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Crime in São Paulo’s metro system: Sexual crimes against women
Vania Ceccato* and Yuri Paz
Corresponding author: Vania Ceccato
Department of Urban Planning and Environment
School of Architecture and the Built Environment
KTH Royal Institute of Technology
Drottning Kristinas väg 30
100 44 Stockholm, Sweden
vania.ceccato@abe.kth.se
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Abstract
The article investigates personal safety conditions in the São Paulo metro, the largest
rapid transit system in Brazil. The study looks at all types of crimes, but devotes special
attention to the nature and spatio-temporal dynamics of sexual crimes against women
while in transit. The methodology combines Geographical Information System (GIS)
and crime records with data collected using Google street view and other secondary data
into a set of regression models. Findings show that sexual violence is concentrated at
the busiest central stations; it often takes place during the morning and afternoon rush
hours, and at stations that also attract all sorts of violence and events of public disorder.
The study finalises with an analysis of the metro’s current prevention practices targeting
women’s sexual victimisation.
Keywords: gendered violence, subway, transit environments, GIS, Google street view.
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Introduction
“I turned around and saw my whole leg soiled. He was closing the zipper of his pants. I
despaired and started screaming for help. I was rescued by other passengers, while
others held the assailant in the carriage…After this, I did not want to take that route
anymore…I decided to go to court against the metro, asking for compensation” (young
woman on the way to work).
Cases as the one above happen every day in the São Paulo metro. A recent survey
shows that two of three women declared being victims of sexual harassment and
violence while in transit, half of them on public transportation, while the proportion
among men is 18 per cent (Datafolha, 2014). Yet, the majority of cases go
underreported. Similarly, in England and Wales, sexual assault and harassment are
generally underreported (for a review, see Gekoski et al, 2015), with estimates of 75-95
per cent of victims never reporting incidents to the police. Women are ashamed and
even if they want to report, they declare finding it difficult to do so for different reasons.
In spite of considerable media attention to the problem (Araújo, 2016; JC, 2016); the
exact nature, location and time of these offences against women remain unknown. The
lack of research on women’s transit safety is not exclusive to Brazil but it is a more
general problem found elsewhere as well (Can, 1995; Gekoski et al, 2015; Madan and
Nalla, 2015; Natarajan, 2016; Seedat et al, 2006; Sham et al, 2013)
This article aims at contributing to this area of research by assessing the nature, the
levels and the patterns of sexual crimes against women (cases of staring, touching,
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groping, ejaculation, exposing genitalia and full rape) in the São Paulo metro system,
the largest rapid transit system in Brazil, and the second largest in South America. The
analysis is performed by comparing reported data with records of other types of crimes
in the subway system in time and space. The aim is achieved by (a) creating a
Geographical Information Systems (GIS) database of incidents and attributes of subway
stations, and demographics and socio-economic characteristics of the surrounding areas,
using Google street view; (b) assessing the relationship between incidents and attributes
of underground stations and surrounding areas, with particular focus on women’s sexual
victimization in transit; and (3) reporting and reflecting upon current crime intervention
programs targeting women’s sexual victimization in the metro system.
The novelty of this study is twofold. The analysis is based on a recently available
dataset from the metro company. Owing to a lack of data and their poor quality, there
has been relatively little research interest in crime and disorder in transit environments
in Brazil. This study makes use of aggregated reported data by type and station (2010-
2015) obtained from the São Paulo metro company and by time (2013-2015) as well as
aggregated data from an sms-service (2014-2015), which is a service to report crime and
other incidents that happen during the trip and/or metro premises. Another novelty is the
opportunity to explore Google street view as a source of information, gathered using
GIS, on the environmental features around the subway stations.
