Crime mapping – A crime analysis in Cluj-Napoca

Ionut Bageac

Babeş-Bolyai University

bageac_ionut@yahoo.com

Abstract. This paper proposes a crime mapping analysis adapted to the case of Cluj-

Napoca city. This research offers a visual layout of the phenomenon of criminality in the

mentioned area and explains the observed variation by reference to social and physical

characteristics. The criminality maps provided in the paper did not represent the only aim

of this research, but the starting point to the development of prevention strategies of the

criminality phenomenon and of the feeling that accompanies it – the fear of crime. The

analyzed data consists of 2163 cases of criminal police reports collected in 2009. The

geographical characteristics of any crime allowed data to be organized around different

districts by using a Geographical Information System (GIS) software, MapInfo

Professional. The results showed some obvious ecological patterns of criminality and a

distribution of crime concentrated in a few agglomerations of hot spots, forming an axis

that crosses the city from the south-west to north-east: Mănăştur – Center – Mărăşti.

Keywords: crime mapping, geography of crime, hot spots, crime prevention.

Introduction

The main objective of this research paper is to create a crime mapping, a cartography of

the crime phenomenon in Cluj-Napoca and to explain the variations recorded in the year of 2009

by using characteristics of the social and physical environment. The study puts into practice a

method of spatial presentation of the social data which is less used in Romania – crime mapping.

This method is used to create criminal maps followed by the elaboration of crime prevention and

reduction strategies. The research interest in this topic comes from its novelty since among all

the research made in our country in the crime sphere very few or none of them treated the

problem of crime mapping, a field which deserves to be explored by the sociologists as well. At

the national level research treating geographical criminology, and the connection between the

phenomenon of crime to that of the social geography of the town or crime mapping in other

words did not stand out. At an international level the interest in such approaches appeared in the

19th

century. The importance of the concept of place and its role in the act of crime opens new

horizons in the study of criminality by using the features of the social environment. Thus, in

order to better explain and to have a general view on the issues of the crime phenomenon these

immoral actions were linked to different social indicators. Sociological explanations which make

use of structure come as a more adequate alternative to the biological and psychological ones of

the individualistic type (agency). By paying attention to the social structure to which these

criminals belong we can also understand the crime at a certain level because the criminal

phenomenon incidence does not have a universal causality. It contains elements which generate

it, which must be searched in the culture of the society where the crime occurs taking into

consideration both the situation in which it happens and the macros social context (Reid, 2003).

What is crime mapping?

The crime has an inherently geographical quality: it appears in a specific space and at a

certain time and is committed by a person who most probably lives in a different place than the

one where the crime occurs (Chainey and Ratcliffe, 2005). Criminological research knew the

importance that these geographical qualities had in their analysis but technology didn‟t allow a

maximum fructification of this element. Once this thing made possible, crime analysis benefited

from great advantages among which a better exploration of the connection between crime and

environment, geographical visual pattern and hot spots identification. Crime mapping

computerization and statistic instruments give to the crime and place study the possibility to

make a small-scale analysis. Even though they gave explanations for the important number of

crimes which took place in certain space, the ecologic studies of big towns - whose contribution

in the crime field is unquestionable - had the disadvantage of hiding important variations in

crimes a thing noticed by John Glyde (1856, in Weisburd, Bernasco and Bruinsma, 2009). Since

the 80s a more advanced technology and criminality statistics at a micro level came to be used

more and more. Thus the study interest focused on this area. Research results become more

refined, crime variations is better captured but the micro analysis of the geographical unity can

also become an issue: it can happen that in a certain area the number of crimes can be small so

that it is not possible to analyze them or to identify patterns or trends.

