Population-level insights
The UPRN brings with it a range of other spatial intelligence. Specifically, the Office for National Statistics (ONS) UPRN Directory (ONSUD) gives the full range of statistical geographies for every UPRN:
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Census Output Area (OA), Lower Super Output Area (LSOA) and Middle Super Output Area (MSOA)
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Ward, parish and electoral division
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Local authority (upper and lower tier) and region
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Health boundaries
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Other administrative and statistical boundaries
ONS maintains a list of codes for all these statistical geographies, which are (or should be) used throughout government to publish a wide range of datasets, including demographic, socio-economic, population health and other statistical insights.
If the UPRN is held in operational systems, operational data can thus be analysed against these datasets, providing valuable insights that may not otherwise be visible.
Example: Asian and black non-English speakers aged 65+, London, Census 2021
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The maps show concentrations of Asian and Black people aged 65 or over who don’t speak English or don’t speak it well. (Source: 2021 Census)
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Data like this could be compared with information about the take-up of targeted services for this age group. It might, for example, identify a need for information in translation for certain communities.
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As ethnicity is a protected characteristic, translated materials could help to meet a service’s anticipatory duty under the Public Sector Equality Duty (see Equality Act 2010).
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Information could also be adapted where necessary to reflect the cultural assumptions and expectations of different ethnic communities. This could be important across a whole range of services, from debt advice to civil emergency planning.
Describe your image
Describe your image
Describe your image
Example: Take-up of Technology Enabled Care, County Durham, 2022
The east coast of County Durham has a high level of deprivation, shown here by the 2019 Index of Multiple Deprivation (IMD). Source: ONS, 2019. Granularity: LSOA. Darker = more deprived.
The percentage of the population claiming disability benefits (DLA, PIP or Attendance Allowance) closely mirrors IMD – perhaps unsurprising in a former mining community. Source: DWP, 2022. Granularity: LSOA. Darker = higher % of population claiming disability benefits.
The proportion of disability benefits claimants using Technology Enabled Care (TEC) has an inverse relationship with deprivation – the more deprived an area, the lower the usage of TEC among disabled residents. Source: Durham County Council (Careline service), 2022. Granularity: LSOA. Darker = more TEC users.
The east coast of County Durham has a high level of deprivation, shown here by the 2019 Index of Multiple Deprivation (IMD). Source: ONS, 2019. Granularity: LSOA. Darker = more deprived.
By comparing operational data with national datasets (in this instance, deprivation and disability indicators), we helped Durham County Council identify an opportunity for targeted promotion of technology enabled care (TEC) among people with disabilities in a deprived area of the county.
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(Three slides - please scroll through)
