SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

MANUEL DAMMERT-GUARDIA, LUIS RIVERA-SEGURA

REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024

PÁG. 62 - 77

ISSN 0717 - 3997 / 0718 - 3607

This work is part of the research “Irregular and informal urban growth in Arequipa, Lima, and Tacna,” funded by CONCYTEC

through the PROCIENCIA program within the framework of the E041-2022-03 Financial Schemes Competition, called “Applied

Research Projects in Social Sciences,” under contract PE501078514-2022. In addition, it had the support of PUCP’s FAI-0037-

2022.

Doctor en Sociología

Profesor de el Departamento de Ciencias Sociales

Ponticia Universidad Católica del Perú, Lima, Perú

https://orcid.org/0000-0001-7583-1878

mdammert@pucp.edu.pe

Magíster en Estudios Urbanos

Asistente de docencia en la Facultad de Letras y Ciencias Humanas

Ponticia Universidad Católica del Perú, Lima, Perú

https://orcid.org/0009-0005-0019-6124

lerivera@pucp.edu.pe

https://doi.org/10.22320/07183607.2024.27.49.05

Recibido: 25-01-2024

Aceptado: 06-05-2024

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

RESIDENTIAL SEGREGATION

IN TWO CITIES IN PERU’S

SOUTHERN MACRO-REGION

MANUEL DAMMERTGUARDIA 2

LUIS RIVERASEGURA 3

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

MANUEL DAMMERT-GUARDIA, LUIS RIVERA-SEGURA

REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024

PÁG. 62 - 77

ISSN 0717 - 3997 / 0718 - 3607

Studies on residential segregation in Peru have privileged the study of the capital city, to the detriment of cities with other

scales and contexts. This article analyzes the residential segregation levels and patterns in two Peruvian cities in the southern

macro-region: Arequipa and Tacna. Based on data provided by the 2007 and 2017 censuses, synthetic and spatial segregation

indicators were analyzed in both cities, taking as reference the educational level of the head of household and calculating the

data on the block, census area, and district scales. The results show that inherited segregation patterns are maintained, while at

the same time, the growing middle-class sectors are included in the more consolidated working-class settlements. In addition,

processes that reinforce segregation were identied, such as the modication of the role of central areas, urban land production

for the upper-class areas, and homogeneity of disadvantages in new areas of urban expansion, which are linked, particularly

in the city of Arequipa, to the occupation of risk areas. The study suggests that these processes shape a low-scale urban

segregation or fragmentation pattern in both cities, which coexists with inherited patterns.

Keywords: residential segregation, segregation patterns, urbanization, urban growth.

Los estudios sobre segregación residencial en el Perú han privilegiado el estudio de la ciudad capital, en desmedro de ciudades

con otras escalas y contextos. En este artículo analizamos los niveles y patrones de segregación residencial en dos ciudades

del Perú, ubicadas en la macro región sur: Arequipa y Tacna. A partir de los datos proporcionados por los censos de los años

2007 y 2017, se analizaron indicadores sintéticos y espaciales de segregación en ambas ciudades, tomando como referencia

el nivel educativo del jefe de hogar y calculando los datos para las escalas de manzana, área censal y distrito. Los resultados

muestran que se mantienen patrones de segregación heredados, al mismo tiempo que los crecientes estratos medios son

incluidos en los asentamientos populares más consolidados. Además, se identicaron procesos que refuerzan la segregación,

como la modicación de la función de las áreas centrales, producción de suelo urbano para estratos altos y homogeneidad de

desventajas en nuevas áreas de expansión urbana, las que están vinculadas particularmente en la ciudad de Arequipa en la

ocupación sobre áreas de riesgo. El estudio sugiere que estos procesos dan forma a un patrón de segregación urbana de baja

escala o fragmentación en ambas ciudades que coexisten con los patrones heredados.

Palabras clave: segregación residencial, patrones de segregación, urbanización, crecimiento urbano.

