• Data Service: Imago – Data Service for Imagery     
• Coverage: UK-wide, at a local level
• Time period: 2015-2024
• Access: Open

What it is

Climate change affects everyone, but some people and places feel it more than others. Local climate conditions already shape health, wellbeing and economic outcomes, and those effects will grow. Yet making sense of climate data at neighbourhood level usually needs specialist skills and software that most social scientists, health researchers and policymakers don’t have.

CLiVE (Climate Local Vulnerability and Exposure) is a suite of open datasets from Imago, SDR UK’s data service for imagery. Each dataset describes and measures a different aspect of environmental risk across the UK, neighbourhood by neighbourhood, using satellite imagery translated into ready-to-use indicators mapped to standard UK geographies.

The suite currently includes datasets covering multiple indicators:

• Air pollution
• Flood risk
• Air temperature
• Precipitation

CLiVE approaches climate hazards and exposures the way most climate scientists do – as a combination of three things: the hazard itself (such as extreme heat or flooding), exposure (whether people, their homes, and infrastructure are in the path of that hazard), and how well-placed people are to cope, taking into account factors like age, health, income, and housing.

All CLiVE datasets are mapped to standard UK statistical areas, so they slot directly alongside data many researchers already use: census data, the Indices of Multiple Deprivation, and NHS small-area statistics.

How researchers can use it

• Study local climate risks without expensive data collection or specialist satellite skills, using ready-made neighbourhood-level statistics.
• Analyse whether deprived neighbourhoods face greater exposure to flooding, heat stress, or poor air quality than wealthier ones, by joining CLiVE data to deprivation indices.
• Link neighbourhood-level pollution or flood exposure to health outcomes such as respiratory illness or post-flood mental health impacts, using NHS small-area statistics.
• Examine the economic consequences of repeat hazard exposure, such as effects on house prices, insurance access, or small business survival, using consistent local measures.

How policymakers can use it

• Identify which neighbourhoods are most at risk from flooding or extreme heat, and least able to cope with the consequences, to prioritise where investment and recovery support should go. The Welsh flooding data makes this particularly relevant to the Welsh Government, the Environment Agency, Natural Resources Wales, and local authority flood risk teams.
• Feed local hazard indicators into Clean Air Zone decisions, air quality action plans, and health inequality strategies. Relevant bodies for this work would be DEFRA, UKHSA, DHSC, combined authorities and city councils.
• Use CLiVE to show where climate impacts will fall hardest on communities least able to cope, to support national and local adaptation planning under programmes such as the National Adaptation Programme. Relevant bodies for this would be DESNZ, MHCLG, and any council writing a climate adaptation strategy.

Illustrative case study snapshot

A Welsh council wants to know which flood-hit communities were least equipped to recover. Using CLiVE’s flood exposure and vulnerability data, an analyst produces a ranked map of the most affected neighbourhoods. This analysis would previously have required commissioning remote-sensing specialists. The map is used to strengthen a bid for flood resilience funding.

Access and availability

All CLiVE datasets are freely available to all via Imago’s data catalogue and are designed for integration with UK administrative, census, and survey data. To access the data, free registration is required.

Powered by SDR UK

Climate data exists but making it usable has traditionally required specialist software, technical skills, and time-intensive processing that most social researchers and policymakers don’t have. Imago, SDR UK’s data service for imagery, does that work on behalf of the research community. It makes satellite imagery useful, usable and used, translating it into ready-to-use statistics, geographically aligned with the census, health, and deprivation datasets researchers already work with. The result is that a public health analyst or local authority planner can connect climate conditions to outcomes in their area without ever needing to handle a satellite image.

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