Do Conventional Organised Crime Groups in Slovenia Prefer a Rural or Urban Environment VARSTVOSLOVJE Journal of Criminal Justice and Security year 2024 pp. 1-32 volume 26 Boštjan Slak, Bojan Dobovšek Purpose: The aim of the paper was to research whether conventional organised crime groups (OCG) in Slovenia prefer rural or urban environments to conduct their activities. And if there are specifics about the environment where OCGs are found to be operating. Design/Methods/Approach: Slovenian police data on offences with organised crime indicators (OwOCI) was the main dataset used. The dataset included the year, name of the settlement and the name of and municipality where OwOCI was recorded. Data was then mapped out using QGIS software. Also, data regarding rural/urban characteristics of Slovenian settlement, urbanisation of Slovenian NUTS regions, etc., was used to examine how can the settlement where OwOCI occurred be characterised in the urban-rural nexus. Findings: The location data was received for 4,549 OwOCI and shows that Osrednjaslovenska region dominates in the statistic (1421 OwOCI in 20092019 period), following by Goriška (834) and Obalno-kraška 643). None of the Slovenian regions is categorized as an Urban region by Eurostat. Slovenia has three Intermediate regions (Gorenjska, Obalno-kraška, Osrednjeslovenska), and the rest are categorized as Predominantly rural. Therefore, using this categorisation, most offences occurred in Predominantly rural: 2,385 vs 2,164 in Intermediate regions. Similarly, when it comes to municipalities, Slovenia currently has only two municipalities with densely populated areas, 44 with areas that are of intermediate-density and 166 with thinly-populated areas. Most offences using this categorisation occur in municipalities with thinly populated areas. Only if examining how many OwOCI occurs in the settlements that by Slovenian criteria have the status of a city then predominantly urban environments dominate. If making calculations only on 4,549 OwOCI and for which location data was received, then the averages are 62.24% (settlement with a status of a city) vs 37.76% (settlement without a city status). If missing data is included, then a ten-year average was 53.40% vs 32.88% for the non- 1 Do Conventional Organised Crime Groups in Slovenia Prefer ... city settlements, considering that there was on average 13.73% of missing location data. Research Limitations/Implications (if applicable): There are three main limitations. Firstly, due to the nature of organised crime, the locale where OwOCI occurred may not be the location where it was recorded. Secondly, due to the safeguarding of anonymity of the victims, perpetrators and others, the received location data is on the level of settlements, which affected the accuracy of the study. Thirdly, the SarS-CoV-19 pandemic has influenced the modus operandi of OCG, thus making statistics of 2020, 2021 and 2022 incomparable to previous years. This was also the reason why these years were not included in the analysis. Practical Implications (if applicable): The results can be used for trend detection, and this provides information for strategies in the field of crime prevention. Secondly, it can be used for a starting point for future studies. Originality/Value: To the best of our knowledge, there was only one previous paper that investigated the locale of OCG in Slovenia. This paper thus provides an insight into this important but rather, overlooked field of research on OCG. Keywords: organised crime, locations, Slovenia, urban-rural, settlements UDC: 343.341(497.4) Ali skupine klasične organizirane kriminalitete v Sloveniji raje delujejo v ruralnem ali urbanem okolju Namen prispevka: Namen prispevka je raziskati, ali skupine organizirane kriminalitete v Sloveniji za svoje delovanje raje izberejo ruralno ali urbano okolje in ali obstajajo kakšne posebnosti glede okolja, v katerem delujejo. Metode: Uporabljeni so bili podatki slovenske policije o kaznivih dejanjih z indikatorji organizirane kriminalitete (angl. offences with organised crime indicators [OwOCI]). Nabor podatkov je vključeval leto, ime naselja ter ime občine, kjer je bil OwOCI zabeležen. Podatki so bili nato kartirani s programsko opremo QGIS. Uporabljeni so bili tudi podatki glede ruralnih/urbanih značilnosti slovenskih naselij, urbanizacije slovenskih regij NUTS itd., da bi preučili, kako lahko naselje, v katerem se je OwOCI zgodil, kategoriziramo kot urbano/ruralno okolje. Ugotovitve: Prejeti lokacijski podatki za 4.549 OwOCI kažejo, da v statistiki prevladuje Osrednjeslovenska regija (1.421 OwOCI v obdobju 2009-2019), ki ji sledita Goriška (834) in Obalno-kraška 643). Eurostat nobene od slovenskih regij ne 2 Boštjan Slak, Bojan Dobovšek uvršča med urbane regije. Slovenija ima tri vmesne regije (Gorenjska, Obalno-kraška, Osrednjeslovenska), preostale pa so kategorizirane kot pretežno ruralne. Z uporabo te kategorizacije je bilo torej največ kaznivih dejanj storjenih v pretežno ruralnih regijah: 2.385 proti 2.164 v vmesnih regijah. Podobno velja tudi za občine, saj ima Slovenija trenutno le dve občini z gosto poseljenimi območji, 44 občin z območji s srednjo gostoto poselitve in 166 občin z redko poseljenimi območji. Največ OwOCI se zgodi v občinah z redko poseljenimi območji. Šele če preučimo, koliko OwOCI se zgodi v naseljih, ki imajo po slovenskih merilih status mesta, potem prevladujejo pretežno urbana okolja. Če naredimo izračune samo na 4.549 OwOCI in za katere smo prejeli lokacijske podatke, potem so povprečja 62,24 % (naselje s statusom mesta) proti 37,76 % (naselje brez statusa mesta). Če so vključeni manjkajoči podatki, je desetletno povprečje 53,40 % proti 32,88 % (naselja brez statusa mesta), pri čemer je bilo v povprečju 13,73 % manjkajočih lokacijskih podatkov. Omejitve/uporabnost raziskave: Obstajajo tri glavne omejitve. Prvič, zaradi narave organizirane kriminalitete se lahko zgodi, da lokacija, kjer se je zgodil OwOCI, ni enaka lokaciji, kjer je bil zabeležen. Drugič, zaradi varovanja anonimnosti žrtev, storilcev in drugih so bili podatki o lokacijah dejanj na ravni naselij, kar je vplivalo na natančnost študije. Tretjič, pandemija SarS-CoV-19 je vplivala na modus operandi skupin organizirane kriminalitete, zato so statistični podatki za leta 2020, 2021 in 2022 neprimerljivi s prejšnjimi leti. To je bil tudi razlog, da ta leta niso bila vključena v analizo. Praktična uporabnost: Rezultate je mogoče uporabiti za odkrivanje trendov, to pa zagotavlja informacije za strategije na področju preprečevanja kriminalitete. Prav tako se rezultati lahko uporabijo kot izhodišče za prihodnje študije. Izvirnost/pomembnost prispevka: Kolikor nam je znano, je bil v preteklosti objavljen le en članek, ki je preučeval lokacijo delovanj skupin organizirane kriminalitete v Sloveniji. Ta članek tako omogoča vpogled v to pomembno, a precej spregledano področje raziskovanja organizirane kriminalitete. Ključne besede: organizirana kriminaliteta, lokacije, Slovenija, urbano-ruralno, naselja UDK: 343.341(497.4) 1 INTRODUCTION Organised crime remains an ambiguously defined concept (Labovic, 2011; Lampe et al., 2006; Woodiwiss, 2003), one that is both popular among scholars (Windle & Silke, 2019) and in political rhetoric. Among research into organised crime, the role of certain territories and locations and whether they are a factor in the development of organised crime and organised crime groups (OCGs) is a consistent theme (Clark et al., 2021; Morselli et al., 2011; Potter & Cox, 1990; 3 Do Conventional Organised Crime Groups in Slovenia Prefer ... Scalia, 2021; Sergi & Storti, 2021; Varese, 2011). Within this stream of the literature are inquiries regarding the rural-urban nexus and how it translates into the development, behaviour, and overall extent of organised crime in territories with either urban or rural traits. It seems that the papers usually focus either on one or a few urban or rural locations and research the environment, location, and social impact but do not research where there is more OCG presence among these two types of environments. Clinard's (1942) study was the earliest study that we could find in which differences between rural/urban perpetrators are researched. His sample included 200 male offenders from Iowa Men's Reformatory (United States of America [US]) who were in-depth surveyed on crucial characteristics of their lives. His findings