Besides the above mentioned advantages a crime mapping analyses makes it easier for

the police investigation, informs, interacts with the community, evaluates the efficiency of

criminality prevention initiatives, gives information about resource targeting as to criminality

prevention. It can also be used as a dynamic analysis which monitors the changes in the crime

distribution in time (Hirschfield and Bowers, 2001). Based on a Geographical Information

System (GIS) programme, crime mapping involves crime data handling and processing, data

which have geographical references, in order to visually display them (Hirschfield and Bowers,

2001). Visual display advantages can be simply summarized by this saying: a picture is more

than a thousand words (Keith, 1999). However crime mapping finality should not be understood

just in terms of creating a map but as a complete visualization, exploration and understanding

crime instrument. Equally, even though it is a good instrument which helps to draw useful

conclusions about the process which generates the crime distribution in a certain way or its

classification in certain clusters, crime mapping analysis does not prove the existence of a causal

connection between the environment elements and a certain distribution of the crimes.

Everything has to be interpreted in terms of probabilities and clues.

Method and data

Transposition of the statistics referring to crimes into visual stimuli was made possible

due to the analysis programme GIS MapInfo Professional. The choice of Cluj-Napoca city as a

unity analysis did not go from the premise that this could present a certain particularity as to the

level or the type of crimes present here. Cluj-Napoca was rather chosen for reasons of data

access and why not due to subjective grounds. As to the decision of choosing as study object

certain types or crimes to the detriment of others this was based on their recurrence and the way

it directly affected an important part of the population. The data analyzed (a total number of

2163 cases) represented some types of criminal events registered by the police in 2009 for which

there was geographical information so that they could be distributed by neighborhoods or town

areas. Since some crime types were not very frequent and could not form a certain model or

cluster I used a classification in three important groups as follows: thefts from cars (1124 cases,

16 of which with unknown localization), thefts from residences or companies (990 cases, 8 of

them having an undefined area) and robberies (we include here robberies in the street, in

residences, in the park, in public transport, in companies, by threat with side arms and having

masked authors as well as other robberies reaching a total number of 46 cases to which we can

add 3 cases where there was no geographical localization).

The purpose of the analysis was to point out a general view on the crime phenomenon

present in Cluj-Napoca in order to create some guiding lines as to the elaboration of some

prevention measures. This fact is due to the lack of access to data for a longer period of time. In

fact this research has a more explorative nature relating more to a methodological exercise.

Before presenting the results I will first point out some shortcoming in data in the criminological

domain which apply in this case and which can change the image that the society has on crime. I

will then give a brief presentation of the steps it takes to make a crime mapping analysis.

A first limit comes from the inherent weakness of the statistics data in the field crime

which we must regard with skepticism. From the moment when the crime is committed to its

official recording some of the crimes might “get lost” along the way, different elements which

affect the data comparability might appear (for instance the way to define in time and space a

crime). Thus one must show carefulness in their interpretation. A crime does not automatically

enter the statistic evidence. It needs to fulfill some criteria (Carrabine, Cox, Lee, Plummer and

Nigel, 2009): the acknowledgement by a witness or the victim that a criminal incident took

place, then the complaint to the police followed by the admission of the police that a potential

criminal incident took place and finally the registration of the incident as not yet confirmed

crime. The criminal reality is not always captured by the figures which describe the types of

crimes so that the amount of crimes officially recorded at a certain moment is just a partial

reflection of the crime phenomenon to which is faced a certain society. A part of this discrepancy

can be understood by the fact that not all the crimes are declared, the population migrates and

distortions in registering a crime by the police might appear, not to mention even the deliberate

hiding of the crimes so as not to damage the good name of a certain area. There is a black

number of the crime (Marsh, Melville, Morgan, Norris, Walkington, 2006) determined by

different elements which lead to the non reporting and obviously to the non registering of the

crimes. First of all, the victims are not aware that they are actual victims of a crime or even if

they are aware they do not want to waste time and money by going to the police. Then, another

cause might be the fear of the retaliation, or the belief that there is no point anyway in letting the

police know, the shame of the victims, their lack of trust in the police. The black list of the crime

is completed by the so called crimes without victims when all the parts involved refuse to let the

police know about a certain crime or cases when the victims are not legally clean so they decide

not to report the criminal act.

The affirmation according to which nothing is subject to a more fake interpretation that

the criminal statistic (Sparks, 1970 in Picca, 1983) or the classification made to the lies by B.