Theoretical background and hypotheses of study
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Crime and sexual victimisation in metro environments
Crime does not happen in a vacuum; it reflects people’s routine activities and daily,
rhythmic habits. Transportation nodes, such as metro stations, are special places in this
matter because they make daily routines happen, allowing the movement of people from
A to B, at the same time that they bring people together in transport nodes, converging
in space and time. Most crimes depend on these interrelations in space and time:
offenders’ motivation, suitable targets and absence of responsible guardians, as
suggested by routine activity theory (Cohen and Felson, 1979). Transit environments
are a fertile territory for sexual and other types of abuse and harassment. An empty train
in the evening might just allow the anonymity that an offender needs to commit a rape
(Ceccato et al, 2017; Gekoski et al, 2015; Hewitt and Beauregard, 2014). Similarly,
crowded rush hours might just provide the right opportunity for groping and all sorts of
inappropriate unwanted sexual behaviours (Madan and Nalla, 2015; Natarajan, 2016).
This is a particular problem in big cities in countries of Global South where the poor
supply of public transportation means that women are relatively more exposed to sexual
victimisation. Thus, it is expected that in São Paulo metro, sexual victimisation as well
other transit offences follow crime-specific spatial and temporal patterns determined by
the conditions of the transportation system, at particular hours of the day, days of the
week and seasonally.
A metro station can be the place where offender and victim awareness spaces converge;
a condition that may lead to crime. The vast majority of crime occurs within the
offender’s ‘awareness and activity space’ (Brantingham and Brantingham, 1995), which
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means that crime place is often a familiar spot for both offender and victim. However,
if, for any reason, an offender does not see the place as a target, or as part of her/his
territory, crime will not happen. Even within this awareness space, the offender’s
judgment is influenced by multi-scale conditions at a particular place. These conditions
are generated by stations’ local environments (Ceccato, 2013; Ceccato and Newton,
2015; Ceccato et al, 2017; Levy et al, 2017; Loukaitou-Sideris, 2012; Loukaitou-Sideris
et al, 2002; Newman, 1972; Uittenbogaard, 2015) but also by the type of neighbourhood
in which the station is located (Mustaine et al, 2006; Shaw and McKay, 1942) as well as
the relative position of both the station and the neighbourhood in an urban area. Thus,
what happens in terms of crime and sexual victimisation in the São Paulo metro system
is bound to be a function of the stations’ local environments and the contexts in which
stations are embedded (neighbourhood and city).
2.2 Situational crime preventing sexual violence in transit environments
One way to prevent crime is making it difficult to happen. Situational crime prevention
focuses on methods to reduce crime opportunities (Clarke, 1997). These strategies focus
on changing the environment and how it is managed, thus closing off opportunities for
crime. A key element in this approach is identifying the underlying drivers of a
particular offence in time and in each particular environment.
The international literature is vast in showing ways places, including transit
environments, can be made safer by applying situational crime prevention (LaVigne,
1997; Levine et al, 1986; Newton et al, 2015), but much more limited in showing ways
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to prevent sexual harassment and sexual violence against women in transit
environments (Gekoski et al, 2015; Madan and Nalla, 2015; Natarajan, 2016; Natarajan
et al, 2015)
Common safety interventions may include design strategies (e.g. improving lighting,
decreasing disruptive objects, increasing visibility, good maintenance of transport
facilities, implementing real time scheduling information), passenger separation
strategies (e.g. women’s only carriages/taxis, splitting passenger flows), improvement of
surveillance (e.g. CCTV, presence of security officers, training of personnel and
passengers to be alert and intervene, hotlines, implementation of emergency buttons in
carriages, sms-services), targeting routes (hotspots known to have higher crime rates),
raising awareness (campaigns to motivate individuals to report, organising public
workshops, actions through social media in collaboration with existing campaigns used
in raising awareness about sexual violence in public spaces (see Lea and Silva, 2017 in
this Special Issue, including women’s voices by using new technology and
crowdsourced data). For an extensive review, see Gekoski et al (2015) covering
evidence from the UK, Australia, New Zealand, USA and Denmark. In summary, these
interventions vary in nature but they also share similarities: they often involve changes
in the transit environment and implementation of new management routines. Our
hypothesis is that also in São Paulo, crime prevention initiatives devoted to sexual
victimisation, if any, are devoted to making passengers aware of the problem and
empower victims to come forward.
