Selected Works

Reducing cartel recruitment is the only way to lower violence in Mexico

with  Rafael Prieto-Curiel (Complexity Science Hub) and Alejandro Hope (Independent, deceased). Published in Science (https://www.science.org/doi/10.1126/science.adh2888).

See commentary by Caulkins, Kilmer and Reuter: https://www.science.org/doi/10.1126/science.adj8911 

Abstract: 

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 recruitment, state incapacitation, conflict, and saturation as sources of cartel size variation. Results show that by 2022, cartels counted 160,000 to 185,000 units, becoming one of the country’s top employers. Recruiting between 350 and 370 people per week is essential to avoid their collapse because of aggregate losses. Furthermore, we show that increasing incapacitation would increase both homicides and cartel members. Conversely, reducing recruitment could substantially curtail violence and lower cartel size.

Organized Crime, Violence and Support for the State

with  Gianmarco Daniele (University of Milan & Bocconi University), Andy Martinangeli (Burgundy School of Business) and Paolo Pinotti (Bocconi University) - Published in the Journal of Public Economics (https://www.sciencedirect.com/science/article/abs/pii/S0047272723002116 ).

Abstract: 

The pervasive violence perpetrated by criminal organizations poses a substantial threat to numerous countries around the world. This study investigates the influence of organized crime violence on public perceptions of organized crime and support for the state. To address this unexplored issue, we conducted an experimental study with a representative sample of 6,000 individuals in Italy, a country with a historical presence of organized crime. Participants' perceptions of crime trends diverged significantly from actual data, overestimating a 26.6% increase in overall homicides and a 17.3% rise in organized crime-related killings over two decades, while data showed a reduction of approximately two-thirds. Exposure to journalistic depictions of violence related to organized crime led to increased trust in state institutions and perception of state performance, irrespective of factual corrections. These findings underscore the potential impact of media narratives on shaping public attitudes toward crime and state authorities.

Recruitment into Organized Crime: an Agent-Based Approach Testing the Impact of Different Policies

with Francesco Calderoni  (Università Cattolica), Aron Szekely (Collegio Carlo Alberto) , Mario Paolucci (Italian National Research Council) and Giulia Andrighetto (Italian National Research Council) - Published in Journal of Quantitative Criminology (https://link.springer.com/article/10.1007/s10940-020-09489-z  ).

Abstract: 

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 and social support to at-risk schoolchildren.

Methods

We developed a novel agent-based model drawing on theories of peer effects (differential association, social learning), social embeddedness of organized crime, and the general theory of crime. Agents are simultaneously embedded in multiple social networks (household, kinship, school, work, friends, and co-offending) and possess heterogeneous individual attributes. Relational and individual attributes determine the probability of offending. Co-offending with organized crime members determines recruitment into the criminal group. All the main parameters are calibrated on data from Palermo or Sicily (Italy). We test the effect of the four policy scenarios against a baseline no-intervention scenario on the number of newly recruited and total organized crime members using Generalized Estimating Equations models.

Results

The simulations generate realistic outcomes, with relatively stable organized crime membership and crime rates. All simulated policy interventions reduce the total number of members, whereas all but primary socialization reduce newly recruited members. The intensity of the effects, however, varies across dependent variables and models.

Conclusions

Agent-based models effectively enable to develop theoretically driven and empirically calibrated simulations of organized crime. The simulations can fill the gaps in evaluation research in the field of organized crime and allow us to test different policies in different environmental contexts.

Criminal Careers Prior to Recruitment into Italian Organized Crime

with Cecilia Meneghini (Università Cattolica & Cambridge University), Francesco Calderoni  (Università Cattolica), and Tommaso Comunale (Centre for the Study of Democracy) - Published in Crime & Delinquency (https://journals.sagepub.com/doi/abs/10.1177/00111287211035994 , pre-print available at: https://www.crimrxiv.com/pub/xz4ynah9/release/1 ).

Abstract: 

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 Italian mafias through logistic regressions considering several criminal career parameters computed on the period prior to recruitment. Results show that recruitment in the mafias is far from a homogenous process. Early recruits report an early criminal onset, lower educational attainment, more serious offenses within a shorter time-span, and more frequent violent co-offending; late recruits show a later onset, more prolific and versatile—but less serious—offending.

