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Applied Geography 42 (2013) 124e132

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Applied Geography

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Fear, crime, and space: The case of Viçosa, Brazil

Akenya Alkimim a,*, Keith C. Clarke b, Fábio S. Oliveira c

aDepartment of Arts and Humanities, Campus Universitário, Federal University of Viçosa, Viçosa, MG 36570-000, BrazilbDepartment of Geography, 1720 Ellison Hall, University of California Santa Barbara, Santa Barbara, CA 93106-4060, United StatescDepartment of Geography, Caixa Postal 719, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil

Keywords:Crime geographyUrban violencePerceptionSpatial patternBrazil

* Corresponding author. Av. Pádua Dias, 11, Cx. Po900, SP, Brazil. Tel.: þ55 19 8289 2821.

E-mail addresses: akenyaalkimim@usp.br, a(A. Alkimim), kclarke@geog.ucsb.edu (K.C. Cla(F.S. Oliveira).

0143-6228/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.apgeog.2013.05.007

a b s t r a c t

Crime is both a factual and perceptual component of the urban landscape, seemingly both a societalpathology and the consequence of economic disparity between social groups. Crime has a spatialstructure that can be revealed by mapping. Urban crime has a spatial multiplier effect that changes thevalues and perceptions of how people see urban space, and which jeopardizes the quality of life of a city’sinhabitants. In this research we examine the question of whether the geography of actual criminal acts isechoed by peoples’ perceptions of crime, what might be termed their “spaces of fear”. We ask how thefear of crime is associated with reported urban crime. Urban crime incidents have been increasing inViçosa, Minas Gerais, Brazil. We assembled crime information about Viçosa from two sources: first, crimeas reported to the police and second, crime as perceived by city residents and measured by surveys andinterviews. Reported criminal acts reveal a clustered geography, focusing particularly on the Downtownarea, where there is a concentration of urban wealth and potential victims are more numerous. Offensesagainst property were focused on Downtown, while offenses against the person were located mostly inperipheral areas. The widespread feeling of insecurity in the city’s neighborhoods, reflecting the fear ofbecoming a victim of violence and crime, was common throughout the city. Results confirmed theconclusion of past studies showing that the fear of violence and crime are not directly related toincreasing numbers of criminal reports. Sites with higher incidence of crimes are not places with higherlevels of fear. Rather than being geographically explainable “spaces of fear”, the spatial distribution of thefear of violence and crime appears to be unrelated in Viçosa, and neither is clustered or dispersed in anymeasurable way.

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Introduction

Fear is a powerful force that shapes our representations of thecity (England and Simon, 2010), far beyond simply the risk of beingvictimized by crime (Garofalo, 1979). The fear of urban crime is acomplex amalgamation of perceptions and experiences, and itmanifests itself in different illusions and beliefs, that can havenegative effects on individuals and decrease the quality of life(Nasar & Fisher, 1993). Fear can create the basis for the organizationof urban space, due to voluntary or involuntary movements ofpeople (Ortega-Alcazar, 2009), directly affecting their quality of life(Crank, Giacomazzi, & Heck, 2003; Franklin, Franklin, & Fearn,2008).

stal 9, Piracicaba CEP 13418-

kenyaalkimim@yahoo.com.brrke), fabiosolos@gmail.com

All rights reserved.

The globalization of urban commerce has unleashed spatialchanges upon the urban landscape, characterized by rapid land usechange, fragmented landscapes and segregated social classes(Gaffney, 2010; Goldberg & Pavcnik, 2007). The segregated suffer achange in lifestyle (Garofalo, 1981; Skogan, 1986) as people aredeprived of the freedom to come and go as they please, and tend tostay in their homes (Box et al., 1988) afraid to walk in neighbor-hoods that they do not know, especially at night (Box et al., 1988;Skogan & Maxfield, 1981).