Disraeli (Carrabine, Iganski, Lee, Plummer, Nigel, 2004): lies, damned lies and statistics are just

some findings of the lacks in the statistic machine especially in the criminal field. Unfortunately,

these limits are imposed by the nature of the social phenomenon so that despite the

methodological lacks, statistics was and will be the first measure of crime. A sort of refinement

of the analysis and a solace for the criminal research came with the appearance of crime mapping

about which Clarke (2004, in Chainey and Ratcliffe, 2005) stated that it will become a research

instrument as important as the statistic analysis at present.

How effectively social data can be spatial represented

Before the presentation of the spatial distribution of the crimes I will briefly describe the

steps which permitted the transformation of tabular information into objects on the map of Cluj-

Napoca. The simple condition at the base of the mapping procedure is the presence of two types

of folders: one named „Map data‟ containing various objects on the map with spatial information

such as areas of a town like in the present case study, an address, a postal code, a latitude, a

longitude and another folder „Tabular data‟ (fig. 1) which contains various events that we want to

visually represent, in our present case different types of crimes. The connection between the

tabular information (which can be in Excel, dBASE, ESRI shapefiles, Ascii delimited text,

Access format or other ) and the objects on the map is called geocoding, a process which allowed

the overlapping of the data on crimes with the objects already on the map (different

neighborhoods were previously defined as polygons written by coordinates X, Y and they were

spatially indexed to the „Map data‟). Using the MapInfo programme the command necessary to

the geocoding process is made from the menu Table > Geocode (fig. 2) and in the dialogue box

which opens one will specify between what tables and columns the correspondence must be

made. There are two geocoding possibilities (fig. 3): an automatic one (the data transferred on

the map will look for a reference in a list of addresses or localizations which already exist,

known under the name of gazetteer) or a manual one to which we resort when an overlapping

with the localization data is not possible. Thus, we insert the data on the map by clicking on the

area where we want them to appear. Once the spatial distribution of the crimes is made the next

step will be to add these criminal events on the map with the purpose of being able to see them.

To do this one will select the command Map menu > Layer control (fig. 4). At this point one

must choose what data must be introduced in that specific layer, by clicking Add layer button.

Maybe the most important element on a crime analysis is the fact that these layers might overlap:

individually the layer of a map does not give too much information but combined with other

layers it will make the map richer in details. So at this point the MapInfo user is able to do

different analyses in function of the targeted objectives. The crime mapping analysis of this case

study was to create some thematic maps which present the density of the crime events for each

neighborhood in Cluj-Napoca. The procedure can also be made by selecting from the Map menu

the subcommand Create thematic map (fig. 5). From this point on the programme offers the

possibility to choose among numerous variants of data representation (fig. 6): „Ranges‟ (different

intensity colors point out a limit at the data level), „Bar chart‟, „Pie chart‟ (the data appear on the

map as graphs), „Graduated‟ (symbols of different shapes are used to represent the data

magnitude), „Dot density‟ (data repartition in different areas under the form of dots), „Individual

(one can choose different representation styles for each analyses unity) or „Grid‟ (the data are

presented as continuous colors gradually along the map).

Study results

Having as a starting point Alexander Lacassagne‟s words (1913) according to which

societies have the criminals they deserve I took the case of Cluj-Napoca and I tried to explore

the connection between a certain part of the criminal phenomenon present here and the

environment. The purpose was that of observing how variations in the numbers and the type of

crimes belong to the particularities of each neighborhood given by the physical features of the

built space but also by the socio demographic features of the persons who populate a certain

town area. Nobody doubts about the existence of the criminal phenomenon in Cluj-Napoca but

those who know these phenomena beyond the idea of an abstract number are few. We know that

there is criminality or that it must exist according to Durkheim‟s idea of crime normality in each

society but we don‟t know how it looks like. Since most of us become aware of the criminality

existence by the actual fear of crime, by the image sometimes misleadingly presented by the

mass media (because few people had the misfortune to face it) I tried to go from the level where

it is represented as a simple number to a visual display of it.