showed that gang participation influencing criminal behaviour was more influential in perpetrators from urban environments. Youth gang studies are often the ones, where the frequency of occurrence is compared among urban/rural environments (Howell & Egley, 2005). The latest study that we could find was by Campana and Meneghini (2024), which utilised crime incident data recorded by Cambridgeshire Constabulary (United Kingdom) and gained insight into the occurrences of 222 OCG members. They, too, noticed that more organised crime is happening in urban areas, but the chances of a prosperous criminal market were the key factors for OCG activities in a locale. While many comprehensive works on organised crime do acknowledge the rural environment as an important environment of operation of OCG (e.g., Abadinsky, 2010; Albanese, 2015; DeVito, 2005; Dobovsek, 2012) the urban environments are predominant in the literature researching the location of organised crime, something that was noticed and criticised by Potter and Gaines (1992) in their seminal work on organised crime in rural America. For identifying criminal networks Potter and Gaines (1992) build on analysis of newspapers of the selected rural region combined with data from Kentucky Justice Cabinet reports, and the U.S. Attorney's office. Then they applied participant-as-observer research method, posing as customers to observe activities at various locations like roadhouses, game rooms, and restaurants. They established that at the time wholesale trade was the most profitable activity of the identified networks. Locally grown marijuana crops and imported cocaine were the most profitable commodities. Other commodities and services such as alcohol procurement, gambling, and prostitution followed. However, research focus on urban organised crime is not surprising, as the urban environment is where there are more customers to whom organised crime provides products and services, such as prostitution, illegal gambling, drugs, and so on, as well as more targets and potential victims (e.g., there are more cars for vehicle thefts and more places to rob). Social ties among the population in the urban environment are also weaker, and interpersonal dynamics are different in urban and rural settings, contributing to the ineffectiveness of crime prevention approaches in the former as informal control is diminished (Eman & Bulovec, 2021; Mesko et al., 2002). Secondly, the characteristics of the urban environment can in certain countries be recognised as a factor contributing to the birth and extension of OCGs. High unemployment, the lack of other ways of making a living, and a concentration of ethnic minorities all occur more in urban than in rural settings. 4 Boštjan Slak, Bojan Dobovšek While some rural regions can also have high unemployment, this factor alone does not facilitate organised crime, despite being a facilitator of crime (Eman & Bulovec, 2021). Similarly, poverty does not seem to have such a direct contributing effect to more crimes being committed, as most theories predict (Hipp & Yates, 2011). The Global Initiative Against Transnational Organized Crime (2019), when analysing hotspots of organised crime in the Western Balkans, recognised three key factors that must overlap for an organised crime hotspot to develop: weak governance, economic vulnerability, and location. Similar factors can be found elsewhere where OCGs develop. For instance in Scotland, where Clark et al. (2021) findings also expose similar factors. Their findings build on case studies, which were found and complemented by interviews with expert stakeholders, open-source documentation and sanitised intelligence from Police Scotland. And similarly, Blume (2021) whose results came of two years of comparative ethnographic fieldwork, found that this holds true in Central America. Almost two decades earlier Hall (2010, p. 8) using a literature review, found frequent mentions of the same factors with some additional ones (e.g., cultural idolisation of gangsters, existence of criminal tradition, etc.) of which several of them are linked to rural environment traits (poor border security, remote terrain, strategic location). Weak governance can be a consequence of local political decision-makers being in bed with OCGs, or there is simply no interest from the central government in governing certain areas in the country. Economic vulnerability is reflected in high unemployment and usually in falling populations. Locations, on the other hand, are diverse, as organised crime hotspots can be near urban areas (ports, airports) as well as in rural areas, where hard-to-reach settlements with loose formal political and law enforcement oversight can be used by OCGs for the production of drugs or for illegal border crossings. However, it is not just hotspots but OCGs themselves that can emerge due to a set of similar factors. An illustrative case being the Sicilian mafia, which started as a group protecting the local population from foreign invaders, a task that generally is part of governmental services. Later on, the same ineffectiveness of the government in the rural south of Italy prevented the dismantling of OCGs (Richards, 1999).1 The findings of the Global Initiative Against Transnational Organized Crime (2019) pertaining the OCGs exploiting remote areas go beyond the Balkan region. Examples of using remote areas for drug production can be found in Colombia, where the coca plant is farmed (Abadinsky, 2010; Thoumi, 2014), and poppy farms in rural areas of Afghanistan, Burma or Laos (Chouvy, 2011). Marijuana was farmed in rural areas of Kentucky in the US (Potter & Gaines, 1992), and such areas remain places of illegal marijuana farming in the country (U.S. Department of Justice, Drug Enforcement Administration, 2021). Chiu et al. (2011) examined court cases involving clandestine drug laboratories in Australia and via the application of crime scrips methodology found that some laboratories processing amphetamine-type illegal drugs could be found in rural areas of the country. Other drug trafficking activities are also conducted in the rural environment, and some degree of drug trafficking usually occurs along 1 Albanese (2015) writes about different origin of the Sicilian Mafia, yet also connected to the absence of legitimate and legal state governance. 5 Do Conventional Organised Crime Groups in Slovenia Prefer ... the so-called green border and in rural areas of the border (DeVito, 2005). The rural environment also includes airstrips where planes with cocaine land and are unloaded, and then the drug delivered to distributors (Potter & Gaines, 1992). The same or similar routes can be used for migrant smuggling and human trafficking. Rural areas also attract illegal waste dumping and wildlife poaching, but there are also numerous other forms of crimes that can be done by OCGs in such areas (Eman & Bulovec, 2021), with the poverty of the rural population being one of the factors that OCGs exploit in human trafficking. In Mexico, OCGs engage in violence and armed conflicts with the military, and when such clashes occur in rural areas this forces many to flee to the cities. When they arrive there, and due to the lack of other employment opportunities and low salaries if a job is even found, many girls and young women are often tricked, forced or kidnapped into prostitution (Acharya & Bryson Clark, 2021). Rural settlements are also locations where members of OCGs hide from law enforcement agencies (DeVito, 2005), or from rival gangs who wish to assassinate them. These factors are also reasons why OCGs have an interest in gaining control over specific areas. This control can be achieved by the use of violence (evident in racketeering schemes or gang-related territorial disputes) or informal governance, where OCGs are providers of (quasi-) legal services, including the control of street crime, offering employment, dispute mediation, etc. (Berg & Carranza, 2018; Blume, 2021; DeVito, 2005; Finckenauer, 2007). In some remote rural areas, especially if there is weak governance or state capture (Blume, 2021), OCGs are sometimes the only organisations that provide these services. The aim of this paper is to research if Slovenian OCGs operate in rural settings and in what capacity. 