The present study
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The study area
The study area is composed of 62 stations in the São Paulo metro (Metropolitano de São
Paulo), excluding some expansion (65 stations total), which is the main rapid transit
system in the city of São Paulo, the largest in Brazil and the second largest system in
South America. The system was founded in 1968 with one line, and today has five lines
(74 km, see Appendix 1) carrying 4,600,000 passengers per day on 154 trains operating
from Sunday to Saturday, from 4:40 AM to midnight (1:00 AM on Saturdays). The
Metro runs within São Paulo municipality - a municipality with 12 million inhabitants.
Another company, São Paulo Metropolitan Train Company (CPTM), serves 22
municipalities that make up the São Paulo Metropolitan Region with a total of 21
million inhabitants, 258.4 km, 92 stations, and 2,900,000 passengers per day (Metrô,
2015).
Data and method
This study makes use of data from Delpom (Delegacia de Polícia do Metropolitano) and
Polícia Civil reported by passengers (2010-2015) obtained from São Metro company
(aggregated by type and station) and by time (2013-2015) as well as from one sms-
service (2014-2015), which is a service to report crime and other incidents that happen
during the trip and/or metro premises. Crime incidents include property and violent
crimes, such thefts, muggings and all sorts of violence, including sexual violence and
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sexual harassment. The sms-data involve minor incidents but can include crimes such as
sexual violence, sexual harassment and groping, as well as non-crimes such as begging,
noise, and incivilities like putting feet on metro benches and irregular commerce inside
metro property. While crime incidents can be split by station, sms-data cannot. In order
to standardise the levels of incidents, number of passengers was also obtained from
Metro, which are estimates of number of passengers in a working day by station and
peak and off-peak hours, as well as the total number of personnel (security and overall)
by station.
There are on average 24 security guards per station, and although security guards rotate
between stations, they tend to be concentrated at large stations: 73 per cent of the
stations lack permanent security guards. This fact is bound to have an effect on
reporting practices of crime and disorder (both in total levels and geography) since
passengers may be discouraged to report if personnel are not around (Ceccato, 2013;
Ceccato and Newton, 2015; Newton et al, 2004). In the São Paulo system, there are
indications of high reporting practices at larger stations. A significant correlation was
found between total personnel by station and total reported events (r=0.57, p=0.01), as
well as between total security guards and number of CCTVs (r=0.29, p=0.05), which
indicates larger stations. If the incidents are witnessed by others, e.g. inside a carriage,
the likelihood that the event is reported also increases (see Lea and Silva (2017), in this
Special Issue). Although coordinates were available for the station, it was not possible
to know the precise location where the event occurred (during the trip, at the station, in
the carriage, or on other nearby premises). The best indication was an estimation from
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the metro that about 40 per cent of reported events took place on the train, inside the
carriage.
Figure 1 – about here.
The environment of metro stations follows some common standards, but they are far
from homogeneous and greatly differ in security a resource, which potentially impacts
the stations’ vulnerabilities to crime and disorder. In terms of CCTV, the average
number of cameras per station is 19, but they also tend to be located at the busiest hubs:
5 per cent of the stations have 50 cameras or more.
In order to assess the influence of the surroundings on crime incidents at each station, a
number of criminogenic land-use indicators were manually collected using Google
street view. Figure 1(a) illustrates the detailed level of the landscape collected about the
environmental features and land use around the subway stations, and Figure 1(b) shows
the combined dataset with other data on land use (e.g., existence of parking lots,
restaurant/bars, banks and schools). These observations were later input into
spreadsheets and then imported into GIS together with other data on land use, crime and
demographic and socio-economic data on the population. Police-recorded data for the
surroundings (from Secretaria de Segurança Pública de São Paulo, SSP) are used here
as a reference. Stations and crimes were mapped as point data, whereas the São Paulo
demographics and socio-economic data (from GeoSampa portal in 2016) were linked to
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small unit statistics around the stations. Whether stations were larger hubs was indicated
by a dummy variable and distance from the station to the city centre, and also included
in the model (Appendix 2). Interviews with personnel were performed in December
2015 to obtain information about crime prevention programs, and in particular about
sexual violence and current prevention measures. Documents and videos were obtained
from the metro itself but also from material available in newspapers and on the internet.
