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Crime in a planned city: the case of Brasília

João Ricardo Faria

MPA Program, University of Texas at El Paso, USA

Laudo M. Ogura

Economics Department, Grand Valley State University, USA

Adolfo Sachsida

IPEA - Instituto de Pesquisa Econômica Aplicada, Brasília, Brazil

Working paper version: there might still be typos and incorrect references. Published version contain additional material (http://dx.doi.org/10.1016/j.cities.2013.03.002).

Abstract: Brasília, the capital of Brazil, offers an interesting ground to study crime factors. The

central area (Plano Piloto) is characterized by greater concentration of economic activities and

stricter planning, which leads to lower population density and higher income than most places in

the periphery. Using an econometric model to analyze two years of crime data for 27 divisions of

the Brasília region, we find that higher overall crime rates in the Plano Piloto are related to the

concentration of commercial activities, vertical housing, lower density and greater population

size, while lower burglary rates reflect the predominance of vertical housing.

Keywords: Crime; Land zoning; Planning.

JEL Codes: R52; K42

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Crime in a planned city: the case of Brasília

Introduction

Brasília was a central planner dream, a Cartesian city with iron-clad zoning built to be the

new capital of Brazil. The core of Brasília is the Plano Piloto (Pilot Plan), conceptualized by two

architects influenced by the modernism of Le Corbusier: Lúcio Costa, responsible for the urban

plan, and Oscar Niemeyer, responsible for building designs. The Plano Piloto was designed to be

a complete city, with workplaces and housing located within its area. It is a peculiar urban place,

characterized by 1) a strict code of use that restricts land functions, separating housing from

other activities; 2) large superblocks, with straight streets with few corners; 3) high-rise

apartment buildings located in residential superblocks, with homogeneous height, built around

the same time (mostly from 1960 to 1980) and sharing a reinforced concrete style; and 4) a

population that is also homogeneous, mostly middle class service workers with similar

educational background and income, distributed uniformly in moderately dense residential zones.

By contrast, places in the periphery developed in a much more spontaneous way, often through

squatter settlements, with flexible zoning (frequently ignored), mixed land use and a more

diverse population in terms of income, human capital, race and regional origins. As a result,

households in the periphery earn on average less than half of the income of residents in the Plano

Piloto (there are few places with high income residents in the periphery). On the other hand,

population density is higher in the periphery, which is the opposite pattern compared to other

large urban areas in the world.

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Most studies find that crime rates are highly correlated to socio-economic, demographic

and/or policy variables, e.g., income, education, unemployment, family structure, race, age,

police effort and/or prison population (Fajnzylber et al., 2000, presented a cross-country study

based on these factors; Levitt, 2004, discussed the role of such factors in the 1990s U.S. crime

decline). Spatial variables (e.g., density and proximity to travel paths, retail stores and transit

stops) have also been studied as crime factors based on how potential criminals and other people

interact. Among others, the literature on spatial factors includes works by Jacobs (1961),

Newman (1972), Brantingham and Brantingham (1981), Wilson and Kelling (1982), Kelling and

Coles (1986) and Zelinka and Brennan (2001). Recent empirical studies include Hillier (2004),

Ackerman and Murray (2004), Ceccato and Oberwittler (2008) and Bernasco and Block (2011).

The peculiar features of the Plano Piloto make Brasília an interesting ground to study crime

determinants, especially factors related to the use of space. In fact, the Plano Piloto’s

characteristics are the opposite of Jacobs’ (1961) indispensable conditions for urban diversity,

liveliness and vitality (the conditions are mixed land use, small blocks, diversity of buildings and

high density). According to Jacobs, urban buoyancy is the crucial determinant of neighborhood

safety because it increases the number of eyes on streets (see also Palen, 2005, and Thomas and

Grigsby III, 2008). One application of this view is the livable communities project (Meck, 1997),

which advocates walkable neighborhoods (see also Daniere, 2000). The SafeScape community

design program (Zelinka and Brennan, 2001) is another variation of this approach, based on the

principle of seeing and being seen,1 advocating high-density mixed land use along common areas

1 This principle advocates intervisibility and enhanced surveillance opportunities, which can be achieved by more permeable layouts and housing types that are supported by studies on mapping and monitoring of pedestrian movement (Hillier and Shu, 1999; Hillier and Shu, 2000).