The spatial reorganization reflects an effort by subsets of thepopulation to modify their physical environment (Brantingham &Brantingham, 1993) to protect themselves from violence and ur-ban crime. Social, ethnic or economic groups that feel threatened byviolence and crime commonly seek out the most isolated neigh-borhoods within urban areas, or even create an enclave or pseudo-fortress (by use of security cameras, doormen, security officers,electric fences, or concrete walls) within the limits of the city tojustify their exclusion (Caldeira, 2000; Carvalho, Varkki, & Anthony,1997; Coy, 2006).

A. Alkimim et al. / Applied Geography 42 (2013) 124e132 125

The fear of violence and crime in many cities has a spatial dis-tribution (Bernasco & Elffers, 2010; Doran & Lees, 2005; McCrea,Shyy, Western, & Stimson, 2005) characterized by spatial segrega-tion and an urban space reallocation (Low, 2006; Vesselinov,Cazessus, & Falk, 2007). Insecurity caused by the increase inviolence and crime (Wood, Gibson, Ribeiro, & Hamsho-Diaz, 2010)leads to the isolation of segments of the population, whether inrestricted and security zones or within high-risk areas. The growinguse of private security (Caldeira, 2000), monitoring by video cam-eras (Bradley & Clarke, 2011), gated communities, and electricfences perpetuates the segregation of some social groups thatperceive their environment as threatening (Grant & Mittelsteadt,2004).

Crime and violence preclude people from enjoying the benefitsoffered by city living, which leads residents to seek safety. Pro-gressively they have transformed that safety into safe places orrefuges, which then become maximum-security enclaves, leadingto self-segregation. According to Souza (2000), self-segregationseems to represent a search for “autonomy,” the recreation of autopian community, characterized by the desire of those with highpurchasing power to separate themselves fromwhat they considerunpleasant or dangerous. Souza noted the irony that self-segregated classes spatially exclude people they would notconsider as neighbors, but welcome them as employees.

Felix (2002) considers increased social and economic inequalityas a motivator of crime, which in turn reinforces or creates newperceptions of urban threat. Does the fear of urban crime showspatial structure, such as clustering, as a consequence of this self-segregation? Socio-economic measures of the population becomedistinct in space, as groups segregate and form a landscape ofexclusion. Does urban crime against property then directly reflectthe wealth segregation and so also become clustered? Differentperceptions of urban threat lead the individual to construct certainspatial relations with the space creating a geography of fear.Different ways exist to measure this fear (McCrea et al., 2005), butone should consider time, space and social context (Farral,Bannister, Ditton, & Gilchrist, 1997).

Fear of violence and crime cannot be only explained by crimerates as an urban threat. Many researchers in different countrieshave found that crimes rates are not a good predictor of fears levels(Liska, Sanchirico, & Reed, 1988; Perkins & Taylor, 1996; Skogan,1986; Skogan & Maxfield, 1981; Warr & Stafford, 1983). Thereforevarious other factors should be considered in predicting fear ofcrime. In this research we extend earlier studies about the lack of arelationship between fear and objective measures of crime.Although is a well known subject in the literature, we focus on thelocal scale, and also replicate the findings in a new place. Accordingto Maxfield (1984) small-scale studies are both informative andneeded.

The main purpose of this study was to investigate the spatialrelationship between the fear of crime and its actual occurrence inthe city of Viçosa, Minas Gerais, Brazil. Viçosa was chosen bothbecause it shows a significant increase in crime, and because it wasthe focus of a broad scale social survey of crime and crimeperception among its inhabitants. We used official data crime fromthe police department to map the distribution of crime, and datagathered from a survey to analyze the distribution of fear withinurban neighborhoods in Viçosa.

The study was able to view crime trends over a longer period oftime in comparison to other studies, and added the mapping of fearin detail. A geographic information system (GIS) played an impor-tant role in the examining of the data in a geographical context(Chainey & Ratcliffe, 2005), using GIS spatial autocorrelation of fearand the spatial analysis of crime. The products of this analysis aremaps that are helpful to show to the public and to inform decision-

making on the spatial distribution of crime. The information pro-duced should provide support for reducing specific crimes byobjectively showing where they actually take place.

