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Record W7057275048

The impact of night-time economy districts on violence and perception of safety at night

2023· dissertation· en· W7057275048 on OpenAlexfundno aff

Bibliographic record

VenueNottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersEuropean CommissionTrent UniversityNottingham Trent University
KeywordsPerceptionVictimisationPromotion (chess)Set (abstract data type)Field (mathematics)Fear of crimeHuman factors and ergonomics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.232
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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