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Spatial analysis of temporal criminality evolution an environmental criminology study of crime in the Maltese Islands.
Abstract
The study, the first of its kind in the Maltese Islands, reviewed crime in a spatio-temporal aspect based on where offenders live, interact and commit crime. The study has sought to develop an understanding of the Maltese Islands’ crime within a social and landuse structure through the employment of high-end GIS tools.
A study at European and Small Islands level resulted in a relative safety-danger dynamic score model that shows that Malta is safe, though progressively decreasing in relative safety. A 40-year analysis depicted increasing crime rates as well as changes in crime categories. Findings highlight a high foreign prisoner component, highly-specific local-offender social situations with residential and poverty clustering. The findings show that the Maltese offender is male, young, a recidivist, increasingly less literate, has had a secondary education, single, unemployed and increasingly partaking to serious crimes.
Residential analysis show a preference for the harbour region where offenders live in areas characterised by poverty that have disproportionate offender concentrations when compared to their shrinking population concentration. Offences committed by convicted offenders fall within high dwelling concentrations, vacant dwelling concentrations, apartment zones and low population density areas. Offender-offence findings show that Maltese offenders commit crime close to their residence mostly travelling less than 5 km.
Reported offence analysis results in high summer rates, with specific weekend to weekday differences, concentrated in a relatively small area within the conurbation with unique hotspots in fringe recreational localities. An analysis of landuse categories identified that residential areas host the highest offence counts, particularly serious crimes, whilst retail-related crime activities directly effect neighbourhoods through distance travelled from the retail entity.
Outputs from the research include a conceptual model based on the crime, social and landuse constructs, a league-table of crime-mapping sites and the creation of a web-enabled Crimemap system for the Maltese Islands.
... The discussion that sits in the following section is based on data analysis carried out over a number of years as linked to juvenile courts and the Corradino Correctional Facilities. Data sources pertain to Testa, S., (2012), Formosa, S., (2007Formosa, S., ( , 2014, and Formosa Pace, J., (2003. ...
... Another survey, based on a digital setup was termed the MEPA employee Dark Figure of Crime Survey 1 0. The survey was distributed to 300 MEPA employees as a controlled case study where employees were asked to report any crimes over 5 years and whether they filed reports to Police. However, both the sample and the reply rate was too low to enable reliable analysis and the author decided that it would not be included, though the framework is now ready for a larger run post this-study (Formosa, 2007). were exported to .csv ...
... Based on CRISOLA (Formosa, 2007): The identification of persons at risk is calculated as based on the Crimestat methodology and the analysis of both individual benefits at NNH1, NN2 and NNH3. Each benefit was identified through its importance in the relative structural dependency aggregates and the persons-at-risk aggregates as per Figure C.25. ...
... The discussion that sits in the following section is based on data analysis carried out over a number of years as linked to juvenile courts and the Corradino Correctional Facilities. Data sources pertain to Testa, S., (2012), Formosa, S., (2007Formosa, S., ( , 2014, and Formosa Pace, J., (2003. ...
... Another survey, based on a digital setup was termed the MEPA employee Dark Figure of Crime Survey 1 0. The survey was distributed to 300 MEPA employees as a controlled case study where employees were asked to report any crimes over 5 years and whether they filed reports to Police. However, both the sample and the reply rate was too low to enable reliable analysis and the author decided that it would not be included, though the framework is now ready for a larger run post this-study (Formosa, 2007). were exported to .csv ...
... Based on CRISOLA (Formosa, 2007): The identification of persons at risk is calculated as based on the Crimestat methodology and the analysis of both individual benefits at NNH1, NN2 and NNH3. Each benefit was identified through its importance in the relative structural dependency aggregates and the persons-at-risk aggregates as per Figure C.25. ...
... The CRISOLA Model (Formosa, 2007) explores the phenomenon of crime and its relationship to social dynamics within a spatial construct (crime, social and landuse termed CRISOLA). This phenomenon of crime research through horizontal approaches were tackled through a project entitled 'JANUS: The Spatial and Socio-Physical Faces of Crime -a hotspot approach to crime mitigation' funded through the ISEC Programme 2009 Action Grants 'Prevention of and Fight Against Crime'. ...