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like parks and community centers.2 In contrast, Newman’s (1972) Defensible Space advocates

closed and impermeable environments that better allow residents to recognize intruders and

challenge them.3 This view is consistent with a patchwork of inward-looking defensible enclaves,

such as modern American suburbia. In crowded travel paths, the absence of capable guardians

offers favorable conditions for criminal activities (Brantingham and Brantingham, 1981).

In short, density and mixed land use can help prevent crime because they increase the

number of eyes on streets, but on the other hand they reduce private control of space. In our

paper, we present a study of crime in the Brasília area motivated by anecdotal perception that the

richer residents in the homogeneous and less dense Plano Piloto enjoy greater safety than

residents in the heterogeneous periphery. While this perception is supported by burglary rate data

and the negative correlation between crime and income (to be presented later), overall crime

rates in the Plano Piloto are actually higher. These differences in crime rates seem to be related

to some of the characteristics of the Plano Piloto, like the concentration of commercial activities,

predominance of vertical housing, lower density and greater population size. Regression results

indicate that these features are associated with higher overall crime rates. However, vertical

housing lessens burglary rates, which explain why they are lower in the Plano Piloto.

The relationship between the development patterns of cities and crime rates in Brazil has

been discussed by a few scholars. Among others, Caldeira (2000) studied the dynamics and

interrelations between crime, urban violence, gated communities, segregation and the

development of the city of Sao Paulo. Coy and Pöhler (2002) analyzed the development of gated

2 Town and O’Toole (2005) remind us that Jacobs (1961) was not referring to suburban settings, only to certain American central cities. Aware of that, we consider Jacobs’ views as only one of the possible explanations for crime in Brasília, where densities are comparable to those in many American central areas. 3 Newman is one of the prominent authors in the literature on Crime Prevention Through Environmental Design (CPTED), which studies spatial design features that help preventing crime (see Crowe, 2000, and Cozens et al., 2005, for literature reviews).

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communities in Sao Paulo and Rio de Janeiro as a response to high crime rates. Souza (1996)

extended his study on the impact of drug trafficking on the development of poor communities in

the periphery of major Brazilian cities. In our paper, we study how the planned nature of the

Plano Piloto created differences in the use of space relative to the periphery and how crime rates

respond to these differences.

The remainder of this paper is structured as follows. In the next section, we present an

overview of Brasília’s history, geography, society and politics to explain the origins of the

differences between the Plano Piloto and the periphery. The following section describes the data

used in the empirical analysis. The last two sections present results and conclusions, respectively.

Brasília: History, Urban Design, Geography, Politics, Society and Economy

The core of Brasília was built in three years, from the approval of Lucio Costa’s city plan in

early 1957 to its inauguration in April 21st, 1960. The rapid pace of construction was a result of

political constraints: since President Juscelino Kubitschek could not run for reelection, the new

capital had to be inaugurated before the end of his five-year term. It represented a huge political

capital for Kubitschek (Kubitschek, 1975), who intended to run for president again in the future.

In fact, the construction of Brasília had widely popular appeal (Santos, 1964, p.398). The urban

plan, the Plano Piloto, is often considered to resemble the shape of an airplane, where

employment is concentrated in the body of the plane and housing is placed in the wings (see

Figure 1 for a map, showing the many zones that separate land uses). Zoning regulations are

rigid and strictly enforced. For instance, economic activities are segregated, with separated zones

for civil and military government buildings, retail stores, hotels, banks, recreational clubs, etc.

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The wings of the Plano Piloto are divided into superblocks, each containing a limited number of

apartment buildings, no taller than six floors and with ample space between buildings. Areas for

local shopping are located between superblocks (see Figure 2 for a representation of typical

superblocks). Other facilities such as schools and churches might exist between superblocks.

Transportation in the Plano Piloto takes place mainly through two perpendicular expressways

(the east-west Eixo Monumental and the north-south Eixão) and two medium-speed avenues (the

L2 and the W3, which are parallel to the Eixão). This design avoids the mixing of pedestrians

and vehicle traffic (Madaleno, 1996).

Figure 1 about here Figure 1: Plano Piloto The map shows the urban area of Brasília (Plano Piloto), divided in its many zones. The city was planned around two expressways, with main economic activities located along the Eixo Monumental and housing located around the Eixão, between the W2 and L2 avenues and west of the W2 avenue. Source: Governo do Distrito Federal (n.d.).

