Post on 24-Jun-2020
Mapping Fear of Crime as a Dynamic Event in the Whole Journey Environment
Authors: Reka Solymosi, Taku Fujiyama, Kate Bowers
ABSTRACT
Perception of security largely influence people’s experiences with all forms of
transport, be it public, private, walking or cycling, and has knock on effects on
transport mode choice, as well as the accessibility and walkability of an area. It
is therefore important to gather accurate spatial and temporal information of
when and where people feel unsafe within the whole journey environment, to
identify problem areas. Currently, fear of crime information is collected using
retrospective surveys such as Transport for London’s attitudes to safety &
security survey, or the Crime Survey for England and Wales, which result in
inaccurate measures segmented over various stages of the entire journey
experience, unable to cover people’s entire activity space. To correctly identify
‘fear hotspots’ in place and time, experience sampling method applied to a
mobile phone application can be used to collect information on people’s
perceptions along with GPS and time-stamp data. By applying this novel
approach to measuring dynamic events on the move as they happen, problem
areas can be identified based on empirical evidence. This is used to target
preventative interventions effectively to improve perceptions of safety and
security in the whole journey environment, and to identify environmental
features associated with these hotspots to inform town planning and local
initiatives on situational prevention.
INTRODUCTION
What is fear of crime
Fear of crime is the negative emotional reaction elicited by crime and its
associated symbols (Warr, 2000; Hale, 1996). Not only does this fear appear in
those who have been victimised, but also those who have not, making it more
prevalent than actual rates of victimisation (Warr 2000). In the 2010-11 year
43% of Londoners reported their quality of life affected by fear of crime (Gray et
al. 2013). Nationwide, according to the British Crime Survey, 17 % of
respondents were very worried about violent crime, 13 % about burglary and
14 % about vehicle crime (Jansson 2007).Regarding feeling safe on public
transport, TfL’s survey reveals that a fifth of Londoners said that they had felt
worried about their personal security in the last three months, with 53% of
these incidents on a bus followed by 24% on the Tube and 20% on the train
(Transport for London 2013).
Why is fear of crime important
Fear of crime, evidently a present issue, is important to investigate as it has its
own, very real, consequences that are distinct from those of crime. People’s
perceptions of the environment in terms of safety impacts on their experiences
in the areas where they live, work, and travel through. Fear of crime as
experienced while traveling is particularly important, as travel mode choice is
influenced by perception of safety in the journey environment; higher fear of
crime is associated with less cycling and walking, and increased use of private
transport modes (Mitra et al. 2010; McDonald et al. 2010). To encourage use of
public transportation, perception of safety during the entire door-to-door
journey needs to be addressed (Brons & Rietveld 2001). “Perceptions of the
likelihood of being a victim of crime or antisocial behaviour affects travel
choices and can act as a barrier to travel” (Transport for London 2011). As such,
fear of crime in the journey environment poses an issue of accessibility. Groups
such as elderly and vulnerable people, women, and ethnic minorities are
disproportionately affected by fear of crime in transport (Loukaitou-Sideris &
Fink 2009) and experience further disadvantage by barring accessibility of
transport. Liska et al. (1988) report that fear of crime defined in terms of low
levels of perceived safety constrains social behaviour, creating a feedback loop
by which more fear leads to more social isolation and subsequently even higher
fear (see also Garofalo 1981). People’s sense of security is also an important
factor in assessing the walkability of an area (Kelly et al. 2011). Walkability of
the environment affects public transport ridership (Ryan & Frank 2009). Indeed,
according to Transport for London survey results, the transport modes most
affected by fear of crime are walking, travelling by bus, and cycling, with people
feeling safest traveling after dark by door-to-door modes such as car and black
cab (Transport for London 2011). Evidently, fear of crime can prevent people
from using environmentally friendly transport modes, and encourages the use
of private vehicles, increasing carbon emissions, and reducing exercise. Overall,
fear of crime in an area can affect accessibility, environmentally favourable
travel choices, and relative disadvantage as well as other social and economic
elements of a community.
This importance of fear of crime is reflected in governmental attitudes as well;
“how safe people feel and how much crime they perceive to be occurring has
become a priority for policing” (London Transport Committee 2008), and
perception of crime and antisocial behaviour is addressed under the Mayoral
goal of improving safety and security on the transport networks in London
(Transport for London 2011). In order to be able to mitigate this effect, it is
important to establish where, when, and how fear of crime is experienced by all
users of an environment, be they residents, workers, people travelling through,
or other users of public spaces, and cover their entire activity space in the
whole journey environment.
