top" align="left">Skanste</td>
<td valign="top" align="center">50.3</td>
<td valign="top" align="center">18.3</td>
</tr>
 <tr>
<td valign="top" align="left">Old town</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">33.7</td>
</tr>
<tr>
<td valign="top" align="left">Riga avg.</td>
<td/>
<td valign="top" align="center">2.7</td>
<td valign="top" align="center">17.4</td>
</tr></tbody>
</table>
<table-wrap-foot>
<p>Source: Authors&#x00027; calculations based on Central Statistical Bureau of Latvia (<xref ref-type="bibr" rid="B14">2025</xref>).</p>
</table-wrap-foot>
</table-wrap>
<p>A key characteristic was the high proportion of rental housing, which exceeded twice the city average in most inner-city neighborhoods. This prevalence of rental accommodation facilitates settlement for newcomers, contributing to the formation of &#x0201C;urban arrival spaces&#x0201D;. These areas are often characterized by diverse international populations, high residential mobility (in- and out-migration), a concentration of disadvantaged residents, and relatively lower rents, though spatial outcomes remain strongly influenced by housing market dynamics (Hans et al., <xref ref-type="bibr" rid="B21">2019</xref>; Haase et al., <xref ref-type="bibr" rid="B19">2020</xref>). Notably, the neighborhoods featuring the highest share of new buildings tended to exhibit the lowest proportion of rental housing. This heterogeneous housing stock&#x02014;encompassing older buildings, renovated structures, limited new builds, and extensive rental options&#x02014;fundamentally shapes the opportunities and constraints faced by emerging ethnic groups seeking residence within Riga&#x00027;s inner-city core.</p>
</sec>
</sec>
<sec id="s4">
<title>4 Data and methods</title>
<sec>
<title>4.1 Data source and preparation</title>
<p>The primary data source for this analysis was the 2021 Population and Housing Census of Latvia, provided by the Central Statistical Bureau (CSB) of Latvia. We utilized anonymized, individual-level census records geo-referenced to precise residential locations. For analytical purposes, these individual data points were aggregated into a regular grid network of 1-hectare (100 m x 100 m) cells covering the study area (<xref ref-type="fig" rid="F1">Figure 1</xref>). This grid-based approach provided spatially consistent units, overcoming the limitations of varying administrative boundary sizes.</p>
<p>The 2021 census identified 330 distinct ethnic groups based primarily on self-identified affiliation, usually tracing direct ancestral lineage. For this study, we focused on the spatial distribution of the two largest established populations (Latvians and Russians) and the three most rapidly expanding emerging ethnic groups identified in the inner core (Indians, Uzbeks, and Vietnamese). A key limitation of the census data is the lack of provision for mixed-ethnic identification and the inclusion of &#x0201C;unspecified&#x0201D; and &#x0201C;unknown&#x0201D; categories. The number of people who did not declare their ethnicity increased by about 5 times between the 2011 and 2021 censuses, and the share of non-responses/non-selection to the ethnicity question in the total population reached 4.4% in 2021 compared to 0.7% in 2011.</p>
<p>Recent demographic trends between 2011 and 2021 underscored the rationale for focusing on these specific groups. City-wide, the combined proportion of the five historically largest ethnic groups (Latvians, Russians, Belarusians, Ukrainians, and Poles) in Riga decreased from 96% to 92%, indicating a gradual decline in the dominance of established communities (Central Statistical Bureau of Latvia, <xref ref-type="bibr" rid="B14">2025</xref>). Against this backdrop, the inner-city core exhibited distinct dynamics, which can be characterized by two key movements detailed in <xref ref-type="table" rid="T3">Table 3</xref>. First, the absolute populations of all established ethnic communities, except Ukrainians, declined within the inner core, but despite the numerical decrease, the proportional share of Latvians in the inner core remained high and stable (64.9% in 2021), far exceeding their share in the rest of Riga (44.6% in 2021). Second, emerging communities from outside Europe experienced exceptionally high relative growth within the inner core. Indian, Uzbek, and Vietnamese populations grew by 95.7%, 89.8%, and 99.1% respectively. Although starting from a very low baseline, this rapid influx led to a substantial increase in their proportional representation (up to 16-fold). This observed demographic shift, characterized by the decline of established populations alongside the rapid, concentrated growth of specific non-European groups, marked the inner core as a key site of transformation warranting detailed multi-scalar analysis of their residential geographies, especially considering their recent arrival and potential socioeconomic vulnerabilities.</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Changes in ethnic group composition and distribution in Riga&#x00027;s inner-city core and the rest of the city, 2011&#x02013;2021.</p></caption>
<table frame="box" rules="all">
<thead>
<tr style="background-color:#919498;color:#ffffff">
<th valign="top" align="left" rowspan="3"/>
<th valign="top" align="left" rowspan="3"/>
<th valign="top" align="center" colspan="4"><bold>% population</bold><xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></th>