In order to avoid possible criminal maps interpretation errors one must say that the

analysis level is the neighborhood or a certain town area so that the spatial distribution of

criminal acts must be understood as such. In other words the points on the map which represent

different types of crimes are randomly distributed in each neighborhood and show the density on

that certain area and not their exact location. Another important fact is the distinction between

the offence map and that of the offender. The first shows the areas where a crime took place

while the other shows the information concerning the offender„s residence. The present analysis

shows only offences‟ maps and that due to the lack of more important data because the available

data represent reporting of the criminal events and not solution to them; this causing the

impossibility to establish the criminal‟s residence. A certain amount of prudence must be

observed when interpreting the maps due to the above mentioned reasons and also to the fact that

by overlapping the crimes with the environment where these occur there is the pitfall of the

„ecological fallacy‟ according to Brantingham and Brantingham (1981, in Maguire, Morgan,

Reiner, 1997). Such a mistake occurs when we make assumptions that certain descriptive

features of an area which also has a great number of offender residents automatically has a lot of

persons liable to commit crimes. Even if the reality shows that the possible criminals live

preponderantly in economically low areas, where there is a high rate of unemployment or areas

having difficulties with ethnic minorities this must not lead to affirmations such as unemployed

persons or certain ethnic groups are those who commit crimes. Thus, in the absence on the

offender‟s map it would not be fair to state that some variables observed at the level of a certain

neighborhood are also reflected at an individual level. In other words we cannot made deductions

starting from the hot spots form Mănăştur neighborhood for instance by saying that the people

here are more criminal than the others. Anyway, we can launch some general hypothesis without

claiming that they apply preeminently in our case as well and having as a base the theoretical

models presented in the specialized literature. On this basis we know that the probability that an

offender might act is diminished by the growing distance between his place of residence and the

area where the possible victim lives. As an explanation of the reasons which sustain these

connection Hakim (1980, in Brown, 1982) brings into discussion the transport fare, the risk of

being identified as a stranger in that specific area and the risk of being unfamiliar in that area.

Even if the offenders prefer to commit crimes close to their homes they will never come to

commit a crime too close to their homes fearing not to be recognized by the neighbors.

The crime spreading concentrated especially in some clusters (hot spots) making up an

axis which crosses the town from south west to north east. The results are pretty clear in the

sense that they form almost identical patterns for all the three types of infractions analyzed. This

can be observed in Map 1. Vulnerable areas for burglars overlap almost symmetrically with

vulnerable areas for thefts in the cars, residences or shops. A possible explanation for this

agglomeration takes into consideration the existence of some common offenders for all these

crimes or just the attraction of some areas for these persons. On the whole the victims

distribution was predicable taking into consideration the fact that results overlap with theoretical

and practical models which exist in the specialty field (a bigger concentration of the crimes in the

centre of a town or the association made between the crimes and the economic condition or the

design of that area).

Map 1: Thefts from cars, houses and shops and robberies

Knowing that we cannot separate the crime from the environment where it appears the

interpretation result will not be based just on the theory but also on the creation of a profile of the

town characteristics in order to see which are those that contribute to the apparition and

development of the crimes. At the beginning Mănăştur neighborhood was a village but in the

70s the town could not resist the second wave of industrialization which included Eastern Europe

(Petrovici, 2010), so that the structure was entirely changed: the houses would be pulled down

and in their place one would build 4, 8 and 10 storey blocks used primarily as rooms for workers

(Lascu and Opris, 1979, in Petrovici, 2010). The area was intensely populated by massive

migration of the villagers coming from different parts of the department. Mărăşti neighborhood

has a similar evolution both regarding the architectonic structure and the type of persons who

lived in it, generally workers coming from the country side and who went to the factories in Cluj-

Napoca. Somewhere at the intersection of these two neighborhoods with the “good ones” Andrei

Mureşanu, Grigorescu or Gheorgheni there is Zorilor neighborhood with 4 and 8 storey blocks

which also hosted a part of the socialist factory workers. Coming back to the posh areas

mentioned above we can say that these areas were different not only from the point of view of

the population (lower density, better economic situation, a better education level and a bigger

number of Hungarians) but also due to the less important number of blocks.

