Skip to main content
SearchLoginLogin or Signup

Parallel Worlds: Revealing the Inequity of Access to Urban Spaces in Mexico City Through Mobility Data

Published onJun 20, 2022
Parallel Worlds: Revealing the Inequity of Access to Urban Spaces in Mexico City Through Mobility Data
·

Abstract

The near-ubiquitous use of mobile devices generates mobility data that can paint pictures of urban behavior at unprecedented levels of granularity and complexity. In the current period of intense sociopolitical polarization, mobility data can help reveal which urban spaces serve to attenuate or accentuate socioeconomic divides. If urban spaces served to bridge class divides, people from different socioeconomic groups would be prone to mingle in areas further removed from their homes, creating opportunities for sharing experiences in the physical world. In an opposing scenario, people would remain among neighbors and peers, creating “local urban bubbles” that reflect and reinforce social inequities and their adverse effects on social mixity, cohesion, and trust. These questions are especially salient in cities with high levels of socioeconomic inequality, such as Mexico City.

Building on a joint research project between Data-Pop Alliance and Oxfam Mexico titled “Mundos Paralelos” [Parallel Worlds], this paper leverages privacy-preserving mobility data to unveil the unequal use and appropriation of urban spaces by the inhabitants of Mexico City. This joint research harnesses a year (2018–2019) of anonymized mobility data to perform mobility and behavioral analysis of specific groups at high spatial resolution. Its main findings suggest that Mexico City is a spatially fragmented, even segregated city: although distinct socioeconomic groups do meet in certain spaces, a pattern emerges where certain points of interest are exclusive to the high- and low-income groups analyzed in this paper. The results demonstrate that spatial inequality in Mexico City is marked by unequal access to government services and cultural sites, which translates into unequal experiences of urban life and biased access to the city. The paper concludes with a series of public policy recommendations to foster a more equitable and inclusive appropriation of public space.

Keywords: Mobility data, urban mobility, spatial fragmentation, urban inequality, public space appropriation.

On mobility data and urban planning

The inclusion of mobility data into urban planning research is a relatively new phenomenon. However, the study of cities and their configuration has a long history (Ratti et al. 2006; Wang et al. 2018; Tu 2020; Tseng, Chao, and Liu 2019; Xue, Xiao, and Li 2020; Mehmood et al. 2017; Shaw 2021).

Urban design studies using mobility data have gone through several stages, incorporating elements of cities’ historical, geographical, and social development. The pioneering studies that examined population distribution in urban space (Mohan and Pant 1982; Mesev et al. 1995) mainly analyzed the dynamics of city life. These studies used national census data to provide a look into the residential distribution of inhabitants, which required years of observation and large-scale units of analysis (Davis and Spearritt 1974; Horvath et al. 1989; Booth 1903; Bromley and Jones 1996). These approaches were later replicated in several other cities, using data of a similar nature (Nagle 2002; Brewer and Suchan 2001; Lazzaro 2003; Elliott 2003; Wiesmann et al. 2014) and also regularly complemented by Point of Interest (POI) analysis (Liu and Su 2018; Silva 2014; Krueger et al. 2019).

Innovations in fieldwork have gone hand-in-hand with technological progress, generating new methodologies that use “nontraditional”1 data, traffic or mobility data from smartphone apps, and biometric data from wearable devices. This has resulted in making mobile phones the most important generator of data on urban dynamics. Today, smartphones provide data on users’ location, movement, and place of residence in cities. Concurrently, urban studies have delved deeper into a broader range of social issues, such as public service provision, infrastructure, communication routes, crime incidence, social housing planning, health service provision, commercial areas dynamics, cultural life, city morphology, and resilience to natural disasters (Liu and Su 2018; Li et al. 2018; Xu et al. 2019; Crooks et al. 2016; Wang et al. 2020; Zhu 2017; Cai, Huang, and Song 2017; Yuan et al. 2016; Shi and Yang 2018; Melikov et al. 2021; Weiss et al. 2018).