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

MANUEL DAMMERT-GUARDIA, LUIS RIVERA-SEGURA

REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024

PÁG. 62 - 77

ISSN 0717 - 3997 / 0718 - 3607

I. INTRODUCTION

Residential segregation (RS, from now on) - understood

as the distribution and concentration patterns of the

population in the region - is one of the most important

fields of analysis in urban studies. RS patterns are

based on socio-economic, demographic, and/or ethnic

criteria to configure opportunity structures that impact

the quality of life. The production of urban space and

the levels of segregation do not depend exclusively

on individual decisions but on the institutional

arrangements of the land and real estate markets,

the location of public investment policies, and the

contextual conditions of each urban agglomeration.

RS “is a multiscale process driven by diverse systemic

mechanisms and contextual factors, their legacies and

transformation, and not by inevitable global forces,

individual behavior, or pure market logic” (Arbaci, 2019,

p5). It depends on each city’s historical trajectory,

multiple dimensions, and institutional arrangements

(Maloutas, 2012). In operational terms, RS refers to the

degree of separation of two or more groups in the same

area (Massey & Denton, 1988).

RS is a field that is not without controversies. Debates

have been identified around whether contemporary

territorial transformations generate conditions for the

increase of RS, large-scale segregation patterns, or

small-scale segregation and socio-spatial fragmentation

(Borsdorf & Hidalgo, 2010; Janoschka, 2002; Prévot

Schapira, 2001; Sabatini et al., 2001), or the relationship

between segregation and inequalities (Ruiz-Tagle &

López, 2014; Sabatini et al., 2020). In methodological

terms, discussions arise on synthetic (such as the

dissimilarity index) and spatial indices (Sánchez &

Gómez, 2021), operationalization (Massey & Denton,

1998), and the impact of the analysis scale on the

results (Marengo & Elorza, 2014; Sabatini et al., 2001),

the use of census tracts or other delimitations. In

addition, whether the RS patterns are similar when

comparing cities of different scales and sizes has

recently been discussed (Garreton et al., 2020; Krupka,

2007; Mayorga, 2021; Monkkonen, 2012).

The following article contributes to these debates

by comparing two regional cities in Peru’s southern

macro-region. The starting point is moving away from

the country’s metropolitan area and capital, Lima, and

its almost 10 million inhabitants, to analyze Tacna and

Arequipa, with populations of around 300,000 and

1 million, respectively (INEI, 2017). These cities were

chosen for the following three reasons: a) Both belong

to the same southern macro-region; b) Arequipa is

the second most populous city in the country; and

c) Both cities have population growth rates higher

than the country’s capital and a significant recent

regional growth. The dimensions are the distribution

and homogeneity/heterogeneity, analyzed from

the socioeconomic variable, taking as a reference

the level of education achieved by the head of

household. The analysis uses spatial and non-

spatial indexes with information from the 2007 and

2017 censuses, and three scales of analysis were

incorporated: block, census area, and district.

The article identifies various debates about RS

in urban studies to delimit its working premises.

Subsequently, the socio-spatial context of the

analyzed cities and the results of the spatial and non-

spatial indices are described. Finally, the article marks

the coexistence of both cities’ large- and small-scale

segregation dynamics as relevant.

II. THEORETICAL FRAMEWORK.

Segregation: debates and scales

RS is one of the central topics in urban studies.

In particular, it is a crucial analytical input to

understanding growth models, location patterns,

relationships between social inequalities and urban

form, and the role of the land and housing market,

among other things. In the United States, the racial,

sociocultural, and socioeconomic ethnic components

have been discussed from different angles (Massey

& Denton, 1998). On the other hand, various

adaptations and uses of the analysis models were

made in Latin America to understand Residential

Segregation (Sabatini, Cáceres & Cerda, 2001) from

critical readings (Ruiz Tagle & López, 2014). RS is

associated with multiple processes (de Queiroz,

2017). Among the main topics, the following stand

out: link with “urban informality” (Clichevsky, 2000),

public policies, housing, and land markets (Águila

& Prada, 2020; Prada-Trigo & Andrade, 2022), and

the effects of migration. At the same time, RS is a

structure of opportunities (Katzman, 2001) associated

with dynamics of labor insertion (Niembro et al.,

2019), income generation (Gomes & de Queiroz,

2021), territorial stigmas (Elorza, 2019), citizen

security (Arriagada & Morales, 2006), access to the

labor market (Niembro et al., 2019) and can influence

- albeit ambiguously - social networks (Marques,

2015) and their resources (Otero et al., 2021).