I must mention the fact that although Grigorescu and Gheorgheni are just two

neighborhoods, for analytical purposes I decided to make a distinction (by tracing an

approximate limit) between the blocks area and the houses area, so they will appear in the next

maps as being four individual neighborhoods. The crime mapping results tend to confirm like in

the case of Cluj-Napoca the fact that the design of the environment, the territoriality have

consequences on the rate of criminality. By analyzing images 2 and 3 we notice that when the

structure of the residential segment is represented by ground floor constructions and maximum

one storey with individual yards, the crimes tend to diminish (Wilson, 1984). We could say that

in cases of theft from cars this is expected to happen because the people keep their cars in the

yard which makes the theft even more difficult. This is not only the case for this map which

confirms the explanation based on territoriality but it can also work for robberies or thefts from

the residences.

Map 2: Thefts from car

The north side of the town (Bulgaria, Iris, Dâmbul Rotund neighborhoods) show a

mingled regime being also residential area (more houses than blocks) and an industrial one

(factories CUG, Mucart, Tehnofrig, etc.). The other residences or areas do not have an out of

common characteristic. They have a structure which is more or less similar to the surrounding

areas so I will go on by explaining the crime as an opportunity.

Map 3: Thefts from house and shops

Map 4: Robberies (in different places and through different ways)

An important amount of the explanations concerning the criminal acts has in its centre the

idea that opportunity makes the thief (Felson and Clarke, 1998, in Weisburd et al. 2009);

according to this the crimes are opportunist by their nature and are not planed in advance. The

fact that we find a bigger crime concentration in some hot spots is due to a better reflection of

some opportunities or favorable conditions in that place. The important number of shops and cars

in the centre and their variation, the agglomeration of the area are such opportunities. These

appear to the possible offender as some available attractive targets which Clark (1999, in

Chainey and Ratcliffe, 2005) defined as having the possibility of being hidden and remote, then

they had to be valuable, to give a degree of satisfaction and there also had to be a demand on the

market for those goods. A similar explanation for the existence of these hot spots can be found in

the classification of the place as something which generates and attracts a crime (Brantingham

and Brantingham, 1998, in Weisburd et al., 2009). The places which generate criminality are the

ones which concentrate in the same place and at the same time many crime opportunities. In such

places the offender acts as a response to the stimulus, a newly created opportunity without

having a pre-established plan. There is however a lot to discuss on this idea since just some of

the persons react though a criminal act in such a situation. Thus a question is welcome here: do

some persons have a more rational thinking and do they better analyze the cost benefits report

involved by a crime? On the other side the areas which attract crimes are places known by the

offender as having different opportunities for crime and where the latter come exclusively in

order to commit a crime. By applying this dichotomy of the place in the particular case of our

analysis we can say that the centre of the town might fit as crime generator while Mănăştur and

Mărăşti neighborhoods are areas which attract crimes.

The crime situation can be very well captured by a Marxist analysis where the town is

seen as a place of conflict and class inequality, Cluj-Napoca being no exception to it. According

to this view the criminal behavior appears as a social claim that the economically deprived

individuals ask for due to the improper and discriminatory treatment coming from the society.