Figure 2 about here Figure 2: Typical superblock plan in the Plano Piloto In the Plano Piloto, residential superblocks (shown as Super Quadra Norte – SQNs) are located around the north-south expressway (Eixão). Local retail areas are located between superblocks (shown as Comércio Local Norte – CLNs). Darkened areas in the figure represent buildings (six floor apartment buildings in the superblocks and three floor buildings in the retail areas). Source: Governo do Distrito Federal (n.d.).

One of the goals behind the creation of Brasília was to change the Brazilian society at a time

when it was considered to be inward-looking, backward, and traditional, divided by a rigid and

unequal class system. A new unit of the Brazilian federation was created, called Distrito Federal

(DF), with an area of 5,802 km2 (2,240 sq mi), located in the central region of Brazil to boost

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economic and social integration between the rich regions in the south and the rest of the country.

The new planned city aimed at creating a new and modern society in its image (Holston, 1989),

that is, Brasília was intended to be an ideal environment for communal and egalitarian

conviviality, a new and just world that “we had always wished for” (Niemeyer, 1961, p.64).

However, one of the main characteristics of the modernist design of the Plano Piloto is the

separation of places of work and residence. Moreover, homogeneous housing placed households

with similar income together, leading to a stratified use of urban land along class lines, since the

poor were excluded from the Plano Piloto and forced to live in the periphery.

The construction of Brasília reinforced income segregation as a large number of workers

from other regions had to be attracted, generally characterized by low skill, income and

education. Many of these workers stayed in the region after the construction was completed,

being accommodated in satellite communities, which mushroomed in the periphery (Carvalho,

1991; Epstein, 1973, discusses the spontaneous development nature of the first satellite towns).

As the DF economy grew, it attracted an increasing number of uneducated and poor migrants,

with shantytowns built overnight removed to new places far from the Plano Piloto. Some of the

new communities were created or allowed to settle down with clear political opportunism

(Castells, 1988), e.g., Samambaia and Recanto das Emas. Figure 3 shows the location of

Administrative Regions, which are divisions of the DF associated with satellite towns (these

divisions do not have independent governments). The figure shows the Plano Piloto located at

the centre of the DF and the approximate location of other urban communities (in most cases,

communities depicted close to each other have contiguous urban areas).

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As a result of the settlement of poorer households in the periphery of Brasília, the DF became

as stratified and unequal as the Brazilian society.4 Inequality can be seen clearly in one

dimension of urban life: transportation. In 2000, residents of Brasília made 78.5% of their daily

trips by private vehicles or taxis despite the availability of a broad public transportation system.

In most satellite towns, this proportion was much lower; for instance, 26.5% in Ceilândia and

21.5% in Planaltina (Governo do Distrito Federal, 2007a).

Figure 3 about here Figure 3 – Administrative Regions in the DF The Plano Piloto (depicted at the center of the map) is the central area of the DF, where the main economic activities take place. There were 31 Administrative Regions in the DF at the end of 2012 (Jardim Botânico, Setor de Indústria e Abastecimento, Vicente Pires, and Fercal are not represented in the map due to undefined borders – the areas of these regions were respectively part of Lago Sul, Guará, Taguatinga, and Sobradinho/Sobradinho 2). In each Administrative Region, the square marker represents the approximate location of the centre of the urban area. Other dark areas in the map represent large bodies of water. Source: Governo do Distrito Federal (2007b).

Besides the differences in residents’ income, the periphery also has very different land uses.

While the Plano Piloto is strictly controlled (with clear separation of land functions and

homogeneous housing or commercial uses in each zone), mixed land use functions are much

more common in the periphery (except for few places occupied by rich households, like Lago

Norte and Lago Sul).5 Satellite towns grew spontaneously as result of large contingents of mostly

poor incoming households. These places developed without much planning and land use control

4 According to Serra et al. (2004, p.14), “in 1991, 90% of homeowners who earned less than the minimum salary lived outside of Brasília in one of the “satellite cities,” while 57% of homeowners who earned more than ten minimum salaries resided within the original layout of Brasília (Plano Piloto).” In 2004, the Administrative Regions of Lago Sul, Lago Norte and Sudoeste/Octogonal also had more than 50% of their households earning at least ten minimum salaries (Governo do Distrito Federal, 2004). 5 Informal work (common in Brazil) also exist in residential areas in the Plano Piloto, but they are not as visible as they are in poorer satellite towns (likely due to stricter zoning enforcement along with higher income of households, who have less need for informal work).

