An Investigation of the Relationship between Crime and Reported Incidents and the Built and Natural Environment in the Region of Waterloo, Ontario
Notice bibliographique
Résumé
In the study of crime and geography, many studies have investigated the spatial relationship between crime and the built and natural environment. However, these studies usually focus on specific environmental characteristics, such as alcohol serving businesses or the presence of vegetation. This study conducts a comprehensive analysis of the spatial relationship between crime and features of the built and natural environment in the sister cities of Kitchener and Waterloo, Ontario, taking into account many factors that may potentially affect crime and reported incidents. This includes built environment features, such as residential buildings, commercial buildings, drinking establishments, and bus stops. Natural environment features, such as parks and the presence of green vegetation were also considered. The measure of crime in this study was a geospatial record (aggregated to the nearest street intersection) of crime and reported incidents where police were called (e.g., emergency call and response) recorded by the Waterloo Regional Police Service (WRPS). Relationships between built and natural environment characteristics with crime and reported incidents were studied using linear regression and logistic regression modelling techniques based on three datasets. The first dataset involved creating a buffer around each street intersection and deriving the proportion of each building type and count of bus stops, streetlights, and alcohol licenses within a static or adaptive radius, which was subsequently compared with the number or presence of crime and reported incidents at each intersection. The second involved developing Adaptive Kernel Density Estimation (AKDE) rasters of each environmental feature and then conducting a regression analysis by comparing the number or presence of crime and reported incidents at each street intersection to its corresponding pixel values. The third involved using buffers to summarize the levels of vegetation cover detected from remote sensing imagery surrounding each street intersection, which was subsequently compared with the number of crime and reported incidents at each intersection. The results of this study identified overall low r-squared values for tested regression models, which suggests that important variables may be missing, such as socio-economic variables that may have a significant role in predicting crime incidents. The model also found that bus stops and alcohol licences were the most important urban environment factors in predicting crime and reported incidents in Kitchener-Waterloo.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».