... The study of a thematic aspect such as crime cannot be isolated from the interactive aspects it is embedded in, as crime does not stand alone: it interacts within a wider and more complex environment. Formosa (2007) identified various elements that are required for the understanding of crime and its horizontal inputs and outputs. The need to bring together each aspect and build a mindmap that helps set out a process to depict a basic and generic model on how crime, social and landuse issues interact together was identified. ...
... The third period pertains to the shoulder week following the festa conclusion, which period pertains to the festa xalata and the cleanup/dismantling period. The process entailed the statistical analysis of the observed offences as against the expected offences, which methodological process is based on the Craglia et al. (2001) method as enhanced within the Maltese context in Formosa (2007). ...
... The discussion that sits in the following section is based on data analysis carried out over a number of years as linked to juvenile courts and the Corradino Correctional Facilities. Data sources pertain to Testa, S., (2012), Formosa, S., (2007Formosa, S., ( , 2014, and Formosa Pace, J., (2003. ...
... Another survey, based on a digital setup was termed the MEPA employee Dark Figure of Crime Survey 1 0. The survey was distributed to 300 MEPA employees as a controlled case study where employees were asked to report any crimes over 5 years and whether they filed reports to Police. However, both the sample and the reply rate was too low to enable reliable analysis and the author decided that it would not be included, though the framework is now ready for a larger run post this-study (Formosa, 2007). were exported to .csv ...
... Based on CRISOLA (Formosa, 2007): The identification of persons at risk is calculated as based on the Crimestat methodology and the analysis of both individual benefits at NNH1, NN2 and NNH3. Each benefit was identified through its importance in the relative structural dependency aggregates and the persons-at-risk aggregates as per Figure C.25. ...
Criminal career research shows that the early age of onset of offending is that of between 8 and 14 years old, whilst offending prevalence peaks between 15 and 19 years. On the other hand, adolescents desist at the age of 20 to the age of 29 thus many children manifesting antisocial tendencies enter adulthood in a conformist way. Desistence has been attribution to marriage, job satisfaction, and internal migration to better neighborhoods or even joining the military. If a child “steps off on the wrong foot” and remains on this unconventional path, the consequences may be perpetuated by persistent offending. In such a situation, it is difficult to make up for lost opportunities in acquiring conventional skills such as academic skills. All children deserve access to a good education that will enable them to find work and develop their potential. Unfortunately, often prisons do not have as their primary focus education but rather focus on security, community safety and incapacitation of offenders. However, if society expects these youths to be able to secure employment, they must leave the custodial centres with some form of qualifications and discipline that enables them to find, secure and maintain a job. Adolescents come to the attention of the juvenile justice system when the police arrest them, and when their deviant behaviour becomes frequent and serious they are referred to sittings at the juvenile court (Lundman, 1993). From these intakes probation officers and social workers become involved and have to take decisions as well as look after juveniles who will be brought before the Maltese Juvenile Court. Malta has one Juvenile Court that was set up in 1986. Consequently those offenders who are less than 16 years of age are prosecuted before the Juvenile Court where they are treated differently, guaranteed special protection, with hearings closed to the public (Juvenile Court Act: Chapter 287 of the Laws of Malta, 1980). The only rehabilitative measures at the disposition of the Law Courts include a probation order and a conditional discharge which are served within the community. On the other hand, for a relatively small percentage of adolescent offenders detention is deemed necessary by the Law Courts. These include those adolescents who have committed a crime which is sanctioned by an imprisonment term; who are also likely to classify as being at risk themselves and/or posit a risk to the community. These children tend to be vulnerable and are exposed to inherent risks related to anti-social tendencies, delinquency and crime. This said, Corradino Correctional Facility (CCF) being the only prison setting is thus the only secure detention setting which could cater for this. In other words, the Law Courts have no other alternative secure setting where to “place” adolescents engaged in crime. However, YOURS (reallocated at Mtahleb since 2013) hosts a wider spectrum of youths since the age threshold within this specialised unit is that of 21 years. In other words such a setting cannot cater for the educational and rehabilitative needs of vulnerable minors (less than 16).
