Data tool aims to help London councils tackle rough sleeping

The London Office of Technology and Innovation (LOTI) is developing a data tool to support London councils in the goal to make rough sleeping rare, brief and non-recurrent.

In a blog post, Jay Saggar, Programme Manager for Data, Smart Cities and Cybersecurity at LOTI, said that strategic decision-makers currently lack the data to understand the pathways into and out of rough sleeping.

“The lack of data hinders the development of preventative approaches,” he commented. “Blind spots include the circumstances and institutions that people are in immediately prior to entering rough sleeping, and the medium and long-term outcomes for those who are provided with services such as hostels or temporary accommodation.”

By being able to analyse how people move through the rough sleeping ecosystem, the goal is that councils can better design services that can be deployed to the right people at the right time to prevent rough sleeping.

The initiative is in collaboration with London Councils, the Greater London Authority and Bloomberg Associates.

Data showed that 3,570 people were sleeping rough in London between October and December 2022 – a 21 percent rise on the same period in 2021. The number of new rough sleepers recorded during this period – 1,700 – was 29 percent higher than the same period the previous year. Mayor Sadiq Khan said the figure was “evidence of the devastating fallout from the cost-of-living crisis”.

Research

During the discovery phase of the project, LOTI used its outcome-based LOTI Data Projects Methodology, working back from the goals defined by London’s Life off the Streets programme.

The process included a user journey mapping exercise and engagement with rough sleeping practitioners through workshops and interviews.

The initial tool will include data from the Combined Homelessness and Information Network (CHAIN), data from hostel providers, and data held in borough Homelessness Case Level Collection (H-CLIC) returns.

Bloomberg Associates has led the work to curate the minimum data set.

One of the initial use cases will be to support reporting on the new Department for Levelling Up Housing and Communities (DLUHC) rough sleeping KPIs that all local authorities will need to report on in the future.

LOTI has secured funding from London Borough Housing Directors and will be procuring support to help design and build the solution.

A Minimum Viable Product (MVP) is scheduled for July this year and will include data from CHAIN and four pilot boroughs – Camden, Lambeth, Hillingdon and Westminster – and their hostel provider.

If the MVP is successful, LOTI will work to onboard more boroughs during the rest of the year.

The project will link individual records but the outputs will be anonymised and aggregated for the strategic use cases that have been identified, Saggar said.

Future

“Our collective ambition goes way beyond the scope of our initial MVP. We would like to see more datasets added to the system to build a more complete understanding of rough sleeping in London as part of our ambition to make rough sleeping, rare, brief and non-recurrent,” Saggar commented. “These could include data from the Probation Service, health and care data and eviction data.

“Once the system is live there is also an opportunity to build a data science capability that can perform more sophisticated analysis. We are excited by the prospect of working with borough data scientists and analysts alongside expertise from research institutions, such as the Alan Turing Institute.”

Other cities are also using data in various ways to address homelessness.

London in Canada implemented an artificial intelligence (AI) tool it developed internally to predict and prevent homelessness. In May last year, Hamilton in Ontario, Canada launched a data dashboard to inform efforts to reduce homelessness. While several cities share homelessness data, Hamilton is aiming to create a more detailed picture by providing more comprehensive information in one place.