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and, although planning was later adopted, the initial spontaneous development remained.

Moreover, enforcement of construction regulations in Brazil is generally loose, especially in less

organized areas, which allowed for squatter settlements and illegal land development in fringe

areas (political pressure has led to the concession of property rights to settlers in many cases). As

a result, compared to the Plano Piloto, satellite towns are generally much more heterogeneous,

with greater density, variety, and mixing of economic activities, housing and population.6

The higher income and lower density of residents in the central area together with the large

variation in the characteristics of Administrative Regions in the DF create an interesting ground

to study crime factors. While the spatial scale of the data available does not allow for identifying

the impact of specific spatial features, our analysis (presented next) shows that crime rates are

related to the presence of economic activities, type of housing, and residential density.

Data

For government purposes, the DF area is divided into Administrative Regions (there were 31

at the end of 2012). The sample used in our work includes the 27 regions that had observations

available for the variables employed in the analysis.7 Crime data is available for the years 2006

6 Brasília (Plano Piloto) was inscribed as a World Heritage site by the United Nations in 1987 (UNESCO, n.d.), which strengthened the preservation of the original urban plan. The satellite towns of Planaltina and Brazlândia, which are the farthest from the Plano Piloto, were founded without planning before Brasília was built; others (Varjão, Itapoã and Riacho Fundo II) originated from squatter settlements. Most satellite towns have experienced large expansions through illegal land development or settlements. Despite the unintended consequences of how the city was planned and how it developed, Spencer (2010) suggests that the success of Brasilia should not be measured by merely assessing its reality in relation to its original intentions because there are ways for users to transform the city through urban redevelopment. 7 The 27 Administrative Regions included in our sample are: Brasília, Gama, Taguatinga, Brazlândia, Sobradinho, Planaltina, Paranoá, Núcleo Bandeirante, Ceilândia, Guará, Cruzeiro, Samambaia, Santa Maria, São Sebastião, Recanto das Emas, Lago Sul, Riacho Fundo, Lago Norte, Candangolândia, Águas Claras, Riacho Fundo 2, Sudoeste/Octogonal, Park Way, Sobradinho 2, Varjão do Torto, Estrutural and Itapoã. The others have limited data

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and 2007, while data for explanatory variables are estimated for the previous years (2005 and

2006, respectively).8 The variables used are listed in Table 1, which presents statistical

summaries. Table 2 presents the correlation matrix of the variables (notice that coefficients of

correlation between independent variables are not high).

Table 1 – Data summary Variable Obs. Mean Std. Dev. Minimum MaximumCRIME 54 3,788.61 1,830.26 1,314.58 11,997.33PROPERTY 54 2,360.41 1,673.50 778.54 9,796.28VIOLENT 54 1,428.21 598.19 319.91 2,710.79STREETTHEFT 54 1,876.25 1,181.81 592.90 7,335.37BURGLARY 54 469.58 216.16 71.50 1,013.71BANK 54 10.80 18.86 0.00 85.95SINGLEHOUSE 54 0.82 0.27 0.00 1.00DENSITY 54 5,631.57 3,722.27 433.13 13,425.24POPULATION 54 82,048.12 79,116.01 5,781.00 354,428.00INCOME 54 2,794.20 2,650.87 471.97 11,510.99POLICE 54 612.02 456.65 81.27 1,660.42GOV 54 0.85 0.23 0.14 1.00Rates are computed per 100,000. Except for crime rates, all other variables are estimated for 2005 and 2006. Details on how variables are estimated and sources used are discussed in the text.

