Community Impact Zones: Stronger Families/Safer Communities


Local councils, the police and health services combined data to identify wards where outcomes are worse than surrounding areas. This used nationally published data e.g. deprivation, nationally submitted but not published data e.g. school attendance by ward, and locally held data e.g. date and time of crimes against children to help understand why outcomes were worse for some people.

The results were shared with local communities to see if it mirrored their stories. Communities then identified the issues and the places (geographies) that defined their community. We took these issues and geographies to layer information from different services into new micro communities to provide greater insight of what was happening to families in these streets. We are now looking to analyse qualitative and quantitative data to develop a model of predictive indicators to help families at an earlier stage.

This project is led by the input of the local community (including voluntary sector and faith groups) who are defining the place we are working in and shared success measures.
All partners will then review the services they provided in these areas deliver improved outcomes.


This project used open data (e.g. demography), nationally collected but not reported data (e.g. school absence by ward) and local service data (e.g. social care referrals) to spotlight areas which had consistently had poor outcomes for people despite considerable resources being placed in that area. The work identified different issues in different areas – e.g. in one area the population was very transient, in another very stable. Analysis showed the inter-relationship between services on people’s lives and moved the focus from service data to people’s life story e.g. identifying an increase in children as victims of crime with growing school absence and 30% of those crimes being in school time.

Active engagement took place with the local communities to identify whether they recognised the description of where they lived, the key issues they faced and the underlying issues behind this. This led to changing the way we described communities – moving from traditional geographical boundaries to local descriptions of communities.

Building on agreed data sharing through the troubled families initiative we identified the number of families within one zone that we would look to support differently, focusing both on the individual support to that family and infrastructure support to the wider local community (e.g. supporting local clubs; placing additional PCSOs in that area). This was done with all partners and the project board. The work remains constantly evolving, reflecting feedback from local practitioners, the local voluntary sector and the local community.

Partners, communities and the board are provided with simple information to inform what is happening in the agreed geography through a number of different visualisation tools. Data from different services is layered over each other to show any commonality and inter-relationships that each may have with the any other. This has led to the targeting of specific streets, engagement with all the houses in those streets about the changes they are looking to see in their neighbourhood.

This is now very much a local community project. It is run by a steering group with an independent chair. A local charity is providing support to organize and evaluate the project.

What should LARIA members learn from your award entry?

Data makes sense to people when it describes their lives, rather than services provided or activities that have happened. There is always a risk and considerable evidence that any amount of data can be ‘Trumped’ with a good personal story, whether this is a true story or not.

The data you have must tell a story. The story that you tell must chime with those whose story it actually is. They must believe that story and that story needs to add to their own story. Data that challenges a person or communities’ story must be told sympathetically until it either becomes that story or is jettisoned in the wider narrative.

The data and the subsequent story remain limited without an idea for change – the policy or behaviours that will change. This must be generated with and led by those who need that change.

So our learning was … data can create a story, which if shared appropriately will generate  the idea to genuinely improve lives