
Gian Maria CampedelliFondazione Bruno Kessler | FBK
Gian Maria Campedelli
PhD
About
67
Publications
24,875
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Citations
Introduction
PhD in Criminology at Università Cattolica del Sacro Cuore, Milan (Italy). Currently: Research Scientist at Fondazione Bruno Kessler.
Former postdoc at the University of Trento, Researcher at Transcrime and visiting scholar at Carnegie Mellon University - SCS.
I work at the intersection of criminology and computational sciences.
Main areas of expertise: Organized Crime, Complex Criminal Phenomena, Terrorism, Network Science, Machine Learning, Statistical Methods.
Additional affiliations
July 2020 - present
January 2018 - July 2018
November 2016 - November 2019
Transcrime
Position
- Researcher
Education
November 2016 - February 2020
Publications
Publications (67)
Recent studies exploiting city-level time series have shown that, around the world, several crimes declined after COVID-19 containment policies have been put in place. Using data at the community-level in Chicago, this work aims to advance our understanding on how public interventions affected criminal activities at a finer spatial scale. The analy...
Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a complex networks approach. This approach will allow to find la...
While indicators assessing the quality of life often comprise measures of crime or fear of crime, these components usually refer to property or violent crimes. More complex crimes, which may significantly impact on the social, economic, and political conditions of local communities, are often overlooked, mostly due to problems in adequately measuri...
Through a novel data set comprising the criminal records of 11,138 convicted mafia offenders, we compute criminal career parameters and trajectories through group-based trajectory modeling. Mafia offenders report prolific and persistent careers (16.1 crimes over 16.5 years on average), with five distinct trajectories (low frequency, high frequency,...
Research on artificial intelligence (AI) applications has spread over many scientific disciplines. Scientists have tested the power of intelligent algorithms developed to predict (or learn from) natural, physical and social phenomena. This also applies to crime-related research problems. Nonetheless, studies that map the current state of the art at...
As Large Language Model (LLM)-based agents become increasingly autonomous and will more freely interact with each other, studying interactions between them becomes crucial to anticipate emergent phenomena and potential risks. Drawing inspiration from the widely popular Stanford Prison Experiment, we contribute to this line of research by studying i...
Does a victim's race explain variation in the likelihood of homicide clearance? Attempts to address this issue date back to the 1970s. Yet, despite its theoretical and policy relevance, we lack a comprehensive and clear empirical answer to this critical question. Here, I causally focus on this problem by investigating racial disparity in homicide c...
Mexican cartels lose many members as a result of conflict with other cartels and incarcerations. Yet, despite their losses, cartels manage to increase violence for years. We address this puzzle by leveraging data on homicides, missing persons, and incarcerations in Mexico for the past decade along with information on cartel interactions. We model r...
While countries differ in how they handle terrorism, criminal justice systems in Europe and elsewhere treat terrorism similar to other crime, with police, prosecutors, judges, courts and penal systems carrying out similar functions of investigations, apprehension, charging, convicting and overseeing punishments, respectively. We address a dearth of...
Objectives
Evaluate the effects that Prudential Center events had on crime in downtown Newark from 2007 to 2015 in terms of incident counts and spatial characteristics.
Methods
We evaluate the effects of events held at the Prudential Center on crime counts via negative binomial regression. Through the Fasano-Franceschini test, we assess whether cr...
Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackl...
Every year, Mexican cartels lose many members due to conflict with other cartels and arrests. Yet, despite their losses, cartels have managed to increase violence for years. We address this puzzle by leveraging data on the number of homicides, missing persons and arrests in Mexico for the past ten years, along with information on the interactions a...
While countries differ significantly in how they handle terrorism, in the west, criminal justice systems tend to treat terrorism similar to other crime, with police, prosecutors, judges and courts, and penal systems carrying out similar functions of investigations, apprehension, charging, convicting, and overseeing punishments respectively. While t...
Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackl...
Machine Learning for Criminology and Crime Research: At the Crossroads reviews the roots of the intersection between machine learning, artificial intelligence (AI), and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that may emerge from this relationship.
As machine lear...
Systemic police violence against Black individuals and marginalized communities in the United States has contributed to reignite the associated debate regarding the fairness, accountability, and ethics of algorithmic decision-making in policing and criminal justice. This chapter seeks to contribute to this critical debate, arguing that although alg...