Even if the socio economic status is not a direct cause for murder it can act as an element of

frustration. From the point of view of this theory the explanation to the criminal answer is not to

be found in the autonomous action of an individual but in a social structure given by economic

conditions, class relations and state answers (Reid, 2003). The distribution of the crimes

confirms the fact that the areas full of such actions are low class neighbourhoods where the

inequalities are more striking. The indutrialization and the urbanisation process that we

mentioned previoulsy affected especially Mănăştur and Mărăşti neighborhoods, hot spots of the

map, creating here an important working class and implicitly social inequalities. Prints on

inequalities are not given only by the conflicts between the different social classes. To this we

can add the fact that such urban low class areas present according to the theory of the cultural

transmission subcultural models which favor the development of criminality. Changes brought

by the urbanization phenomenon can also be noticed in the migratory process of the area. The

powerful development that Cluj-Napoca went though in the last years attracted many persons

from the rural areas so that an understanding of the crime cannot be made without taking into

consideration the surroundings of the town. The extreme areas of the axis SW-NE are also the

most exposed to the impact of the offenders due to the infrastructure (way of access which offer

quick ways of escape) but also due to the expansion of the town in those direction: Floresti

village very close to the Mănăştur neighborhood and Apahida, close to Mărăşti.

Policing and crime prevention

The present study does not have only a theoretical finality to explain which are the effects

that generate some crime models and to represent them graphically but it also has practical

public purpose since the results of this research can be taken into consideration by the authorities

when they take crime prevention and reduction measures. Another possible application for this

research could be the evaluation of these prevention measures. At a more abstract level crime

mapping can facilitate the police investigation by offering clues about the environment of the

typical criminal and thus diminishing the list of suspects in certain cases. Due to the efficiency

that such research brings to the public sphere it is proper to explore new elements in this field by

doing subsequent research both at a micro and at a macro level in the context where the crime

phenomenon is a social fact that every society has to deal with.

I think that no matter the society the crime cannot be discussed in term of finality. It is no

longer an unusual phenomenon but rather a common one (Picca, 1983) present under different

forms in all the social environments. Thus the differences between the societies are given by the

crime number and not by its absence. This is not to say that we must declare ourselves powerless

in front of it. If we cannot stop it we can reduce it by preventing it. Without having an answer we

can asks ourselves why we do not prevent it so that it disappears. Maybe because a criminal act

contains a number of behaviors whose mechanisms we do not totally know so we do not know

what to prevent or maybe such an act wouldn‟t be welcome if we think of the crime as Durkheim

does: as an element of cohesion and a moralizing one.

Crime prevention is not an element of the modern thinking. 200 years ago Cesare

Beccaria (1764, 2008) pointed out the prevention side to the detriment of the repressive one,

talking about the fact it is better to prevent crimes than to punish them. Also when the

punishment is imposed in order to amend a criminal act the accent must be put on the certitude

that it will be applied rather than on its intensity and duration. Sharing J. Jacobs„s idea (1961, in

Loukaitou-Sideris, 1999) a good prevention method can be created by environmental design if

the way in which a neighborhood is built makes it possible to a great amount the natural

surveillance. To be on guard while in the street, to be present in the social space for a long time

is linked to the fear of the crime. Thus a good crime management will have to take into

consideration that the diminishing of this fear will lead to the informal social control especially

by night, a time when many persons fear to go in the street. Other solutions to crime generating

mechanisms could be found in the opportunity reduction. Increased attention must be given to

the centre of the town where there is the false idea that it is a safe area and the best preventive

answer must be given by the possible targets that have to be more watchful. It goes without

saying that we must not exclude elements such as camera surveillance in shops, alarms or

guards, locking the cars and using an anti theft system for them. Anti criminal politics which are

based on the increase of the police patrols must be careful not to reach a satiation level, a

situation which could become uncomfortable for the population. Knowing the distribution of the

crime risk for each part of the town such situation can be avoided by directing the police

resources especially in these hot spots. In fact a good control of the situation does not always

mean resorting to the police. We must also understand the specific behavior and to act by means

of other social institutions such as the family or the school.

Conclusions

By trying to see which are the vulnerable areas of Cluj-Napoca as to thefts from cars,

residences, shops but also other types of burglary I used a crime mapping type analysis which

allowed the spatial distribution of these events. The interest was not just to put into practice a

certain methodology but the crime maps resulted from this analysis made up reliable instruments

for the interpretation of the crime phenomenon present in this town by overlapping the actions

described above with the elements of the social and physical environment. It can be said that the

paper was a sort of cross sectional study where I described the degree of criminality that the

population of Cluj-Napoca had to face in 2009 followed by the identification of a some possible

causes which were behind these crimes. By using crime mapping analysis the present case study

also tried to bring a public utility by creating crime reduction and prevention policies. On the

basis of the visual representation of the crimes the police can direct the resources in a more

efficient way operating especially in areas with a high infractional risk.