Background in Fear of Crime research
Fear of crime has been studied extensively, and this background aims to give
only a brief introduction into its methodologies, as relevant to measuring
perceptions of security in the whole journey environment. Traditionally,
measurement of fear of crime is done using household surveys. A variant of the
question “How afraid are you walking alone at night?” is asked from
respondents, as well as a series of follow up questions, asking about the
perceived likelihood of falling victim to a particular offence. In the Crime Survey
for England and Wales (CSEW, formerly British Crime Survey (BCS)),
respondents are asked whether they are ‘very’, ‘fairly’, ‘not very’ or ‘not at all’
worried about becoming a victim of crime. Past research has claimed that this
involves an assessment of individuals’ perceived likelihood of becoming a victim
of crime, and the level of worry they attribute to this event, based on the
assumed probability rather than actual experience (Gray et al. 2008). Perceived
risk here is measured as solely subjective probability (Jackson & Gouseti 2013),
omitting many of the complexities involved with individuals experiencing fear of
crime in their lives.
Consequentially, such methodologies can result in the overrepresentation of
actual rates of fear of crime (Farrall & Gadd 2004; Fattah & Sacco 1989; Yin
1980; Bernard et al. 1984) and neglecting the possible impact of the perceived
seriousness of crime and victimization (Warr & Stafford 1983; Jackson 2011) as
well as the sense of control over the event (Jackson & Gouseti 2013: 4). Further,
“these intensity measures may often collate not just everyday worries or fears,
but also some emotionally tinged ‘attitude’ towards risk (Jackson 2006) or
future-oriented anxiety (Sacco 2005; Gray et al. 2010) which they do not
account for in the interpretation of these results. Instead the findings are
presented as accurate representations of people’s everyday experiences, when
it is widely considered that they are not.
To attain a more accurate picture of fear of crime as experienced, questions
about the frequency and intensity of the experience were intrudiced in the
2003-4 sweep of the BCS (Farrall & Gadd 2004; Farrall et al. 2007; Gray et al.
2008; Gray et al. 2010). These new measures focus on instances of ‘worry’,
referring to concrete mental events of concern (Farrall & Gadd 2004) rather
than ‘anxiety’, which refers to a more diffuse mental state (Gray et al. 2010).
This movement towards measuring fear of crime as an event experienced in
everyday life rather than an merely an underlying attitude or anxiety shows a
shift in focus towards analysing fear of crime as an experiential event. However,
by asking whether in the last 12 months the respondent has felt worried about
being a victim of crime, and asking them to remember how many times this
happened and how worried were they in each of these occasions (Experience
and Expression in the Fear of Crime, 2003-2004), a number of original issues
with the CSEW/BCS remain unaddressed. Firstly, these new measurements still
do not get around “problems associated with self-reports, including errors in
recall, demand effect, or reluctance to disclose emotions” (Warr 2000). Issues
with recall have always been a part of quantitative survey research in the social
sciences (Loftus et al. 1992; Bernard et al. 1984). Specific to fear of crime, it is
important to note that memories of emotional experience from longer than
about two-weeks prior draw on semantic knowledge and general beliefs related
to the particular event, rather than the specifics of the event itself (Robinson &
Clore 2002b; Robinson & Clore 2002a; Gray et al. 2011). Therefore even though
such experience-based questions move closer to capturing the more expressive
dimensions of public insecurities about crime (Gray et al. 2011), it still does not
reflect fully the dynamic way in which this is experienced in every-day life.
Further, these surveys use respondents’ place of residence when assigning a
geography to fear of crime. Therefore we gain a static image of how safe people
perceive their own neighbourhoods, and lose information from those who may
not live there, but do travel to, or through, that area. However it has been
shown in past research that fear of crime varies based on many variables,
besides just who feels it (age, gender, etc.). Factors such as time of day,
familiarity with an area , and psychological distance (Jackson & Gouseti 2013) all
affect perceptions of safety. It has even been shown to vary with the purpose of
the journey, that is whether the person was carrying out a voluntary or
compulsory routine activity (Rengifo & Bolton 2012). Therefore these new
measures while shifting emphasis towards addressing fear of crime as
experienced in every-day life, still fall short of being able to capture its dynamic
nature. Even surveys that concentrate on travel, such as the TfL Attitudes to
Safety & Security Survey only cover segmented elements of the whole journey
environment, and do not cover people’s entire activity space door-to-door.
