Predictive prevention · urban security · Italian field experience
A documented research and field experimentation pathway focused on moving from post-event response to the anticipatory reading of the conditions that make predatory crime more likely.
XLAW® is the name of a technical and methodological experience developed to support the prevention of predatory urban crime through georeferenced predictive alerts, recurring criminal model analysis and selective resource deployment.
The method focuses on thefts, robberies, pickpocketing, scams and other urban events characterized by recurrence, spatial-temporal regularity and observable operating patterns.
XLAW® is a technological and methodological invention conceived and implemented to test, for the first time in Italy, an innovative approach to urban security based on the possibility of preventing widespread urban illegality through a predictive logic.
The system generates and strategically uses georeferenced predictive alerts of possible crimes, processed through a predictive model based on machine learning.
Unlike control or alarm systems that operate after the event, XLAW directs prevention toward places and times where risk is expected to reconfigure.
The field experimentation, conducted across multiple cities and independently assessed, documented the possibility of making territorial control more dynamic, selective, precise and effective.
The goal is not to increase controls indiscriminately, but to reduce the waste of resources and energy through a better operational awareness of risk.
XLAW shifts the strategic construct of territorial control from a reparative vision of damage to a probabilistic vision of risk.
Giacomo Di Gennaro, University of Naples Federico IIXLAW introduced a different interpretation of risk: not only places where past crimes clustered, but recurring configurations in which offender, victim, target, place, day and time form an observable trajectory.
Control is oriented toward the points and time intervals where risk configurations are more coherent and imminent.
Operational action follows the likely sequence of the predatory phenomenon rather than generic social alarm.
The aim is not only to react after damage, but to disrupt the criminal sequence before it materializes.
The older website content is reorganized here into readable documentary capsules, preserving the substance while making the experience clearer and easier to navigate.
XLAW was developed to shift urban security from post-event response to anticipatory action before risk becomes damage.
The core of the experience is not an alert after crime, but georeferenced strategic information that guides prevention in time and space.
The method moves beyond traditional hot spots and observes recurring configurations of place, offender, victim, target, day and time.
Prevention becomes more precise when resources are not deployed generically but follow the evolving sequence of risk.
The experimentation also addressed crime displacement, documenting a reduction of the phenomenon without migration to alternative areas.
XLAW documents method, sources and results and is not based on individual profiling, biometric identification or scoring of people.
A defining feature of XLAW is its non-person-based design: the method is not intended to identify, classify or profile individuals, but to read aggregate risk configurations, spatial-temporal sequences and operational conditions useful for prevention.
XLAW does not assign dangerousness scores to citizens, does not use biometrics and does not build individual profiles.
The experience is presented through method, sources, results, publications, validations and public evidence, avoiding opaque or non-explainable logic.
Without claiming formal certification, XLAW is aligned with a modern view of AI: proportionality, transparency, human oversight, minimisation of impact on individual rights and a risk-based orientation.
The project emerged from long-term study of urban deviance. Theft, robbery, snatching, pickpocketing, fraud and other predatory crimes were observed as phenomena showing recurrence, territorial persistence and reconfiguration across time and space.
The model combines operational observation, criminology, urban context analysis, social dynamics, machine learning and deterrence theory.
XLAW moves beyond simply reading places where crimes have already occurred, focusing instead on the conditions that make a criminal pattern likely to reconfigure.
Operators are supported by strategic information that enables more precise, dynamic and risk-aware deployment.
The results fit within a deterrence logic: increasing the probability of disrupting the criminal design may move a system from a high-violation equilibrium to a lower-violation equilibrium.
XLAW was recognised as a digital innovation case for the application of artificial intelligence to the prevention of predatory crime and urban security. The SMAU recognition strengthens the public traceability of the experience and its value as a documented Italian field experimentation.
The field trial was conducted across multiple urban contexts, comparing actual events with predictive alerts generated by the system.
Origin of the research, development and initial operational experimentation.
Extension of the experimentation into a different urban context.
Verification of transferability to another territorial setting.
Application in a complex urban and tourist environment.
Additional operational verification of method and protocol.
Application documented also through public media evidence.
The documentary publication reports a comparison between actual events and predictive alerts generated within the same daily period.
| City | Reported predictive accuracy | Context |
|---|---|---|
| Naples | 84% | urban field trial |
| Salerno | 87% | urban field trial |
| Prato | 89% | urban field trial |
| Venice | 83% | urban field trial |
| Parma | 82% | urban field trial |
| Modena | 86% | urban field trial |
The documentary images summarize the cities involved, verification method, predictive accuracy, results, displacement, participation and media impact.
The synthetic framework of prediction accuracy in the trial cities.
The representation of prevention and economic results in the cities involved.
The media dissemination of the XLAW experience across press, web and TV.
XLAW should be read as a documented field experience: book, DOI publication, validations, trials, press coverage, videos and public materials form its historical and scientific evidence base.
The study behind XLAW software for predicting and preventing crime.
The book reconstructs the theoretical and operational roots of the method, including the hunting reserve theory and the transition from post-event response to preventive precision.
The official Italian field experimentation.
A documentary synthesis of method, protocol, field results, cost-benefit implications, conclusions and references.
Start of the research work on predatory urban crime and territorial control strategies.
Initial operational experimentation and early applications of the prevention paradigm.
Academic dialogue and consolidation of the criminological foundations.
Public recognition and SMAU Digital Innovation Award.
Publication of Sicurezza 4P and consolidation of the foundational documentary basis.
Publication of the Italian field experimentation synthesis with Zenodo DOI.
XLAW remains a historical and scientific reference for the shift from events to conditions in predictive urban security.