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024

PÁG. 62 - 77

ISSN 0717 - 3997 / 0718 - 3607

Different debates regarding the analyzed literature

can be highlighted. Faced with the question about

the particularity of the RS patterns in Latin American

cities (Rodríguez & Arriagada, 2004), an argument was

made in favor of a large-scale pattern as a result of the

characteristics of the massive urbanization process of

the 20

century, the barriers of access to housing in the

formal markets, and the processes of irregular access

and self-construction. On the other hand, the move

to smaller-scale segregation, or fragmentation, was

proposed (Borsdorf & Hidalgo, 2010; Prévot Schapira,

2001). An intermediate line shows that the RS patterns

are explained by the forms of measurement and work

scale or by their coexistence (Aguilar & Mateos, 2021).

A second debate is about the relationship

between segregation, extension, population, and

metropolization, which were extended to comparative

studies that identified general characteristics of

RS patterns between cities of different sizes, with

or without metropolitan conditions. Garreton et

al. (2020) proposed an analysis model where a

correlation between size and level of segregation was

demonstrated for Chile. According to Krupta (2007),

size and segregation have no significant differences.

Monkkonen (2012) analyzed more than 100 urban areas

in Mexico and concluded the following: “The historical

urban development processes are more important in

determining segregation patterns than the universal

land market factors” (p. 143). The contexts are specific

configurations of historical, territorial, and institutional

processes to understand the RS patterns (Theodore et

al., 2009). Namely, segregation is a concept linked to

the context (Maloutas, 2012), where people are more

inclined towards cities other than the capitals.

In the case of intermediate cities, Toro and Orozco

(2018) discussed the particularities of segregation

patterns in intermediate cities, highlighting the

possibility of different processes - such as forms of

negotiation and tolerance of proximity between

social groups. Paying particular attention to real estate

growth, for their part, Águila and Prada (2020) identified

in the city of Valdivia, Chile, a “segregation marked by

opposite poles, i.e., there is a voluntarily segregated

group, which is a high socioeconomic class [...] and a

peripheral cordon” (p.39). For the case of intermediate

cities in Mexico, Ruiz et al. (2021) concluded that the

growth of intermediate cities follows a periphery

pattern, with homogeneous old areas, indigenous

populations in dispersed localities, and swaths of

“newcomer” populations in disadvantaged situations.

III. CASE STUDY

Cities of Peru’s Southern Macro-region

Similar to regional trends (Cebrián et al., 2022), Peru’s

urban growth in the 20

and 21

centuries was mainly

defined by the sustained increase in urban population,

which rose from 35.4% in 1940 to 82.4% in 2017. The

urban primacy, with a third of the national population

residing in the capital (Lima), forms part of the centralism

and weaknesses of networks of cities integrated into the

national territory (Espinoza et al., 2022; Galarza, 2011).

The country’s System of Cities and Population Centers

revealed that the cities with the highest demographic

growth between 2007 and 2017 are not the capital. The

population growth rates of intermediate major cities and

regional metropolises are higher than those of national

metropolises (Table 1). Metropolitan Lima saw the most

significant growth in the 20

century, with intercensal

growth rates of 5.2 (1940-1961), 5.7 (1961-1972), 3.7 (1972-

1981), 2.7 (1981-1993), and 2.0 (1993-2007). This meant

rising from about 500,000 inhabitants in 1940 to almost 10

million in 2017. Although the growth rate has decreased,

it is still the country’s most important urban area.