Table 2 - Correlation matrix (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (1) CRIME 1.00 (2) PROPERTY 0.95 1.00 (3) VIOLENT 0.41 0.10 1.00 (4) STREETTHEFT 0.96 0.92 0.39 1.00 (5) BURGLARY -0.01 -0.06 0.15 -0.15 1.00 (6) BANK 0.85 0.93 -0.01 0.87 -0.15 1.00 (7) SINGLEHOUSE -0.44 -0.56 0.22 -0.48 0.58 -0.54 1.00 (8) DENSITY -0.39 -0.40 -0.08 -0.33 -0.32 -0.30 -0.01 1.00 (9) POPULATION 0.25 0.16 0.32 0.38 -0.29 0.14 -0.09 0.11 1.00 (10) INCOME 0.28 0.50 -0.54 0.29 0.09 0.60 -0.31 -0.43 -0.15 1.00 (11) POLICE 0.32 0.37 -0.08 0.25 0.09 0.45 -0.06 -0.16 -0.32 0.26 1.00 (12) GOV 0.19 0.28 -0.19 0.25 -0.43 0.33 -0.33 -0.06 0.20 0.38 0.30 1.00

availability; they are Jardim Botânico (created in 2004), Setor de Indústria e Abastecimento (created in 2005, zoned exclusively for non-residential use), Vicente Pires (created in 2009) and Fercal (created in 2012). 8 Previous year data for explanatory variables are considered to control for possible endogeneity with crime (e.g., crime is affected by population size, but population might be affected by the occurrence of crimes).

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Crime data is obtained from the DF Civil Police (Policia Civil do Distrito Federal, 2011). In

Table 1, crime rate refers to the number of crimes per 100,000 people, based on the estimated

population for the respective year. The variable CRIME corresponds to the total rate of crimes

against people (businesses are excluded, since we are only interested in how people are affected

by land use in this study). Following the usual categorization of crimes, PROPERTY represents

the rate of non-violent thefts and burglaries. VIOLENT is the rate of crimes involving violence

or threat, including robberies, homicides, attempted homicides, assaults and rapes.9

In addition, because we are also interested in how spatial factors affect crime activities, we

created the following subcategories:

a) STREETTHEFT, representing the rate of stealing of non-vehicle property from people

on streets (by either violent or non-violent means), and

b) BURGLARY, representing the rate of stealing property from homes (forcibly or not).

Demographic, socio-economic and housing data used in this work comes mostly from a

household survey (Pesquisa Distrital por Amostra de Domicilios), which was assembled first in

2004 and more recently in 2010-2012 by the DF Government (Governo do Distrito Federal,

2004, 2012). When needed, data for 2005, 2006, and 2007 are estimated by linear interpolation.

The following are the explanatory variables used in the analysis.10

9 The variable CRIME is the sum of PROPERTY and VIOLENT. The variable PROPERTY includes thefts on streets and inside houses or vehicles (bus, train, etc.). Note that our work focuses on the victimization of people by crime rather than on the existence of criminal activities. Because the crime data used is based on crime reports by the victims or the police, it is likely that there is severe underreporting especially for less severe types of crime like street theft. The crime data used ignores illegal activities like drug trafficking and sales tax evasion, which are common, especially in poor areas, but are hardly documented. 10 Because of the small sample size, we left only explanatory variables that were justifiable and statistically significant in the final regressions. Age, race, proportion of female household heads, unemployment rate and high school education were also considered, but the coefficients in the regressions were insignificant at the 0.05 probability level and had little effect on the results for the other independent variables.

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BANK is the rate of bank branches per 100,000 people and it is used to measure the presence

of commercial activities.11 The number of bank branches is obtained from the Brazilian Central

Bank (Banco Central do Brasil, 2007). Commercial activities attract traffic of people on streets,

which could help prevent crimes if perpetrators shy away in the presence of passers (Jacobs,

1961). On the other hand, greater density of targets on streets can attract criminals if there are no

capable guardians (Brantingham and Brantingham, 1981; Hillier, 2004; Bowes, 2007; Bernasco

and Block, 2011).

SINGLEHOUSE is the proportion of single family housing (defined as either detached

houses or slum shelters) and it is employed as a measure of the degree of horizontal residential

land use. On one hand, horizontal housing can make it easier for residents to monitor activities in

their neighborhood, thus preventing crime (Jacobs, 1961). On the other hand, horizontal houses

have easier accessibility, which might lead to higher burglary rates (as found by Budd, 1999, in a

study of burglaries in the United Kingdom).

DENSITY is computed by dividing the estimated population of each Administrative Region

by its urban area in square kilometers.12 Greater residential density should help increase

monitoring of streets by residents (Jacobs, 1961). In the same line of reasoning, several authors

have argued that larger area makes it harder for the police to monitor crime activities (Wheaton,

2005).

