Research by borough’s data science team feeds into new licensing policy and parliamentary debate
The London Borough of Barking & Dagenham’s data science team has used recent findings from its data science team to determine elements of its gambling licence policy.
Pye Nyunt, the borough’s Insight Hub manager, told innovation charity Nesta’s City Data Analytics conference that the data has been used to gain a better understanding of how local betting shops can influence gambling addiction.
The model took in demographics, the proximity of schools and colleges to betting shops, local mental health problems, the presence of homeless shelters, food banks and payday loan shops. It used tree based models to come up with the spatial indices, z-scores (which indicate how many standard deviations an element is from the mean) to normalise the data, and kernel density estimations to approximate how many vulnerable people were living close to the betting shops.
“The model enabled us to understand where the most vulnerable people are in the borough,” Nyunt said.
Concentrated abuse
The team initially expected to find that gambling addiction was scattered across Barking & Dagenham, but found it was in fact concentrated in three wards. It also established that the shops were clustered together to attract gamblers who had exhausted their credit for fixed odd betting terminals in one to go to another.
It calculated that, while the business rate revenue from the 47 betting shops in the borough was £300,000 per year, the social cost in dealing with the 1,400 gamblers was much higher at £800,000.
In addition to using the evidence in its own gambling licensing policy, which has been published recently, Barking passed it on to Sir Robin Wells, mayor of neighbouring Newham, as evidence in his lobbying of the Gambling Commission. Nyunt said it has been used in parliamentary debates and could influence future legislation.
He concluded: “The major lesson is that too often in local authorities we begin with the sentence ‘I think this is a problem’. This is an example where we have shown that by using data we have been able to convert that to ‘I know this is a problem’.”
Photo: Graeme Main/MOD, Open Government Licence, via Wikimedia Commons