2 RESEARCH ON THE LOCATION OF ORGANISED CRIME IN SLOVENIA Some research on Slovenian crime locations has been carried out (Eman & Bulovec, 2021; Eman & Hacin, 2018, 2021; Mesko et al., 2002; Mesko, 2003), but there is not much focus on organised crime, although in discussions and writings on OCGs and/or their activities, the geographical location and characteristics of Slovenia are frequently recognised as important (Dobovsek, 1997, 2012; Dvorsek, 1995; Jager et al., 2005; Maver, 2002). The location of Slovenia (being located on the so-called Balkan route) jointly with the fact that it has a connection to international waters has always played a part in the form and scope of criminality. Organized crime included as is evident from the historical records that show that even from 16 century and up to the end of world war two there was present smuggling, trafficking in merchandise (even opium and later heron), armed robberies and many other activities trademarked to organised crime (Celik, 2001, 2005; Darovec, 2005; Holz, 2001; Korinsek, 1952, 1954). When Slovenia was part of socialist Yugoslavia, organised crime was also present even though not officially recognised. And for times after the independence, Dvorsek (1995), a scholar and once also director of Slovenian Criminal Investigative Police, has illustratively written that (small-sized) Slovenia now has OCGs that deal with and do what 6 Boštjan Slak, Bojan Dobovšek OCGs from all the bigger (and biggest) countries. Also attributing the locale of Slovenia to be a contributing factor to this. An international project called Organised Crime Outlook aim was to first identify factors and elements that influence organised crime development in a specific country and then predict its future development based on these elements. The authors of the Slovenian report recognised the location as a factor befitting OCGs and its smallness and lack of big cities as a factor discouraging OCG activities (Jager et al., 2005). In all these and other sources, the rurality of the country is not directly mentioned. Green borders and an abundance of forestry, of course, played a role in early OCG activities as they proved cover for smuggling, trafficking, and hiding from law enforcement. After the independence, when new OCG activities included (or better said, an increase in specific) forms developed (such as car theft, human trafficking, and prostitution), the benefiting factors of rurality, in a sense, additionally decreased. One of the scenarios of organised crime development in Slovenia, developed by Jager et al. (2005) predicted that improved transportation technology would be a strong beneficial factor for OCGs operating in Slovenia. After the independence, Slovenia was fast pacing the development of highways throughout the country - building so-called highways cross. Connecting the corners of a country and linking it via highway with other countries. And it seems that this has influenced organised crime as Furdi (2014, p. 1) in the only study exclusively focused on the locations used by OCGs, looking at the most typical OC activities between 2008-2012, noted that "Drug trafficking is one of the biggest problems in the country since it occurs throughout the whole territory and is growing over the years. The analysis shows the movement of this type of crime along major traffic axes and major cities. In contrast, the operation of human and arms trafficking, and other hazardous substances, occurs in less populated areas of Slovenia and is declining over time."2 In other contemporary studies, OCGs appear more - as Haysom and Shaw (2017) would say - as a passing reference, in which gangs or other OCGs are seen as possible perpetrators of crimes that are (partly) geographically and criminologically analysed (Eman et al., 2012; Eman & Hacin, 2018; Kuhar, 2017; Mesko, 2003). The current study was thus conducted to address this gap in the literature about the locations of organised crime in Slovenia, and especially with regard to the rural-urban nexus. 2.1 Methodology The methods for the analysis of the locations where OCGs in Slovenia operate were classic desk research, desk research using Artificial intelligence tools and other software tools to search and analyse similar themed and contented papers (e.g., Litmaps, n.d.; SciSpace, n.d.) frequency analysis of statistics, and location mapping. Beside Law enforcement agencies (LEA), NGO reports, the empirical organised crime database develop by Slak (2019)3 and other research and writings 2 The text was translated from Slovenian by the authors of the paper. 3 To analyse the connection between organised crime and the legal-formal economy in Slovenia, 66 prosecutor files describing such cases were reviewed and analysed. Based on the data found in these files an extensive database on the characteristics of a) Slovenian OCGs and b) characteristics of the business subjects that are in ownership or (in)formal control of members of OCGs, were created. The Slovenian OCG membership characteristics, preferred activity, type of management and other such issues were reviewed. With regard to business subjects that are in 7 Do Conventional Organised Crime Groups in Slovenia Prefer ... on organised crime (e.g., Dobovšek, 1997, 2012; Jager et al., 2005; Mozetič & Jager, 2005; Slak et al., 2015), there were two main data sources used: I. Slovenian police data on the location of organised crime,4 which was obtained with a request that was sent to the police for data on offences with organised crime indicators (OwOCI). When police officers log instances of crime into their databases they mark if they have traits of organised crime, based on Europol criteria. The request sent to the Slovenian police also asked for statistics on the locations of these OwOCI, provided as either points of occurrences (geographical coordinates of OwOCI) or on the level of settlement or even wider areas where OwOCI occurred. In this way the police could provide data that, in their view, presented a suitable balance between safeguarding the anonymity of victims, perpetrators, witnesses and others involved on the one hand, and on the other enabling research on the locations used by OCGs to outside researchers and analysts. Safeguarding anonymity is difficult in a small country like Slovenia, which also has a high standard of human rights when it comes to criminal investigations (Maver, 1997). Moreover, this also safeguard tactics and methods of police work. The location data received was data on OwOCI per settlements and municipalities for 2000-2019. This decreases the precision of the analysis, but increases anonymity, which was one of the main concerns when designing the study. II. The general raw statistics of offences recorded by the police in a certain year, which is available on the Slovenian police website (Policija, n.d.) in the form of anonymised annual datasets (ADOs). The data include basic information, such as the yearly ID number of the offence, the month and year, the hour and day of the week when the offence was committed, the designated police directorate, the related chapter and article of the criminal code, the type of crime (general, economic, or juvenile), and other details, providing an initial record of the offence. Further investigations can change how a specific event is classified and treated. There are also some aspects of the reports that are subjective, the location of the crime being one of them. For example, if a vehicle in which drugs are hidden is found on a natural surface - forest, field, and so on - then the place can be either the vehicle or in nature. Therefore, these ADOs are to be used with some caution. The datasets in .csv form covering 2009 until 2023 were downloaded,5 joined in a single query, and then data for OwOCI were filtered out. There were 17,646 such entries, but because the datasets ownership or (in)formal control of members of OCGs, special focus was put on the type of registered activities, location, size, management style, etc. Both databases thus present a rather deep look into Slovenian organised crime (Slak, 2019). 4 In their annual report (Police, 2021, p. 30) explains: "In order for an organised criminal activity to be defined, mandatory criteria and at least two of the seven variable [selective] criteria must be met. The mandatory criteria are the existence of a group of at least three people, activity in a longer period of time, gaining illegal proceeds and/or social power and committing criminal acts that are prosecuted ex officio. The variable criteria are the use of violence and/or corruption, operation at the international level, participation in money laundering, having internal rules of behaviour and distributing roles and tasks among the members of the group, entrepreneurial manner of operation and influencing the media, economy, state administration and/or politics." 