Recent research has also focused on sensitive sociopolitical issues, such as the differential experiences of women in diverse cultural contexts (Seager 2003; Hawken et al. 2020), spatial segregation based on racial differences (Brewer and Suchan 2001; Musterd and Kempen 2009; Bakelmun and Shoenfeld 2020; Shelton, Poorthuis, and Zook 2015; Medhipanah 2020; Massey 2020), poverty (Glasmeier 2006; Teșliuc et al. 2016), and inequality (Gergő et al. 2021, Monroe 2017). Finally, several articles have examined the methodological limitations of the area (Glaeser et al. 2018).

However, many highly significant questions remain to be explored by combining different disciplines with (e.g., geography, sociology, anthropology) traditional and nontraditional data sources.

In this context, the main objectives of this paper are 1) to contribute to the existing body of knowledge on urban segregation by leveraging nontraditional data sources in the case of Mexico City, and 2) to investigate if people from different socioeconomic backgrounds interact in the urban space or if they live in “Parallel Worlds.”

A data-driven approach to inequality in cities

The study of inequality in urban areas has been widely analyzed in the sociological, ethnographic, urbanistic, economic, and geographical literatures (Lee, Sissons, and Jones 2016; Trounstine 2020; O'Connor et al. 2001; Fairbanks and Lloyd 2011; McFarlane 2013; Buitelaar, Weterings, and Ponds 2017; Goldsmith and Blakely 2010).

Traditionally, census data, surveys, and other records (sourced from national statistical offices and ethnographic fieldwork) have been the indispensable raw material for observing and analyzing spatial inequality (Turner 1954; Jensen 1969; Nord 1980; Fields 1979; Garofalo and Fogarty 1979).

Early studies focused on the United States, Brazil, Mexico, Iran, the Philippines, Taiwan, Thailand, Pakistan, and Colombia (Fields 1979; Murray 1969), before progressively extending to other countries and regions. Subsequently, urban studies on inequality have proliferated, likely spurred by the rapid growth of cities and the global economic shifts that led to and were fueled by rural-urban migrations.

In recent years, the development of technologies and their widespread uses have paved the way for an entirely new vision of inequality. Studies such as Glaeser, Resseger, and Tobio (2009) highlight the importance of looking at localities (i.e., smaller geographic units). As a result of observing mobility rates in localities, researchers such as Bayón (2015), Quesada and Peinado (2019), and Krozer (2020) found that demographic distribution in urban space was stratified. Bayón noted that people’s movements formed “bubbles” within the city, where inhabitants with similar economic status coexisted.

Recently, a growing number of studies have relied on new data sources and approaches that have the potential to shed new light on urban inequality. Studies such as Tóth et al. (2021), Morales et al. (2019), Morales and Bar-Yam (2019), Dong et al. (2020), Moro et al. (2021), and Sun et al. (2013) demonstrate how mobility data can be used to analyze daily movements in urban areas and how different population groups move around the city differently. In addition, studies such as Tóth et al. (2021) showed that places where diverse population groups concur, could foster social interactions.

Tóth et al. (2021) have also analyzed georeferenced data to demonstrate that cities are configured in a way that prevents the equitable enjoyment of the urban infrastructure by its inhabitants. The availability of personal vehicles, for example, gives the privileged sector access to an exclusive version of the city. Moro et al. (2021) found that urban design was not the only contributing factor to the persistence of exclusivity bubbles and that mobility behaviors played a determining role. Moro et al. (2021) analyzed mobility data and highlighted the effect of daily mobility decisions on urban interactions.

By applying these data-driven approaches to the Mexican context, we seek to analyze everyday inequality in a megacity such as Mexico City, where social inequality is a long-standing phenomenon. This has been previously studied by Saraví (2015), who analyzed social stratification in Mexico City, while Duhau and Giglia (2008) examined spatial inequality in the urban sphere. Both works recognized Mexico City as a space of marked socioeconomic differences, which have been maintained over time and eventually rendered “invisible” due to their ubiquity.

Inequality and segregation in Mexico City: On parallel worlds

Poverty and inequality have been long-standing features of large Mexican cities. This situation is concerning due to Mexico’s overwhelmingly urban population: according to the 2020 national census, almost 80% of the country’s total population lived in urban areas (INEGI 2020). Furthermore, two out of three people living in poverty reside in urban areas (CONEVAL 2018). The latest poverty measurements at the municipal level suggest that Mexico’s three largest metropolitan areas (Mexico City, Guadalajara, and Monterrey) account for 20% of people living in poverty, with seven out of ten individuals residing in Mexico City (CONEVAL 2015b). Mexico’s social structure has long been characterized by a profound inequality in the living conditions of its residents, as demonstrated by the following indicators.

