Which Location are you Most likely to be Assaulted with a Weapon?

With the press reporting an ever increasing number of stabbing’s and violent assaults we thought we’d dig into the data and see what’s going on. Is this type of crime increasing? Or is it media hype?

Note you’ll find complete details on the data used along with how you can run your own analysis at the end of this article.

Crime Developments in the UK

Before any analysis, we must first generate a better understanding of crime patterns, specifically to answer the following question:

Are Crimes on the Rise? If so, which parts of the country are being affected the most?

To start with, we mapped the number of crimes in the most serious categories against the year of occurrence. An immediate finding is that reported UK crime data contains a lot of ‘Anti Social Behaviour’ related incidents, this is a large category as it contains everything from drinking alcohol in a park to kids on street corners causing a disturbance. As an over arching figure, this largely explains the decreasing trend over the years as due to it’s size it directly translates into an overall decrease in crime in UK. Whether this is due to the police not responding as often due to reduced resources, or there are less incidents is unknown, all we can say is that reported low level crime is decreasing and pulling the overall number down.

However, a decrease in the overall crime rate doesn’t necessarily mean our safety has improved. Many sources (such as the Independent) suggest that violent crimes in London are on a rapidly rising trend, as seen with the recent media frenzy over the keywords ‘Stabbings’, ‘Knife Crime’ and ‘Murder’.

Knife crime isn’t reported separately so we decided to look at changes in the possession of weapons to see if this has increased. The ‘Possession of Weapon’ category, can serve as an indication of the likelihood of an occurrence of a violent crime. The visualization on the left verifies that despite overall crime rates decreasing, the likelihood of being caught with a weapon has almost tripled since 2013. Our initial thought was that this was due to the police putting more focus on the possession of weapons, but stop and search figures show that there were an average of 18 stop and searches per 1,000 people in 2013/14, by 2016/17 this had fallen to 5. This would lead us to assume that there is less focus.

Before we can say knife crime is definitely on the increase we need to look at the distribution of crime by area.

The map visualizations below demonstrate the distribution of the 4 categories of crime we decided to look at across England and Wales.

Overall, a common trend is observed where the following cities tend to have high crime rates across all categories:

• London
• Newcastle
• Manchester
• Sheffield

These numbers are relative due to the differences in the population at each location. However even taking this into account London has a far higher proportion of reported weapon possession incidents compared to the others.

Investigating the Rise of Weapon Crime

With the ‘possession of weapon’ crime category identified as a rising threat in England and Wales, prominent speculations include demographics causes such as age and economic status, while others spectate gangs connected to crimes driven by drugs and anti-social behaviour activities. This section aims to understand the underlying motivators for occurrences of weapon crime by testing against the most prominent speculations.The visualisations below provide some initial insights:

Population Age: Occurrences of ‘possession of weapon’ crime peak at area with average population age between 25 to 50.

Anti-Social Behaviors: No observable trend

Drugs Related Crime: No observable trend

Household Deprivation: No observable trend

Note: Correlations between population qualification and weapon crimes were also run, but no insights were gained.

The results would indicate that the common assumptions are incorrect or the reported incidents are occurring away from home locations.

Which Location Are You Most likely To Be Assaulted With A Weapon?

Finally, to answer the core question of this article, we have mapped the number of reported ‘possession of weapon’ crimes to the property category of the location. This indicated that over 70% of the crimes were caught within the proximity of a ‘shop’, disproving popular beliefs that there is a higher risk of weapon crime in pubs, quiet car parks or housing estates.

Taking it further, to identify the specific type of shops within the category of ‘shop’, the data were also mapped to the commercial owner of the property. It was identified that gambling facilities (such as Betfred and William Hill) showed the highest count of reported weapon related crimes in their proximity. This is just indicative of the areas where these shops are based as a pose to the actual retailer being the focal point.

Conclusion

Our analysis shows:

• Overall reported crime is falling but mainly due to the decreases in Anti-Social Behaviour.
• The reported ‘Possession of Weapon’ category is rapidly increasing whilst the number of stop and searches and decreased.
• Weapons are not being found on deprived housing estates or areas with large number of youths.
• People are being caught in possession of weapons on the high street. Mainly near premises located in larger numbers in deprived areas such as betting shops

In conclusion our research shows the commonly held belief that people are more at risk in dark alleys or housings estates isn’t proven in the data. Surprising people are more likely to be caught carry a weapon on the high street.

Want to Create Your Own Analysis?

This articles utilized a range of different data to create interesting insights toward the more severe crimes in the UK, while diving into a deeper level on ‘possession of weapon’ related crimes by testing against its causes and location of occurrence. However, many more analysis can be made by diving into the 130 different datasets hosted in DoordaStats.

At Doorda we have mapped all our statistical data to a postcode level, which means you can easily add more granular insights to your geographical analysis. Our next article will be on imports and exports data, demonstrating the implication of the recent US trade tariffs toward UK companies.

Data Used

We’re happy to offer access to our data so just drop us an email and we’ll provide a login to our base datasets hosted on BigQuery. This allows you to connected any tool you like, Python, Tableau, Power BI up to you. We will NOT use your details for marketing purposes. But we’d love to know what you uncover!

DoordaStats

We used the spine in DoordaStats as a base starting point. This consists of basic details like postcode, output area, postcode introduction date, latitude, longitude along with population and household numbers.

Crime Data

DoordaStats consists of data on more than 20 different categories of incidence and crime, for the purpose of the analysis we will only be focusing on the occurrences of severe/ serious crimes in London. They are:

• Possession of Weapon: Found under the file name `r1_incident_possession_of_weapons`.
• Anti Social Behaviors: Found under the file name `r1_incident_anti_social_behaviour`.
• Criminal Damage and Arson: Found under the file name `r1_incident_criminal_damageand_arson`.
• Drugs: Found under the file name `r1_incident_drugs`.

Area Demographics Data

To identify the causes of serious crimes, we correlated the crime data with some area demographic datasets, including:

• Population Age: Found under the file name `r1_people_population_age_bands`.
• Population Education Level: Found under the file name `r1_people_highest_level_qualification`.
• Household Deprivation: Found under the file name `r1_household_deprivation_dimensions`.

Property Ownership Data

Finally, to identify the places in which weapon related crimes are likely to take place, the crime data was mapped to our occupancy dataset. This contains commercial occupant name and details on the type of of property . The data are found under the file name`business_rate`.

Connect to Doorda Host (BigQuery) and Merging with Stats Spine

Doorda’s data are hosted on Google’s BigQuery platform, it is easily accessible using a wide variety of tools including python, R and tableau.

The following documentations will provide an in-depth guide for connecting to Doorda’s database on BigQuery (Once yo have you r login details):

• Using python: https://www.doorda.com/kb/article/connecting-to-doorda-host-big-query-with-python.html
• Using R- Studio: https://www.doorda.com/kb/article/getting-started-with-doorda-host-and-r-studio.html
• Using tableau: https://www.doorda.com/kb/article/getting-started-with-doorda-host-using-tableau.html

All datasets within DoordaStats contain postcode information, which can be used as a key for merging your unique dataset for analysis.

Notes: it is recommended to export the final dataset onto your local machine to avoid repeating the same process of pulling and merging datasets should you restart the python script.

CRIME DATA IS THE ONLY TIP OF THE ICEBERG OF DOORDA’S STATISTICS DATA , AS DOORDASTATS ALSO ALLOWS YOU TO DIVE INTO HOUSEHOLD EXPENDITURE, LIVING ARRANGEMENTS AND ECONOMIC WELL-BEING DATA.