Methods

The study area is located in Viçosa, Zona da Mata of MinasGerais, Brazil with an area of approximately 299 km2 (IBGE, 2007).Viçosa County has a population of 70,401 inhabitants (IBGE, 2007),and has witnessed high growth rates in recent years. As reported byRebeiro Filho (1997), in 1950 the population was 36,558 in-habitants, and the city doubled that number by 2000, to 64,854inhabitants (IBGE, 2007). The city has a floating population ofapproximately 12,000 inhabitants, mostly composed of students,which is not included in the IBGE census (IBGE, 2002).

The municipality’s population density is 249.2 inhabitants/km2,withmost of the population density concentrated in the Downtownarea, near the campus of Federal University of Viçosa (UFV) wheremost of the commercial activities are situated. Data from ViçosaCity Hall (PMV, 2007), define the Downtown (Centro) as the part ofthe city with the greatest concentration of buildings. The analysis ofurban crime and fear used in this study was processed and evalu-ated using a Geographic Information System (ArcGIS 10e ESRI) andJMP Pro 10. Population and median household income data weregathered from the census tract survey for 2000 (IBGE, 2002). Dataon crime incidence, household income, crime per capita and apublic survey were combined in those programs for the study.

Crime report input and data processing

Data for crimes registered in Viçosawere obtained from the 97thSpecial Military Police Company (PM). The data recorded the ab-solute number of incidents reported in the county, and assignedcrimes to blocks. Thirty neighborhoods areas (polygons) wereselected for crime study according to the blocks defined by the PM,all located in the urban area. The database provided by the PM wasbased on Occurrence Reports (BO) covering the period 2002e2006.

The datawere organized in Excel tables containing the followingattributes derived from the BO: city code, nature of crime, place andmonth of occurrence. We selected eight types of crimes that mostcontribute to the feelings of fear and insecurity. The types of crimewere as follows: Homicide e Homicídio Tentado (individual actswith the intent to kill or taking the risk of killing someone as apredictable consequence of the acts) and Homicídio Consumado(the individual has no intention to kill, nor takes the risk of killingsomeone as a predictable consequence of their acts) e these wereclassified as offenses against the person; Furto consumado a resi-dência (breaking, entering and stealing property, taking anotherperson’s possessions without their permission or consent), Furtoconsumado a transeunte em via pública (theft of a person’s pos-sessions in a public place), Furto consumado a pessoas em esta-belecimento comercial (breaking, entering and stealing fromcommercial facilities), Furto qualificado a residência urbana e

arrombamento (breaking, entering and stealing from urban resi-dences), Roubo consumado a transeunte (taking a pedestrian’spossessions by force or threat of force against the victim), Roubo amão armada a transeunte e assalto (taking a pedestrian’s posses-sions with the use of a weapon) e these were classified as offensesagainst property.

The data obtained from the PM were partial because they areoccurrence records. In some cases, certain crimes are not reportedto the police. Victims do not always report occurrences of crime dueto the time and trouble of the process, so the data obtained mostlikely are underestimates of crime, especially against property. Af-ter data assimilation, processing and analysis, thematic maps were

A. Alkimim et al. / Applied Geography 42 (2013) 124e132126

made in order to show the spatial incidences of crimes occurring inthe neighborhoods of Viçosa over time.