<th valign="top" align="center" colspan="2"><bold>% change</bold></th>
</tr>
<tr style="background-color:#919498;color:#ffffff">
<th valign="top" align="center" colspan="2"><bold>Inner-city core</bold></th>
<th valign="top" align="center" colspan="2"><bold>Rest of the city</bold></th>
<th valign="top" align="center"><bold>Inner-city core</bold></th>
<th valign="top" align="center"><bold>Rest of the city</bold></th>
</tr>
 <tr style="background-color:#919498;color:#ffffff">
<th valign="top" align="center"><bold>2011</bold></th>
<th valign="top" align="center"><bold>2021</bold></th>
<th valign="top" align="center"><bold>2011</bold></th>
<th valign="top" align="center"><bold>2021</bold></th>
<th valign="top" align="center"><bold>2011&#x02013;2021</bold></th>
<th valign="top" align="center"><bold>2011&#x02013;2021</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td/>
<td valign="top" align="left">Latvians</td>
<td valign="top" align="center">64.7</td>
<td valign="top" align="center">64.9</td>
<td valign="top" align="center">43.7</td>
<td valign="top" align="center">44.6</td>
<td valign="top" align="center">&#x02212;3.5</td>
<td valign="top" align="center">&#x02212;5.5</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Ethnic minorities</td>
<td valign="top" align="center">35.3</td>
<td valign="top" align="center">35.1</td>
<td valign="top" align="center">56.3</td>
<td valign="top" align="center">55.4</td>
<td valign="top" align="center">&#x02212;4.3</td>
<td valign="top" align="center">&#x02212;9.0</td>
</tr>
<tr>
<td valign="top" align="left" rowspan="6">Declining ethnic groups</td>
<td valign="top" align="left">Russians</td>
<td valign="top" align="center">71.0</td>
<td valign="top" align="center">59.8</td>
<td valign="top" align="center">75.3</td>
<td valign="top" align="center">69.1</td>
<td valign="top" align="center">&#x02212;19.4</td>
<td valign="top" align="center">&#x02212;16.5</td>
</tr>
 <tr>
<td valign="top" align="left">Ukrainians</td>
<td valign="top" align="center">5.6</td>
<td valign="top" align="center">6.3</td>
<td valign="top" align="center">6.5</td>
<td valign="top" align="center">6.6</td>
<td valign="top" align="center">7.0</td>
<td valign="top" align="center">&#x02212;7.8</td>
</tr>
 <tr>
<td valign="top" align="left">Belarusians</td>
<td valign="top" align="center">5.0</td>
<td valign="top" align="center">4.8</td>
<td valign="top" align="center">7.4</td>
<td valign="top" align="center">7.1</td>
<td valign="top" align="center">&#x02212;8.9</td>
<td valign="top" align="center">&#x02212;13.3</td>
</tr>
 <tr>
<td valign="top" align="left">Poles</td>
<td valign="top" align="center">3.8</td>
<td valign="top" align="center">3.4</td>
<td valign="top" align="center">3.4</td>
<td valign="top" align="center">3.2</td>
<td valign="top" align="center">&#x02212;13.2</td>
<td valign="top" align="center">&#x02212;14.0</td>
</tr>
 <tr>
<td valign="top" align="left">Jews</td>
<td valign="top" align="center">4.0</td>
<td valign="top" align="center">2.7</td>
<td valign="top" align="center">1.1</td>
<td valign="top" align="center">0.8</td>
<td valign="top" align="center">&#x02212;35.3</td>
<td valign="top" align="center">&#x02212;35.7</td>
</tr>
 <tr>
<td valign="top" align="left">Lithuanians</td>
<td valign="top" align="center">1.9</td>
<td valign="top" align="center">1.8</td>
<td valign="top" align="center">1.5</td>
<td valign="top" align="center">1.5</td>
<td valign="top" align="center">&#x02212;9.5</td>
<td valign="top" align="center">&#x02212;11.6</td>
</tr>
<tr>
<td valign="top" align="left" rowspan="6">Growing ethnic groups</td>
<td valign="top" align="left">Vietnamese</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.4</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">99.1</td>
<td valign="top" align="center">83.8</td>
</tr>
 <tr>
<td valign="top" align="left">Indians</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">1.6</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.2</td>
<td valign="top" align="center">95.7</td>
<td valign="top" align="center">88.9</td>
</tr>
 <tr>
<td valign="top" align="left">Uzbeks</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">0.9</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.3</td>
<td valign="top" align="center">89.8</td>
<td valign="top" align="center">80.7</td>
</tr>
 <tr>
<td valign="top" align="left">Chinese</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">0.2</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">76.9</td>
<td valign="top" align="center">82.1</td>
</tr>
 <tr>
<td valign="top" align="left">French</td>
<td valign="top" align="center">0.2</td>
<td valign="top" align="center">0.5</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">60.5</td>
<td valign="top" align="center">59.2</td>
</tr>
 <tr>
<td valign="top" align="left">Azeris</td>
<td valign="top" align="center">0.2</td>
<td valign="top" align="center">0.4</td>
<td valign="top" align="center">0.3</td>
<td valign="top" align="center">0.3</td>
<td valign="top" align="center">47.2</td>
<td valign="top" align="center">&#x02212;10.9</td>
</tr></tbody>
</table>
<table-wrap-foot>
<p>Source: Authors&#x00027; calculations based on Central Statistical Bureau of Latvia (<xref ref-type="bibr" rid="B14">2025</xref>).</p>
<fn id="TN3">
<label>&#x0002A;</label>