Life-Course Criminal Trajectories of Mafia Members

with Francesco Calderoni  (Università Cattolica), Tommaso Comunale  (Università Cattolica), Cecilia Meneghini  (Università Cattolica) - Published in Crime & Delinquency (https://journals.sagepub.com/doi/full/10.1177/0011128719860834 ).

Abstract: Through a novel dataset comprising the criminal records of 11,138 convicted mafia offenders, we compute criminal career parameters and trajectories through Group Based Trajectory Modelling. Mafia offenders report prolific and long spanning criminal careers (16.1 crimes over 16.5 years on average), and the sample yielded five distinct trajectories comprising a low frequency, a high frequency, an early starter, a moderate persistence, and a high persistence group. While showing similarities with those of general offenders, we discuss different interpretations of the criminal careers based on the organized crime literature. The long-lasting and active careers of mafia offenders show that involvement into organized crime is a negative turning point with significant impact on the individuals’ lives.

Security Governance: Mafia Control over Ordinary Crimes 

with Alberto Aziani (Università Cattolica) and Serena Favarin (Università Cattolica) - Published in the Journal of Research in Crime and Delinquency (https://journals.sagepub.com/eprint/GZENH97QTKG7RBBXWHGT/full).

Abstract:  

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 that deserves to be considered.

Method:

To understand whether mafias’ territorial control is associated with lower levels of ordinary criminality, we conduct a panel data analysis on 110 Italian provinces (2004 to 2015). System generalized method of moment and Driscoll–Kraay standard errors are performed to test our hypothesis. This study exploits an aggregated measure of thefts, robberies, and assaults as dependent variable. A standardized index derived from the number of active mafia groups in a province is our proxy of mafia control.

Results:

The article statistically shows that mafias limit ordinary criminality, whereas less stable and unstructured criminal groups do not.

Conclusions:

The results indicate that crime prevention and the maintenance of public order should be considered among the pillars of mafia’s governance. By controlling and reducing ordinary crimes, mafias overcome the role of law enforcement and institutional justice increasing consensus among the population. Consequently, the state may better contrast mafias by becoming a stronger supplier of security.

A Security Paradox. The Influence of Governance-type Organized Crime Over the Surrounding Criminal Environment 

with Alberto Aziani (Università Cattolica) and Serena Favarin (Università Cattolica). Accepted for publication at the British Journal of Criminology

Abstract:  This study empirically demonstrates how governance-type organised 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 other crimes are offset by the tangible and intangible revenues that it facilitates. Indeed, combating volume crimes fosters an environment in which OCGs can conduct their activities unfettered by other criminals and law enforcement agencies, safeguard those businesses that pay them protection, and curry favour amongst the population. Consequently, the present study verifies the validity of the security governance paradigm by conducting an econometric analysis of eleven different volume crimes.

Systematic Review of the Social, Psychological and Economic Factors Relating to Involvement and Recruitment into Organized Crime 

with Francesco Calderoni, Tommaso Comunale, Elisa Superchi, Martina Elena Marchesi (all Università Cattolica). Chapter in the book "Understanding Recruitment to Organized Crime and Terrorism: Social, Psychological and Economic Drivers" (Springer), edited by David Weisburd, Badi Hasisi, Ernesto U. Savona, Badi Hasisi, Francesco Calderoni. Link: https://link.springer.com/chapter/10.1007/978-3-030-36639-1_8 

Abstract: 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 factors leading to recruitment into OCGs, (ii.) highlighting whether these factors are independent of one other or they are correlated, and (iii.) assessing the validity and generalizability of research findings. The review searched all possible relevant studies indexed in selected databases and published in five languages (i.e. English, French, German, Italian, and Spanish), without limitations as to their year of publication or geographic origin. Starting from 48,731 potentially eligible records, integrated with experts’ suggestions, this systematic review includes 47 empirical studies employing quantitative, qualitative, or mixed-methods approaches. The findings show that social and economic factors are the most commonly reported factors relating to recruitment into OCGs, while psychological factors are marginal. Although factors are highly interrelated and shared across OCGs, their predominance varies across types of criminal organizations. For instance, individuals join mafias and gangs attracted by strong group identity, whereas individuals enter DTOs mainly for financial gain. In light of these findings, recommendations for future research and implications for prevention policies are discussed.