Fear survey and data processing

The study used data gathered from the respondents to a PublicOpinion Survey on Urban Violence conducted in Viçosa in 2007. Atotal of 450 questionnaires were used in face-to-face interviews in34 neighborhoods of the city. The questionnaires were based onobjective questions, with 22 questions asked by in-person inter-view using randomly selected adults. Of the questions, 14 wereused to analyze the fear of crime and violence. For example, therespondents were asked “If you had to walk at 10 p.m. in yourneighborhood, howwould you feel?”. Responses were checked by theinterviewer according to the following scale range: (4) Very safe;(3) Safe; (2) Unsafe; (1) Very unsafe. Another question asked:“Thinking about the city of Viçosa, do you consider that the chanceof having your bag or wallet taken is: (4) Very high; (3) High; (2)Low; (1) Very low?” The use of objective questions facilitated theprocessing and data analysis. On the other hand the questionnairedid not facilitate the responses of individuals who did not know theanswer or had difficulty in answering.

The determination of the sample size to find the number ofquestionnaires that were necessary to make representative con-clusions about the whole city was based on the following formula:

n ¼ s2xpxqxNE2ðN � 1Þ þ s2xpxq

(1)

where:

n¼ Sample sizeN¼ Population sizes2¼ Confidence level, in s’s numbers (equivalent to 95% confi-dence level)p¼ proportion of the characteristics surveyed in the universe(equivalent to 50% chance of the respondent to feel insecureabout crime and urban violence)q¼ Proportion of the universe that does not have the charac-teristic studied (q¼ 100� p)E2¼ Estimation error

For the purpose of statistical analysis the sample calculationwasbased on a population of 80,000 inhabitants. 10,000 were added tothe sample because it was considered a reasonable number toincorporate the floating population of Viçosa into the study. Theestimation error was set at 5%.

Our particular survey was an adapted version of the surveyapplied by the Getúlio Vargas Foundation (FGV) to measure the fear

Fig. 1. Total of crime reports in Viçosa 2002e2006. The chart shows the total number of all ccrimes not considered in this study.

index inmajor Brazilian cities in 2002. The quantitative informationwas founded on the experience and judgment of the respondents. Itwas based on composite priorities or preferences of a set of alter-natives, which were subject to a numerical ranking 1e4 that indi-cated an order of preference among the alternatives. The 1e4 ratingvalues were grounded on a Likert scale, an ordinal scale that has asan attribute the ordering of objects according to certain charac-teristics, serving to name, identify and (or) categorize people, ob-jects or facts. It is based on the premise that the measured generalattitude draws on beliefs, and the force that holds these beliefs andthe values related to the object of study provide information aboutthe respondents’ feelings, in spite of them being subjective. This isbecause they specify the level of their agreement or disagreementwith the proposed questions by choosing one of the ranged alter-natives (Likert, 1932).

The procedure adopted to calculate the fear index for eachneighborhood in Viçosa was obtained using the following formula:

100� ððSum of the general index� 14Þ=42Þ (2)

where:

100¼ fear index expressed as a percentage.Sum of the overall index¼ sum of the all answers to the fourteenquestions.14¼ number of questions used to measure the fear index.42¼multiplication of the greater number used (4) by thenumber of favorable assertive (14) minus the multiplication ofthe lesser number used (1) by the number of unfavorableassertive (14).

The number “99” and alternative “NA” (none of above), con-tained in the questionnaires, were assigned a value of zero for thepurposes of calculation. The raw data were compiled in Excelspreadsheets and processed in ArcGIS using the Geostatisticalanalysis toolbox.

A cluster and outlier analysis was conducted using a binarymatrix. The matrix defined the relationship among all theneighborhoods based on the area and Delaunay triangulation.The Delaunay triangulation creates neighbors using Voronoipolygons, which ensure that every area has at least one neighbor.The triangles were formed by the GIS around points where datawere located. With the cluster and outlier analysis tool we wereable to identify a spatial outlier taking into consideration thedissimilar values in comparison to the other neighbors. A lownegative z-score for a feature in the attribute table indicates astatistically significant (0.05 level) spatial outlier. The results ofthis processing specify if a neighbor has a high value and issurrounded by neighbors with low values (HL) or if the neighbor

rimes against persons and property reported in the city over the years, including those

Fig. 2. A statistically significant spatial outlier (Downtown) determined by the lownegative z-score. HL indicates that the relationship with the number of crime is High-Low, which means that Downtown is surrounded by neighborhoods with low valueand has values that are significantly higher than its neighbors.