<p>Share of specific ethnic groups is calculated as a proportion of all ethnic minorities, i.e., excluding Latvians and those whose ethnic group is unspecified or unknown.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>4.2 Addressing scale dependency: the k-nearest neighbors (k-NN) approach</title>
<p>To address the research questions regarding concentration, isolation, and segregation across varying spatial scales, and to mitigate the MAUP inherent in fixed administrative units, we employed a multi-scalar analytical approach based on k-nearest neighbors (k-NN), facilitated by advancements in software and high-resolution data availability.</p>
<p>The k-NN method was selected as it effectively mitigates the MAUP, enables nuanced multiscale analysis, and enhances inter-study comparability (Marci&#x00144;czak et al., <xref ref-type="bibr" rid="B38">2023</xref>). Multiscale measures derived from this approach reflect individual exposures to socio-spatial contexts, thereby providing a more comprehensive understanding of the multiscale nature of geographical context (Petrovi&#x00107; et al., <xref ref-type="bibr" rid="B49">2018</xref>). The k-NN method has proven instrumental in revealing subtle ethnic segregation patterns, as evidenced by numerous empirical studies, where residential segregation can simultaneously increase at one spatial scale while decreasing at another (&#x00160;imon et al., <xref ref-type="bibr" rid="B53">2023</xref>). Segregation varies across contextual dimensions&#x02014;including inter- and intra-city differences shaped by policies&#x02014;as well as scalar and ethnic-group-specific dimensions driven by individual and institutional forces, with k-NN facilitating micro- and meso-level analyses that help to reveal highly localized concentrations, which are often masked at broader spatial scales (Malmberg et al., <xref ref-type="bibr" rid="B35">2018</xref>; Marci&#x00144;czak et al., <xref ref-type="bibr" rid="B38">2023</xref>; Nielsen and Hennerdal, <xref ref-type="bibr" rid="B44">2017</xref>; Sleutjes et al., <xref ref-type="bibr" rid="B55">2018</xref>). In our study, the application of k-NN method facilitated a more comprehensive examination of the residential patterns of emergent ethnic groups at the inner-city core. Nonetheless, we recognize the conclusions of other researchers that this methodology does not encompass all potential advantages and does not entirely resolve the existing MAUP, as the current continuous grid coverage overlooks several physical aspects of the urban environment (Amcoff, <xref ref-type="bibr" rid="B1">2025</xref>). While physical geographical constraints on mobility may influence meso-scale indices (&#x000D6;sth and T&#x000FC;rk, <xref ref-type="bibr" rid="B48">2020</xref>), this effect is considered mitigated within our study area, as discussed previously.</p>
<p>The k-NN method constructs local environments, or &#x02018;&#x00027;bespoke neighborhoods&#x02018;&#x00027;, around each individual (or in this case, each grid cell centroid) based on population proximity rather than predefined boundaries. For each 1-hectare grid cell, the neighborhood is formed by expanding outwards until a specified population size (k) of nearest residential neighbors (individuals) is encompassed. This approach creates individualized, overlapping neighborhoods defined by the user-specified k-values, effectively mitigating MAUP and allowing for a nuanced assessment of segregation patterns across a continuum of spatial scales.</p>
<p>We implemented this approach using the EquiPop Flow software (&#x000D6;sth, <xref ref-type="bibr" rid="B46">2024</xref>). Calculations were performed for eight different k-values: 50, 100, 200, 400, 800, 1,600, 3,200, and 6,400 nearest neighbors. This range allows examination of patterns from highly localized micro-scales (e.g., k = 100, representing local neighborhood) to broader meso- and macro-scales (e.g., k = 6,400, representing broader community) (&#x000D6;sth et al., <xref ref-type="bibr" rid="B47">2015</xref>; &#x000D6;sth and T&#x000FC;rk, <xref ref-type="bibr" rid="B48">2020</xref>). Within Riga&#x00027;s inner-city core, these k-values corresponded to neighborhoods with average radii ranging from approximately 36 meters (k = 50) to 607 meters (k = 6,400), and areas from 0.4 hectares to 115.8 hectares, respectively, reflecting the varying spatial extents captured by different scales.</p>
</sec>
<sec>
<title>4.3 Quantitative indices</title>
<p>To measure different dimensions of spatial distribution, we calculated three indices for each of the five ethnic groups (Indians, Uzbeks, Vietnamese, Latvians, and Russians) at each analytical scale (k-value). The selected indices were the location quotient (LQ), the spatial isolation index (SII), and the index of segregation (IS), following the methodology of Tammaru et al., <xref ref-type="bibr" rid="B60">2016</xref> and van Ham et al. (<xref ref-type="bibr" rid="B62">2021</xref>), adapted for the k-NN context based on the methodology of Imeraj et al. (<xref ref-type="bibr" rid="B26">2018</xref>).</p>
<p>Location quotient (LQ) measures the relative concentration of an ethnic group within a specific k-NN neighborhood compared to the group&#x00027;s overall representation in the inner-city core study area. It identifies areas of over- or under-representation at different scales. The formula is:</p>