Aim of this paper
The project presented in this paper will attempt to address these shortcomings
and omissions in past fear of crime literature and by doing so complement and
build upon past research, to help provide a more holistic picture of fear of
crime, as experienced by people in their everyday lives. It aims to investigate
the feasibility of applying a novel methodology to fear of crime research in
order to gather data about people’s perceptions of safety in their entire activity
space, and locate them in place and time dynamically.
Purpose of this paper
The study presented here approaches fear of crime from the framework of “the
criminology of everyday life” (Garland 2001). This approach views the criminal
event as the endpoint of a decision process (which can be conscious or
subconscious), influenced by personal (e.g. readiness to commit crime), and
environmental (e.g. suitable target and lack of capable guardian) factors
(Brantingham & Brantingham 1993). Similarly, the fear of crime event can be
considered as something experienced by someone with a certain readiness to
experience fear of crime, which can depend on a number of factors which the
individual brings with them (e.g. age, gender, psychological distance, familiarity
with an area) as well as elements present in the environment (e.g. signal crimes,
graffiti). By collecting micro level spatial and temporal data on fear of crime as
experienced in people’s entire activity space, it would be possible to discern
whether fear of crime shows concentration at hotspots in place and time, like
crime. This knowledge can be used to design targeted, efficient and effective
situational interventions to enhance perceptions of safety in the built
environment. When such micro-level variation for crime data was identified, it
resulted in more effective targeted interventions, and exposed how previous,
inaccurate, measurement lead to inefficient and misguided interventions (e.g.
see Ratcliffe & McCullagh, 2001). Therefore, by discerning the feasibility of
measuring fear of crime as a dynamic event in the whole journey environment
with specific data on spatiotemporal variation, this paper can take the first step
towards identifying what are the circumstances that promote fear of crime, and
how situational interventions can be implemented to enhance perceptions of
security.
METHOD
Experience sampling
Experience sampling method (ESM) “captures the representation of experience
as it occurs, or close to its occurrence, within the context of a person’s everyday
life” (Christensen, Barrett, Bliss-moreau, Lebo, & Kaschub, 2003: 54). It offers a
method for researchers to seek information about the daily events and
experiences that make up people’s lives (Csikszentmihalyi & Larson 1987), and
provides a powerful way to understand psychological phenomena (Christensen
& Barrett 2003). The use of experience sampling emerged in the domain of
psychology, as a reaction to “a large body of research demonstrating the
inability of people to provide accurate retrospective information on their daily
behaviour and experience” (Csikszentmihalyi & Larson, 1987: 526). As in fear of
crime research, criticisms of questionnaires claim they tend to measure
people’s generalized knowledge or theories about their experiences, rather
than the episodic or experiential representations (Christensen et al. 2003).
Answers to such retrospective questionnaires, of which the CSEW/BCS is one,
tend to therefore reflect experiences that have been filtered and reconstructed.
These problems associated with using CSEW/BCS and other questionnaire
surveys measuring fear of crime can be addressed by the approach of using
ESM. As retrospective reports distort toward an ‘average’ experience, a more
effective way to “capture emotions, motivations, and cognitive processes is by
asking people to describe them at the moment they occur” (Hektner et al. 2007:
24). By asking people to report their subjective experiences with fear of crime
as they go about everyday tasks, we can build on their reports to identify trends
and patterns that may emerge from their experiences.
However, one downside to using ESM is that it has been seen as more costly
than questionnaires, and more taxing on respondents, since they have to
remember to carry the survey with them to complete it at the appropriate time,
and to eventually meet with the researcher in person or post their responses
once they have completed the survey. A solution to this limitation can be found
by applying this methodology to use with mobile phone applications.
ESM and mobile phones
Mobile applications offer a convenient platform to survey people about their
every-day activities as they are not an extra burden to carry around, but
something that people already have. Further, the use of mobile phones offers
added benefits; sensors such as GPS and an internal clock allow for the
collection of data such as geographic location and time of response without
having to explicitly ask the participant. This feature helps to reduce burden on
participants, as well as to ensure greater accuracy by eliminating the
opportunity for human error. An example of such a tool is a mobile phone
software called EpiCollect, which collects data “submitted by phone, together
with GPS data, to a common web database and can be displayed and analysed,
along with previously collected data” (Aanensen et al. 2009). EpiCollect has
been used for various studies from epidemiology (e.g. Olson et al., 2013) to
ecology (e.g. Madder et al., 2012).
Another example of a mobile application using experience sampling to collect
data on participants’ subjective perceptions is the Mappiness project.
Mappiness extends experience sampling “ to incorporate satellite (GPS) location
data” by using an app to collect a panel data set from volunteers (MacKerron
2012). In this way, MacKerron (2012) was able to collect information on
people’s happiness from a large sample of accurately geocoded responses, and
calculate particularly good indicators of environmental quality, and make
conclusions about momentary happiness and its environmental correlates.
