Table 2 Performance of RTM using hit rate as the forecasting performance measure
From: Systematic review and meta-analysis of risk terrain modelling (RTM) as a spatial forecasting method
Study | Crime type | Hit rate (high-very high risk cells) |
|---|---|---|
Valasik et al., (2019) | Lethal violence | 23% |
Kocher and Leitner (2015) | Burglary | 25% |
Kocher and Leitner (2015) | Auto theft | 25.7% |
Assault | 29% | |
Homicide | 32% | |
Valasik (2018) | Gang violence | 33% |
Dugato et al., (2018) | Burglary | 35.7% |
Kocher and Leitner (2015) | Assault (spring) | 37% |
Anyinam (2015) | Robbery | 39% |
Theft | 40% | |
Ohyama and Amemiya (2018) | Thefts from vehicles | 40.9% |
Anyinam (2015) | Non-fatal shootings | 41% |
Alcohol related traffic crashes | 41% | |
Caplan (2011) | Shootings | 42% |
Dugato (2013) | Robbery | 43% |
Onat and Gul (2018) | Terrorist acts | 43% |
Kocher and Leitner (2015) | Robbery | 43.5% |
Kocher and Leitner (2015) | Assault (summer) | 44.4% |
Violent crime | 45% | |
Drawve et al., (2016) | Gun crime | 48% |
Marchment et al., (2019) | Bombings | 50% |
Marchment et al., (2019) | Bomb hoaxes | 50% |
Daley et al., (2016) | Child maltreatment | 52% |
Anyinam (2015) | Homicide | 57% |
Escuerdo & Ramírez (2018) | Illicit drug markets | 64% |
Dugato et al., (2017) | Mafia homicide | 85% |
Onat et al., (2018) | Drug crime | 85% |