The Criminal Careers of Italian Mafia Members 

with Ernesto Ugo Savona, Francesco Calderoni, Tommaso Comunale, Cecilia Meneghini , Marco Ferrarini (all Università Cattolica) . Chapter in the book "Understanding Recruitment to Organized Crime and Terrorism: Social, Psychological and Economic Drivers" (Springer), edited by David Weisburd, Badi Hasisi, Ernesto U. Savona, Badi Hasisi, Francesco Calderoni. Link: https://link.springer.com/chapter/10.1007/978-3-030-36639-1_10 

Abstract: 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 11 thousand individuals and 182 thousand offences. The study explores the career of mafia members following a three-level approach, analyzing the macro, meso, and micro dimensions of the criminal careers of the mafiosi. At the macro level, Italian mafias’ member show different types of criminal trajectories, with a significant portion of the sample exhibiting a late onset and late persistence pattern. At the meso level, the four main types of mafias (the Sicilian Cosa Nostra, the Neapolitan Camorra, the Calabrian ‘Ndrangheta, and the Apulian mafias) report very similar traits although some distinctive patterns emerge. At the micro level, there are differences in the criminal career between early- and lately- recruited member, with the former showing higher frequency of violent, volume crime and the latter a more complex, white collar profile. A further exploration of the PMM data shows an escalation in both the number and the seriousness of crimes before joining the mafias, which subsequently stabilize afterwards.  

Campbell Collaboration Protocol: Organised crime groups: A systematic review of individual-level risk factors related to recruitment 

(with Francesco Calderoni,  Elisa Superchi, Tommaso Comunale, Martina Elena Marchesi) - Published in Campbell Systematic Reviews Journal (https://onlinelibrary.wiley.com/doi/epdf/10.1002/cl2.1022 ). Link to the Title Registration:  https://www.campbellcollaboration.org/library/organised-crime-recruitment-risk-factors.html  

Arrests and convictions but not sentence length deter terrorism in 28 European Union member states 

with Michael Wolfowicz (Hebrew University of Jerusalem), Amber Seaward (University College London) and Paul Gill (University College London). Published in Nature Human Behaviour (https://www.nature.com/articles/s41562-023-01695-6  ).

Abstract:  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 research on potential deterrent effects against terrorism by analysing data on terrorism offending, arrests, charges, convictions and sentencing over 16 years in 28 European Union member states. Applying both count and dynamic panel data models across multiple specifications, we find that increased probability of apprehension and punishment demonstrate an inverse relationship with terrorism offending, while the rate of charged individuals is associated with a small increase in terrorism. The results for sentence length are less clear but also indicate potential backlash effects. These findings unveil overlaps between crime and terrorism in terms of deterrent effects and have implications for both the research agenda and policy discussion.

The geometrical shapes of violence: predicting and explaining terrorist operations through graph embeddings

with Janet Layne (Boise State University),  Jack Herzoff (Boise State University) and Edoardo Serra (Boise State University). Published in the Journal of Complex Networks (https://academic.oup.com/comnet/article/10/2/cnac008/6564024 ).

Abstract:  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 attacks plotted by Al Shabaab, Boko Haram, the Islamic State and the Taliban in the 2013–2018 period. First, we present LabeledSparseStruct⁠, a graph embedding approach, to predict the group associated with each operational meta-graph. Second, we introduce SparseStructExplanation⁠, an algorithmic explainer based on LabeledSparseStruct, that disentangles characterizing features for each organization, enhancing interpretability at the dyadic level. We demonstrate that groups can be discriminated according to the structure and topology of their operational meta-graphs, and that each organization is characterized by the recurrence of specific dyadic interactions among event features.

Learning future terrorist targets through temporal meta-graphs 

with Mihovil Bartulovic  (Carnegie Mellon University) and Kathleen M. Carley  (Carnegie Mellon University) - Published in Scientific Reports (https://www.nature.com/articles/s41598-021-87709-7 ).

Abstract:  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. Focusing on three event dimensions, i.e., employed weapons, deployed tactics and chosen targets, meta-graphs map the connections among temporally close attacks, capturing their operational similarities and dependencies. From these temporal meta-graphs, we derive 2-day-based time series that measure the centrality of each feature within each dimension over time. Formulating the problem in the context of the strategic behavior of terrorist actors, these multivariate temporal sequences are then utilized to learn what target types are at the highest risk of being chosen. The paper makes two contributions. First, it demonstrates that engineering the feature space via temporal meta-graphs produces richer knowledge than shallow time-series that only rely on frequency of feature occurrences. Second, the performed experiments reveal that bi-directional LSTM networks achieve superior forecasting performance compared to other algorithms, calling for future research aiming at fully discovering the potential of artificial intelligence to counter terrorist violence.