11 Because commercial activities depend on financial intermediaries, we expect BANK to be a good proxy for the existence of economic activities. The rates for 2005 and 2006 are estimated using population in the respective years, but the number of branches is for 2007 (geographical distribution of branches was not available for earlier years). 12 The area of most Administrative Regions is available from the DF government, but in most cases the urban portion represents only a very small share of the total area. As a better alternative, we estimated the approximate urban areas using satellite images from Google Maps (2012).

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POP (population size) is taken into account because larger urban agglomerations typically

offer greater pecuniary benefits and lower probability of arrest (Glaeser and Sacerdote, 1999).

INCOME refers to monthly average household income measured in 2004 inflation-adjusted

Brazilian currency (real). Higher income should reduce the supply of crimes due to less need for

residents to commit crime. On the demand side, higher income attracts criminals looking for

valuable properties, but also leads to greater spending in private security (see Lee and Pinto,

2009, for a recent paper on private crime prevention).13 Police efforts are also likely to be higher

in richer areas due to greater urbanization or political influence of the rich (however, these

effects should be captured by the control variables POLICE and GOV, which are discussed

next). Hence, violent crime rates are likely to be lower in high income areas, although property

crime rates could be higher.

POLICE is the rate of military police officers per 100,000 people in 2008 (based on data

from Cardoso, 2008; data for earlier years was unavailable).14 Policing should prevent crime

(Levitt, 1997, estimates the impact of exogenous police hiring on crime, finding a negative effect

of policing), but greater policing is typically a response to higher crime rates in the area, so it is

not uncommon to uncover a positive relationship between the two. Moreover, greater access to

government services (including police) facilitates crime reporting, thus making crime rates

appear higher (this possibility is also discussed by Levitt, 1997). Indeed, anecdotal evidence

suggests that crime victimization is severely underreported in Brazil in areas of weak

13 Studies on crime in Brazil (e.g., Souza, 1996, Caldeira, 2000, and Coy and Pöhler, 2002) often highlight the role of fear of crime and urban violence in the development of neighborhoods, especially richer ones, where households protect themselves with high walls, private security apparatus or street patrol, or the creation of gated communities. 14 The police officer rate is computed based on the distribution of officers across Administrative Regions reported by Cardoso (2008), who obtained data from the DF Military Police (Polícia Militar do DF) and the DF Planning Agency (CODEPLAN). In Brazil, states and the DF have their own Military Police forces, which deal primarily with crime prevention, while the Civil Police forces are responsible mostly for crime investigation.

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government presence. To better control for underreporting, we include a proxy for government

presence (GOV), which is the proportion of residences served by paved roads.15

Empirical Analysis

In Table 3, crime rates in the Administrative Region of Brasília (Plano Piloto) are compared

with the rates in the periphery. Brasília exhibits greater overall rates of reported crime (see

CRIME in Table 3). However, the higher rates pertain mostly to street crimes, while burglary

rates are much lower. Looking at the other variables, it is clear that Brasília has greater economic

activities (higher BANK), greater proportion of vertical housing (lower SINGLEHOUSE),

lower density, bigger population size, higher income, and stronger presence of the police and

government. These differences can help explain the higher street crime rates and lower burglary

rates observed in Brasília. For instance, Hillier (2004) proposes that types of crimes are affected

differently by spatial accessibility. Accordingly, street criminals can find targets and hide or run

away more easily in crowded places, which are common in the proximity of commercial

activities, while home burglars need accessibility, which is restricted by vertical housing.

15 Paved road was selected as the proxy for government presence because it seems to be the most evident type of missing infrastructure in areas neglected by the government in the DF. Other public services with data available (e.g., transportation, garbage collection, sewage collection and water supply) are often provided by private parties.

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Table 3 – Comparison of characteristics Variable Brasília mean Periphery meanCRIME 11,209.40 3,513.55PROPERTY 9,198.00 1,946.22VIOLENT 2,011.39 1,567.33STREETTHEFT 6,895.62 1,836.79BURGLARY 223.27 429.66SINGLEHOUSE 0.11 0.87BANK 85.95 6.11DENSITY 3,364.00 6,290.13POPULATION 201,840.00 152,421.50INCOME 5,364.13 2,110.84POLICE 1,039.58 417.14GOV 0.99 0.88Means weighted by POP. Sources are listed in the text.

