Using machine learning to conduct crime linking of residential burglary
Datos Bibliográficos
| ID | 6431093 |
|---|---|
| Autores | Eric Halford (0000-0002-3913-1679), Ian Gibson (0000-0001-9154-8247) |
| Año | 2024 |
| Volumen | 80 |
| Páginas | 100716-100716 |
| Fecha de publicación | 2024-12-10 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | International journal of law, crime and justice (JOURNAL) |
| Identificadores de la revista | ISSN: 1756-0616 • E-ISSN: 1876-763X |
| Editorial | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijlcj.2024.100716 |
| OpenAlex | W4405223883 |
| Idioma | EN |
| Citas recibidas | 1 |
| Referencias citadas | 41 |
Business · Criminology · Sociology · Computer Science · Crime Patterns and Interventions · Crime, Deviance, and Social Control · Cybercrime and Law Enforcement Studies · Artificial Intelligence
Criminality of place
Variable selection using random forests
Conditional variable importance for random forests
Random Forests
When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns
A machine learning approach to police recruitment
Space–Time Patterns of Risk
Burglars on the Job, Streetlife and Residential Break-ins
Using offender crime scene behavior to link stranger sexual assaults
How Do Residential Burglars Select Target Areas
Social Change and Crime Rate Trends
| Obras citantes distintas | 1 |
|---|---|
| Citas por año | 1 |
| Intervalo de citas | 2026 - 2026 (1) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 1 |