A. Alkimim et al. / Applied Geography 42 (2013) 124e132 127

has a low value and is surrounded by neighbors with highvalues (LH).

A spatial autocorrelation analysis based on Global Moran’s Iwasused to evaluate the fear data pattern using a confidence level of

Fig. 3. Spatio-temporal distribution of crime. The proportional circle maps show the spatia2002e2006.

95%. The method calculates the mean and variance and subtractsthe mean of each feature value, creating a deviation from the mean.Deviation values using the distance of each neighborhood aremultiplied to create a cross product, where zi is the deviation of theattribute for feature i from its mean, wi is the spatial weight be-tween feature i and j, n is equal to the total number of features, andS0 is the sum of the spatial weights:

I ¼ nS0

Pni¼1

Pnj¼1wijzizjPni¼1z

2i

(3)

Where:

S0 ¼Xn

i¼1

Xn

j¼1wij (4)

A z-score and p-value were also obtained, which evaluate thesignificance of the calculated Moran’s I Index. The z-score is thenumber of standard deviations that the observation is from themean, and the p-value is the probability of the spatial pattern beingcreated by some random process.

A regression analysis was performed to model and examine therelationship between the fear of violence and crime and occur-rences of crimes in Viçosa in 2006. The “Fit Y by X” tool in the JMPProgramwas used to perform a Least Squares regression techniqueto fit a line that minimizes the sum of the square distances fromeach individual point in the graphic to the value that the line pre-dict for that individual occurrences of crimes, where a singleregression equation represents the correlation between those var-iables. The result determines if there is a correlation between them,and in the case that correlation exists, the degree to which occur-rences of crimes make positive or negative changes in the fear ofviolence and crime in the population. Despite the fact that the datacollected include the number of occurrences of crimes in Acamari,this neighborhood was not part of the regression analysis becausethe access to that neighborhood was not allowed. So, no ques-tionnaires were applied to that gated community.

Results and discussion

Viçosa is a city that attracts people, especially students, due tothe presence of UFV, as well as other private colleges. The history ofthe city is closely linked to the growth of UFV. As a research andlearning center, Viçosa is characterized by annual student

l and temporal distribution of crime for each neighborhood in Viçosa during the years

Fig. 4. Occurrences of crime by neighborhoods in Viçosa per 1000 residents in the years 2002e2006. The maps show the spatial and temporal difference in per capita crime rates.

Fig. 5. Median household income salary in Reais. The data was based on the Censusblocks 2000 (IBGE, 2002). They were integrated to represent the median income ineach neighborhood in the city. The area in hollow has no income data value.

A. Alkimim et al. / Applied Geography 42 (2013) 124e132128

migration flows and a floating population that contributes tobusiness in the city.

Due to the rapid growth in recent decades, which has not beensubject to urban planning, Viçosa faces several problems. Amongthem is an increase in urban violence generating a state of inse-curity among inhabitants. It is noteworthy that the growth inviolence has followed the same pace as the city growth and con-stitutes an important index to determine the inhabitants’ qualityof life.

According to the survey, 72.34% of the respondents said thatviolence in Viçosa has been growing in recent years. When it comesto how people perceive violence, 46.90% of the respondents thinkor worry about violence and crime all the time, followed by another21% who think about it a few times a day. It is plausible to infer,based upon the police reports, that this significant concern aboutomnipresent violence and crime could be in part explained by theconsiderable increase in the number of crimes that have occurredin the city. The total number of crimes in the city has indeed beenrising, as shown in Fig. 1.

The period of 2002e2006 was marked by a significant increasein total crimes in Viçosa, an increase of about 42%. The growth wasin all types of crime, however the number of occurrences of of-fenses against property had the largest growth, going from 1725occurrences in 2002 to 3306 in 2006, an increase of about 92%.During the period 2002e2006, among the offenses classified asagainst property, Assalto a mão armada a transeunte increased fourtimes and Roubo a transeunte increased by six times.