To better understand what affect crime rates in the Brasília region, we resort to regression

analysis. The independent variables are BANK, SINGLEHOUSE, DENSITY POP, INCOME,

POLICE, GOV and a dummy variable for year. An OLS regression model is used, with

variables expressed in natural logarithm form, except for SINGLEHOUSE and GOV, which are

percentage rates. Results are presented in Table 4, where each column refers to a different crime

rate taken as dependent variable (for instance, results for CRIME are shown in column (1),

results for PROPERTY are in column (2), and so on).

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Table 4 – OLS regression results (1) (2) (3) (4) (5) CRIME PROPERTY VIOLENT STHEFT BURGLARY

coeff. t-stat coeff. t-stat coeff. t-stat coeff. t-stat coeff. t-stat SINGLEHOUSE -0.470 * -2.18 -0.494 * -2.04 -0.096 -0.44 -0.732 ** -3.01 1.272 ** 6.38 (0.216) (0.243) (0.216) (0.243) (0.199) ln(BANK) 0.213 ** 5.08 0.285 ** 5.97 0.181 ** 4.07 0.210 ** 4.02 0.027 0.48 (0.042) (0.048) (0.045) (0.052) (0.056) ln(DENSITY) -0.266 ** -4.75 -0.198 ** -2.99 -0.293 ** -5.11 -0.342 ** -5.91 -0.276 ** -3.57 (0.056) (0.066) (0.057) (0.058) (0.077) ln(POP) 0.151 ** 3.09 0.092 1.87 0.201 ** 3.58 0.254 ** 4.61 0.081 1.72 (0.049) (0.049) (0.056) (0.055) (0.047) ln(INCOME) -0.308 ** -3.64 0.027 0.28 -0.814 ** -9.29 -0.418 ** -3.99 -0.025 -0.21 (0.084) (0.096) (0.088) (0.105) (0.121) ln(POLICE) 0.094 1.65 0.049 0.83 0.060 0.98 0.091 1.30 0.128 * 2.32 (0.057) (0.059) (0.061) (0.070) (0.055) GOV 0.449 1.75 -0.066 -0.22 1.075 ** 5.11 0.761 * 2.62 -0.439 -1.33 (0.256) (0.306) (0.210) (0.290) (0.331) D2007 -0.112 -1.78 -0.129 -1.90 -0.080 -1.13 -0.092 -1.30 -0.124 -1.80 (0.063) (0.068) (0.070) (0.071) (0.069) constant 10.179 ** 7.06 7.803 ** 5.59 12.110 ** 7.68 9.753 ** 5.38 6.169 ** 3.95

(1.441) (1.395) (1.577) (1.813) (1.564) Observations 54 54 54 54 54 R2 0.81 0.88 0.76 0.83 0.70 F-test 26.16 ** 47.89 ** 44.89 ** 45.49 ** 21.96 ** Dependent variable: ln(crime rate per 100,000 people). Robust standard errors (to correct for potential heteroskedasticity) are reported inside the parentheses. Variance is weighted by the inverse of the population. Statistical significance levels: * p<0.05, ** p<0.01.

The regressions indicate that BANK (a measure of the intensity of commercial activities) is

positively related to crime rates, except for burglaries, suggesting that higher traffic of people on

streets caused by the presence of business activities turn streets into crime hot spots.

Horizontal housing (SINGLEHOUSE) is associated with less crime in general (especially

property crimes and street theft), but more burglaries. The correlation is particularly large for

burglaries (a one percentage point higher SINGLEHOUSE is associated with a 1.3 percent

increase in burglary rate, holding other variables constant). Lower street crime rates might be a

result of the greater ability of residents to monitor streets made possible by horizontal housing.

On the other hand, higher burglary rates are justified by easier accessibility to houses compared

to apartment buildings in the DF.

17

Residential density (DENSITY) also seems to lower crime rates, perhaps because it is easier

to monitor streets in denser places (the effect of density is stronger for violent crimes than for

property crimes and is especially larger for street crimes).

As expected, bigger urban agglomerations (places with greater POP) have higher crime rates,

while INCOME is negatively related to crime rates, with an especially large and statistically

significant effect on violent crimes.16

Last, the presence of the police (POLICE) and accessibility to government services (GOV)

are found to be positively related to certain types of crimes (burglaries in the case of police and

violent and street crimes in the case of government), results that might reflect higher crime

reporting rates in places where the presence of police and government are felt more strongly (as

non-serious crimes tend to be largely underreported in Brazil). The greater presence of police can

also indicate the existence of crime hot spots caused by unobserved factors.