A Complex Networks Approach to Find Latent Clusters of Terrorist Groups 

with Iain Cruickshank  (Carnegie Mellon University) and Kathleen M. Carley  (Carnegie Mellon University) - Published in Applied Network Science (https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0184-6 ).

Abstract:  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 latent clusters of similar terror groups using information on their operational characteristics. Specifically, using open access data of terrorist attacks occurred worldwide from 1997 to 2016, we build a multi-partite network that includes terrorist groups and related information on tactics, weapons, targets, active regions. We propose a novel algorithm for cluster formation that expands our earlier work that solely used Gower’s coefficient of similarity via the application of Von Neumann entropy for mode-weighting. This novel approach is compared with our previous Gower-based method and a heuristic clustering technique that only focuses on groups’ ideologies. The comparative analysis demonstrates that the entropy-based approach tends to reliably reflect the structure of the data that naturally emerges from the baseline Gower-based method. Additionally, it provides interesting results in terms of behavioral and ideological characteristics of terrorist groups. We furthermore show that the ideology-based procedure tend to distort or hide existing patterns. Among the main statistical results, our work reveals that groups belonging to opposite ideologies can share very common behaviors and that Islamist/jihadist groups hold peculiar behavioral characteristics with respect to the others. Limitations and potential work directions are also discussed, introducing the idea of a dynamic entropy-based framework. 

Pairwise Similarity of Jihadist Groups in Target and Weapon Transitions 

with Mihovil Bartulovic  (Carnegie Mellon University) and Kathleen M. Carley  (Carnegie Mellon University)- Published in Journal of Computational Social Science (https://link.springer.com/article/10.1007/s42001-019-00046-8 ).

Abstract: 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, this study employs transition networks to construct trails and analyze the behavioral patterns of the world's five most active jihadist groups using open access data on terror attacks from 2001 to 2016. Within this frame, we propose Normalized Transition Similarity (NTS), a measure that capture groups' pairwise similarity in terms of transitions between different temporally ordered sequences of states. For each group, these states respectively map attacked targets, employed weapons, and targets and weapons combined together with respect to the entire sequence of attacks. Analyses show a degree of stability of results among a number of pairs of groups across all trails. With this regard, Al Qaeda and Al Shabaab show the highest NTS scores, while Taliban and Al Qaeda prove to be the most different groups overall. Finally, future work directions are also discussed. 

On Terrorism Memory and Predictability via Graph-Derived Multivariate Time Series: Integrating Meta-Networks and Deep Learning 

(single Author). Presented at: 2019 International Conference on Computational Social Science (IC2S2). 

Synopsis:  I here propose an hybrid framework that feeds graph-derived multivariate time series into recurrent neural networks to predict most risky targets in 3-day time window units. With respect to more ordinary statistical techniques for time series, the aforementioned deep learning architectures can more easily handle non-linearity and are more powerful when dealing with complex data structures. The intuition behind the methodology of this work lies in two hypotheses: 

1. Taking into account the interdependent relations between attack characteristics via a dynamic meta-network approach provides an information structure which is capable of illuminating hidden patterns across multiple event dimensions;

2. If memory-like processes exist also in terms of event characteristics beyond mere event spatio-temporal clustering, algorithmic techniques should be explored and tested to learn patterns and forecast terrorism in a richer fashion.

Using data on attacks retrieved from the Global Terrorism Database (N= 14,297), a complex multi-mode network for each of the the world's five most active jihadist groups from 1970 to 2016 is created. These groups are the Islamic State, Taliban, Boko Haram, Al Shabaab and Al Qaeda. The modes of the groups' networks include data on employed tactics and weapons, attacked countries and hit targets. Each group's overall multi-mode network is then decomposed into three-day time units and every bipartite matrix resulting from the disaggregation is multiplied by its transpose in order to obtain one-mode weighted undirected networks: degree centrality is computed and employed as a proxy of the importance of a given entity (e.g. Firearms in the Weapons matrix) within each time window. The process allows then to extract multivariate time series of the centrality of each node for all groups that are subsequently fed into three types of neural networks: Simple Recurrent Neural Networks (SRNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

Specifically, this work uses multivariate time series of Weapon, Tactic and Country modes as the components of the feature space created to learn and forecast the target types that are at the highest risk of getting attacked in the next time step. Although the neural networks learn the continuous centrality values, the performance of the algorithms is assessed considering only the centrality-based rank of the most probable targets, regardless of the error between the real and predicted centrality values.