By building a mobile application to make possible experience sampling of fear of
crime, and also the collection of spatial and temporal information about the
fear of crime events, many of the issues mentioned above with fear of crime
research methodologies can be addressed, and benefits similar to those that
came out of Mappiness’ methodology may be achieved.
FOCA
The fear of crime application (FOCA) was developed in Java programming
language, for use on Android mobile devices. It was written and tested by the
author and is not based on code from any other mobile application. It was
created using the Android Software Development Kit in the Integrated
development environment for Java, Eclipse. It was designed to be simple,
efficient, usable, and to be able to call built-in applications, such as the GPS
sensor, using intents, in order to fully make use of the capabilities of the mobile
phones. Learning how to do this eliminates the need for using separate GPS
trackers and paper surveys or diaries that users may forget at home or find
taxing to wear or use.
The basic functions of the application are to allow individuals to submit reports
of fear of crime events they experience and collect spatial & temporal data as
well as demographic information. The following will outline and illustrate how
this works:
1. Participants download the application from an app store onto their own
mobile devices.
2. The first time the app is launched, users fill out a quick demographic
survey to collect information such as age, gender, etc. This only needs to
be completed once, and is linked to all submitted reports.
3. Participants are sent reminders or “ping”s to complete the questionnaire.
This can be set to remind them at certain times (e.g. during morning peak
travel times and evening peak travel times) or in certain locations (e.g.
when they come within 50 meters of Camden Town Station), and
corresponds to a signal-contingent protocol of the experience sampling
method. As early as 1985, ‘beeper technology’ was being used to signal
to participants a request for them to record their experiences while going
about their days (Pervin 1985). This method is most suited for projects
that measure on-going behaviours, susceptible to retrospective memory
bias and to cognitive or emotional regulation, such as fear of crime. To
carry out this type of experience sampling, there are traditionally two
required instruments; one is a signalling device that emits the ping
according to a pre-determined schedule, and the other is an experience-
sampling form (ESF) (e.g. a short answer questionnaire) where the
participant records information on the momentary situation and
psychological state (Csikszentmihalyi & Larson 1987). This method is
useful to ensure that enough responses are submitted during the course
of this study. The innovation of using an app is to combine these two
tools.
4. When participants receive this ping they open the application and
complete the survey questionnaire (ESF) about their current state.
5. Participants can also report something un-prompted; if they experience a
fear of crime event at a non-ping time, they can still send a report about
it. This option incorporates an event-contingent protocol of experience
sampling. This method involves reporting an experience immediately or
closely following the event of interest (Christensen & Barrett 2003), in
this case, an experience of fear of crime. Since we are trying to measure
instances of fear of crime as they are experienced in every-day life, the
opportunity for people to report something as it happens is necessary.
6. An issue with asking people to report fear of crime events using
smartphones is the possible danger into which participants may put
themselves or their valuables. When experiencing a fear of crime event, a
participant may not be inclined to make their valuable mobile device
visible and vulnerable. To account for this, retrospective annotation was
chosen as a third option to send a report. In this version participants are
offered the option of finding the location of the event on the map, and
telling how many hours ago the incident happened, allowing for an
adjusted GPS and time-stamp value that is still accurate to the report.
7. Finally the data collected will tell us who sent the report (demographic
information), where it was sent from (GPS), when it was sent (date and
time), what the person felt (fear of crime), which option they used to
send the report (responding to a ping, voluntarily, or retrospectively), and
any additional questions put to participants.
8. See Figure 1 for a diagram
Figure 1: Methodology of the experience sampling application
FEASIBILITY STUDY
Sample characteristics
In order to determine the feasibility of this methodology when applied to the
study of fear of crime, a pilot study was conducted. We recruited people who
live or work in one particular part of London, to ensure that despite the small
sample size (only a pilot study) responses will not be scattered all over the city.
Of the 34 people who registered for the study, 7 dropped out, leaving a final
sample of 27 people. 52% were female and 48% male. Mean age was 27.32, the
youngest participant being 20, and the oldest 47. 22% were in full time
employment at the time, 11% in part time employment, and 67% were full time
students. Regarding ethnicity, the sample contained 1 Asian person, 1 Chinese
person, 3 Latin Americans, 2 people who did not answer this question, and the
majority were white British (41%) or white Other (30%).