5 Before 2009, the Police ADOs included separate datasets for events and datasets for the person involved. After 2009, the dataset provided by the police is a joined set with limited differentiations over the years, making creating useful, organised crime datasets for post-2009 more credible and stable. 8 Boštjan Slak, Bojan Dobovšek include data on the perpetrators - which in cases of organised crime are usually more numerous - and data on the victims, as well as a limited number of offences from previous years,6 this means that the data needed to be further filtered and redesigned. A combination of the date of the offence and case serial number was used as a filter variable to generate the number of OwOCI for 2009-2023. This gave a total of 7,682 records of OwOCI. There are notable differences compared to the data received from the first request to the police, described above (seen also in Figure 1 below). Which is logical as these datasets are snapshots and are not latter corrected and amended when more is known. Investigations sometimes reveal that certain offences cannot be listed as OwOCI. In one year, the difference was +132 events and the second biggest difference was -89 events. The absolute average of the difference was 59 events.7 2.2 Limitations There are several major limitations that were encountered in this study. Firstly, the location data on OwOCI offences is usually logged into the database when crimes are reported, criminal charges are brought forward, or arrests are made. This therefore can also mean that the "location" is the police station (and this dose happen an as seen from table 2). Also, it must be considered that there are cases where OCGs operate in one or multiple areas, but its members are charged in a different one. Paradoxically, the possibility of errors stemming from this data was in some ways overcome by using data on the settlements or municipalities and not on geographical coordinates. However, it should also be noted that the lack of data on geographical coordinates reduces the precision of the results of this study, as there could have been cases where OwOCI occurred within the borderline area of a town or city, and thus although the location has rural characteristics it was counted as an urban environment. Yet even if geographical coordinates data is available there need to be caution in interpretating it, as these are organised crime offences and static geographic points, while organised crime activity requires the OCG member to move around and meet in various places. Several OwOCI are carried out away from where the OCG members live and/or perform their managerial tasks in the organisations. The arrangements and deals on drugs or migrant trafficking are usually carried out in other locations, like bars or business offices, which are used as a cover. There are only a few activities where the location of management, planning and carrying out the activities are all the same, as is the case with forced prostitution in nightclubs. Therefore, the geographical coordinates of locations where LEAs make arrests when perpetrators are caught in flagrante delicto during drug sales or when illegal migrants are caught at the borders thus only represent one of several locations where the related OCGs 6 There are some cases of offence where someone reports them, or police get to know about them years after they have occurred. While this is recorded into dataset of the currentyear, the actual date of crime commission is also entered into the report. In the used dataset, offences from 2002 were even recorded. In our calculations, the year when the event occurred was used and not the year of detection. 7 We were considering whether if only cases for which a criminal charge resulted should be included in the study. However, this showed that there were even further discrepancies between the data on request and datasets from Police ADOs. 9 Do Conventional Organised Crime Groups in Slovenia Prefer ... conduct their activities. The drawbacks in using this data are typical for the data that is frequently used by organised crime researchers (Windle & Silke, 2019). Because our study uses police data that means that it omits the most successful - and thus rarely, if at all, uncovered - organised crime groups. Similar are limitations regarding the forms and types of OwOCI. While the used dataset did not include data on type (e.g., article from criminal code), we can, from other sources (annual police report and annual raw statistics), see that most offences are conventional (drug trafficking, human smuggling, etc.), yet not exclusive, since offences as corruption offences, and economic crimes were also present. However, it's unclear which particular offences were exactly those that populate the database with location data. But common logic indicates that conventional forms were dominating. Based on this we can also say, that excluded are those types of OCGs and activities which are perhaps most damaging to societies. Types that Labovic (2011) names as institutional, Chambliss (1989) as State-organized crime or Ruggiero (2015) as crimes of the powerful. Another major drawback is based on the nature of the geographical data used. The data presents a snapshot of the state of the environment, but the environment is constantly developing. Some areas could thus have a lower degree of urbanisation in an earlier part of our analysed period. Moreover, there are environmental elements that are not included in GIS datasets, such as the fence built on Slovenia's southern border with the aim of stopping migrants crossing and people smuggling. This affected where activities of OCG were done. 2.3 Results The Republic of Slovenia gained its independence from former Yugoslavia in 1991, and transformed itself into a modern, democratic and relatively economically developed country. In 2004 it joined the European Union and NATO, and is also in the Schengen Area. Slovenia covers 20,271 square kilometres and has a population of 2.1 million. Ljubljana is the nation's capital and its largest - and arguably only - city. Historically, due to being part of a socialist and communist country prior to its independence, it officially did not have any OCGs, as this was seen as a trait of capitalist countries, yet in reality it did have crimes with all the traits of organised crime, like trafficking in drugs and other contraband. Indeed, trafficking and smuggling were and remain common in Slovenia, as the country is located on the so-called Balkan route, with access to the sea and increasingly developed highways. Due to its geographical smallness, there have never been extreme difference among different parts of the country, but some regions were and are more developed than others. Osrednjeslovenska, the region that contains Ljubljana, has always been the most prosperous area, while Pomurska, for instance, has had the highest level of unemployment. On the national level, organised crime statistics have varied over time, mainly due to differences in organised crime measurement and recording. The number of OwOCI from 2000 until 2023 and the number of offences without OCI can be seen in Figure 1 below. 