<disp-formula id="E1"><label>(1)</label><mml:math id="M1"><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>L</mml:mtext></mml:mstyle><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>Q</mml:mtext></mml:mstyle><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>,</mml:mo><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>x</mml:mtext></mml:mstyle><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>,</mml:mo><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mrow></mml:msub></mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mfrac><mml:mo>&#x000F7;</mml:mo><mml:mfrac><mml:mstyle mathvariant='bold'><mml:mtext>X</mml:mtext></mml:mstyle><mml:mstyle mathvariant='bold'><mml:mtext>T</mml:mtext></mml:mstyle></mml:mfrac></mml:mrow></mml:math></disp-formula>
<p>where x<sub>i, k</sub> is the ethnic group size in neighborhood i with k-nearest neighbors; k is the number of nearest neighbors; X is the total ethnic group size in the study area; T represents the total population of the study area, serving as a benchmark to determine whether an ethnic group is more or less concentrated in an individualized neighborhood compared to its average presence across the entire study area. Consistent with Brown and Chung (<xref ref-type="bibr" rid="B11">2006</xref>), we defined a LQ range between 0.85 and 1.20, where a value of 1.20 or above indicates significant concentration and a value of 0.85 or below indicates under-representation.</p>
<p>Spatial isolation index (SII) measures the probability of an individual from an ethnic group encountering another individual from the same group across k-NN neighborhood. It reflects the degree of potential intra-group interaction within localized environments. The formula is:</p>
<disp-formula id="E2"><label>(2)</label><mml:math id="M2"><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>SI</mml:mtext></mml:mstyle><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>I</mml:mtext></mml:mstyle><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mstyle displaystyle='false'><mml:msub><mml:mo>&#x02211;</mml:mo><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>=</mml:mo><mml:mstyle mathvariant='bold'><mml:mn>1</mml:mn></mml:mstyle></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy='false'>(</mml:mo><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>x</mml:mtext></mml:mstyle><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>,</mml:mo><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mrow></mml:msub><mml:mo>&#x000D7;</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>x</mml:mtext></mml:mstyle><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>,</mml:mo><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mrow></mml:msub></mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mfrac><mml:mo stretchy='false'>)</mml:mo></mml:mrow></mml:mstyle></mml:mrow><mml:mrow><mml:mstyle displaystyle='false'><mml:msub><mml:mo>&#x02211;</mml:mo><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>=</mml:mo><mml:mstyle mathvariant='bold'><mml:mn>1</mml:mn></mml:mstyle></mml:mrow></mml:msub><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>x</mml:mtext></mml:mstyle><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>,</mml:mo><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mstyle></mml:mrow></mml:mfrac></mml:mrow></mml:math></disp-formula>
<p>where k is the number of nearest neighbors; x<sub>i, k</sub> is the ethnic group size in neighborhood i with k-nearest neighbors; <inline-formula><mml:math id="M3"><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:mfrac></mml:math></inline-formula> is the proportion of the ethnic group in neighborhood i with k-nearest neighbors. Values range from 0 (0% likeliness of encountering a co-ethnic neighbor) to 1 (100% certainty of encountering only co-ethnic neighbors).</p>
<p>Index of segregation (IS) measures the degree to which an ethnic group&#x00027;s spatial distribution across k-NN neighborhoods differs from that of the rest of the population. The formula is:</p>
<disp-formula id="E3"><label>(3)</label><mml:math id="M4"><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>I</mml:mtext></mml:mstyle><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>S</mml:mtext></mml:mstyle><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:msub><mml:mo>=</mml:mo><mml:mstyle mathvariant='bold'><mml:mn>0</mml:mn></mml:mstyle><mml:mo>.</mml:mo><mml:mstyle mathvariant='bold'><mml:mn>5</mml:mn></mml:mstyle><mml:mstyle displaystyle='true'><mml:munder><mml:mo>&#x02211;</mml:mo><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>=</mml:mo><mml:mstyle mathvariant='bold'><mml:mn>1</mml:mn></mml:mstyle></mml:mrow></mml:munder><mml:mrow><mml:mrow><mml:mo>|</mml:mo> <mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>x</mml:mtext></mml:mstyle><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>,</mml:mo><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mrow></mml:msub></mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>X</mml:mtext></mml:mstyle></mml:mfrac><mml:mo>&#x02212;</mml:mo><mml:mfrac><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle><mml:mo>&#x02212;</mml:mo><mml:msub><mml:mstyle mathvariant='bold'><mml:mtext>x</mml:mtext></mml:mstyle><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>i</mml:mtext></mml:mstyle><mml:mo>,</mml:mo><mml:mstyle mathvariant='bold'><mml:mtext>k</mml:mtext></mml:mstyle></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mstyle mathvariant='bold'><mml:mtext>T</mml:mtext></mml:mstyle><mml:mo>&#x02212;</mml:mo><mml:mstyle mathvariant='bold'><mml:mtext>X</mml:mtext></mml:mstyle></mml:mrow></mml:mfrac></mml:mrow> <mml:mo>|</mml:mo></mml:mrow></mml:mrow></mml:mstyle></mml:mrow></mml:math></disp-formula>