Considering the factors analyzed above, it seems that higher overall crime rates in the Plano

Piloto (shown in Table 3) reflect the predominance of vertical housing, concentration of

commercial activities, lower density, larger population size and stronger presence of the

government and the police (the existence of crime hot spots might be a reason for greater police

efforts in the Plano Piloto). On the other hand, lower home burglary rates in Brasília are

explained by the predominance of vertical housing.

16 The relationships between crime and income might also be reflecting the correlation between crime and variables like unemployment, education and race, which were not included in the final regressions, but are highly correlated to income. When included, those variables were not statistically significant, but they did reduce the significance of INCOME, a likely result of multicolinearity. The negative relationship between IN COME and VIOLENT is likely much stronger in reality since underreporting tends to be severe among the poor (either because they feel unsafe to report violent crimes or they have little trust in the effectiveness of the justice system in Brazil).

18

The implications of our results are limited by the scale of analysis, which ignores differences

in crime distribution within Administrative Regions, thus restricting the ability to disentangle

spatial factors from other determinants of crime. For instance, the existence of crime hot spots

can only be tested with a fine scale of analysis.17 If data becomes available, it would be

interesting to study crime in the DF at the neighborhood or street level. For now, our study

identifies some of the most important determinants of crime at the city level in the DF, which can

be helpful in designing better crime prevention policies.

Conclusion

Criminal behavior is influenced by a multitude of factors, including demographic, socio-

economic, policy and spatial variables. In this paper, we study the case of the capital of Brazil,

Brasília (Distrito Federal – DF), motivated by its peculiar planned characteristics. The central

area (Plano Piloto) is the place where most economic activities occur, with strict zoning laws that

separate land uses and promote vertical housing with moderate residential density. The

population is highly homogenous, mostly middle class service sector workers. The periphery

(satellite towns) has more relaxed land use, leading to greater heterogeneity, density and mixing

of population, housing and businesses. Income is generally lower in the periphery, while density

is higher.

Public policies regarding planning and police efforts should take into account how spatial

and socio-economic features of each community play a role in the safety of residents and people

17 Hillier (2004, 2007) suggests the use of computer-based techniques (e.g., Space Syntax) for spatial modelling of crime, allowing to better identify spatial factors. Johnson and Bowers (2004) find spatial clustering of burglaries over time. Bowers et al. (2004) suggest that crime hot spots can emerge as an epidemic, with a recent crime causing “infection” (that is, future crimes). See also Buchanan (2008).

19

on streets. Our work attempts to provide evidence on the impacts of some important crime

factors. The sample used in the analysis includes 27 Administrative Regions in the DF, with

crime data available for the years 2006 and 2007. An OLS regression model is used for the

empirical analysis. The independent variables included are the presence of commercial activities,

type of housing buildings, population size and density, income level, presence of police and

government services, and a dummy variable for year. Other socio-economic variables were also

considered, but they are highly correlated to income in the sample and result in statistically

insignificant relationships).

We find that overall reported crime rates were greater in the central area than in the rest of

the DF in 2006 and 2007 (despite the higher income of the residents of the Plano Piloto),

However, burglary rates were lower in the central area. Further analysis based on regression

results suggests that the higher overall crime rates reflect the greater presence of commercial

activities, predominance of vertical housing, lower density and larger population size, while

lower burglary rates reflect the predominance of apartment buildings. These findings indicate

that urban features such as the types of housing and the presence of economic activities affect

crime rates and the types of crime committed. Moreover, an important lesson is that crime is

pervasive even in a carefully planned city.

Future research can focus on the use of multi-year data, when it becomes available, to

provide more robust econometric results. In addition, it would be helpful to gather neighborhood

or street level data to identify the location of crime hot spots in the DF and more specific spatial

factors that lead to higher crime rates in the central area. The findings uncovered in this work and

future analyses should help policymakers to better understand the tradeoffs between different

types of urban space. For instance, as shown in this paper, horizontal housing helps curtail street

20

crimes, but gives easier access to burglars. On the other hand, traffic of people due to

commercial activities seems to correlate with greater criminal activities, while greater population

density helps curtail them.

21

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Figure 1:

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Figure 2

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Figure 3