10 Boštjan Slak, Bojan Dobovšek ■ Number of offences with OCI (source: Slovenian police, on request) ■ Number of offences with OCI - "raw" data (source: Police ADOs) Number of offences without OCI 900 ¡3 H 1300 O so 8[|1100 K ^ M M ^ ™ S ž C 5 ^ fe £ CD 6 ^ o t Ph £ 1 00 100,010 90,010 80,010 70,010 60,010 50,010 40,010 30,010 20,010 10,010 10 ooooooooo If considering only police data that was received on request, then it can be seen that in 2000-2019, a total of 9,696 OwOCI were recorded, on average accounting for 0.67% of total recorded offences. There is an increase in recorded OwOCI towards the end of the analysed period, as in 2018 these accounted for 1.07% of the total, and in 2019 1.47%. This compares with 1.38% in 2000 and 1.12% in 2001. However, it should be noted that these earlier numbers were influenced by the not-yet-settled or fully adopted Europol methodology of measuring organised crime - in 1999, the number of such offences was 1,134 (Svetek, 2001). Figure 1 illustrates a three- to four-year surge in OwOCI during the period analysed. If incorporating also the data from annual Police reports and general raw statistics, the finding do not change and crime rate of OwOCI remains stable from 0.9 to 1.0%. Due to changes in the police methodology for recording OwOCI that occurred in 2000, changes in the number of Slovenian cities (in 2005 the government granted a further 16 settlements the status of a town ("Sklep o podelitvi statusa mesta naseljem v Republiki Sloveniji [Decision Granting the Status of Town to Settlements in the Republic of Slovenia]", 2005), and because Slovenia became a member of the European Union in 2004 and in 2007 a member of the Schengen Area (European Union, n.d.), the analysed period was set from 2009 to 2019. Data on OwOCI for 2009-2019 is presented in Figure 2 below. Also included is information on how much location data for these OwOCI were received. Figure 1: Statistics on the number of criminal offences without an organised crime indicator (OCI) and the number of criminal offences with OCI for the 2000-2022 period (source: Slovenian police, on request and Policija (2023) for years 2020, 2021 and 2022, therefor marked with* and ADOs (Policija, n.d.) for the year 2023, marked with **) 11 Do Conventional Organised Crime Groups in Slovenia Prefer ... Figure 2: Statistics on the number of criminal offences without an organised crime indicator (OCI) and the number of criminal offences with OCI and how much of the location data for the offences with OCI we have for the period 20092019 (source: Slovenian police, on request) Figure 3: Location of offences with OCI - NUTS 3 level ■ Number of offences with OCI ■ Number of offences without OC □ Number of offenses with OCI and for which we have location data There were 5,349 OwOCI recorded between 2009 and 2019. The average percentage of organised crime within the total number of crimes recorded for the period is 0.68%. Location data was received for 4,549 offences out of these 5,349, or for 85%. Coverage varies over the years - the least data was received for 2012, where location data is only available for 62% of OwOCI, while most data was received for 2011, for 97% of cases. First, an analysis of OwOCI per NUTS 3 statistical region was made, with the results shown in Figure 3. t Croatia 12 The figure presents the distribution of all OwOCI in the period 2009-2019 by NUTS 3 region, and the table presents the data by year.* Boštjan Slak, Bojan Dobovšek Figure 3: Location of offences with OCI - NUTS 3 level Gorenjska Goriška Jugovzhodna Slovenija Koroška Obalno-kraška Osrednjeslovenska Podravska Pomurska Posavska Primorsko-notranjska Savinjska Zasavska Total Urban-Rural Typology** I PR PR PR I I PR PR PR PR PR PR 2009 1 113 20 17 141 21 39 36 1 15 1 405 2010 4 46 22 6 22 122 51 18 7 15 5 318 2011 3 62 38 3 51 101 13 7 20 3 10 8 319 2012 16 42 32 1 65 90 21 2 8 33 21 331 2013 5 39 81 11 161 116 45 2 7 13 46 1 527 2014 18 39 6 20 100 105 23 21 12 1 17 362 2015 14 72 26 1 23 88 85 6 4 1 21 341 2016 10 77 4 3 53 156 14 48 3 2 36 1 407 2017 1 146 3 89 52 19 41 24 2 25 8 410 2018 22 43 34 54 171 82 11 3 11 73 6 510 2019 6 155 25 1 8 279 89 14 8 12 22 619 Total 100 834 291 46 643 1421 463 191 143 86 301 30 4549 * Data received from the Slovenian police, on request. The figure was made using QGIS software and data from the Republic of Slovenia, Portal Prostor (n.d.). ** Typology is based on datasets from 2016 obtainable at Eurostat (n.d.-b) and defined at Eurostat (n.d.-c). In Slovenia, only the Intermediate (I) and Predominantly Rural (PR) classifications appear.8 Osrednjeslovenska region has, by far, the most OwOCI. Second and third are Goriška and Obalno-kraška. This is predominantly consistent throughout the analysed period. An increase in OwOCI over time usually is reflected in several regions. For example, 2009 and 2013 saw a surge in recorded OwOCI, and the numbers are higher in several regions and not just one. The results outlined above are logical, as the Slovenian 8 Eurostat (n.d.-a) defines a rural area as "all areas outside urban clusters. 'Urban clusters' are clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000." It classifies a region "on the basis of the share of their population in rural areas" in one of the three categories: "'Predominantly rural' if the share of the population living in rural areas is higher than 50; 'Intermediate' if the share of the population living in rural areas is between 20 and 50; Predominantly urban' if the share of the population living in rural areas is below 20." "To resolve the distortion created by extremely small NUTS 3 regions, for classification purposes regions smaller than 500 km2 are combined with one or more of their neighbours."(Eurostat, n.d.-c). 13 Do Conventional Organised Crime Groups in Slovenia Prefer ... capital Ljubljana is in the Osrednjeslovenska region and Ljubljana is the city-municipality9 with the most OwOCI. The reason that Goriška and Obalno-kraška are second and third for occurrences with OwOCI can be attributed to the fact that these two regions border or are near Italy, which also has a high occurrence of organised crime and is a desirable destination for migrants. The theory is in part confirmed by ADOs (Policija, n.d.) as Police Directorate Koper has the second highest number of OwOCI for Prohibited crossing of state border or territory in 2009-2023. It also has the second highest number of OwOCI in general. Police Directorate Nova Gorica, in turn, had a small number of these crimes in 2009-2023. However, it has the second highest OwOCI for Unlawful manufacture and trade of narcotic drugs, illicit substances in sport and precursors to manufacture narcotic drugs. The Slovenian-Italian border has historically always been burdened with problems of trafficking and drugs. Korinšek (1954) writes about a very successful smugler who operated in Slovenia and Italy and was arrested on the border in 1954. Osrednjaslovenska and Obalno-kraška are in the Eurostat (n.d.-b, n.d.-c) categorisation marked as intermediate urban regions, while Goriška is a predominantly rural region. None of the Slovenia regions is categorised by Eurostat (n.d.-b, n.d.-c) as urban-type. Using this classification thus mean that, in a European context, organised crime in Slovenia does not occur in environments that would be labelled as urban, and instead 52% of OwOCI occur in regions that are categorised as predominantly rural, and 48% as intermediate regions. If adjusting for 15% missing data, then 45% of OwOCI occur in regions that are categorised as predominantly rural and 41% regions are categorised as intermediate. To see, if the results differ if examining the data on smaller scale an analysis on the level of municipalities and settlements were done. In order to analyse the data on municipality level, the Eurostat (n.d.-a) degree of urbanisation typology was used. Which "is a territorial typology used for classifying municipalities by their population density type." (Statistical Office of the Republic of Slovenia, n.d.-b) The analysis for the whole 2009-2019 period as a unified unit could not be done, because the changes in the number of municipalities in Slovenia affected the population size per municipality and the areal size of municipalities. Moreover, natural migration flows have been occurring and influencing the population size of municipalities. Combining data from Eurostat and the Statistical Office of the Republic of Slovenia (n.d.-a), a comparison of the degree of urbanisation of municipalities over time was made. It was noticed that there was no change between 2011-2013 and 2017-2019. Therefore, the distribution of municipalities with OwOCI per degree of urbanisation was calculated in two steps. Firstly for the years 2011-2013, and then 2017-2019. All years for which the degree of urbanisation data was not found (2009, 2010, 2014, 2015, 2016) were excluded from the calculations.10 Table 1 presents the results of this analysis. 9 City-municipality is a classification of the municipality in cases where the municipality includes a city with more than 20,000 inhabitants and meets certain other criteria. 