Spatial pattern analysis of robbery and spousal assault in Vancouver between 1989 and 2000 utilizing Geographic Information Systems
Notice bibliographique
Résumé
It has been repeatedly shown that there are temporal and spatial concentrations of crime. Various research indicates that a motivated offender has a greater chance of committing a crime near his or her home base, and may also travel to familiar places where more potential targets exist. Thus, the areas where motivated offenders live and spend time, or pass by frequently in their daily activities will tend to have more occurrences of criminal events influencing patterns in crime. Traditionally, it has been thought that spatial patterns of crime are more related to stranger-to-stranger property and / or violent crimes than crimes occurring between known-to-known people. In this research, it is argued that patterns of crime exist whether it is crime occurring between stranger-to-stranger or known-to-known. Therefore, the purpose of this research was to examine whether spatial concentrations of crime exist in two types of crime: robbery and spousal assault. The present research explored spatial patterns of spousal assault and robbery by mapping out the Vancouver Police Department's data of calls for service in selected years between 1989 and 2000 utilizing Geographic Information Systems (GIS). It was found that in both crimes, spatial patterns exist and these patterns were stable during the observed time periods. By examining repeat victimization of locations, it was supported that repeatedly victimized locations disproportionately contribute to both spatial crime patterns and crime rates. As a last step, Location Quotients of Crime (LQC) were calculated for both crimes to assess the relative risks and centres of both crimes. As expected from the theoretical frame of environmental criminology, spatial patterns of robbery and spousal assault were different. As this research demonstrates, analyses utilizing GIS can offer useful information regarding crime hot spots and the extent of crime concentration in a given geographical area. In the future, findings from such research need to be employed in an effort to improve crime prevention, detection, and forecasting.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».