The predominance of Lima is also reflected in urban

studies, which presented excessive attention to this to the

detriment of other processes at a national level (Calderón

& Vega-Centeno, 2016). Studies focusing on the capital

usually argue that this case is representative of the rest

of the country’s urban processes. This sometimes leads

to the reproduction of what Vergara and Salazar (2021)

mention, namely studying cities of different scales as

if they were “mini-metropolises.” There are important

exceptions, such as territorial studies (Vilela, 2023),

debates on the role of intermediation of intermediate

cities (Canziani & Schejtman, 2013), and the possibilities

they offer for decentralized economic development

(Espinoza et al., 2022), as well as efforts to analyze

territorial and environmental conditions in Arequipa

(Zevallos, 2020), the water distribution system also in

Arequipa (Zapana et al., 2021), and the role of the state

in the production of land and housing in Tacna (Abanto,

2017), among others.

According to Law No. 31313, “Sustainable Urban

Development Law” (2021), it is mentioned that Peru’s

national territory is subdivided into urban macrosystems.

Due to its economic and demographic importance, the

southern macrosystem has Arequipa as a dynamic city

(regional metropolis). Tacna (a major city) is located

in this same macro system. Both cities have a smaller

population than the capital, even though Arequipa has

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

MANUEL DAMMERT-GUARDIA, LUIS RIVERA-SEGURA

REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024

PÁG. 62 - 77

ISSN 0717 - 3997 / 0718 - 3607

4 According to the national regulations, there are 8 classication ranges of urban agglomerations, the main ones being: 1) National metropolis

(Lima) (10 million inhabitants); 2); regional metropolis (500,001 or more); major city (100,000 to 500,000); intermediate city (20,000 to 100,000);

and minor city (5001 to 20,000).

Table 1. Growth rate and population in department capitals4. Source: MVCS (2016); INEI (2017)

one million inhabitants and Tacna has less than 300,000.

Therefore, the population and territorial extension had

considerable growth in both cases. Between 1984 and

2017, the built-up area of Arequipa grew more than

threefold (Figure 1), especially in recently urbanized areas

on land classified as a non-mitigable risk zone (Arequipa

Provincial Municipality, 2015). In the case of Tacna, the

built-up area has grown sixfold (Figure 2). However, due

to the growth of the urban area, these areas are exposed

to more significant anthropogenic hazards that limit their

habitability (quarries, pig farms, sanitary landfills, among

others) (Tacna Provincial Municipality, 2013).

IV. METHODOLOGY

The microdata of the 2007 and 2017 Population and

Housing Censuses (CPV, in Spanish) were used, which

were processed in a Geographic Information System

Department capital 2007 2017 City typology Growth rate

Puerto Maldonado 57,035 85,024 Main intermediate city 4.1

Ayacucho 151,019 216,444 Major City 3.7

Abancay 51,462 72,277 Main intermediate city 3.5

Chachapoyas 23,202 32,026 Intermediate city 3.3

Moquegua 50,799 69,882 Main intermediate city 3.2

Huánuco 148,665 196,627 Major City 2.8

Moyobamba 39,250 50,073 Main intermediate city 2.5

Arequipa 806,782 1,008,290 Southern Macrosystem Regional Metropolis 2.3

Piura 377,896 473,025 Northern Macrosystem Regional Metropolis 2.3

Cajamarca 161,215 201,329 Major City 2.2

Huancavelica 40,004 49,570 Intermediate city 2.2

Cusco 348,935 428,450 Central Southern Macrosystem Regional

Metropolis

2.1

Ica 232,054 282,407 Main Major City 2.0

Trujillo 766,082 919,899 Northern Macrosystem Regional Metropolis 1.8

Huancayo 382,478 456,250 Central Macrosystem Regional Metropolis 1.8

Pucallpa 272,251 326,040 Central Macrosystem Regional Metropolis 1.8

Huaraz 99,462 118,836 Major City 1.8

Tacna 242,670 286,240 Main Major City 1.7

Metropolitan Lima 8,472,092 9,562,280 National Metropolis 1.2

Puno 119,116 128,637 Major City 0.8

Tumbes 91,365 96,946 Main intermediate city 0.6

Chiclayo 527,250 552,508 Northern Macrosystem Regional Metropolis 0.5

Iquitos 367,153 377,609 Northern Macrosystem Regional Metropolis 0.3

Cerro de Pasco 61,046 58,899 Main intermediate city -0.4

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024

PÁG. 62 - 77

ISSN 0717 - 3997 / 0718 - 3607

Figure 1. Map of location and urban growth of Arequipa. Source: Population and Housing Census (CPV) 2007 and 2017 (INEI), Google Earth