The results showed a bigger concentration in Mănăştur and Mărăşti neighborhoods and in

the centre of the town. This might be explained by the scenery of the built space and the structure

of the population for the two neighborhoods and by the important number of opportunities and

the anonymous and transitory character of the connections between the persons in the centre. As

to the urban context I could notice that the blocks (which also involve a greater density in these

neighborhoods) favor the apparition of crimes due to the possibilities of hiding that it offers to a

potential offender but also due to a more weak informal surveillance. Mănăştur and Mărăşti

neighborhoods confirmed this hypothesis. The centre functioned like a crime generator due to the

important amount of opportunities that a possible offender might find here but also due to the

fact that the anonymous and transitory character of the relations encourages such a person to act.

The paper could be developed by exploring some directions among which the most

interesting would be an analysis of the crimes for longer periods of time which could permit the

observation of the dynamics and the trend of such social acts. Also another subsequent research

direction could take into consideration the creation of a map with the offender‟s dwellings

(possible only for solved cases) in order to see which is their zone of action. Eventually new

analyses could be made for a restricted level of analysis such as the street which could

investigate how the crime dissemination varies inside a neighborhood.

References:

*** MapInfo Professional 10.0, (2009). Pitney Bowses Inc.

Alba, R. D., Logan, J. R., Bellair, P. E. (1994). Living with Crime: The Implications of

Racial/Ethnic Differences in Suburban Location, Social Forces, 73(2), 395-434.

Anselin, L., Cohen, J., Cook, D., Gorr, W., Tita, G. (2000). Spatial analysis of crime. In

Measurement and analysis of crime and justice, Washington D.C.: U. S. Department of

Justice, 4:213-262.

Beccaria, C., (1764). (2008). On Crimes and Punishments, translated by Thomas, A., Parzen, J.,

Toronto: University of Toronto Press.

Brantingham, P.L., Brantingham, P.J. (1998). Mapping Crime for Analytic Purposes: Location

Quotients, Counts, and Rates. In Weisburd, D., Mcewen, T., Crime Mapping and Crime

Prevention, New York: Criminal Justice Press, 263-288.

Brown, M. (1982). Moddeling the spatial distribution of suburban crime, in Economic

Geography, 58(3), 247-261.

Carrabine, E., Iganski, P., Lee, M., Plummer, K., Nigel, S., (2004). Criminology: A sociological

introduction, 1st ed., London and NY: Routledge.

Carrabine, E., Cox, P., Lee, M., Plummer, K., Nigel, S., (2009). Criminology: A sociological

introduction, 2nd

ed, London and New York: Routledge.

Chainey S., Tompson, L. (2008). Crime mapping case studies: practice and research, London:

John Wiley & Sons.

Chainey S., Ratcliffe, J. (2005). GIS and crime mapping, London: John Wiley & Sons.

Cohen, E. L., Felson, M. (1979). Social Change and Crime Rate Trends: A Routine Activity

Approach, American Sociological Review, 44(4), 588-608.

Cordner, G. (2010). Reducing Fear of Crime. Strategies for Police, Washington D.C.: U. S.

Departament of Justice.

Cote, S. (2002). Criminological Theories: Bridging the Past to the Future, London: Sage

Publications.

Durkheim, E. (1895). (2002). Regulile metodei sociologice, Iasi: Polirom.

Durkheim, E. (1897). (1993). Despre sinucidere, Iasi: Institutul European.

John Eck, J. (2005). Crime hot spots: What they are, why we have them, and how to map

them. In Mapping crime: understanding hot spots, Washington D.C.: U.S. Department of

Justice.

Ellis, L., Beaver, K., Wright, J., (2009). Handbook of crime correlates, Oxford: Academic Press.