Instead of using crude data of crime events by station, rates per 10,000 passengers were
calculated based on the passenger flow at each station. The purpose was first to explain
the variation in station-specific rates for various types of incidents and later compare
them with those found for sexual harassment and/or sexual violence. The dependent
variables are rates by passenger flow for selected offences from data from the station.
The independent variables are a set of four factors which are the result of a factor
analysis in SPSS of over 25 variables (for details see e.g. Costello and Osborne (2005))
characterising environmental attributes of the stations and demographic, socio-
economic, and land-use covariates of the surrounding areas following the conceptual
model suggested by Ceccato (2013).
Results
Sexual crimes against women and other crimes in the São Paulo metro
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A small share of cases of sexual harassment and/or sexual violence is reported in the
São Paulo metro (Figure 2(a)). Yet, it is believed that two-thirds of female passengers
have experienced it (Datafolha, 2014) and that it is on the rise (Araújo, 2016; Souza and
Valle, 2017). From total reported crimes, 12 per cent of incidents constitute cases of
staring, touching, grouping, ejaculation, exposing genitalia and up to full rape. The
crime category is the same but the reaction varies, as some women get stunned, others
feel scared or traumatized, while others feel desperate. Despite a recent increase in
reporting figures (Diógenes, 2017), many cases go unreported because many women are
ashamed to report or do not find metro personnel to record the incident. These
underreporting figures should be kept in mind when interpreting the results in this
study.
Figure 2(a) indicates that the large majority of reported incidents (62 per cent) is
composed of property crimes such as thefts and robberies, and according to Valle and
Bottini Filho (2014), mobile phones are a major target. Fights and other types of
violence composes nearly a fifth of the records. Important to note is that there are other
events that are also underrepresented in this dataset (some of them are gathered
elsewhere, e.g. via SMS-service, and will not be discussed here), such as cases of
physical damage against property, begging and other types of public disorder, which in
many other transit system may constitute the large majority of recorded incidents. In
Stockholm’s metro system, only 15 per cent of incidents are actually crime (Ceccato,
2013).
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Figure 2 – (a) about here.
Clear hot spots of sexual harassment and/or sexual violence emerge at stations of São
Paulo’s inner city. The highest numbers of cases are on the Red and Blue Lines: Sé
station (with about half of the cases), Brás, Luz, Bresser-Mooca, Anhangabaú and Pedro
II. In the most recent surveys, the Red Line is also pointed out by passengers as being
more problematic, especially in terms of security, accessibility and comfort (Araújo,
2016).
There are distinct peaks of recorded incidents of sexual harassment and/or sexual
violence in the São Paulo metro that correspond to people’s daily routine activities.
Most of these events happen from Monday to Friday (20 per cent on Mondays and
Tuesdays and only 6 per cent on Saturdays) when people are going to or returning from
work. The most significant peak in cases happens during rush hour in the morning,
between 8:00 and 9:00, and in the late afternoon between 18:00 and 19:00 (Figure 2(c)),
also confirmed for 2016 data by Souza and Valle (2017), when the most trains and lines
are overcrowded (note that trains run from 4:40 am to midnight, and until 1:00 am on
Saturdays). There are slightly increases in the number of cases in June and July.
However, a longer time series of data would be necessary to test differences in seasonal
patterns. High rates of reporting are more often found at stations located within a 3 km
radius from the city centre (Figure 2(d)), following a distance decay distribution from
Sé station. As Figure 3(a) shows, transit overcrowding facilitates all sorts of incivilities
and ‘legitimate’ inappropriate behaviour, not in the least sexual harassment and sexual
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violence in the São Paulo metro, an old problem that only recently has prompted
passengers’ protests (Figure 3(b)).
Figure 3 – about here.
Modelling sexual crimes against women in transit
Findings from modelling and factor analysis indicate that high rates of recorded sexual
harassment and/or violence tend to be more common at more central stations (these
stations have relatively large numbers of CCTVs and metro personnel, presence of
physical and social disturbance, presence of dark corners), they are also well connected
to the urban fabric, accessible but relatively small, and surrounded by more affluent
neighbourhoods (close to bicycle storage, restaurants).