Figure 2. Map of location and urban growth of Tacna. Source: CPV 2007 and 2017 (INEI), Google Earth

IV. METHODOLOGY

Map of Arequipa

and its urban

growth

Map of Talca and its

urban growth

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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ISSN 0717 - 3997 / 0718 - 3607

(GIS) environment, Quantum GIS software (QGIS),

GeoSegregation Analyzer, and Geode. For the data’s

georeferencing, the scope was calculated by dividing

the number of blocks by the data extracted from

the blocks of the National Institute of Statistics and

Informatics (INEI, in Spanish) cartographic database5.

Once georeferenced in blocks, the data were added

at the census area level (delimitation of the INEI that

houses an average of 63 blocks).

After the exploratory analysis, the head of household’s

educational level was determined as an analysis

parameter, given the available data, the explanatory

capacity, and the revised bibliography. Similarly,

Rodríguez and Arriagada (2004) and Marengo and Elorza

(2014) included heads of households under 25 due

to their small proportion in the analyzed cases6. The

educational level achieved variable was categorized

into low, medium, and high. The low group includes

the elementary or primary level of education (no level,

initial, primary, and special primary); the middle group

includes secondary education; and the high group

for complete or incomplete technical, university, and

postgraduate education.

To evaluate the complementarity of spatial and non-spatial

indices, two standard indices in RS analysis were chosen as

synthetic indices: the Duncan dissimilarity index (DI) (Equation 1)

and the segregation index (SI) (Equation 2) (Aparicio et al., 2013).

Equation 1

Equation 2

For the spatial indices, the standard deviation of the socio-

educational groups in each census area was used, dened as the

average of the dierences between each data and the arithmetic

mean of the set according to each census area (Toro & Orozco,

2018). In addition, the Morán local spatial association index (LISA)

was used to locate sector groupings.

V. RESULTS

The intercensal analysis allowed for identifying variations in the

composition of socio-educational groups (Table 2). In both cities,

Arequipa

2007 2017 Absolute Relative

Low

41 522 46 923

5 401 13,01%

19,49% 15,64%

Medium

64 452 110 849

46 397 71,99%

30,26% 36,96%

Higj

107 039 142 152

35 113 32,80%

50,25% 47,40%

Tacna

2007 2017 Absolute Relative

Low

15 591 16 918

1 327 8,51%

22,56% 19,22%

Medium

25 841 38 526

12 685 49,09%

37,40% 43,76%

Higj

27 668 32 598

4 930 17,82%

40,04% 37,03%

Table 2. Absolute and relative intercensal variation of the head of household educational level in Arequipa (2007 – 2017). Source: CPV 2007 and

2017 (INEI)

5 A scope of 82.3% was obtained in Arequipa and 85.9% in Tacna in 2007.

6 In Arequipa, in 2017, heads of household under the age of 25 accounted for 6% of the total.

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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ISSN 0717 - 3997 / 0718 - 3607

the middle group had the highest growth, and the low

and high groups declined in absolute and relative terms.

In the city of Arequipa, the high group remained the

most predominant, while in Tacna, the middle group

displaced the high group.

In the case of the high socio-educational group, the

analysis of the intercensal variation at the census

area level shows patterns in both cases. In both cities,

the most significant reduction of heads with a high

educational level occurs in census areas near the historic

center, followed by the decrease of this group in areas

of urban expansion associated with forms of working-

class urbanization. In addition, a growth of the group of

heads of households with a high educational level was

observed in the sectors characterized as rural areas and

rustic islands according to the regulatory plans: Cerro

Colorado in Arequipa and the district boundary of Tacna

and Gregorio Albarracín (Figure 3).