... The CRISOLA Model (Formosa, 2007) explores the phenomenon of crime and its relationship to social dynamics within a spatial construct (crime, social and landuse termed CRISOLA). This phenomenon of crime research through horizontal approaches were tackled through a project entitled 'JANUS: The Spatial and Socio-Physical Faces of Crime -a hotspot approach to crime mitigation' funded through the ISEC Programme 2009 Action Grants 'Prevention of and Fight Against Crime'. ...
... The study of a thematic aspect such as crime cannot be isolated from the interactive aspects it is embedded in, as crime does not stand alone: it interacts within a wider and more complex environment. Formosa (2007) identified various elements that are required for the understanding of crime and its horizontal inputs and outputs. The need to bring together each aspect and build a mindmap that helps set out a process to depict a basic and generic model on how crime, social and landuse issues interact together was identified. ...
... The third period pertains to the shoulder week following the festa conclusion, which period pertains to the festa xalata and the cleanup/dismantling period. The process entailed the statistical analysis of the observed offences as against the expected offences, which methodological process is based on the Craglia et al. (2001) method as enhanced within the Maltese context in Formosa (2007). ...
Social interactionism occurs in space-time where the phenomenon morphs from the thematic activity to the geographical space it occurs in and the spatial relationships between the actors. The Maltese festa posits a ripe scenario for analysis of such interactionism and the inherent effects on safety and security. Whilst the fundamental festa scope banks on the sacred aspect, the activity moved through a village centre to a wider interactive (entire town) secular reality. This study investigates the occurrence of offending during the festa and shoulder weeks for potential relationships between the spaces relevant to the activity through a study of expected and observed offences. The CRISOLA model serves as the basis for this study in the fields of crime, social issues and landuse and their impacts on safety and security within the villages hosting the festa. The festa as cause of crime by the relevant parties and significant others and its impact on social cohesion and operational requirements serves as basis for proactive measures to be taken by the religious, secular and enforcement entities.
... Spatial data analysis is therefore capable to bring latent or new patterns to the fore, to get insights into spatial processes, to infer broader trends and to generalise to larger realities from smaller manageable samples in a reproducible and explicit fashion, and to ultimately turn information into useful knowledge (Baddeley, Rubak, & Turner, 2016;Conolly & Lake, 2006;O'Sullivan & Perry, 2013;O'Sullivan & Unwin, 2010;Rogerson, 2001). As such, GIS and spatial data analysis prove crucial to research fields as diverse as environment and earth science (Formosa, 2015;Tian, 2017), ecology (Wiegand & Moloney, 2014), agriculture (Plant, 2012, public health (Baluci, Vincenti, Conchin, Formosa, & Grech, 2013;Kurland & Gorr, 2014), socio-economic (Wang, 2014) and forensic disciplines (Elmes, Roedl, & Conley, 2014), crime studies (Chainey & Ratcliffe, 2005;Formosa, 2007), archaeology/anthropology (Conolly & Lake, 2006;Nakoinz & Knitter, 2016;Wheatley & Gillings, 2002). ...
... Mapping landscapes through photogrammetry, laser-mapping and GPR were attempted through a conceptual view to creating substantially detailed models that could be modelled in a 3D environment that enables users to explore otherwise unavailable landscapes (Lovett et al, 2015). The porting of such outputs to other domains such as offender studies (Formosa Pace, 2014), offence studies (Formosa, 2007), crime scene generation, inaccessible spaces analysis (Obanawa et al, 2014), erosion studies and heritage outputs during covid-19 closures proved the affectivity of such processes (Bustillo et al, 2015)). ...
The Digital Transformation of the Real World entails the need to move from analogue to digital to virtual in an attempt to recreate that reality into a digital twin that is enhanced through multi-domain, multi-disciplinary integration systems. The What factor in the W6H model (What, Why, Who, Where, When, How and Why Not) takes central stage in this publication through the plethora of research studies that pivot around the location kernel. The past two decades have been dedicated by scholars and in turn society to employing a bottom-up approach to the concept of digitisation and digitalisation. Through data, information and in turn knowledge, action can now be taken up by policy-makers and decision-takers to ensure that all are ready for a new research and analytical operand: an operand that pivots on the Digital Transformation through strategic, operational and tactical activities as the fourth industrial revolution takes hold (Lachvajderova at al (2021); Vrana et al, (2022).