<p>where x<sub>i, k</sub> is the ethnic group size in neighborhood i with k-nearest neighbors; X is the total ethnic group size in the study area; k is the number of nearest neighbors; T is the total population in the study area. Values range from 0 (perfect integration), where the group&#x00027;s distribution mirrors that of the rest of population, to 1 (complete segregation), where the group is exclusively concentrated.</p>
<p>We calculated the segregation indices using the Geo-Segregation Analyzer v.1.2. (Apparicio et al., <xref ref-type="bibr" rid="B4">2014</xref>) and performed all subsequent spatial analysis and map production in ArcGIS Pro.</p>
</sec>
</sec>
<sec sec-type="results" id="s5">
<title>5 Results</title>
<p>The multi-scalar analytical strategy employed in this study yielded significant insights into the contemporary residential geographies of Riga&#x00027;s inner-city core, revealing stark contrasts between established populations and emerging ethnic communities, thereby addressing the central research questions regarding scale-dependent concentration, isolation, and segregation. The findings, derived from location quotient (LQ), spatial isolation index (SII), and index of segregation (IS) analyses of 2021 census data processed through a k-nearest neighbor framework, not only quantified current patterns but also situated them within Riga&#x00027;s unique post-socialist context and the broader dynamics of new migration in Central and Eastern Europe.</p>
<sec>
<title>5.1 Aggregate concentrations and ethnic geographies</title>
<p>LQ analysis underscored the divergent spatial concentration processes operating within the inner core. The relatively even distribution of the established Latvian and slightly more concentrated Russian populations, exhibiting low and scale-stable maximum LQ values (<xref ref-type="fig" rid="F2">Figure 2</xref>), aligned with Riga&#x00027;s historical legacy of comparatively low ethno-social segregation documented in the literature. Conversely, the emerging Indian, Uzbek, and Vietnamese communities demonstrated patterns characteristic of recent migration flows concentrating in specific urban locales and were highly sensitive to scale. Their exceptionally high maximum LQ values at micro- and meso-scales, despite more moderate, seemingly balanced mean LQs&#x02014;a consequence of zero presence in many generally populated local units&#x02014;signified intense clustering rather than widespread integration. This finding validated the necessity of the multi-scalar methodology adopted, as aggregate measures or single-scale analyses would obscure the profound localized overrepresentation shaping the initial settlement geography of these newcomers.</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>Maximum and mean location quotient in inner-city core of Riga, by ethnic group and k-nearest neighbors in 2021. Source: Authors&#x00027; calculations based on Central Statistical Bureau of Latvia (<xref ref-type="bibr" rid="B14">2025</xref>).</p></caption>
<alt-text>Two line graphs compare LQ max and LQ mean values based on k-nearest neighbors for five ethnic groups: LAT, RUS, IND, UZB, and VIE. The left graph shows LQ max decreasing steeply as k-nearest neighbors increase. The right graph indicates LQ mean values stabilizing around one after an initial fluctuation. Both graphs share the x-axis labeled with k-nearest neighbors ranging from 50 to 6400.</alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frsc-07-1612980-g0002.tif"/>
</fig>
<p>Spatial mapping across varying scales (k = 200, 800, 6,400; <xref ref-type="fig" rid="F3">Figure 3</xref>) further illuminated the distinct territorial expressions of these groups within Riga&#x00027;s transforming inner-city landscape. While all emerging groups exhibited high concentration clusters at all scales, their specific geographies differed: Indians showed clustering across several neighborhoods with a notable locus in the southwest, including Old Town, and balanced representation in the southeast; Uzbeks displayed partial overlap with Indians but were underrepresented in the east and also showed a distinct presence near established Russian communities in the north, particularly the western section; Vietnamese presented the most spatially confined pattern, concentrated strongly in the southeast across all scales. In contrast, at the micro- and meso-scale, both Indians and Uzbeks showed clusters of over- and underrepresentation across most study area, indicating localized concentrations within a broader distribution. Crucially, the macro-scale analysis revealed a convergence zone in the southern inner core for all studied groups. This area aligned with the characteristics of an &#x0201C;urban arrival space&#x0201D; identified in the contextual analysis&#x02014;marked by higher residential density and rental housing prevalence indicative of transient populations, relative affordability attracting diverse socio-economic strata, and fragmented gentrification manifesting as uneven urban redevelopment&#x02014;suggesting its pivotal role in accommodating diverse contemporary inflows.</p>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption><p>Distribution of location quotients of Latvians, Russians, Indians, Uzbeks, and Vietnamese in the inner-city core of Riga among 200, 800, and 6,400 nearest neighbors in 2021. Source: Authors&#x00027; calculations based on Central Statistical Bureau of Latvia (<xref ref-type="bibr" rid="B14">2025</xref>).</p></caption>