Table 1 – about here.
Similar pattern are found for overall violence (Table 1), with the exception that stations
with high rates tend to be located more in the outskirts, possibly in regional centres,
close to shopping malls. For thefts and robberies (and total crimes), the conditions
(indicated by significance of Factors 1, 2 and 3) found at stations located in inner city
areas are better predictors of these property crimes. For total crime, factors reflecting
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internal features of the stations, their locations and surroundings explain around 30 per
cent of the variation of total crime in the São Paulo metro; this percentage varies by
offence type, and for the model of sexual crimes against women, it explains a quarter of
the variation of the dependent variable.
Current practices against sexual crimes against women in transit
The São Paulo metro develops security strategies with the purpose of curbing crime on
its premises. The program “You are not alone” (Você não está sozinha) that started in
2014 is devoted to combat the practice of sexual harassment and/or sexual violence in
the metro. The program has several areas of action, including some that follow
situational crime prevention principles, such as earmarking resources for CCTV and
security personnel, training of personnel, providing information over a longer period of
time (e.g. a video about the program that has been showing in each carriage since 2015)
to empower women to report and to engage staff and passengers to intervene and
support victims, thus transforming passengers into ”capable guardians” (Figure 4). All
these initiatives have so far received mixed reactions.
Figure 4 – about here.
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An issue that has been a source of criticism of the metro’s campaign is that it is limited
in creating incentives for sexual abuse detection, and by doing so, that it is neglecting
victims’ needs and support. The second is that it relies heavily on users to report these
offences, but very little is suggested about the company’s role after the event has
already happened. It has been suggested that the campaign takes away the metro’s
responsibility and moves it to victims and passengers to deal with the problem.
According to the metro company:
“there are over a thousand agents trained to act for the benefit of all passengers, be
they men or women, seniors, adults or children” (Metrô, 2017)
Yet not much is known about the distribution of trained guards and other personal and
their practices. Newspapers show examples that the post-complaint support from the
company may be lacking. If victims are not helped in real time (as illustrated in the next
section), it may be difficult to expect any long-term effect of the campaign:
“It was crowded, but there was room to be relatively far away. I was surprised that he
was behind me… I sent a message to the sms-service and to my surprise I received ‘a
standard message’ from them … and I did not get any support from the service at all”
(student, on the way to the university).
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On the other hand, a positive impact of the campaign has certainly been the increase in
reporting and protesting (Figure 3(b)). According to Soares (2015), the survey was
performed by the metro itself and shows that 83 per cent of respondents spontaneously
recalled the campaign; 82 per cent believed that by reporting, cases of abuse should
decrease; and two-thirds of interviewed users who assessed the campaign felt more at
ease in personally reporting cases of sexual abuse after the campaign.
Discussion of results
Crime and poor perceived safety in public transportation are crucial issues which
disproportionately affect women worldwide (Peters, 2013). Sexual crimes against
women in transit constitute a large share of these crimes. In the São Paulo metro, 12 per
cent of total reported crime incidents constitute cases of for example, staring, touching,
groping, ejaculation, exposing genitalia and full rape–crimes that are highly
underreported. This type of offence concentrates at inner city stations of São Paulo,
particular on the Red Line. Typical but not exclusive to large cities of the Global South,
overcrowding is an important facilitator of this offence. Paths of millions of people
overlap in these transportation nodes (Brantingham and Brantingham 1995), making
some of them offenders and others, victims. Most cases happen on weekdays when
people are going to or returning from work, corresponding well with people’s daily
routine activities (Cohen and Felson, 1979).