Detecting Latent Terrorist Communities Testing a Gower’s Similarity-Based Clustering Algorithm for Multi-partite Networks 

with Iain Cruickshank  (Carnegie Mellon University) and Kathleen M. Carley  (Carnegie Mellon University). Published: Proceedings of 2018 Complex Networks and Their Applications. Link: https://link.springer.com/chapter/10.1007/978-3-030-05411-3_24  

Abstract: 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 cluster perpetrators. Data include information on weapons, tactics, targets, and active regions. We show how different dimensional weighting schemes lead to different types of grouping, and we therefore concentrate on the outcomes of the unweighted algorithm to highlight interesting patterns naturally emerging from the data. We highlight that groups belonging to different ideologies actually share very common behaviors. Finally, future work directions are discussed. 

Complex Networks for Terrorist Target Prediction 

with Iain Cruickshank (Carnegie Mellon University) and Kathleen M. Carley  (Carnegie Mellon University). Published: Proceedings of 2018 International Conference on Social Computing, Behavioral- Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS). Link: https://link.springer.com/chapter/10.1007%2F978-3-319-93372-6_38 

Abstract: 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 analysis of terrorist events. Therefore, in this work we first introduce a novel method to analyze the heterogeneous dynamics of terrorist attacks through the creation of a dynamic meta-network of terror for the period 1997–2016. Second, we use our terrorist meta-network to test the power of Network-based Inference algorithm in predicting terrorist targets. Results are promising and show how this algorithm reaches high levels of precision, accuracy, and recall and indicate that network outcomes can be used in broader machine learning models. 

Disentangling community-level changes in crime trends during the COVID-19 pandemic in Chicago

with Serena Favarin (Università Cattolica), Alberto Aziani (Università Cattolica) and Alex R. Piquero (University of Miami and Monash University). Published: Crime Science . Link: https://link.springer.com/article/10.1186/s40163-020-00131-8 

Abstract: 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 analysis relies on a two-step methodology. First, it estimates the community-wise causal impact of social distancing and shelter-in-place policies adopted in Chicago via Structural Bayesian Time-Series across four crime categories (i.e., burglary, assault, narcotics-related offenses, and robbery). Once the models detected the direction, magnitude and significance of the trend changes, Firth’s Logistic Regression is used to investigate the factors associated to the statistically significant crime reduction found in the first step of the analyses. Statistical results first show that changes in crime trends differ across communities and crime types. This suggests that beyond the results of aggregate models lies a complex picture characterized by diverging patterns. Second, regression models provide mixed findings regarding the correlates associated with significant crime reduction: several relations have opposite directions across crimes with population being the only factor that is stably and positively associated with significant crime reduction.

Exploring the Immediate Effects of COVID-19 Containment Policies on Crime: an Empirical Analysis of the Short-Term Aftermath in Los Angeles

with Alberto Aziani (Università Cattolica) and Serena Favarin (Università Cattolica). Published: American Journal of Criminal Justice . Link: https://link.springer.com/article/10.1007/s12103-020-09578-6 

Abstract: 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-intervention time windows—from March 4th to March 16th and from March 4th to March 28th 2020—to dynamically assess the short-term effects of mild and strict policies. In Los Angeles, overall crime has significantly decreased, as well as robbery, shoplifting, theft, and battery. No significant effect has been detected for vehicle theft, burglary, assault with a deadly weapon, intimate partner assault, and homicide. Results suggest that, in the first weeks after the interventions are put in place, social distancing impacts more directly on instrumental and less serious crimes. Policy implications are also discussed.

Temporal Clustering of Disorder Events During the COVID-19 Pandemic

with Maria Rita D'Orsogna (UCLA and CSUN). Published: ArXiv . Link: https://arxiv.org/abs/2101.06458 

Abstract: 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 as tallied by the "COVID-19 Disorder Tracker" initiative by focusing on the three countries with the largest number of incidents, India, Israel, and Mexico. By fitting Poisson and Hawkes processes to the stream of data, we find that disorder events are inter-dependent and self-excite in all three countries. Geographic clustering confirms these features at the subnational level, indicating that nationwide disorders emerge as the convergence of meso-scale patterns of self-excitation. Considerable diversity is observed among countries when computing correlations of events between subnational clusters; these are discussed in the context of specific political, societal and geographic characteristics. Israel, the most territorially compact and where large scale protests were coordinated in response to government lockdowns, displays the largest reactivity and the shortest period of influence following an event, as well as the strongest nationwide synchrony. In Mexico, where complete lockdown orders were never mandated, reactivity and nationwide synchrony are lowest. Our work highlights the need for authorities to promote local information campaigns to ensure that livelihoods and virus containment policies are not perceived as mutually exclusive. 