<alt-text>Twelve grid-based maps show the spatial distribution of five different ethnic groups in a city: Latvians, Russians, Indians, Uzbeks, and Vietnamese. Each group is represented in three columns with varying smoothing parameters (k=200, k=800, k=6400). The color scale indicates concentration, from dark blue (low) to dark purple (high). Maps illustrate how population concentrations change with different smoothing levels.</alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frsc-07-1612980-g0003.tif"/>
</fig>
<p>Beyond this convergence, the divergent spatial patterns offered insights into the complex interplay of preferences, constraints, and ethnic infrastructure shaping settlement. The Indian concentration may reflect attraction to specific urban amenities, whereas the Uzbek proximity to Russian populations potentially indicates leveraging existing linguistic and social networks, consistent with K&#x00159;&#x000ED;&#x0017E;kov&#x000E1; and &#x00160;imon (<xref ref-type="bibr" rid="B32">2022</xref>) observations on the role of prior migration history on the availability of ethnic infrastructure. Simultaneously, the apparent spatial avoidance patterns&#x02014;Latvians underrepresented near Russians, and Russians underrepresented where emerging groups concentrate&#x02014;provided empirical traces of the potential socio-spatial separation within this diversifying post-socialist context, possibly linked to the broader processes of socio-economic divergence and gentrification previously discussed.</p>
</sec>
<sec>
<title>5.2 Spatial isolation and segregation</title>
<p>Turning to quantitative measures of isolation and segregation, the findings reinforced the scale-dependent nature of these phenomena. The SII indicated relatively stable potential for intra-group contact for established groups across scales, while for emerging groups, the higher, yet low probability of encountering co-ethnics within immediate micro-neighborhoods diminished to zero probability in broader contexts (<xref ref-type="fig" rid="F4">Figure 4</xref>). Vietnamese consistently exhibited the highest isolation, reflecting their tight geographical clustering.</p>
<fig id="F4" position="float">
<label>Figure 4</label>
<caption><p>Spatial isolation index and segregation index in the inner-city core of Riga, by ethnic group and k-nearest neighbors in 2021. Source: Authors&#x00027; calculations based on Central Statistical Bureau of Latvia (<xref ref-type="bibr" rid="B14">2025</xref>).</p></caption>
<alt-text>Two line charts. The left chart shows the Spatial Isolation Index against k-nearest neighbors, with lines for LAT, RUS, IND, UZB, and VIE, all remaining relatively flat. The right chart depicts the Segregation Index against k-nearest neighbors, with LAT and RUS decreasing slightly, while IND, UZB, and VIE decline more steeply.</alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frsc-07-1612980-g0004.tif"/>
</fig>
<p>The IS demonstrated a markedly greater sensitivity to scale variation and inter-group variations than the SII and revealed profound levels of spatial separation for emerging groups, particularly at finer scales (<xref ref-type="fig" rid="F4">Figure 4</xref>). Micro-scale IS values exceeding 0.7 starkly quantified the local unevenness, standing 3&#x02013;4 times higher than those for Latvians and Russians and confirming the emergence of significant segregation patterns despite Riga&#x00027;s historical context of low ethnic segregation. The pronounced scale sensitivity of the IS, decreasing significantly but remaining high even at macro-scales (more than 2 times higher than those for Latvians and Russians), underscored the critical importance of the multi-scalar approach adopted. It is also important to note a methodological nuance: indices like the IS are sensitive to group size and can produce inflated values when applied to small ethnic populations at a fine neighborhood level, as minor clustering of a small group can result in a distribution that appears highly uneven (Stonawski et al., <xref ref-type="bibr" rid="B58">2022a</xref>). Furthermore, the differential rates at which IS declined for each emerging group&#x02014;highest for Uzbeks and lowest for Vietnamese&#x02014;suggests that segregation intensity and its scale-dependency are influenced by complex factors beyond mere group size, likely including cultural differences and underlying socioeconomic vulnerabilities discussed earlier. This observation is supported by the work of Stonawski et al. (<xref ref-type="bibr" rid="B58">2022a</xref>), who also identified that even groups of a similar size can experience highly different levels of concentration.</p>
<p>In essence, these results painted a complex picture of Riga&#x00027;s inner-city core undergoing dynamic diversification. While the established groups maintained patterns resonant with historical integration, emerging communities were carving out distinct, highly concentrated, and significantly segregated niches within the urban fabric. The intensity and nature of these patterns were profoundly scale-dependent, highlighting the necessity of nuanced, multi-scalar analysis for understanding contemporary urban segregation processes, particularly within the evolving context of new migration to post-socialist European cities.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s6">