10 Though data (population and area of municipalities) was available for other years, we did not make our own calculations of the degree of urbanisation and only official data from Eurostat (nd-e) was included. For 2017 data was not found on the official Eurostat webpage, but on that of the Statistical Office of the Republic of Slovenia (nd-a). 14 Boštjan Slak, Bojan Dobovšek Period 2011-2013 Number of municipalities = 211 Period 2017-2019 Number of municipalities = 212 Total both periods Typology* Number of municipalities in category** No. of offences with OCI % of OwOCI % w/o per typologyI missing data Number of municipalities in category** No. of offences with OCI % of OwOCI per typology % w/o missing data No. of offences with OCI % of OwOCI per typology % w/o missing data Densely-populated areas 2 298 21.10% 25.32% 2 460 24.69% 29.89% 758 23.15% 27.91% Intermediate density areas 38 444 31.44% 37.72% 44 462 24.80% 30.02% 906 27.66% 33.36% Thinly-populated areas 171 435 30.81% 36.96% 166 617 33.12% 40.09% 1,052 32.12% 38.73% Missing data*** 235 16.64% 324 17.39% 559 17.07% Total 211 1,412 100.00% 100.00% 212 1,863 100.00% 100.00% 3,275 100.00% 100.00% Table 1: Municipalities with offences with OCI by degree of urbanisation * The typology is defined on the Statistical Office of the Republic of Slovenia (n.d.-b) webpage: "Degree of urbanisation is a territorial typology used for classifying municipalities by their population density type. It covers the whole geographical territory of the European Union. The statistics shown by the degree of urbanisation typology give an analytical view to the densely and sparsely populated areas. According to the degree of urbanisation areas are classified into three types: • densely-populated areas (cities, large urban areas), • intermediate density areas (towns and suburbs, small urban areas), • thinly populated areas (rural areas). In densely populated areas at least 50% of the population lives in high-density clusters, while in intermediate density areas less than 50% of the population lives in rural grid cells and less than 50% lives in high-density clusters. Thinly-populated areas have more than 50% of the population living in rural grid cells." (Statistical Office of the Republic of Slovenia, n.d.-c). ** Calculations based on data from Eurostat (n.d.-b, n.d.-c, n.d.-d) and the Statistical Office of the Republic of Slovenia (n.d.-a). *** Calculated by subtracting the sum of OwOCI per period and for which location data was received from the total number of OwOCI that was received in datasets from the Slovenian police on request. 15 Do Conventional Organised Crime Groups in Slovenia Prefer ... Based on the Eurostat (n.d.-a) categorisation, most OwOCI occurred in rural areas. However, there is a high concentration in Slovenia's main urban areas, as the number of OwOCI in two municipalities categorised as densely populated is not much smaller compared with areas in other categories. This high concentration of OwOCI in urban clusters aligns with other research on variations in crime in Slovenia. For example, it was noted that 90% of crimes in Slovenia occur in urban areas (Eman & Bulovec, 2021; Eman & Hacin, 2018), among which the city of Ljubljana dominates with one-third of all crimes (Eman & Hacin, 2018). According to Eurostat, in Slovenia there are only two cities - Ljubljana as a medium-large city and Maribor as a small city (Ministrstvo za naravne vire in prostor, 2024). In contrast, the Slovenian government grants the status of the town to any "urban settlement that differs from other settlements by size, economic structure, population density and historical development and has more than 3,000 inhabitants." According to the Ministry of Natural Resources and Spatial Planning information on the government web page (Ministrstvo za naravne vire in prostor, 2024), there are 69 settlements with this status in Slovenia. Two received their status in 2020 and were therefore not considered towns in the framework of this study. All other settlements received the status of a town before 2009. The data on OwOCI that was received along with data on where theses offences occurred were then ranked in one of two categories - a settlement that has status of a town or a settlement without this status. Figure 4 presents how many OwOCI for which location data were received occurred in settlements with city or town status. Figure 4: Offences with OCI and for which location data was received with regard to the type of settlement where the crime occurred 16 In the period 2009-2019, OwOCI were predominantly recorded in settlements with the status of a town or city. For the ten-year average the figures are 53.40% and 32.88% for urban and non-urban settlements, with an average of 13.73% missing location data. If making calculations based only on the 4,549 OwOCI for which location data was received, then the averages are 62.24% for settlements with the status of a city or town vs 37.76% for those without this status. In 2013 the _data is almost equally distributed, with just one more OwOCI occurring in towns Boštjan Slak, Bojan Dobovšek and cities than other settlements. The greatest contrast was in 2017, when most of the offences with OCI were recorded in non-urban settlements. Despite further analysis, we could not find a reason for this anomaly. TTo gain a more in-depth understanding and identify patterns in the locations of OwOCI occurrences, settlement and offence data were mapped onto a map of Slovenia using QGis software. • Major roads and traffic infrastructure (data obtained from the Republic of Slovenia, Portal Prostor (n.d.)). • Major settlements (data for population of settlements was obtained at Statistical Office of the Republic of Slovenia (n.d.)). • Relief, major rivers, lakes and sea (using the National Topographic Model, National Topographic Map, National General Map all obtained from the Republic of Slovenia, Portal Prostor (n.d.)). • Statistics on the settlements with OwOCI. Figure 5 presents the results. Figure 5: Settlements where offences with OCI occurred in the period 20092019 mapped out on Slovenian territory Figure 5 indicates that there is a wide variation with regard to the locations of OwOCI. The national capital, Ljubljana dominates the statistics, being the only settlement where there were over 1,000 OwOCI recorded in a ten-year period. It is also evident that there are specific clusters of OwOCI, often occurring near the biggest Slovenian cities. Some OwOCI occurred in rural settings, although also with great variations among specific areas. To safeguard anonymity, however, we do not present data by year, where this would be more evident. Border areas are a place with more OwOCI. Nova Gorica is perhaps the most illustrative example of this, as the border between Italy and Slovenia goes literally through the town. The proximity of water sources does not seem to play a role in where OwOCI are committed, but roads do seem to have an impact, with more such crimes along major highways. However, all these are interpretations based on a visual_ 17 Do Conventional Organised Crime Groups in Slovenia Prefer ... inspection of the map in Figure 5, as no geographical statistical analysis was done. And all the results must be viewed through the prism of the characteristics of Slovenia, its small size and dispersed settlements. Some additional insight can be noted data in ADOs (Policija, n.d.) where the top 10 OwOCI and their dispersion among locations is seen from table 2. Drug trafficking dominates in traffic areas and illegal border crossing in specially protected areas. 