This publication is the result of decades of collaboration in the academic, operational and strategic domains as exemplified by over 40 authors, each an established researcher in their own domain. What they succeeded in doing, is the bringing together of a diversified approach to integrative efforts for the spatial and virtualization resultant from the GI-based approach to spatial relationships to integration and immersion.
The publication contains twenty-four chapters authored by the SIntegraM and
SpatialTrain project contributors: a tsunami of information gleaned from data and in turn knowledge readied for positive action in the physical, natural and social environments. The publication which is the third of three books is categorised in four domains: Spatial technologies: GI and its real-life application, Project based Approaches: Benefits gained from National, EU and International Projects Domain, Thematic: Environmental, Social and Development Planning Domains, Emergent Realities: Virtuality, Augmented Reality and Innovation Domain. As in all three publications, such chapters can be recategorised by method, delivery, analytics and other facets, each sustained by the other domains as they seek fruition towards integration and eventually wellbeing.
... GIS provides facilities to acquire spatial data, to integrate them in a common framework, and to meaningfully organize and interrelate spatial information. GIS and spatial data analysis prove crucial to research fields as diverse as environment and earth science [3,4], ecology [5], agriculture [6], public health [7,8], socio-economic [9] and forensic disciplines [10], crime studies [11,12], archaeology/anthropology [1,[13][14][15]. One of the aspects that GIS may help exploring is understanding the ways in which movement relates and engages with the surrounding space. ...
Cost-surface and least-cost path analyses are widely used tools to understand the ways in which movement relates and engages with the surrounding space. They are employed in research fields as diverse as the analysis of travel corridors, land accessibility, site locations, maritime pathways, animal seascape connectivity, transportation, search and rescue operations. This work describes the ‘movecost’ package, designed for the free R statistical environment, which provides the facility to produce, in a relatively straightforward way, various accumulated slope-dependent cost surfaces and least-cost outputs from different models of movement across the terrain. The package motivation and significance are described, and the main software characteristics are outlined by means of an illustrative example.
... The setting up of the procedural structures enabled the author to investigate the potential for the creation of a SEIS based on a criminological construct relative to environmental criminology theory also known as urban ecology. As a case study to investigate the integration of data from the various themes into one integrated system, the CRISOLA (crime, social, landuse) model (Formosa, 2007) was implemented based on the SEIS design. The model reviews the different urban, social and crime data and integrates them into a spatial structure which delivered some interesting cross-thematic results. ...
The study of urban ecology cannot be separated from geographical space; however the limitation of access to spatio-temporal information is a reality. Creating a crime information system for the Maltese Islands has entailed bridging the gap between analogue social information and spatial planning information which rarely talk. This paper covers the process employed to initiate an understanding of the legislative and operational tools available to crime and security geographers through to the preparation for the launching of country-wide baseline datasets for effective future socio-technic analysis. The decade long process to implement a major project using ERDF funds is at the final stages prior to the initiation of cross-thematic studies that span the physical and social domains. Both environmental and green criminology is now set to take off employing one of the most comprehensive GI systems spanning urban and rural offences (person and property-oriented), census data together with the natural, social and physical environments. The study reveals issues on access to data, mitigating processes undertaken and the forward planning initiatives to ensure free dissemination of environmental data to the academic and general public. Initial studies based on the analysis of crimemaps, poverty and crimes related to the environment show correlation between the different social and geographical spaces.
With few ground-breaking exceptions, mainly framed in the context of archaeological research, GIS and quantitative methods have not been used so far in Malta to better understand the development and layered making of the landscape in relatively recent historical times. The present work aims at describing the main achievements of the authors’ research (developed in the context of the five-year ERC-funded FRAGSUS project) into the Maltese economic and historical landscape and will provide insights into how modern GIS-based quantitative approaches can be used in conjunction with qualitative data (for instance, cadastral maps and ethnographic accounts) to shed light on human-environment interaction in Malta during the last two centuries.
