The impact of night-time economy districts on violence and perception of safety at night
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".