Furto qualificado a residência urbana, Furto a residência and Furtoa transeunte contributed most to the significant number of offensesagainst property. They had an increase of 29%, 118%, 375%, respec-tively, in the year 2006 in comparison to 2002. In comparison tooffenses against the person, offenses against property, in the urbanarea, were 60% of all occurrences in 2002 and 73% of all occurrencesin 2006. Offenses against the person went from 1175 in 2002 to1254 in 2006, an increase of 7%. What is noticeable is not the factthat the increase was relatively small (compared to the totalnumber of crimes that occurred in this class), but that the magni-tude of the total numbers of crimes in Viçosa remained high duringthis period.

As can be seen in the cluster and outlier analysis (Fig. 2), thedissimilarity in the Downtown area is more pronounced than onewould expect in a random distribution, which make this area aspatial outlier. The reason for the high crime rate can be explainedby the concentration of most of the city’s commercial activities such

as a shopping mall, theater, grocery stores, and by the greatmovement of people, besides the proximity to UFV. Downtown isthe location of criminal acts because the offenders can easilydetermine a susceptible victim in the area. According to the police,young students e ages 18e25 years e are the main victims ofcrimes that occur in Downtown, since that area is their major route

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to go to school and to gather. Fig. 3 shows the annual distribution ofthe crime occurrences in Viçosa, presenting a significant increase inthe number of crimes in some areas over the years. As can beobserved in Fig. 4, Downtown has higher crime rates per capita incomparison to other neighborhoods.

The spatial distribution of crime assumes particular character-istics in the landscape, according to the juxtaposition of poor urbanareas with wealthy areas. Offenses against the person, for example,tend to occur more frequently in the peripheral areas, while of-fenses against property have higher rates in the central areas,where wealth is concentrated (Fig. 5). As stated by Francisco Filho(2003), the distribution of crimes is related to an urban stratifica-tion imposed by a process that segregates people by their pur-chasing power, and as a consequence, they are isolated in areas thathave a certain demographic uniformity. As a result, crime reflectsthese characteristics and develops a dependent spatial structure.

Urban morphology, mostly derived from economic change, hasforced the poor population into more peripheral areas, and thegrowth of Viçosa has reinforced this spatial separation. This has led tothe development of dense peripheral neighborhoods with a lack ofinfrastructure and assistance even for the most basic needs. Theoccupationof peripheral areas inViçosa, on the otherhand, is not onlyby the poorest, but also, in some cases, by high-income inhabitants inan attempt to “escape” from the violence and crime of the centralarea. However, the peripheral areas where they live in luxury gatedcommunities (Acamari) have a different urban morphology to thoseoccupied by the poor (Vereda do Bosque) (Fig. 6).

The high numbers of crimes in Viçosa have contributed to theloss of the inhabitants’ quality of life, who feel deprived of theircitizens’ rights, especially when it comes to safety issues. In somecases they have to limit their social and physical activities to avoidcertain places or situations. From the survey, it was determined that

Fig. 6. The spatial segregation in Viçosa. On one side there is the Acamari neighborhood (A)and planned homes. In contrast, the neighborhood Vereda do Bosque, known as Carlos Dias osewage networks, and space for recreational activities, presence of houses that do not meetpeople in the neighborhood very difficult. Not to mention the main local access to this neigmakes the mobility of the elderly and pregnant women who live there extremely hard. Sou2009.

insecurity caused by the fear of becoming victims of violence andcrime is a common feeling among the majority of the people thatparticipated in the interview. This tells us that no matter whetherthey are inhabitants of the wealthy areas, equipped with infra-structure, or the poorest neighborhoods, mainly composed ofsprawled subdivisions or settlements without urban planning, theyall share an everyday perception of risk of crime (fear).

