The impact of night-time economy districts on violence and perception of safety at night
Notice bibliographique
Résumé
This project aims to advance the understanding of the impact of Night-Time Economy (NTE) districts on violence and perception of safety at night in UK cities. \nNTE districts have been long recognised by academics working in the field of the night-studies. However, there is a scarce number of studies focusing on assessing their characteristics, like different mixes of economic activities, offers, the level of disorder, infrastructure and services. \nFollowing the environmental criminology theoretical framework, the combination of these factors can lead to a set of opportunities for crime as they influence what kind of NTE visitors are attracted and which kind of activities are encouraged and allowed. A better understanding of how these contexts can impact violent crimes and perception of safety at night should help design urban strategies aiming at improving NTE districts. \nIn this study, statistically significant clusters of NTE activities are identified in different UK cities using the Point of Interest (POI) dataset in combination with the Optimised Hot Spot spatial analysis on AcrGIS. Then, the researcher employs Google Street View to assess the environmental characteristics of individual NTE districts. Finally, the researcher uses a combination of statistical models to test the possible significant correlations between individual environmental characteristics with different levels of violent victimisation and perception of safety at night. Factors considered include the mix of activities offered by NTE venues, alcohol promotion strategies, the density of NTE venues and retail, infrastructure, elements of disorder, alongside socio economic and routine activity characteristics of the population. Data about violence and perception of safety at night are extracted and manipulated from the CSEW at the MSOA level. \nUsing this combination of approaches, the research proposes a new time-saving protocol for identifying, visualizing, understanding, and monitoring NTE clusters in relation with violence and perception of safety trends. The results show that different types of NTE activities cluster in the urban environment, therefore forming identifiable NTE districts. \nIn the NTE districts identified, there are features which are more common than others - like the presence of alcohol promotion signs, entertainment activities, graffiti and litter on the street. In terms of violence and perception of safety at night, at the city level, a higher level of deprivation seems to be the main predictor for increased violence and decreased perception of safety at night. On the other hand, the routine activities and demographic characteristics of the population show the presence of complex interactions with the phenomena of interest. When zooming on those areas overlapping with NTE districts, it emerges that the presence of activity nodes and increased footfall can significantly predict a higher level of violent victimisation at night. Also, the number of people aged under 30 years old remains correlated with a higher level of violent victimisation at night. Interestingly, at this level, the least deprived areas are not found to be safer at night in terms of violent crimes. Results in relation to the impact of NTE districts on the perception of safety at night are more inconsistent and more research needs to be conducted in this field.
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».