In 2020, Mexico City had a population of approximately 9 million inhabitants, which accounted for 7.3% of the national population. According to the United Nations Development Programme (2014), two of the city’s municipalities, Benito Juárez and Miguel Hidalgo, were ranked the highest nationally in the Human Development Index. In contrast, Iztapalapa, located just a few kilometers away, has been considered the municipality with the third-highest number of people living with incomes below the poverty line (more than 800,000 people) (CONEVAL 2015a).

These drastic contrasts are reflected in Mexico’s relatively high Gini coefficient of 0.53 (SEMARNAT 2020), with Mexico City having the highest income inequality (CONEVAL 2018). In Mexico City, one-third of the population lives in poverty, while another one-third of residents are considered “vulnerable” (i.e., at risk of poverty due to insufficient access to social services or income) (CONEVAL 2018). The variance across income distribution reflects an important urban divide. Households in the top decile of the distribution receive 20 times the income of the bottom 20% (Bleynat and Segal 2021). In addition to income, inequality is reflected in labor conditions, educational levels, labor market integration, access to health services, and social security, among others.

Over time, the prevalence of these inequalities has led to a visible polarization of the social structure. This process of division and segregation by social classes is directly related to the concentration of certain social groups in specific areas of the city and the degree of social homogeneity of particular areas (Saraví 2008).

The notion of “Isolated Worlds,” as Saraví describes them, which in this case are called “Parallel Worlds” (Mundos Paralelos), refers to two vastly different demographic groups (in terms of economic conditions)—selected from the highest and lowest distribution decile in Mexico City—who live in the same city but in totally different contexts. Duhau and Giglia (2008) also refer to this “parallelism” as a distancing and alienation from the presence of “others” in the city, caused by the absence of spaces for social encounters, and such distancing ends up causing a fracture. This fragmentation of society can thus be linked to the existence of privileged spaces and sectors that leave the most disadvantaged in isolation (Saraví 2008). For instance, households living in the low-income deciles in Mexico City experience lower-quality educational infrastructure and longer commuting times between school and home (Bleynat and Segal 2021).

Consequently, the urban experience of individuals varies according to their economic position, which, in turn, determines the urban spaces to which they have access. Concerns regarding regulating the use and organization of public spaces have led several authors to introduce and delve into the concept of urban experience (Thomasz 2009).

Another relevant component for this analysis consists in exploring the relationships—or, rather, the lack thereof—between the richest and the poorest in urban Mexican society. Understanding and analyzing social fragmentation represents a qualitative leap in comprehending inequality. It requires consideration of economic differences as well as the social, cultural, and subjective dimensions that generate spaces of unequal inclusion and mutual exclusion (Saraví 2015).

Such exclusion occurs through multiple and diverse practices of discrimination toward individuals of “other” socioeconomic levels. Classification strategies, formalization of social encounters, and the closure of various spaces generate a sense of belonging among “us”2 and estrangement between “us” and “others” (Saraví 2015).

Mexico City is characterized by a deep inequality in the living conditions of its population. The northeast, center, and some southern parts of the city are the places of residence of the most privileged3 groups, who benefit from the highest levels of income, education, and urban infrastructure. At the other extreme, the east of the city is made up of municipalities in which the most marginalized sectors reside; here, the indicators related to living conditions show the greatest deficiencies (Saraví 2008).

In his analysis of “Isolated Worlds” (2008), Saraví found that low-income individuals have a very high probability (greater than 90%) of interacting with others of the same condition in the course of their usual routines. At the same time, there is a minimum probability of encountering high-income individuals. However, the situation for high-income individuals differs in statistical terms. The privileged sectors’ probabilities of interacting with people of the same conditions is 20–30%, while the probability of crossing paths with low-income people is 70–80% (Ariza and Solís 2005).

INCOME LEVEL

INTERACTION PROBABILITY WITH PEOPLE OF THE SAME CONDITION

Low income

<90%

High income

20%–30%

Table 1. Source: Elaborated by the authors with information from Saraví (2008).

Against this background, the present article examines the everyday movement in the city of these Parallel Worlds.