Since this was a pilot study to ascertain the feasibility of using the novel
methodology proposed by this paper for the study of fear of crime, the
representativeness of the sample was not a concern. However it is important
that this data can be collected by the application, because for the real study, it
will be vital to ensure that the sample we use is representative of the
population at large.
Response characteristics
Besides demographic information, the pre-experiment survey also asked the
same retrospective question that is asked by the CSEW /BCS, for comparability.
Responses revealed that the pilot sample’s distribution of fear of crime
measured with this retrospective survey matched that of findings for the whole
country with the BCS; our sample’s fear of crime was not significantly different
from that of the overall population.
Regarding the experience sampling study, participants submitted a total of 467
data points over the course of two weeks. Number of reports per person varied
from a minimum of 2 and a maximum of 44. On average, people submitted, 16
reports (median was 13.5) with a standard deviation of 11.2 (Figure 2).
Figure 2: Distribution of reports sent by participants
Of all these responses, 121 were sent using retrospective annotation (about a
quarter of all responses). However, the majority of these were sent within the
hour (n = 94), where participants indicated that there was 0 hours delay
between the event and them reporting it. Out of the reports where a delay in
reporting was indicated, the shortest time between event and report was one
hour, and the longest 23 hours, with a mean delay of 5.6 hours (median = 3).
However it is interesting to note that the retrospective analysis option was not
chosen to report instances when participants were fairly or very worried about
crime. Considering that many of these reports were sent also with 0 hours
delay, it is possible that the use of option 3 with the map was utilised by
participants as a way to get around the problems or delays associated with the
phone’s GPS finding signal. Instead, reports of being fairly and very worried
about crime were submitted in almost equal parts using option 2, un-prompted
self-reports (53%,) and using option 1, response to signal-contingent protocol
pings (47%s).
Regarding sampling times of the day, Figure 3 shows the number of responses
sent per hour.
Figure 3: Fear of crime reports by hour of day
Adjusting for time in each segment, we received in total 17.3 reports for every
hour in the morning commute section, 20.3 for each hour during the day, 37.3
for each hour within the evening commute, and 14.9 reports per hour on
average for the night period. While evening commute is over-sampled with this
method, it is important to keep in mind that the night time frame contains
several hours where most participants are inactive (sleeping). Therefore this
method is considered effective for sampling different times of the day. Further,
sampling people at various times means that we get information about people
when they are out and about making journeys through London, and are not
restricted to collecting information about their place of residence.
The phone’s inbuilt GPS sensor also proved available to be utilised for collecting
the location of where people were feeling safe or unsafe during those two
weeks. Data about time and location of reports revealed multiple reviews of the
same locations, which will allow for the creation of aggregate ‘fear maps’.
Figure 4 shows the pilot data for Camden and Islington boroughs aggregated to
street level. This map reveals variation in fear of crime in very small areas, which
highlights the need for precise measurement over aggregate categories that
homogenise areas.
Figure 4: Fear of crime map for pilot study
DISCUSSION
Applicability of ESM via mobile app to FOC
The results from the pilot indicate that this method is indeed applicable to the
study of fear of crime. It gives an insight into people’s everyday experiences,
and is not restricted spatially to the places where they live or work. Instead it is
available to people as they move about their entire activity space, allowing
them to send reports about any location that they encounter at any time. The
flexibility in time also moves us away from the day-night dichotomy present in
current research using retrospective surveys. Narrowing down exactly at what
times certain areas are perceived to be unsafe can help strategically target
limited resources in order to enhance feelings of security in problem areas.
Further the low-level variation in fear of crime reported within these
preliminary results further underlines the need for a tool that can allow people
to report where they feel unsafe on a micro-level, in the whole journey
environment.
CONCLUSIONS
Of course this study is a pilot study and will be followed by the actual
experience sampling study of fear of crime, that will cover greater areas of
London, include a large and more representative sample, and include validation
of the technique and findings.
One possible issue is that online-based data collection is based on volunteer
sampling, rather than probability sampling (Lefever et al. 2007). However it is
still possible to use theoretical sampling, in an effort to provide a cross-section
of different socio-economic, age, and gender backgrounds (Innes 2004). To be
able to stratify our sample based on these variables, the methodology may
propose a large sample from which we can select strategically based on
demographic information collected by the pre-experiment questionnaire.
Next steps will also include validation of this study by carrying out interviews
with experience sampling study participants to ascertain whether the data
collected by FOCA accurately reflects their everyday experiences with fear of
crime.
Overall, this new insight into fear of crime can inform efficient, effective and
targeted interventions on the local level, and enhance perception of safety in
the environment, also enhancing walkability, people’s willingness to cycle, and
choose public modes of transport.
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