Figure 3. Intercensal variation according to census area in both cities. Source: CPV 2007 and 2017 (INEI).

Although the intercensal variation provides information on

the changes in the group structure in each city, synthetic

segregation indices characterize the segregation distribution

dimension. For Arequipa, the dissimilarity index shows that

segregation increased between the high and medium

groups and decreased between the medium and low

groups (Table 3). Similarly, in Tacna, segregation increased

between the high and medium groups and decreased

between the medium and low groups (Table 4).

Results similar to the previous ones were obtained when

analyzing the Segregation Index (SI) (Table 5). Segregation

increased in both cities’ upper and middle groups at the

census area level. On the contrary, segregation would

have been reduced on a block scale in both cases. This

shows that measuring segregation with non-spatial indices

is susceptible to the scale and conrms the increase in

segregation between the middle and upper groups in both

cities.

Absolute intercensal variation of the high

socio-educational group by census area in

Arequipa

Absolute intercensal variation of the high

socio-educational group by census area in

Talca

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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Arequipa (2007)

Group Low Medium High

Low 14,27 38,20

Medium 14,27 27,82

High 38,20 27,82

Arequipa (2017)

Group Low Medium High

Low 11,12 38,80

Medium 11,12 32,17

High 38,80 32,17

Tacna (2007)

Group Low Medium High

Low 13,50 35,37

Medium 13,50 29,35

High 35,37 29,35

Tacna (2017)

Group Low Medium High

Low 13,90 34,52

Medium 13,90 31,81

High 34,52 31,81

Table 3. Index of dissimilarity of socio-educational groups in Arequipa, according to census area (2007 - 2017). Source: CPV 2007 and 2017 (INEI)

Table 4. Dissimilarity index of socio-educational groups in Tacna, by census area (2007 - 2017). Source: CPV 2007 and 2017 (INEI)

The variation compared to the mean or standard deviation of

the dierent socio-educational groups was analyzed to explore

the spatial dimension of segregation. Following Toro and

Orozco (2018), the following gures compared the measures

of social homogeneity and the predominant socio-educational

group in each city. In Arequipa, the socio-spatial transition

pattern includes a sector close to the historical center where

a homogeneous high group predominates, followed by an

intermediate cordon where the high group predominates in

a more heterogeneous way, and nally, a peripheral cordon of

medium and low groups, which is very heterogeneous (Figure

4). For 2017 (Figure 5), the central, high, and homogeneous

sector is expanding due to real estate projects for the middle

class (for example, in Cerro Colorado). The area of the historical

center remains less homogeneous, which is associated with

the intercensal variation described above. Likewise, the

average socio-educational group increase is expressed in the

higher frequency and increased heterogeneity observed in

the intermediate and peripheral cordon. However, sectors of

very heterogeneous middle and lower groups persist in the

peripheries and areas of urban expansion. Sectors located in

peripheral districts and working-class settlements are also

related to the agglomerations of vulnerable groups in the city

(Figure 6).

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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Arequipa

2007 2017

Low

Block 42,93 39,64

Census area 28,40 25,15

Medium

Block 34,91 32,50

Census area 17,94 22,81

High

Block 44,13 41,19

Census area 31,53 34,01

Tacna

2007 2017

Low

Block 37,32 33,92

Census area 22,05 19,56

Medium

Block 31,53 29,85

Census area 17,95 21,01

High

Block 42,27 39,21

Census area 31,34 33,16

Table 5. Segregation index in Arequipa and Tacna 2007 – 2017. Source: CPV 2007 and 2017 (INEI)

Figure 4. Predominant socio-educational group and social homogeneity in Arequipa, 2007. Source: CPV 2007 (INEI).

Educational level reached by the head of

household, 2007

Social homogeneity, 2007

(Standard dev. of the educational level %

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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Figure 5. Predominant socio-educational group and social homogeneity in Arequipa, 2017. Source: CPV 2017 (INEI).