Information dynamics revolve around the W6H factors: What, Who, Why, When, Where, How and Why Not? Research focuses on one, two or all such factors. GIS and the study of spatial science revolves around the Where Factor. Locational science combines the entire data cycle: design, capture, cleaning, analysis, outputs and dissemination. Each step revolving around the issue of location: a unique space that inhabits a singular x,y coordinate upon which attribute or thematic data is sewed. This combination of a point in space and the relative thematic elements creates information: information refers to the meaning given to a datum: each on its own does not necessarily garner insight but once wed, the result depicts an information module which combined further with context renders knowledge and in turn wisdom and potentially action.
The publication contains three dedicated editorial papers and sixteen chapters authored by the SpatialTrain contributors: new knowledge categorized in four domains: Cultural Heritage, Social Wellbeing, Infrastructure and Spatial Planning and Ancillary Domains.
Criminal's planning a quick getaway will have to pit their wits against a GIS in future. A new system, set up by Barcelona police is helping to calculate escape routes from crime locations.
The contents of the second paper on "Urban Crime Areas" may be subsumed under the following major divisions: first, the relationship between the spatial distribution of crime and natural areas in the large city; second, generalized patterns of crime as measured by gradients and by isopleth techniques; third, consideration of the relative constancy and uniformity of crime patterns from one place to another and over a period of time; fourth, exploration of the utility of typological techniques in the analysis of urban crime areas; finally, theoretical and methodological conclusions, especially with reference to certain hypotheses pertaining to the ecology of crime.
Three distinct but related models of fear of crime have emerged in the research literature: victimization, disorder, and community concern/community control. Of the three, the community concern/community control model remains least developed. This study seeks to extend the research by including several distinct dimensions of the community concerns model, along with variables drawn from the victimization and disorder frameworks, in an overall model of fear of crime. The model then is analyzed with data from a random sample of residents from a northwestern U.S. city. The inclusion of variables from all three perspectives results in a model that accounts for a substantial amount of the variance in fear of crime. The model next is examined for three subsamples of respondents drawn from low-, medium-, and high-disorder neighborhoods. Finally, policy implications, particularly for the community policing movement, are discussed.
Various authors have tested for the presence of a relationship between unemployment and crime, employing time-series or cross-sectional data. However, little use has been made of the British Crime Survey. Consequently, in the present paper, we test for the presence of a relationship between male unemployment and crime, using regional and area-level crime data from the 1992 British Crime Survey, with socio-economic and demographic data being taken from the 1991 census. Use of these data sets allows us to look at four differing measures of crime: we can study possible relationships between male unemployment and the incidence, and prevalence, of both property and personal crimes. Area-level data are also employed to test more ecological explanations of spatial crime patterns.
This book presents the results of 20 years of ecological research into the nature of the relationship between the distribution of delinquency and the pattern of physical structure and social organization of 21 American cities. Uniform findings in every city confirm the hypothesis that the physical deterioration of residential areas accompanied by social disorganization is greatest in a central zone in the business district, intermediate in a middle zone, and lowest in the other zones, and that there is a progressive decline in the incidence of delinquency from the innermost zone where it is most concentrated to the peripheral areas. Delinquency is found to be highly correlated with changes in population, inadequate housing, poverty, presence of Negroes and foreign-born, tuberculosis, mental disorders, and adult criminality. The common basic factor is social disorganization or the absence of community effort to cope with these conditions. Causation of juvenile delinquency is to be sought more in terms of the community than of the individual. 107 maps pertaining to the cities studied and 118 tables relating to population and delinquency rates are included as well as a chapter describing the Chicago Area Project as a demonstration of the effective mobilization of community forces to combat delinquency and crime. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Research and theorizing about the fear of crime has, in the main, been dominated by researchers who have relied upon sociological or socio-demographic variables to account for variations in fear levels. Whilst this body of work has contributed greatly to our understanding of the fear of crime, we are still far from a full understanding of this important and most corrosive aspect of contemporary society. This article re-examines what is currently just about the only social psychological model of the fear of crime. By incorporating social psychological and socio-demographic variables, the authors are able to account for approximately one third of the variance of the fear of crime.