Key findings from the phase one interviews were as follows:
A number of actions were identified to improve the quality of information available to decision-makers at the local level, and the standards of data analysis. These included:
a need for more comprehensive and consistent data collection;
increased sharing of data between partner agencies;
the nature of information sharing was generally ad hoc and relied on individuals, although occasionally at partnership meetings aggregated/analysed information was shared regularly;
improved standards of analysis to inform policy enforcement, implementation of prevention strategies, deployment of resources, and for monitoring and evaluation, and;
improved access, suggested by several stakeholders, to information from hospital attendances and ambulances responses.
A number of stakeholders agreed that a single multi-purpose database would be highly beneficial. However, a number of concerns and obstacles to achieving this were identified both organisationally and in terms of resources. The following concerns were identified:
agencies collect data for a variety of reasons other than for the management of areas with licensed premises;
comprehensive capture of these data would be resource intensive;
current systems do not easily allow data to be exported,
there are limitations in current data collection techniques;
some organisations store their data on more than one system, therefore obtaining information relevant to ASPs from their systems would be far from straightforward and time consuming;
there are legislative and cultural barriers to sharing individual level data (data protection).
the cost needed to develop such a system;
the extent and level of training required;
a lack of time and resources to interrogate the data; and
the task would be too complicated and might not contribute sufficient added value to justify the effort
Key findings from phase two of the research included the following:
The key sources of data identified for the construction of a single database/system were licensed premise data, police recorded crime data, trading standards data, A&E data, and ambulance data
A number of functions were identified for the development of a single database, and these can be classified as short-term operational responses, mid to longer-term strategic policy decision making, and research functions. Key functions of the database identified were:
to administer licensing applications;
to monitor individual premises, individual persons (both irresponsible managers and repeat offenders), and areas with high concentrations of premises;
to compile evidence for licensing hearings and reviews;
to identify, prioritise and carry out targeted enforcement activity;
to corroborate and share knowledge; and
to remove duplication of effort.
There were a number of difficulties encountered during the creation of the pilot databases. As stated previously, it was not possible to acquire all data sources for each of the three case study areas as some organisations were unwilling or unable to share disaggregate data (even with personal information removed). Indeed, only two case study areas could be used for the final analysis and the key obstacles faced here were that:
A&E data and ambulance data could not be acquired for any of the three case study areas during the time frame of this research (reflecting the concerns expressed by practitioners in phase one about the sharing of health data). This is particularly important when considering the known the under reporting of crime to police; and
In addition to this, several of the datasets required time-consuming manual processing to prepare them for analysis due to the format in which they were currently produced. This stage of the process added considerable time (several weeks) to the creation of the pilot databases.
In order to test the usability of the pilot database a number of research questions were generated in conjunction with the PUG. These included an examination of:
the spatial relationship between ASPs, trading hours and crime;
the relationship between ASP density and crime;
the spatial relationship between ASP density by type and crime;
the extent to which specific combinations of licensed premises explain the variations in the different types of crime; and
the extent local enforcement (trading standards) matched concentrations of licensed premises and crime.
Key findings from this analysis were as follows.
Concentrations of ASPs:
ASPs are spatially concentrated (in one of the wards in the case study areas the density was found to be 7 households per ASP).
The number of ASPs and levels of crime in these areas of concentrated drinking were disproportionately higher than their share of the residential population.
Therefore, the residential population (currently used as the denominator to construct crime rates) may not be the most appropriate measure; for example in the case of violent crime, the ‘total number of licensed premises’ or ‘land area in hectares’ might be better denominators to use.
The relationship between ASPs and crime:
Correlation analyses were used to produce a more systematic examination of the relationships between crime and ASPs.
In both case study areas, higher numbers of ASPs (taking into account both the densities of ASPs in a ward, and the population rate) were associated with higher crime rates (supporting the findings of previous studies).
The strongest correlations revealed that higher levels of violent crime were statistically more likely in the areas with higher numbers of ASPs and longer trading hours.
Indeed, in the two case study areas, the correlation between ASPs and violence against the persons was 0.905 and 0.775.
The relationship between ASP type and crime
The overall mix of premise types (based on all ASP types in each ward) appeared not to be related to the ward’s crime rate; that is wards with an equal share of ASPs in each category did not register higher crime than elsewhere.
However, regression analyses were used to explore how far specific combinations of ASP types explained variations in crime rates.
This analysis suggested that certain combinations of ASPs accounted for a large proportion of the variation in crime rates.
Pubs, bars and nightclubs were the strongest predictors of variations in crime
The only other ASP categories to predict variations in crime were ‘Takeaways’ in one case study area and ‘Stores and off-licences’, and ‘Members/social clubs’ in the other.
Therefore, neither restaurants, supermarkets, nor other types of licensed premise were strong enough to be predictors of crime in either case study area.