Legend

Standard deviation

Figure 6. Agglomeration of middle and lower socio-educational groups in Arequipa, 2017. Source: CPV 2017

(INEI).

Educational level reached by the head of

household, 2017

Social homogeneity, 2017

(Standard dev. of the educational level %

LISA of medium socio-educational groups

LISA of low socio-educational groups

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Figure 7. Predominant socio-educational group and social homogeneity in Tacna, 2007. Source: CPV 2007 (INEI).

Figure 8. Predominant socio-educational group and social homogeneity in Tacna, 2017. Source: CPV 2017 (INEI).

Educational level reached by the head of

household, 2007

Social homogeneity, 2007

(Standard dev. of the educational level %

Educational level reached by the head of

household, 2017

Social homogeneity, 2017

(Standard dev. of the educational level %

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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A similar spatial pattern was identified in the case of

the cities of Tacna and Arequipa (Figure 7). By 2017, the

high and homogeneous central sector expanded to

the southeast (Cercado de Tacna) and the northwest

(Pocollay) due to the real estate development of gated

condominiums. On the other hand, the increase of

the middle group was expressed in the frequency and

more significant heterogeneity in the intermediate and

peripheral cordon, with greater incidence in the south

of the city (the district of Coronel Gregorio Albarracín).

Likewise, sectors with very heterogeneous medium and

low groups persist in the city’s northern districts (Alto

de la Alianza, Ciudad Nueva) (Figure 8). This pattern is

confirmed by the results of the agglomeration nuclei of

the medium and low groups located in the south and

north, respectively (Figure 9).

VI. DISCUSSION

Unlike other studies, the results of the synthetic and

spatial indices were complementary, demonstrating the

importance of looking closer at using different types of

indices to understand RS (Sánchez & Gómez, 2021). As well

Figure 9. Agglomeration of medium and low socio-educational groups in Tacna, 2017. Source: CPV 2017 (INEI).

as in studies on non-metropolitan cities (Águila & Prada,

2020; Ruiz et al., 2021) and in metropolitan areas, such

as Lima (Fernández de Córdova, Moschella & Fernández-

Maldonado 2021), it was found that the RS follows an

urban pattern of periphery where central sectors of

upper classes and heterogeneous peripheral cordons are

differentiated.

The cases of Arequipa and Tacna show how historical,

institutional, and contextual factors define the urban

pattern of urban segregation. In Arequipa, the historic

center and its surroundings continue to undergo an urban

transformation from a residence space to a space for

commerce and tourism services (Meza & Condori, 2018). In

addition, the results show that the number of upper-class

residents decreased in the central area and increased in

the agricultural land area due to real estate developments.

On the other hand, the peripheral cordons diversify and

include the growing middle class and the agglomeration

nuclei of the lower class. It should be noted that the recent

urban expansion occurs in sectors of non-mitigable risk

and is characterized by rapid growth and low population

density. On the other hand, Tacna shares the dynamics

described previously, although it has variations according

to its context. In particular, in the 21

century, the most

SEGREGACIÓN RESIDENCIAL EN DOS CIUDADES DE LA MACRO REGIÓN SUR DEL PERÚ

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significant expansion occurred in the south of the city, which

includes the growing middle class to a greater extent. In

contrast, the north of the city widely concentrates the most

vulnerable population and is exposed to dangers of natural and

anthropogenic origin.

VII. CONCLUSIONS

It is concluded that RS patterns are associated with the

production processes of working-class urban settlements in the

century. This is evident by the permanence of a large-scale

urban pattern of segregation in both cities. However, the shift

towards the service economic sector, particularly tourism, and

the production of urban land for upper classes in rural areas

close to urban centers give way to low-scale segregation. In this

sense, the coexistence of two types of segregation in both cities

is shown to be associated with contextual and territorial factors.

This result was possible because a methodology that combines

synthetic and spatial indices for RS analysis was applied. In the

same way, the use of GIS allowed the identication that the

recent urban expansions are taking place on risky land that

cannot be developed, adding complexity to the management of

this urban problem.

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