This unorthodox introduction to this book not only illustrates the rationale and structure of this book; in fact, it has two specific aims. First, it seeks to contextualize the longstanding roots of the relationship between artificial intelligence (AI) and the study, analysis, and prediction of crime. Second, it provides a historical accounting on...
Building upon decades of empirical research and leveraging contamination from other fields, criminology has lately witnessed a significant increase in the use of computational methods. Machine learning could establish itself as the next substantial fracture within the methodological landscape in this field. This chapter will first discuss three of...
Understanding what phenomena cause delinquent and criminal behaviors or which policy interventions work in curbing crime have been the central goals of decades of crime research, not only within the blurred boundaries of “criminology.” This chapter first aims at reviewing the most important statistical approaches to estimate causal effects both in...
The pervasiveness of artificial intelligence (AI) in our world is continuously growing. AI not only contributes to revolutionize science in many ways: it is also part of the everyday lives of billions of people around the world through a vast spectrum of industrial applications that are now well embedded in our daily routines and lifestyles. This c...
Behaviours across terrorist groups differ based on a variety of factors, such as groups' resources or objectives. We here show that organizations can also be distinguished by network representations of their operations. We provide evidence in this direction in the frame of a computational methodology organized in two steps, exploiting data on attac...
Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Acc...
Objectives: We test the effects of four policy scenarios on recruitment into organized crime. The policy scenarios target (i) organized crime leaders and (ii) facilitators for imprisonment, (iii) provide educational and welfare support to children and their mothers while separating them from organized-crime fathers, and (iv) increase educational an...
Purpose
To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States.
Methods
First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Acco...
Background
Studies from multiple contexts conceptualize organized crime as comprising different types of criminal organizations and activities. Notwithstanding growing scientific interest and increasing number of policies aiming at preventing and punishing organized crime, little is known about the specific processes that lead to recruitment into o...
Capturing dynamics of operational similarity among terrorist groups is critical to provide actionable insights for counter-terrorism and intelligence monitoring. Yet, in spite of its theoretical and practical relevance, research addressing this problem is currently lacking. We tackle this problem proposing a novel computational framework for detect...
Capturing dynamics of operational similarity among terrorist groups is critical to provide actionable insights for counter-terrorism and intelligence monitoring. Yet, in spite of its theoretical and practical relevance, research addressing this problem is currently lacking. We tackle this problem proposing a novel computational framework for detect...
Despite growing evidence about heterogeneous pathways leading individuals into organized crime, there is limited knowledge about the differences in the criminal career between individuals who entered criminal organizations in their youth and those who joined at an older age. This study assesses the differences between early and late recruits in the...
Relying on a sample of 1,381 US-based multiple homicide offenders (MHOs), we study the duration of the careers of this extremely violent category of offenders through Kaplan–Meier estimation and Cox Proportional Hazard regression. We investigate the characteristics of such careers in terms of length and we provide an inferential analysis investigat...
The COVID-19 pandemic has unleashed multiple public health, socio-economic, and institutional crises. Measures taken to slow the spread of the virus have fostered significant strain between authorities and citizens, leading to waves of social unrest and anti-government demonstrations. We study the temporal nature of pandemic-related disorder events...
In the last 20 years, terrorism has led to hundreds of thousands of deaths and massive economic, political, and humanitarian crises in several regions of the world. Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets. F...
In the last 20 years, terrorism has led to hundreds of thousands of deaths and massive economic, political, and humanitarian crises in several regions of the world. Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets. F...
Criminal
organizations
exploit
their
presence
on
territories and local communities to recruit new workforce in order to carry out their criminal activities and business. The ability to attract individuals is crucial for maintaining power and control over the territories in which these groups are settled. This study proposes the formalization, devel...
The COVID-19 pandemic has unleashed multiple public health, socio-economic, and institutional crises. Measures taken to slow the spread of the virus have fostered significant strain between authorities and citizens, leading to waves of social unrest and anti-government demonstrations. We study the temporal nature of pandemic-related disorder events...
Recent studies exploiting city-level time series have shown that, around the world, several crimes declined after COVID-19 containment policies have been put in place. Using data at the community-level in Chicago, this work aims to advance our understanding on how public interventions affected criminal activities at a finer spatial scale. The analy...
This work investigates whether and how COVID-19 containment policies had an immediate impact on crime trends in Los Angeles. The analysis is conducted using Bayesian structural time-series and focuses on nine crime categories and on the overall crime count, daily monitored from January 1st 2017 to March 28th 2020. We concentrate on two post-interve...