<title>6 Discussion</title>
<p>The empirical findings of this study, which revealed high, scale-dependent residential segregation for emerging ethnic groups within Riga&#x00027;s inner-city core, necessitate a critical engagement with established theoretical frameworks, particularly spatial assimilation theory. While this theory posits a gradual dispersal from initial co-ethnic clusters contingent upon cultural and socioeconomic integration, the patterns observed in Riga present risks of growing spatial inequalities. Although it remains early to ascertain long-term outcomes, the intensity of emerging ethnic group local clustering raises important questions about the future socio-spatial fabric of the city.</p>
<p>The uncertainty surrounding the assimilation pathway for Riga&#x00027;s emerging groups is further amplified by broader empirical evidence questioning the universality and pace of spatial integration. Literature consistently highlights persistent ethnic hierarchies and limitations on upward spatial mobility, particularly for non-European migrants, even when controlling for income gains (Wessel et al., <xref ref-type="bibr" rid="B65">2017</xref>; Kadarik, <xref ref-type="bibr" rid="B27">2020</xref>). Other studies indicate that ethnic desegregation can occur based on upward economic status (Catney and Simpson, <xref ref-type="bibr" rid="B13">2010</xref>; Catney, <xref ref-type="bibr" rid="B12">2016</xref>), however, when it occurs, is often a slow process, spanning more than a decade and typically involving only a fraction of an immigrant cohort (Andersen, <xref ref-type="bibr" rid="B2">2016</xref>; Vogiazides and Chihaya, <xref ref-type="bibr" rid="B63">2020</xref>), with assimilation potentially proceeding even more gradually for groups originating from lower-income or culturally distinct countries (Murayama and Nagayasu, <xref ref-type="bibr" rid="B42">2021</xref>). A similar pattern has also been observed in Prague, where groups with greater cultural and socio-economic distance from majority population exhibit higher residential segregation (&#x00160;imon et al., <xref ref-type="bibr" rid="B53">2023</xref>). These insights are highly pertinent to Riga, suggesting that the pronounced initial clustering observed for Indians, Uzbeks, and Vietnamese may reflect not only voluntary self-segregation but also structural barriers, making a smooth transition toward dispersal unlikely without targeted interventions.</p>
<p>The concentration of emerging groups within specific inner-city areas identified as &#x0201C;arrival spaces&#x0201D;&#x02014;characterized by higher rental housing prevalence and relative affordability&#x02014;underscores the critical mediating role of the housing market (Hans et al., <xref ref-type="bibr" rid="B21">2019</xref>). As noted by Imeraj et al. (<xref ref-type="bibr" rid="B26">2018</xref>), liberalized private housing markets can inadvertently exacerbate ethnic concentration. This context renders simplistic policy solutions, such as promoting mixing solely based on socioeconomic criteria, potentially ineffective or even counterproductive. Such approaches often underestimate systemic barriers like discrimination and can yield unintended consequences, including accelerated gentrification or deepening segregation within specific housing tenures (Bolt et al., <xref ref-type="bibr" rid="B10">2010</xref>). The findings for Riga highlight this complexity; for instance, the spatial patterning of the Uzbek community, partially overlapping with areas of higher Russian concentration, suggests a significant influence of pre-existing ethnic infrastructure, a factor often overlooked in standard mixing policies. Therefore, effective integration strategies need to be sensitive to the multi-scalar nature of the observed segregation, acknowledge the importance of ethnic networks and infrastructure, confront potential discrimination, and operate within the realities of Riga&#x00027;s specific post-socialist housing legacies and ongoing fragmented gentrification. Ultimately, effective strategies must address the structural inequalities, housing market dynamics, and potential discriminatory barriers that shape residential outcomes to foster genuine integration opportunities within this evolving urban landscape.</p>
</sec>
<sec sec-type="conclusions" id="s7">
<title>7 Conclusions</title>
<p>This study presented a geographically detailed analysis of ethnic residential patterns within the inner-city core of Riga, focusing on emerging ethnic groups. By utilizing individualized scalable neighborhoods, we addressed key research questions concerning the scale-dependent nature of concentration, spatial isolation, and segregation. In answer to our first research question, the analysis demonstrated high levels of residential concentration for emerging groups (Indians, Uzbeks, Vietnamese), particularly at micro-scales and manifesting spatially as distinct clustering, which contrasted sharply with the relative dispersal of established groups. Addressing the second question, the analysis revealed high segregation for emerging groups, particularly at local scales, which decreased substantially yet remained elevated at broader scales. Spatial isolation, while lower overall, also showed scale dependence for these groups, decreasing from micro- to macro-scales. Together, these findings revealed patterns of large variability across spatial scales and ethnic groups, and with indices tending to decrease as the scale broadens, confirmed the most pronounced segregation is at the hyper-local level. This study confirmed that substantial variability across scales persists even within a relatively compact urban zone, underscoring the importance of methodological approaches that capture this complexity.</p>