Modelling results also confirm this offence’s concentrated pattern at São Paulo inner
city stations. Modelling results also show distinct patterns for property (city centre
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dynamics) and violent crimes (smaller stations). Although these findings are new, they
are not exceptional when compared with findings from studies elsewhere (Ceccato,
2013; LaVigne, 1997; Newton et al, 2015; Smith, 2008). Important to note is that since
data from the São Paulo metro suffer from relatively low reporting practices, results
should be considered with care. As for other types of crimes, sexual crimes against
women in São Paulo metro are associated with particular features of the stations as well
as the stations’ locations and surroundings. This offence pattern is better explained by
the criminogenic conditions found also for violence at central stations (e.g. presence of
physical and social disturbance, presence of dark corners, relatively large numbers of
CCTVs and metro personnel), stations well connected to the urban fabric, and
surrounded by more affluent neighbourhoods.
In a methodological note, this study also illustrates the potentialities of combining data
from different sources on the stations and surrounding areas into a single geographical
framework using GIS. In particular, the potential of using Google street view as a
source of information about the environmental features when data collection in the field
is not an alternative. Although information cannot be collected inside the stations,
Google street view shows an enormous potential for checking the outdoor environment
around the subway stations, at least during day time.
In recent years, the metro has implemented a campaign to combat sexual harassment
and/or sexual violence, which has so far been successful in increasing the number of
reported cases (Soares, 2015), but may be limited in improving women’s safety. A
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problem is that the program has highly relied on reporting to those ‘who are around’,
which means cases are underreported at smaller stations, since personnel tend to be
concentrated at larger stations. The deficiency of the reporting system in immediately
providing a response is also worrying since it will certainly affect women’s willingness
to report an event in the future–which means that the problem persists.
Conclusions, limitations and implications
Any type of prevention measure to combat sexual harassment and/or sexual violence in
the metro must consider the fact that these offences show distinct temporal peaks that
correspond to people’s daily routine activities. Spatially, the distinct pattern by type of
offence and station as illustrated in this study calls for safety interventions that are
adapted to the types of stations (indoor and outdoor environments) but also their
specific neighbourhoods and city contexts. For sexual harassment and violence against
women in transit, for example, a partial solution to the problem is to create special
programs focused on central stations, particularly on the Red Line and during peak
hours in the case of São Paulo. A solution is to implement in the metro system joint
safety initiatives directed to women’s safety together with the so-called Delegacias das
Mulheres (women’s police station), which are specialized units within the police
attended by all female staff. These women’s police station were established in late
1980s in Sao Paulo and is still is considered one of the most important models around
violence against women in the juridical field in Brazil. Moreover, it should be noticed
that women may not want to go to the police, be interrogated, go to court, be cross-
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examined (it can time consuming and traumatic). Therefore there must be other avenues
women can take than reporting, such as NGO’s, women’s groups, as there might be
more support in these types of initiatives than the police can offer.
It would be naïve to believe that tackling the problem of central stations is enough to
reduce the problem overall. Women’s safety in the São Paulo metro reflects a more
fundamental problem that is not properly tackled, namely, ensuring individuals’ rights
to public transportation. As it is now, the metro system runs over capacity; buses, vans
together with commuting trains and metro are not enough to move people around. Most
passengers are ‘transit captives’ (Yu and Smith, 2014), which means that they do not
have any other reasonable means, apart from public transportation, to get from one
place to another. Yet, in order to avoid the safety problem, women change routes, quit
jobs or define other strategies to cope with unsafe transit environments.
In the future, in order to better understand the barriers to tackling problems of women’s
safety while in transit, ‘a whole journey approach’ must be in place (Smith, 2008). A
limitation of this current study is that the analysis has been restricted to the situational
conditions of crime at and surrounding the metro stations. In practice, ‘a whole journey
approach’ means that one must highlight aspects of the transit journey for female
passengers, which requires combined attention from transportation providers, local
governmental authorities (including police departments), policy makers and other
stakeholders. Fundamental in this process is to incorporate women’s voices and views
into transport services. Assuming that passengers have access to the technology, the use
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of ICT technology and social media can be a faster way of informing passengers and
reporting problems. Coupled to crime detection, a basic requirement is having systems
to support victims in the metro that can give assistance to women after an incident has
already happened.
An limitation of this study is that it has focused on women’s victimisation only, and
therefore did not address unwanted sexual behaviour on public transport against other
groups, in particular those who are potentially more targeted (e.g., the LGBTQ
community). There has been evidence that gay men and transgendered persons are often
targets of harassment and violence in the São Paulo metro (see e.g. UOL (2014)) but
also elsewhere (Gekoski et al, 2015).