Parallel Worlds: What Data Says About Inequality in Mexico City

Data and technical approach

The present work uses mobility data obtained from Cuebiq4—as part of its “Data for Good” program—and adopts AGEB (Basic Geostatistical Area, from its name in Spanish) as the basic building block for spatial analysis. An AGEB is a geographical area occupied by a set of blocks delimited by avenues, large streets, or any other clearly defined physical barriers (INEGI 2010a). The choice of this granularity is due to its prevalence in geographic studies and available data sets in Mexico.

To obtain the income data, we used two sources of information: Mexico’s National Population Census (INEGI 2010b) and the National Survey of Household Income and Expenditures (INEGI 2010c). While the first provides data from every household in Mexico, it does not report income. In contrast, ENIGH does report income estimation, but it is only representative at the national level. To obtain robust income estimations at the AGEB level, we used Small Area Estimations (SAE)—a unit model inspired by Molina and Rao (2010). For a more detailed discussion of the estimation model, refer to Oxfam México, Data-Pop Alliance, and Cuebiq Inc. (2020).

The idea behind SAE is to model income using socioeconomic variables that are present in both the census and ENIGH.

The methodology consists of three steps:

  1. Based on ENIGH, we selected household level variables that correlated with income to generate a regression model of household income.

  2. We generated estimated parameters for these variables with data from the census at the household level.

  3. Finally, we aggregated the data at the AGEB level. Mexico City AGEBs were divided into ten groups to create deciles, based on the mean income of each AGEB.

The demographic variables used for the SAE at household level were the age of the head of household; household gender ratio; proportion of children 0–5 years old; proportion of people older than 65 years old; and the schooling level of the head of household. The estimation also took into consideration different household assets and their quality: number of bathrooms and washing machines, sewerage, flooring, electricity, piping, refrigeration, access to an automobile, ownership of a personal computer, and whether at least one person in the household had a cell phone.

Economic deciles and population under study. For our comparative analysis, we chose two contrasting populations as samples. The first group, which resides in the municipalities of Iztapalapa, Xochimilco, and Tláhuac, corresponded to the low-income deciles 2, 3, and 4. By contrast, a population of users was taken from deciles 9 and 10 of the Miguel Hidalgo municipality (see Figure 1, left panel). Yet, our two sample populations can also be considered similar regarding composition and activity. They shared the same number of users (736 users) and a similar number of stays: 210,971, stays were detected for users in low deciles and 216,254 for high deciles.

Stays detection. We initially analyzed human mobility data for the time span between March 2018 and March 2019. This data was shared by Cuebiq, which collects GDPR-compliant data from anonymized users who provided informed consent to share data for research purposes. We extracted the users’ stays or stops of at least 20 minutes through the detection.stops function of the Python Sci-Kit Mobility library (Pappalardo et al. 2021). The stays allowed us to gain a functional understanding of people’s activities by analyzing their proximity to POIs. For example, if a group of users is identified with stays of at least 20 minutes in the vicinity of a coffee shop, it is assumed that these users visited (and carried out some kind of activity in) this place. Thus, when a stay is related to a specific POI, we refer to it as a visit. For all urban places or POIs—with the exception of restaurants and bars—a radius of 100 meters was determined as the threshold for visiting the place in question. For restaurants and bars, which generally tend to be smaller, a radius of 50 meters was determined. Finally, the stays were also used to infer the AGEB of residence of the population under study. For this purpose, the AGEB corresponds with the highest number of stays between 10:00 p.m. and 03:59 a.m. for at least 20 days (see Figure 1).

Appropriation Index. For the purpose of identifying the spatial encounters of both populations, we incorporated the georeferenced information of urban places described by the National Directory of Economic Units (INEGI 2019). To track these encounters, we calculated the distance between each of the users’ stays and the POIs. The points of encounter were identified with a spatial appropriation index that takes into account the quotient of concentration (the proportion of both populations’ visits). This appropriation index was defined with equation (1).

Equation (1)

  • E is the number of stays of the users studied;

  • L refers to the population of low deciles;

  • H refers to the population of high deciles;

  • i is the area defined by a radius of 0 to 100 m from the coordinates of each POI; and

  • T is a surrounding area, and it spans 0–200 m.