The value obtained in the Moran’s I analysis shows that theproportion of people who fear violence and crime does not appearto be clustered or dispersed in any way, since the z-score of 1.30 isnot statistically significant. For this reason, the spatial pattern ap-pears indistinguishable from a random pattern. This fact makes usbelieve that the feeling of fear is evenly distributed among the in-habitants of the city, regardless of the area where they are located,poor or wealthy.

In relation to crime and urban violence, the data obtainedthrough questionnaires indicated that most of the respondents feelinsecure (fear of being victimized), i.e., 55.06%, which is the averagepercentage of general fear in the neighborhoods. The results of theregression analysis revealed that the percent of variance accountedfor the predicted variable (fear) from the predicting variable (oc-currences of crime) was 4%, which means that 96% of the varianceamong people in the neighborhoodswith fear of violence and crimewere unpredicted or unaccounted for by occurrences of crimes. Thisis evidence that crime rates were not a good predictor of the fearaccording to Pearson’s coefficient of determination (Fig. 7). The p-value yielded in the analysis of variance was not significant, whichindicates that the regression model had not enough predicted po-wer to fit the data better than would by chance. Thus, the findingsreported here are consisted with the studies of non-correlationbetween the feeling of fear and crimes rates. Places like Down-town, Santo Antônio, Bom Jesus, Fátima, Nova Era, Nova Viçosa, and

with good infrastructure like paved streets, wooded areas and trees, recreational sitesr Rebenta Rabicho, is a place devoid of infrastructure such as trash collection, water andany criteria of planning, unpaved streets and narrow alleys that make the movement ofhborhood that is via a staircase, as can be seen from Fig. 6B (on the right), which oftenrces: Figure A on the left: Viçosa Cidade Aberta, 2013. Figure A on the right: Portugal,

Fig. 7. Summary of the regression analysis of fear index and occurrences of crimes in 2006 in Viçosa. The results confirm the non-correlation between the fear and occurrences ofcrime.

A. Alkimim et al. / Applied Geography 42 (2013) 124e132130

Santa Clara that had high numbers of crimes did not show highlevels of fear compared to other neighborhoods.

This study did not measure any factor that could be a goodpredictor of fear of crime in Viçosa. However, from the perceptionpoint of view based on self-reporting, it can be inferred that thereare infrastructurally-based “spaces of fear” in Viçosa. Although wedid not statistically established a relationship between other factorsin predicting fear of crime in this study, it is plausible to bring outthat the city landscape contributes to the increase of this feeling,including some places where the fear is indeed more pronounced.Downtown is the area with the highest rate of crime incidence, butironically it is the place where most people actually feel safe incomparison to other neighborhoods, regardless of the time (day ornight). This can be explained by how people perceive the risk ofbeing exposed to crime in that neighborhood, since the placedoes not show significant levels of incivilities, and has a greatflow of people moving in the streets. This fact, however, cannot

be observed in other neighborhoods and surrounding streets,considering the type of infrastructure in these places, mainly due tosignal disorders such as lack of light, presence of narrow streets andforested areas, vacant lots, vandalism, gang activities and loitering.These findings are in accordance with those of Lagrange, Ferraro,and Supancic (1992), Ferraro (1994), Kelling & Coles (1996),Robinson, Lawton, Taylor, and Perkins (2003), Brown et al. (2004),Innes (2004), Jackson (2004), Wyant (2008) and Seymour, Wolch,Reynolds, and Bradbury (2010) that a neighborhood’s physicalenvironment is often as powerful in generating feelings of fear andinsecurity as the actual existence of crime.

Our analysis suggests that Viçosa is characterized more by an“architecture of fear”, which is more likely to predict fear of crime.The presence of signal disorders in specific areas of the city hasmoreinfluence on the feeling of fear by the inhabitants than others.Certain features in the landscapemay intensify fear because theyaresites that could allow the criminal to observe their victim before a

A. Alkimim et al. / Applied Geography 42 (2013) 124e132 131

criminal act without being observed themselves, such that a victimsometimes has no chance to escape from an approaching criminal.