18 Boštjan Slak, Bojan Dobovšek Table 2: Top 10 OwOCI and how are they distributed among locations of where they occurred/where recorded Unlawful manufacture and trade of narcotic drugs, illicit substances in sport and precursors to manufacture narcotic drugs Prohibited crossing of state border or territory Trafficking in human beings Illegal manufacture of and trade in weapons or explosive materials Grand larceny Forgery of documents Counterfeiting money Extortion and blackmail Forgery or destruction of official documents, books, files or archives Acceptance of bribes Total Traffic area 1342 (19.53%) 139 (2.02%) 5 (0.07%) 13 (0.18%) 34 (0.49%) 18 (0.26%) 9 (0.13%) 23 (0.33%) / / 1583 (23.04%) Specially protected areas 22 (0.32 %) 1329 (19.34%) 1 (0.01%) 6 (0.08%) / 139 (2.02%) 2 (0.02%) 1 (0.01%) / / 1500 (21.83%) Accommodation space or the surroundings (yard, garden etc.) 1096 (15.95%) 2 (0.02%) 112 (1.63%) 48 (0.69%) 58 (0.84%) 5 (0.07%) 6 (0.08%) 41 (0.59%) / / 1368 (19.91%) Other 960 (13.97%) 89 (1.29%) 50 (0.72%) 118 (1.71%) 12 (0.17%) 7 (0.1%) 8 (0.11%) 21 (0.3%) / / 1265 (18.41%) Premises for sports, recreation, or entertainment 230 (3.34%) / 77 (1.12%) 6 (0.08%) 5 (0.07%) / 34 (0.49%) 17 (0.24%) / / 369 (5.37%) Sales spaces 77 (1.12%) 1 (0.01%) / / 55 (0.8%) / 50 (0.72%) 4 (0.05%) / / 187 (2.72%) Administrative premises of, a company or other organisation 9 (0.13%) 1 (0.01%) 2 (0.02%) 2 (0.02%) 5 (0.07%) / 4 (0.05%) 5 (0.07%) 48 (0.69%) 48 (0.69%) 124 (1.8%) Official premises of the police 11 (0.16%) 69 (1%) 30 (0.43%) 1 (0.01%) / 1 (0.01%) / / / / 112 (1.63%) Premises of other institutions 1 (0.01%) 4 (0.05%) 1 (0.01%) / / 3 (0.04%) / / 33 (0.48%) 31 (0.45%) 73 (1.06%) Premises for monetary and financial services 11 (0.16%) / / 10 (0.14%) 2 (0.02%) 1 (0.01%) 33 (0.48%) / / / 57 (0.82%) Natural surface 30 (0.43%) 16 (0.23%) / 1 (0.01%) / / / 4 (0.05%) / / 51 (0.74%) Communal space 22 (0.32%) 2 (0.02%) / / 6 (0.08%) / 1 (0.01%) 1 (0.01%) / / 32 (0.46%) Vehicles 11 (0.16%) 4 (0.05%) 11 (0.16%) 2 (0.02%) 3 (0.04%) / / / / / 31 (0.45%) 19 Do Conventional Organised Crime Groups in Slovenia Prefer ... Space for industrial, agricultural or craft 17 (0.24%) / / / 8 (0.11%) / 3 (0.04%) 2 (0.02%) / / 30 (0.43%) production and services Institutional spaces, local assembly bodies, political or special 2 (0.02%) 16 (0.23%) / / / 5 (0.07%) / 1 (0.01%) / / 24 (0.34%) interest organisations Open or closed storage spaces 9 (0.13%) 1 (0.01%) / 1 (0.01%) 8 (0.11%) / / / / / 19 (0.27%) Premises for educational protection and educational activity or the immediate 11 (0.16%) / / / 1 (0.01%) / / 6 (0.08%) / / 18 (0.26%) surroundings (yard, school playground) ) No data 3 (0.04%) 3 (0.04%) / / / / 3 (0.04%) / / / 9 (0.13%) Premises for cultural activity 8 (0.11%) / / / / / / / / / 8 (0.11%) Premises for healthcare services and the 2 (0.02%) / / / / / 3 (0.04%) / / / 5 (0.07%) surroundings Construction site 2 (0.02%) / / / 1 (0.01%) / / / / / 3 (0.04%) Premises for religious rituals / / / / 1 (0.01%) / / / / / 1 (0.01%) Total 3876 1676 289 208 199 179 156 126 81 79 6869 (56.42%) (24.39%) (4.2%) (3.02%) (2.89%) (2.6%) (2.27%) (1.83%) (1.17%) (1.15%) (100.00%) Based on ADOs (Policija, n.d.) 20 Boštjan Slak, Bojan Dobovšek To gain some more detailed insight into to urban-rural nexus, examples of OCG activities in rural settings were sought in the literature and other sources. In order to present the findings of the desk research in the most efficient way possible, a table with three distinct categories of connections of OC and the rural environment was developed. Firstly, a category that includes the activities of OCGs that are offered as products and services in a rural environment; secondly a category that includes activities where the rural area is used as an environment to facilitate the activities of OCGs; and thirdly a category where the rural environment can be found in both roles. In the next step, we listed various activities between these three main categories. The table was then populated with cases, examples and comments that derived from desk research. Table 3 presents a summary of our findings. 21 Do Conventional Organised Crime Groups in Slovenia Prefer ... Table 3: Cases and examples on the connection between organised crime in Slovenia and rural settings Connection of organised crime and the rural environment Sub-category* Cases/examples in rural Slovenia** OCGs as services providers Illegal gambling No cases found. Selling of illegal substances Media reports (Lovrec, 2021) and the ADOs (Policija, n.d.)*** show that illegal drugs are consumed in non-urban environments. From research (Pajnik & Kavčič, 2008; Slak et al., 2016; Slak, 2019) and media reports (Hočevar et al., 2012; Prostitution (forced and pimping) Tomažič, 2021) it is evident that prostitution in Slovenia is often performed in so-called nightclubs, which are often connected to organised crime. Some of these nightclubs are located in rural areas. Providing counterfeit goods Trafficking in counterfeit goods is present in Slovenia. However, no case could be connected to the rural environment. 22 Boštjan Slak, Bojan Dobovšek Arms trafficking (production, storage, manufacturing, and distribution) ADOs (Policija, n.d.) show that one such offence was recorded (in 2017). Art and cultural heritage crimes Slovenia has many sites of cultural heritages in rural areas, but these do not seem to be targeted by OCGs as no related case has been found to date. The only case that was found to be connected to OCGs and rural environment - albeit in a limited manner - is the case of paintings stolen from the Slovenian painter Ivan Grohar. The paintings were taken from his house in a rural area and later found during the search of the home of a suspect in the notorious SKB robbery (Jakopec, 2012; Kuhar, 2017; Predanič, 2013). Dismantlement of stolen vehicles Only media reports can be found (e.g., Fajfar, 2017) that show that this occurred in rural settings. Among the organised crime cases analysed in Slak (2019) there was an OCG that performed this activity, but the workshop that was used to dismantle the vehicles was in an urban environment, albeit not in settlement with the status of a town or city. To safeguard the anonymity of the people involved, the more precise location was not given in Slak (2019). Drug trafficking (production, distribution, and sale) Media sources (e.g., U. P., 2010) provide numerous examples of when drugs, mainly marijuana, is farmed in rural areas. Indeed, the police state that Slovenia is almost self-sufficient when it comes to this drug (Lovšin, 2015), how many growing sites are in rural areas is not known. Research (Eman & Bulovec, 2021) shows that in the Pomurje region some remote areas are used for marijuana cultivation. OCGs using rural areas for facilitating their activities Drug smuggling (cross border and/or transport within the country) ADOs (Policija, n.d.) show several recordings of offences with OCI where natural surface were used for the transport of illegal drugs. Environmental crimes (including wildlife poaching) Statistics from Slovenian prosecutors (Vrhovno državno tožilstvo Republike Slovenije, 2021), LEA reports (Policija, 2021), and research studies (Eman et al., 2012; Eman & Bulovec, 2021) show that environmental crimes occur in Slovenian rural areas. While data from these sources do not always provide a direct link to OCG, the modus operandi eliminates any other option. In contrast, police ADOs (Policija, n.d.) directly list some OwOCI related to the unlawful handling of protected animals and plants in Obalno-kraška region. Finding victims of human trafficking Only Slovenian cases of Roma underage girls who were forced into marriage can be listed here. Some Roma settlements in Slovenia are in rural areas, and forced marriages are a form of human trafficking (Reported and discussed in Debelak, 2020; Mežan, 2014). Human trafficking (via green borders and/or rural border crossings) In Slovenia, most of the human trafficking is connected to forced prostitution in nightclubs. In most cases, victims are foreign women brought to Slovenia legally and by official travel routes. Only a few cases were found where women were brought illegally, e.g., by boat (Pajnik & Kavčič, 2008). Migrant and people smuggling Media reports (e.g., Furlan-Rus, 2013) and police statistics ADOs (Policija, n.d.) show recorded OwOCI for the prohibited crossing of the state border or territory occurring in rural areas. Non-violent crimes OwOCI of fraud and tampering with evidence was found in police statistics (Policija (n.d.)). OCG members hiding from rival OCGs No cases found, and also highly unlikely based on the characteristics of Slovenian OCGs. OCG members hiding from LEA in rural settings No cases found, and also highly unlikely based on the characteristics of Slovenian OCGs. Violent crimes ADOs (Policija, n.d.) show that robberies, extortions with violence and even kidnapping have occurred in natural surfaces. 