These distances were used for all POIs analyzed, except restaurants and bars, where i corresponds to a radius of 0–50 m and T to a radius of 0–100 m. The distances that define i were chosen based on studies indicating that these distances are critical to accurately capture probable user visits. The distances T were taken as a buffer area that permits discrimination in discerning the degree to which visitors are attracted to a POI (Sypion-Dutkowska and Leitner 2017). Moreover, we also identified empty places: spaces that exclusively had visits from either the population of high deciles or the population of low deciles.

Encounter environment: Our approach focused on identifying places with potential encounter environments. Among the limitations of this study is the fact that people sharing a physical space does not necessarily imply interactions. However, some evidence suggests that places visited by an array of people from different socioeconomic backgrounds provide an opportunity for social bond formations (Tóth 2021), in contrast to those locations exclusively visited by people from homogeneous backgrounds. Another limitation is the impossibility of distinguishing hierarchical relationships between people visiting the same place. That means that even if people socialize in the same physical space, the connections could be diverse, such as between workers and clients. Future work could address this problem by adding a temporal dimension, with a possible solution being to study the length of time people spent in a given place to determine whether it corresponds to a work shift (~8 hours) or a visit (e.g., 1–2 hours).

Maps by neighborhoods. To identify patterns of use of urban space at a more intuitive level, we mapped the city at the neighborhood level. Both the encounter places and the empty places were assigned to their corresponding neighborhoods using their geographic coordinates. Only those neighborhoods with the highest density of POIs are shown on the maps. For this, we calculated the N50. To obtain this N50, we ordered the number of POIs per neighborhood from highest to lowest and chose only those neighborhoods that contained the 50% of POIs of the upper half of the list.

Equipment Score. We constructed a score of urban infrastructure and services per neighborhood using nine different indicators based on the number of educational (primary schools, middle schools, high schools, universities), cultural (libraries, museums), athletic (sports clubs), health (hospitals), and work (corporate) spaces. So as to assign a weight to each indicator, we utilized the Budget Allocation Process. We allocated a value of 1 to each indicator and performed an additive aggregation or sum of the indicators per neighborhood. Finally, we mapped this aggregation by neighborhoods in the city.

Results

The analysis of the human mobility data at the POIs described above revealed the daily dynamics of the high- and low-decile populations. To begin with, the central and right panels of Figure 1 show the neighborhoods with the highest concentration of public and private universities in Mexico City and reveal a polarized spatial configuration. The neighborhoods where public and private universities are located differ depending on whether the users reside in high or low economic deciles (the neighborhoods coded in yellow and blue, respectively). In addition, certain neighborhoods (particularly the Central neighborhood) were identified as points of encounters for both deciles and coded in green, most of which are concentrated in the Cuauhtémoc municipality.

Figure 2 shows the distribution of neighborhoods that stand out for the higher prevalence of visits to museums, libraries, restaurants, and bars by different types of users. Neighborhoods are considered to be frequented by either the population of high deciles (in yellow) or the population of low deciles (in blue). Regarding these four categories of POIs (museum, library, restaurant, and bar), there is a clear spatial division. Neighborhoods in the southeast were preferentially visited by users from low deciles. Users from high deciles tended to gravitate toward the northeast.

In the case of museums (Figure 2, panel A), the Cuajimalpa and Álvaro Obregón municipalities were visited by users from high deciles. By contrast, in the case of low-decile users, no visits to museums were identified in the Xochimilco municipality (one of the three residential areas for low-decile users), but visits were identified in the Coyoacán municipality. Regarding libraries (Figure 2, panel B), it should be noted that only one neighborhood in Miguel Hidalgo municipality displayed a high prevalence of users from high deciles. For users from low deciles, a small number of neighborhoods were also identified in the Xochimilco and Tláhuac municipalities, while the greatest concentration lay within the Iztapalapa municipality. Interestingly, the Benito Juárez municipality stands out as possessing neighborhoods with libraries that act as encounter places (in green) for both populations.