Conclusion

Social inequality daily confronts people with different life per-spectives in Viçosa: high-income residentse living inwealthy areaswith a variety of types of infrastructure available, and the poor eliving in areas totally devoid of such facilities. They represent re-alities that are totally antagonistic, where space becomes the ma-trix of the conflict that has its origin in the great wealth differencebetween these groups.

Insecurity causes part of the population to isolate themselves ingated communities transforming them into islands of false security,surrounded by numerous technological security devices such asgates and cameras. This alters the urban space, creating small for-tresses that are representative of a fragmented space. The majority,who are unable to protect themselves from such violence, alsochoose to isolate themselves in their own homes made safer withlocks, or by changing their routine in an attempt to avoid becomingvictims of urban crime.

The actual criminality in Viçosa assumes unique characteristicsin each place, but focuses preferably in the Downtown area, wherethe urban wealth is concentrated. Offenses against property alsofocus on Downtown, while offenses against the person are locatedin the peripheral areas. Despite the fact that criminal occurrencesare concentrated in certain areas of the city, the feeling of fear ofviolence and crime seems to be more evenly distributed, whichmeans it is geographically constant, since the fear index did notappear clustered or dispersed any different from a random patternin the spatial autocorrelation analysis.

There was no evidence based on the regression analysis con-firming a relationship between the feeling of fear and occurrencesof crime in the population. The regressionmodel did not differ fromone that has no predicted power. Therefore, this research confirmspast studies, showing the lack of a correlation between feeling offear and crime rates.

The geography of urban crime in Viçosa has changed the valuesand the perception of how people perceive their city, which hascontributed to its deterioration, and changed its spatial configura-tion creating “spaces of fear” unrelated to the actual pattern of thethreat. The relationship between crime and insecurity shows asocio-spatial structure that has become embedded into the urbanfabric of Viçosa, building and/or organizing space. This change isperceptible for all people regardless of age, gender or social status.

Fear is an attitude of caution that the individuals have in face ofsigns of violence and urban crime. Thinking about it, what shouldbe done to alleviate this fear? Social integration and harmonizing ofeconomic disparities are just palliative measures. The solution forthis problem goes beyond one-dimensional measures, such asmoreeffective policing. People have lost confidence in the police and inthe criminal justice system, because of corruption and impunity.They feel coerced and afraid to report criminal acts, since theybelieve that the offender will escape punishment and will comeafter them for revenge. Besides, criminals often are better armedthan the police.

The urban governance should not just recognize the fear ofviolence and crime as a pathological problem in Brazilian society,but should urgently address this problem because it changes thenatural spaces creating a geography of fear. They should identifyinformation such as those generated here as key information in fearreduction strategy. Implications for the perception of safety by thepopulation should consider planning, developing efforts and aneffective criminal justice system, making the people aware of thesechanges through public campaigns, media, etc. We hope that this

study draws attention to crime and its perception, not just in asmall city like Viçosa, but also across the country, since these pat-terns have been recognized in many places in Brazil.

Lastly, the use of cartographic and geostatistical analysis provedhighly informative in this study. Fragmented data were convertedinto structured data that allowed a trend analysis of spatial andtemporal occurrence of crimes from 2002 to 2006, and revealed thespatial distribution of crime and fear in Viçosa.

Acknowledgments

We want to thank for all their hard work the brothers andfriends of the first author who contributed in the application of thesurvey questionnaires, and also those who participated in theinterview as respondents, sharing with us their feelings of fear. Theauthors also thank the reviewers of an earlier draft for their valu-able comments that significantly helped us to improve the paper.

Appendix A. Supplementary data

Supplementary data related to this article can be found athttp://dx.doi.org/10.1016/j.apgeog.2013.05.007.

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