Emsley, C. (2007). Crime, Police and Penal Policy: European Experiences 1750-1940, New

York: Oxford University Press.

Ferraro, K.F. (1995). Fear of Crime. Interpreting Victimization Risk, New York: State

University of New York Press.

Gilling, D. (1997). Crime Prevention. Theory, policy and politics, London, New York:

Routledge.

Harries, K. (1999), Mapping Crime: Principle and practice, Washington D.C.: U.S. Department

of Justice.

Hirschfield, A., Bowers, K. (2001). Mapping and analyzing crime data: lessons from research

and practice, England and New York: Taylor & Francis.

Hollway, W., Jefferson, T. (1997). The Risk Society in an Age of Anxiety: Situating Fear of

Crime, The British Journal of Sociology, 48(2), 255-266.

Iganski, P. (2008). Hate crime and the City, Bristol: The Policy Press.

Innes, M., (2003). Understanding social control. Deviance, crime and social control, Open

Berkshire: University Press.

Lacassagne, A. (1913). Les transformations du droit pénal et les progrès de la médecine

légale, de 1810 à 1912, In Archives d’anthropologie criminelle, 321-364.

Loukaitou-Sideris, A. (1999). Hot Spots of Bus Stop Crime: The Importance of

Environmental Attributes. Journal of the American Planning Association, 65(4).

Lowman, J. (1986). Conceptual Issues in the Geography of Crime: Toward a Geography of

Social Control, Annals of the Association of American Geographers, 76(1), 81-94.

Maguire, M., Morgan, R., Reiner, R., (ed.), (1997), The Oxford Handbook of Criminology,

Oxford: Clarendon Press.

Marsh, I., (ed.), Melville, G., Morgan, K., Norris, G., Walkington, Z., (2006). Theories of crime,

London and New York: Routledge.

Manning, K. P. (2008). The technology of policing: Crime mapping, information technology,

and the rationality of crime control, New York and London: New York University Press.

Moss, K., Stephens, M. (ed.). (2006). Crime reduction and the law, London and NY: Routledge

Moyer, I. L. (2001). Criminological Theories, London: Sage Publications.

Newman, O., Creating Defensible Space, (1996). U.S. Department of Housing and Urban

Development Office of Police Development and Research.

Pain, R.H. (1997). Social Geography of Women‟s Fear of Crime, Transaction of the institute

of British Geographers, New Series, 22(2), 231-244.

Petrovici, N. (2010). For a relational postsocialist urbanization: bringing back the

epistemologically dispossessed, attend to be published.

Picca, G., (1983). La criminologie, Paris: PUF.

Reid, L. (2003). Crime in the city – A political and economic analysis, New York: LFB Scholary

Publishing.

Schmid, C. F. (1960). Urban Crime Areas: Part II, American Sociological Review, 25(5), 655-

678.

Wang, F. (2005). Geographic information systems and crime analysis, Hershey, London: Idea

Group Publishing.

Weisburd, D., Bernasco, W., Bruinsma J.N. G. (2009). Putting crime in its place: units of

analysis in geograpghical criminology, New York: Springer.

Wilson, C. (1984). A criminal history of mankind, London: Granada Publishing.

Tables and figures:

Fig. 1: Tabel browser: tabular information (various types of crimes) that was to be visually

displayed.

Data source: May 2010, Police of Cluj-Napoca.

Fig. 2; Fig. 3: Running the geocode

process and selecting the correspondence

between tabular information and objects

on the map.

Fig. 4: The figure from above allows the layer

control window to be opened (the figure on

the right). Once opened, a layer can be added,

organized, editated or removed. Also, a layer

can be visible or not on a map.

Fig. 5: How to open a „thematic map‟.

Fig. 6: Step one of three for different ways of

representing data on a map: Ranges, Bar charts,

Pie Charts, Graduated, Dot density, Individual,

Grid. The other two steps allow the user to

select between table and fields for which the

previous selection to be applied and some

settings to customize the visual display.