Evidence on the impact of the Prudential Center on crime in downtown Newark

with Eric Piza (Northeastern University), Alex Piquero (University of Miami) and Justin Kurland (independent). Published in the Journal of Experimental Criminology. Link: https://link.springer.com/article/10.1007/s11292-023-09576-8 

Abstract: 

Purpose

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 crimes that occurred during events spatially differ compared to the incidents in no-event hours. Finally, we employ logistic regression to assess the correlation between crime locations and activity at the center.

Results

Five event types (out of nine) are statistically associated with increases in crime. Spatially, differences in the distribution of incidents when the facility is active partially emerge. Two out of six location types (streets and parking lots) correlate with activity at the center.

Conclusions

The complex array of crime-related effects that the center has on downtown Newark suggests tailored policies discriminating between event and location types for enhancing public safety.

Explainable machine learning for predicting homicide clearance in the United States

Published in the Journal of Criminal Justice. Link:  https://www.sciencedirect.com/science/article/abs/pii/S0047235222000186 

Abstract: 

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 Accountability Project. The most accurate algorithm among all (XGBoost) is then used for predicting clearance outcomes state-wise. Second, SHAP, a framework for Explainable Artificial Intelligence, is employed to capture the most important features in explaining clearance patterns both at the national and state levels.

Results

At the national level, XGBoost demonstrates to achieve the best performance overall. Substantial predictive variability is detected state-wise. In terms of explainability, SHAP highlights the relevance of several features in consistently predicting investigation outcomes. These include homicide circumstances, weapons, victims' sex and race, as well as number of involved offenders and victims.

Conclusions

Explainable Machine Learning demonstrates to be a helpful framework for predicting homicide clearance. SHAP outcomes suggest a more organic integration of the two theoretical perspectives emerged in the literature. Furthermore, jurisdictional heterogeneity highlights the importance of developing ad hoc state-level strategies to improve police performance in clearing homicides.

Survival of the Recidivistic? Revealing Factors Associated with the Criminal Career Length of Multiple Homicide Offenders

Published in Homicide Studies. Link:  https://journals.sagepub.com/doi/10.1177/10887679211010882 

Abstract: 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 investigating correlates of career duration. The models indicate that females, MHOs employing multiple methods, younger MHOs and MHOs that acted in more than one US state have higher odds of longer careers. Conversely, those offending with a partner and those targeting victims from a single sexual group have a higher probability of shorter careers. 

Crime, inequality and public health: a survey of emerging trends in urban data science 

with Massimiliano Luca (University of Bozen & FBK), Simone Centellegher (FBK), Michele Tizzoni (FBK), and Bruno Lepri (FBK). Published in Frontiers in Big Data . Link: https://www.frontiersin.org/articles/10.3389/fdata.2023.1124526/full 

Abstract: 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 tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

Where Are We? Using Scopus to Map the Literature at the Intersection Between Artificial Intelligence and Crime 

Published: Journal of Computational Social Science . Link:  https://link.springer.com/article/10.1007/s42001-020-00082-9 

Abstract: 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 the intersection between AI and crime are lacking. What are the current research trends in terms of topics in this area? What is the structure of scientific collaboration when considering works investigating criminal issues using machine learning, deep learning and AI in general? What are the most active countries in this specific scientific sphere? Using data retrieved from Scopus database, this work quantitatively analyzes published works at the intersection between AI and crime employing network science to respond to these questions. Results show that researchers are mainly focusing on cyber-related criminal topics and that relevant themes such as algorithmic discrimination, fairness, and ethics are considerably overlooked. Furthermore, data highlight the extremely disconnected structure of co-authorship networks. Such disconnectedness may represent a substantial obstacle to a more solid community of scientists interested in these topics. Additionally, the graph of scientific collaboration indicates that countries that are more prone to engage in international partnerships are generally less central in the network. This means that scholars working in highly productive countries (e.g. the United States, China) tend to collaborate with researchers based in their same countries. Finally, current issues and future developments within this scientific area are also discussed.