In the case of restaurants (Figure 2, panel C), the Miguel Hidalgo and Cuauhtémoc municipalities are composed of neighborhoods that attract high-decile users. In the case of visits from the population of low deciles, it is possible to identify a mosaic of various neighborhoods extending along the south and southeast of the city, encompassing the municipalities Iztapalapa, Tláhuac, and Xochimilco but also including parts of Coyoacán, Benito Juárez, and Venustiano Carranza. Finally, in the case of bars (Figure 2, panel D), a similar configuration to that of restaurants was observed. High-decile users gravitated toward a concentration of neighborhoods in Miguel Hidalgo, Cuauhtémoc, and Benito Juárez, while low-decile users preferred a mosaic of neighborhoods toward the southeast of the city. While this asymmetry suggests a division of social life in Mexico City, it is also notable that the Central neighborhood functioned as an encounter point (in green) for both groups of users in all cases (Figure 2, panels a, b, c, d). The Hipódromo neighborhood is also interesting because, despite concentrating visits by users from high deciles to museums, libraries, and restaurants (Figure 2, panels a, b and c), it acted as an encounter place in the case of bars (Figure 2, panel d). Similarly, the Roma Norte and Juárez neighborhoods (Figure 2, panel c) were preferred by users from high deciles in the case of restaurants but can be considered encounter places in the case of bars (Figure 2, panel d). By their part, Guerrero and Morelos neighborhoods received more visits from high-decile users regarding museums (Figure 2, panel a) but functioned as exclusive places for users from low deciles in the case of bars (Figure 2, panel d).

Last, we considered the characteristics of the Central neighborhood that facilitate encounters between socioeconomic groups. In particular, it exhibits a high level of urban infrastructure (Figure 3, panel A) as well as a highly connected transport network comprising metro stations, metrobus, trolleybus, and numerous taxi sites. From the stay data, we built an origin–destination matrix to

explore how the Central neighborhood is connected to other neighborhoods. Panel B in Figure 3 shows a diagram that communicates the interconnection of neighborhoods. In the zoom panel, we found that the Central neighborhood is the area most connected to different neighborhoods (see pink square with an array of different colors), indicating a high level of connectivity.

Figure 1. AGEBs of Residence and universities as POIs.
The residence of high and low deciles users was inferred at the AGEB level, coded in purple and pink. The names of some municipalities are shown (left panel). Neighborhoods with public and private universities are shown in the central and right panel. Green labels neighborhoods of encounter. Yellow and blue label neighborhoods of empty places for high- and low-decile users, respectively. Source: Oxfam México, Data-Pop Alliance, and Cuebiq Inc. (2020) [The map has been modified for this publication]

Figure 2. Spatial use and configuration for museums (A), libraries (B), restaurants (C), and bars (D).
Lowercase letters (a, b, c, d) represent a zoom-in on the dotted squares in the corresponding upper map. Source: Oxfam México, Data-Pop Alliance, and Cuebiq Inc. (2020) [The map has been modified for this publication]

Figure 3. Infrastructure score and neighborhood transit.
The Central neighborhood stands out (along with university city, also in yellow) as a place exhibiting a high number of educational, cultural, sport, health, and work spaces (panel A). The Central neighborhood is the area with the most diverse connections from different neighborhoods (Panel B). Source: Oxfam México, Data-Pop Alliance, and Cuebiq Inc. (2020) [The map has been modified for this publication]

Policy recommendations and policy discussion

Public spaces are places that ideally could forge connections between different social groups. In Mexico City, these spaces can function as encounter places between different economic classes but also as empty spaces that are appropriated by each sector (Oxfam México, Data-Pop Alliance, and Cuebiq Inc. 2020).

There are recreational spaces that are perceived as places of leisure and entertainment by consumers, while for others, they represent a workspace. Different visions of the city are formed and different experiences are created in urban spaces, both of which contribute to generating a sense of belonging to a place (or lack thereof) (Oxfam México, Data-Pop Alliance, and Cuebiq Inc. 2020).

Since some studies suggest that people tend to choose shopping malls according to their socioeconomic status and the distance from home (Beiró et al. 2018), regulations and urban policies must include the promotion of mixing between social classes in common spaces in their agenda, which can, in turn, lead to increased social cohesion.