23 Do Conventional Organised Crime Groups in Slovenia Prefer ... The rural environment in dual roles - either a place for offering goods and services, or facilitating activities No cases found, and also highly unlikely based on the characteristics of Slovenian OCGs. Furthermore, similar to the findings of Berg Crimes and non-illegal activities and Carranza (2018), who note that stronger social ties deter OCG violence, Slovenian rural areas are characterised as more socially for exerting territorial control close-knitted (Bucar Rucman, 2019), something that would also deter such OCG activities. However, incidents of extortion were recorded in rural areas, yet it could not be confirmed if these were racketeering schemes. If so, then this nullifies our assumption. Money laundering No cases found. Occurrence of corruption OCG activities are in a sense no different in modus operandi in rural and urban settings, and thus corruption is a tool that is used in both areas by OCGs to enable or protect their activities from LEAs (Potter & Gaines, 1992), however, no cases where OCGs utilised corruption in rural areas could be found in Slovenia despite the fact that police ADOs (n.d.-b) show that cases of corruption have OCI, and Dobovsek (2019) writes that there are numerous cases of corruption on the local level throughout Slovenia. * These (sub)categories were developed/included based on a number of earlier studies (Abadinsky, 2010; Berg & Carranza, 2018; DeVito, 2005; Eman & Bulovec, 2021; Medel & Thoumi, 2014; Thoumi, 2014; Wadsworth, 1975). **Cases that were found in the media were included only if the related article mentioned a location such as forest, green borders, etc., or buildings such as a barn, stable and so on, or the article had enough details that the location could be found on Google Maps and confirmed to be in a rural environment. *** For this table we consider only OwOCI that in ADOs (Policija, n.d.) had Natural surface in location variable. 24 Boštjan Slak, Bojan Dobovšek 3 DISCUSSION AND CONCLUSIONS While studies showed that OCGs can achieve a good operational environment in more upscale (Haysom & Shaw, 2017) or rural (Blume, 2021) areas, trademarks of communities (Potter & Cox, 1990) and environment (The Global Initiative Against Transnational Organized Crime, 2019; Haysom & Shaw, 2017) or their interplay (Acharya & Bryson Clark, 2021; Blume, 2021; Hobbs, 1998; Potter & Gaines, 1992) does have a substantial impact on the developmental and existential scope or form of organised crime in a certain location. Regarding the type of organised crime activities that occur in more rural areas, our desk research shows that such areas are used chiefly for drug production and migrant smuggling, with some instances of prostitution in nightclubs. Urban clusters remain the main locations of OCG operations. These findings echo those of Furdi (2014) in finding that OCGs operate in a dispersed manner throughout Slovenian territory, with concentrations in Slovenian cities and a pattern that OCGs operate along the national highways. And these findings are in line with Potter and Gaines's (1992) research on types and forms of OCG activities in Kentucky, where marijuana was grown in rural areas, and OCG-run prostitution was present in accessible but still off-town locations. However, due to the small size of Slovenia, a far lesser extent of these activities is present. No evidence of OCG as a governing power, such as those in Central America (Berg & Carranza, 2018; Blume, 2021) or the Balkans (The Global Initiative Against Transnational Organized Crime, 2019) was found. These findings give arguments to scholastic thought that even in globally linked crime, OCGs are influenced by local social dynamics (Hall, 2010; Hobbs, 1998), and the key to understanding organised crime is, therefore, its fluidity of actors, commodities and services within geographical space (Hall, 2010). Offences of people smuggling are on the rise, especially starting with the migration crisis developed from the Syrian war and other conflicts, escalating in 2015 to the European migration crisis. These offences are most notable along the Slovenian green border, where local communities are victims of offences that these migrants do when they travel through Slovenian border settlements. Residents of these settlements also live in fear of being attacked by them. While some profit from being guides for migrants and are connected to smuggling counterparts in other countries. Women who are employed as prostitutes in nightclubs that are located outside of Slovenian cities come from European countries or even Latin America. Therefore, even the rural environment of one of the world's smallest countries is strongly globally connected. This makes the argument that is to be agreed with Clark et al. (2021), who argue that viewing the criminal markets as urban or rural is outdated. Or with Donnermeyer (2019) who, also warns about the outdatedness of the- rural versus urban dichotomies. This is clear from our findings in which the definitions underlying the rural-urban categorisation and the level of data used strongly influence the findings on where organised crime occurs in Slovenia. When examining the rural-urban typologies of the municipalities of Slovenia in which OwOCI occurred, the highest percentage of municipalities with OwOCI fall into the category of thinly populated areas. Although only two Slovenian municipalities fall into the category of densely populated areas, they collectively 25 Do Conventional Organised Crime Groups in Slovenia Prefer ... have about 10% fewer OwOCI cases. This suggests that, despite the lower numbers, urban centers are still key locations for significant OCG activities. On the region level, once again, data shows that in international comparison, Slovenian organised crime does not happen in urban regions as none is marked as such. Therefore, in sum, while in the Slovenian context some locations where Slovenian OCGs operate can be marked as urban, yet in a global or just transnational context, it could be marked as a semi-rural or rural environments. On the other hand, for investigators and strategists who develop preventive measures, there is still value in acknowledging that something is happening in rural settings as the social, operational and other dynamics are different. Criminal intelligence is hard to obtain in rural and closer knitted communities. And so is undercover operations. Even patrolling the fields and forest demands suitable equipment, skillset and knowledge. And perpetrators are aware of this. Further and more in-depth studies should thus be made regarding the role of rural settings and organised crime in Slovenia and in general. Using geographical coordinates and the full statistical powers of GIS software tools to test the statistical significance of the location of OwOCI and proximity of highways, green borders, green areas and other elements could give more detailed and reliable results. Similarly, pairing or statistically testing OwOCI municipality or other territorial data with the related rates of unemployment, GDP, population composition, etc. would address the shortcomings of organised crime research that were identified by Windle and Silke (2019). The results of such studies, combined with the work of Sergi and Storti (2021) who noted that OCGs leave different footprints on the environments in which they operate, will operate in or otherwise engage with, could be very useful for law enforcement activities and crime prevention in general. 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Skupno poročilo o delu državnih tožilcev za leto 2020 [Joint report on the work of public prosecutors for the year 2020]. https://www.dt-rs.si/files/documents/Letno%20 poroc%CC%8Cilo%20DT%20za%20leto%202020.pdf Wadsworth, M. E. J. (1975). Delinquency in a national sample of children. British Journal of Criminology, 15(2), 167-180. Windle, J., & Silke, A. (2019). Is drawing from the state 'state of the art'?: A review of organised crime research data collection and analysis, 2004-2018. Trends in Organized Crime, 22(4), 394-413. Woodiwiss, M. (2003). Transnational organized crime: The strange career of an American concept. In M. E. Beare (Ed.), Critical reflections on transnational organized crime, money laundering and corruption (pp. 3-34). University of Toronto Press. About the Authors Boštjan Slak, Ph.D., assistant professor of Criminology at the Faculty of Criminal Justice and Security, University of Maribor, Slovenia. E-mail: bostjan. slak@um.si Bojan Dobovšek, Ph.D., full professor of Criminology at the Faculty of Criminal Justice and Security, University of Maribor, Slovenia. E-mail: bojan. dobovsek@um.si 32