<p>Furthermore, distinct geographical patterns emerged, including the clustering of emerging groups in an &#x0201C;arrival space&#x0201D; within the south of Riga&#x00027;s inner-city core, aligning with observations in other European settings on &#x0201C;urban arrival spaces&#x0201D;, where ethnically diverse populations, elevated rates of population turnover, and comparatively lower rents foster residential areas favorable to immigrants (Haase et al., <xref ref-type="bibr" rid="B19">2020</xref>; Gerten et al., <xref ref-type="bibr" rid="B17">2023</xref>). The noted overlap between Uzbeks and Russians also suggests the influence of pre-existing ethnic infrastructure. These findings indicated that both self-segregation and place stratification mechanisms may be at work, potentially leading to challenges related to socio-spatial inequalities and complicating straightforward spatial assimilation narratives, especially given the uncertain assimilation trajectories for non-European immigrants (Drouhot and Nee, <xref ref-type="bibr" rid="B16">2019</xref>; Zuccotti, <xref ref-type="bibr" rid="B66">2019</xref>).</p>
<p>Our findings contribute significantly to understanding ethnic residential patterns in this specific urban context of urban shrinkage and post-socialist transition and lay groundwork for observing the progression of ethnic residential patterns in Riga as the city continues to experience increased diversity. Acknowledging the time-bound scope of this analysis, which cannot definitively assess residential trajectories, future longitudinal studies are pivotal to determine if current patterns represent a temporary stage or more entrenched segregation. Moreover, research incorporating activity spaces and daily mobility patterns would yield a more holistic understanding of inter-ethnic interactions beyond residential areas, thus addressing the limitation of this study&#x00027;s emphasis on nighttime segregation (Silm and Ahas, <xref ref-type="bibr" rid="B52">2014</xref>).</p>
<p>Ultimately, our findings suggest that although emerging ethnic groups currently represent a relatively small segment of Riga&#x00027;s population, their further expansion and significant spatial concentration within the inner-city core may presage challenges related to socio-spatial inequalities. Thus, the results carry substantial policy implications, underscoring the need for policymakers to acknowledge the multi-scalar and potentially enduring nature of segregation for emerging groups. Rather than presuming that spatial assimilation will naturally transpire over time, policies should address structural inequalities and specific housing obstacles faced by non-European immigrants in securing housing across diverse neighborhoods, moving beyond simplistic socio-economic mixing approaches. As Riga and similar Central and Eastern European cities navigate increasing diversity, developing a nuanced, scale-sensitive understanding of ethnic segregation, supported by appropriate methodological approaches, is essential for promoting inclusive urban development and social cohesion.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s8">
<title>Data availability statement</title>
<p>The georeferenced census data utilized in this study are governed by an agreement between the Central Statistical Bureau of the Republic of Latvia and the University of Latvia. Disaggregated ethnic data are deemed sensitive, and their dissemination may potentially compromise individual privacy. The data in question were anonymized and processed in compliance with a confidentiality agreement, adhering to all data protection, privacy regulations, and contractual obligations. For further information regarding data usage, please contact <email>maris.berzins&#x00040;lu.lv</email>.</p>
</sec>
<sec sec-type="author-contributions" id="s9">
<title>Author contributions</title>
<p>SB: Methodology, Writing &#x02013; original draft, Investigation, Visualization, Validation, Formal analysis, Software, Writing &#x02013; review &#x00026; editing. MB: Data curation, Formal analysis, Conceptualization, Writing &#x02013; review &#x00026; editing, Supervision, Writing &#x02013; original draft.</p>
</sec>
<sec sec-type="funding-information" id="s10">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Recovery and Resilience Facility Project &#x0201C;Internal and External Consolidation of the University of Latvia&#x0201D; Under Grant No. 5.2.1.1.i.0/2/24/I/CFLA/007.</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s11">
<title>Generative AI statement</title>
<p>The author(s) declare that Gen AI was used in the creation of this manuscript. During manuscript preparation, the AI language model Gemini Advanced 2.5 Pro (Google) was utilized for language editing.</p>
</sec>
<sec sec-type="disclaimer" id="s12">
<title>Publisher&#x00027;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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