The goal is already set. For a city like São Paulo to be ‘sustainable’, it needs to fulfil the
mobility needs of its citizens–regardless of age, gender, sexual orientation, ethnic
background–via an accessible, reliable and safe transportation system. Despite its
limitations, this study has made a contribution to this goal by providing an overview of
the problem of sexual harassment and violence against women in transit.
Acknowledgements
The author thanks Metrô de São Paulo for providing data for the analysis, in particular
Marcelo Borg and Francisco Pelegate. Thanks also go to Erick Tonin for supporting the
process of data acquisition.
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Tables, figures and appendices – Ceccato and Paz
(a)
(b)
Figure 1 – (a) Stations’ surroundings inspected using Google street view; (b) Stations locations and
characteristics of the surroundings in GIS.
percent of incidents are actually crime (V. Ceccato, 2013).
N= 6262
(a)
(b)
(c)
(d)
Figure 2 – (a) Types of reported incidents in the São Paulo metro; (b) Total indecent, sexual assault &
rape, 2010-2015; (c) Distribution of incidents over hours of the day by type; (d) Rates of sexual
harassment and/or sexual violence by passengers and stations 2013-2015 by distance of the city centre
(in kilometres).
(a)
(b)
Figure 3 – (a) Overcrowding is a chronic problem at peak times in São Paulo metro. Source: Vania
Ceccato, 2016; (b) Campaigners sexual crimes against women in São Paulo metro: “In this station a
women was raped, React!”. Source: Julia Chequer.
Figure 4 – Whose responsibility? Photo from metro campaign against sexual abuse: ‘All metro users
against sexual abuse’ (E todos os usuários do metrô contra o abuso sexual).
Table 1 – Modelling results, OLS, Y = Incidents per passengers flow, 2013-2015.
Sexual harassment/
violence
Violence
Theft/
robbery
Total
crime
Factor 1 -‘Big, central,
trouble’
0,028*
(1.994)
0,00003
(0.945)
0,0004***
(2.102)
0.004***
(3.540)
Factor 2 -‘End-line,
visible, poor’
0.002
(0.139)
0,00004
(0.100)
-0,0003**
(-1.695)
-0.002**
(-2.068)
Factor 3 -‘Small,
accessible, affluent’
0,041***
(2.962)
0,000009***
(2.776)
0,0003***
(2.061)
0.002**
(2.205)
Factor 4 -‘Open,
transition, shopping malls’
-0,031**
(-2.291)
-0,000009***
(-2.71)
-0,000217
(-1.195)
-0.001
(-1.116)
R-square
.25
.23
.20
.30
Appendix 1 – “O Metrô” - The São Paulo metroy (65 stations).
Appendix 2 – The database of study (selected variables)
Variable description
Source
Number of passengers between 7am and 9am
Metro company
Number of passengers between 5pm and 7pm
Metro company
Number of passengers between 10pm and 12pm
Metro company
Number of passengers in working day
Metro company
Number of employees working to support and maintain the station
Metro company
Number of station’s security employees
Metro company
Visibility from outdoors/indoors
Google Street View
Close to one or more public roads
Google Street View
Close to one or more bus stops
Google Street View
Close to one or more taxi stops
Google Street View
Close to one or more green areas (parks, woods)
Google Street View
Presence of any litter/any other physical deterioration outside
Google Street View
Presence of seats/benches outside
Google Street View
Presence of civil guards outside
Google Street View
Presence of police outside
Google Street View
Presence of bars, restaurants
Google Street View
Presence of shopping mall
Google Street View
Presence ATM/Bank
Google Street View
Presence hiding corners/dark corners
Google Street View
Presence of large cross section/junctions
Google Street View
Cases of detected weapons in the streets
SSP
Total population in the area
GeoSampa
Total of population receiving minimum salary
GeoSampa
Total population in the area up to 5 years of elementary school
GeoSampa
Distance to the city centre
Meters
Distance to schools
kilometres
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