Research on Artificial Intelligence (AI) applications has spread over many scientific disciplines. Scientists have tested the power of intelligent algorithms developed to predict (or learn from) natural, physical and social phenomena. This also applies to crime-related research problems. Nonetheless, studies that map the current state of the art at...
Relying on a sample of 1,394 US-based multiple homicide offenders (MHOs), we study the duration of the careers of this extremely violent category of offenders through Kaplan-Meier estimation and Cox Proportional Hazard regression. We investigate the characteristics of such careers in terms of length and we provide an inferential analysis investigat...
This chapter provides the first analyses of the criminal career of the Italian mafias members. The analysis is based on the unique Proton Mafia Member dataset, provided by the Italian Ministry of Justice, with information on all individuals who received a final conviction for mafia offences since the 1980s. The PMM includes information on more than...
This chapter presents a systematic review of the social, psychological, and economic factors relating to involvement and recruitment into organized crime groups (OCGs), including mafias, drug trafficking organizations (DTOs), gangs, and other criminal organizations. This review has three objectives: (i) identifying the most commonly reported factor...
The global spread of 2019-nCoV, a new virus belonging to the coronavirus family, forced national and local governments to apply different sets of measures aimed at containing its outbreak. Los Angeles has been one of the first cities in the United States to declare the state of emergency on March 4th, progressively issuing stronger policies involvi...
This study empirically demonstrates how governance-type organized crime groups (OCGs) operate as an enforcer against volume crimes in the communities they control and argues that their ability to mitigate volume crimes forms an integral component of controlling their territory in the long term. This is because the costs incurred from deterring othe...
Jihadist terrorism represents a global threat for societies and a challenge for scientists interested in understanding its complexity. This complexity continuously calls for developments in terrorism research. Enhancing the empirical knowledge on the phenomenon can potentially contribute to developing concrete real-world applications and, ultimatel...
This paper provides a narrative synthesis of the results of a systematic review of the social, psychological and economic factors leading to recruitment into organised crime. This is based on the analysis of evidence emerging from 47 qualitative, quantitative and mixed-method studies published in or before 2017. While the selected studies varied ma...
Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a complex networks approach. This approach will allow finding la...
Criminal organizations exploit their presence on territories and local communities to recruit new workforce in order to carry out their criminal activities and business. The ability to attract individuals is crucial for maintaining power and control over the territories in which these groups are settled. This study proposes the formalization, devel...
Objectives
This study tests whether mafias, as archetypical criminal organizations that exert control over local communities, protect their territories against ordinary criminality. Our hypothesis is that mafias have both the incentives and the capacities to supply security governance to specific territories. This is a distinctive feature of mafias...
PROTON D5.1 presents two agent-based models (ABMs), one on recruitment in
organised crime network and the other on radicalisation and terrorist
recruitment. The report presents each model in sequence, addressing the
design of the models including their theoretical framework, state of the art,
and model overview. It then outlines the calibration, va...
Criminal organizations exploit their presence on territories and local communities to recruit new workforce in order to carry out their criminal activities and business. The ability to attract individuals is crucial for maintaining power and control over the territories in which these groups are settled. This study proposes the formalization, devel...
Tactical decisions made by jihadist groups can have extremely negative impacts on societies. Studying the characteristics of their attacks over time is therefore crucial to extract relevant knowledge on their operational choices. In light of this, the present study employs transition networks to construct trails and analyze the behavioral patterns...
Finding hidden patterns represents a key task in terrorism research. In light of this, the present work seeks to test an innovative clustering algorithm designed for multi-partite networks to find communities of terrorist groups active worldwide from 1997 to 2016. This algorithm uses Gower’s coefficient of similarity as the similarity measure to cl...
Developments in statistics and computer science have influenced research on many social problems. This process also applies to the study of terrorism. In this context, network analysis is one of the most popular mathematical methods for analyzing terrorist organizations and dynamics. Nonetheless, few studies have applied network science to the anal...
This report presents the study of the criminal careers of Italian mafia members and mafia leaders, with a specific focus on recruitment. The analysis applies for the first time the criminal careers framework (firstly developed by Blumstein in 1986) to the population of individuals convicted for mafia offenses between 1982 and March 2017 provided by...
This report presents a systematic review of the social, psychological, and economic factors relating to criminalisation and recruitment to organised crime groups (OCGs). It encompasses different types of OCGs, namely mafias, drug trafficking organisations (DTOs), gangs, and a residual category including other criminal organisations.
This chapter i...