In light of this, the government of Mexico City may wish to invest in programs that promote the use of museums and libraries by diverse societal sectors, especially residents of low-income neighborhoods. Museums are spaces for meeting, promote arts, culture, and science and can be a resource with immense potential to enrich the cultural lives of children, youth, and adults and their ability to learn and broaden horizons, regardless of their families’ socioeconomic status. A relevant example is the “Papalote y tu Colonia”5 program, offered by “Papalote Museo del Niño.” The program consists of free workshops focusing on the culture of peace in a local museum in Cuernavaca, Mexico, for children living in neighborhoods with high rates of marginalization, violence, and poverty. It aims to promote the values ​​of peaceful conflict resolution, human rights, and inclusion, among others. Adopting these types of educational initiatives in Mexico City would allow museums to serve as democratized spaces where all citizens can interact and share values. It would also strengthen the notion of the rights of city inhabitants to all its spaces, and reduce equality gaps in terms of access to culture.

On a similar note, and as shown in the results, education in Mexico City is significantly affected by class divides. Therefore, educational spaces are of particular interest in creating public policies, as they play a key role in identity construction (Oxfam México, Data-Pop Alliance, and Cuebiq Inc. 2020). Effective policies to reduce differences in the quality of education should take into account the economic dimension and the spatial patterns created by segregation. Hence, educational policymakers should be concerned with more than academic test scores. It is essential to build a more integrated society by breaking down social barriers and placing children of different socioeconomic origins in the same classrooms. In this way, public policy can positively influence interracial and interclass social attitudes and behavior (Hares 2018).

This idea is supported by a variety of studies in the United States that have proven that desegregation significantly improved the educational attainment, income, health, and incarceration rates of low-income students (Cortright 2018; Zamorano and Kulpa 2014; Greene and Pettit 2016). At the same time, it did not affect the academic results of the higher-income students (Hares 2018).

Beyond academics, Rao (2019) observed a positive effect of mixing social classes on perceptions of altruism and empathy through a natural experiment in India. In this case, India established clause 12 of the Law on the Right to Education in 2009, which requires all private schools to reserve at least one-quarter of their seats for children from disadvantaged families (Rao 2019). The author found that proximity to their low-income peers made the higher-income students more prosocial (Rao 2019). The mixture of classrooms also changed children’s behavior toward others outside of school (Hares 2018).

In Mexico City, family income can determine the educational destiny of new generations. We conceptualize encounter spaces as a way to combat the classism and elitism that characterize education in the city. Private schools should contribute to social cohesion by increasing the enrollment of economically disadvantaged students, similar to the Indian example.

Conclusion

One of the most striking findings of this paper was the urban vitality of certain areas (such as the Cuauhtémoc municipality), which provide a favorable environment for different population groups to meet. These encounter points probably exist due to the high level of urban facilities, such as transport systems, and the fact that they concentrate schools, sports clubs, cultural centers, hospitals, and workspaces. However, we also found that frequenting museums or sports clubs is difficult for populations residing in the periphery due to factors such as the access cost. This inequality in access to social spaces is particularly marked in San Miguel Teotongo, Barrio la Magdalena, and Ejército Oriente (Oxfam México, Data-Pop Alliance, and Cuebiq Inc. 2020).

We analyzed how fragmentation and spatial segregation in Mexico City shape how citizens navigate the city, grouping them with some and distancing them from others, creating “Parallel Worlds.” They are worlds of discounters, where physical reality legitimizes the socioeconomic inequality between individuals. (Oxfam México, Data-Pop Alliance, and Cuebiq Inc. 2020).

Parallel Worlds also demonstrates the potential of utilizing mobility data. Furthermore, it contributes to the study of poverty as a nonstatic phenomenon and how it accompanies a city’s inhabitants to every space they transit through on a daily basis. The unequal and differentiated use of urban spaces also exposes how Mexico City is appropriated in different ways by the various sectors that make up the city. Such a perspective is made possible by the availability of nontraditional data, which allows for a glimpse into a small part of daily life in megacities.

The analysis carried out in this paper reveals that different worlds cohabit without meeting, unable to recognize each other, a phenomenon already analyzed in other cities and therefore necessary in the case of Mexico. This type of study allows us to understand the extent to which inequality has become normalized and rendered invisible.

Acknowledgments

This article would not have been possible without the exceptional support of Cuebiq and its “Data for Good” program, Dr. Esteban Moro, Brennan Lake, Luis García Rueda, Ivette Yáñez Soria, Anthony Deen, and Milena Dovali Delgado. Their expertise and knowledge were instrumental in the realization of this project.

Comments
0
comment

No comments here

Why not start the discussion?