Water companies are required to identify and support customers in vulnerable circumstances through the Priority Services Register. The register is populated reactively, and is rarely projected forward against demographic, health, or environmental trajectories. Without that projection, investment and support programmes risk leaving vulnerable people behind.
South Staffs Water and Cambridge Water wanted a defensible, sector-leading view of vulnerability across their combined service areas, projected forward to 2040. The work was scoped with Sustainability First, who acted as intermediary and policy partner throughout.
KELP integrated Priority Services Register data with ONS Census and various health data from various sources. Each need code (age, health condition, mobility, mental health, communications, financial, life stage) was modelled with its own prevalence stratification, then projected forward against ONS population trajectories.
Baseline and projected need code datasets at LSOA resolution, detailed methodology, and recommendations now feeding the client's vulnerability strategy, targeted support design, and regulatory narrative. The full methodology and analysis are publicly available.
"KELP helped us deliver a sector leading view of our customer base Priority Services Register health needs. This insight has formed the backbone of the decisions made in our latest vulnerability strategy and will help us offer more pro-active and targeted support to our customers"
Nick Hollaway, South Staffs Water and Cambridge WaterThe vulnerability forecast is informing investment priorities, targeted support programmes, and regulatory submissions at South Staffs and Cambridge Water. The methodology has now been extended to a major UK Electricity Distribution Network Operator, scaling to three licence areas covering over eight million customers.
Selected outputs from the founder's academic research portfolio. Each is independently published and peer-reviewed, and informs the methodological toolkit KELP brings to client work.
A deep learning approach to reconciling water quality measurements across different satellite sensors, addressing one of the practical barriers to operational long-term monitoring across mission gaps.
A new index for monitoring cyanobacteria occurrence using optical water type analysis, transferable to other freshwater systems where consistent water quality monitoring is needed.
Satellite remote sensing combined with spatial analysis to characterise unprecedented harmful algal bloom dynamics linked to elephant deaths in Botswana. Earth observation applied to an ecological crisis event.
An open Landsat-based retrieval tool for monitoring water quality in East African lakes, built to support catchment monitoring at sites with limited in-situ measurement. The most concrete piece of public software from the KELP foundation.
Deep learning applied to satellite imagery on a cloud platform to map and track the changing extent of Southeast Asia's mangroves, monitoring one of the most carbon-rich and threatened coastal ecosystems over space and time.
Full publications list on Google Scholar.