HeatView Technical Details

HeatView’s methodology offers a robust, scalable, and cost-effective solution to manage and mitigate the impacts of extreme heat. It integrates 18 different features from satellite imagery datasets, including often-overlooked overnight temperatures.

Satellite Data Sources

HeatView processes multi-year satellite imagery to generate detailed heat maps and hazard scores. The following satellite data and sources underpin its methodology:

• Landsat-8/9: Offers insights into modern and historical trends in land surface temperature behavior, enhancing understanding of local temperature distribution.
• MODIS: Helps understand the temperature differences between day and night, which are essential indicators of the urban heat island effect and aid in regional evaluations.
• Sentinel-2: Provides high-resolution data with frequent collections and various spectral bands, aiding in the understanding of cooling factors like green spaces in urban environments.

All satellite data is processed and analysed through state-of-the-art platforms, ensuring consistency and precision.

Temperature Metrics

Susceptibility to heat stress is assessed through a multi-temporal spatial and statistical analysis of open satellite imagery datasets from various Earth observation programs.

The data analysis and classification focus on the previous three years, with special attention given to the hottest period of each year. A total of 54 features (18 for each year) are utilized to examine patterns in land surface temperature, the heat island effect, and factors contributing to cooling.

Temporal Analysis

We use various temporal aggregations and statistically assess the features within the whole year, the hot season (April to September), and the classical summer (June to August) for the last three years.

Statistical Hazard Scoring

HeatView employs statistical distribution analysis to classify heat hazard levels on a 50-metre hexagon grid. This data can be further aggregated to other spatial units according to your unique requirements (postcodes, addressed, neighbourhoods etc.). Scores range from 1 (Very Low) to 5 (Very High), reflecting the relative risk of extreme heat events.

Grid-Based Analysis

HeatView utilizes the H3 Hexagonal hierarchical geospatial indexing system at Resolution 11 to organise and present data.
This grid system provides:

Resolution: Each hexagon measures approximately 50 metres across and covers an area of 0.2 hectares.

Connectivity: The hexagonal structure minimizes distortion and reflects real-world spatial relationships more effectively than traditional square grids.

Data Integration

To enhance satellite imagery, HeatView incorporates auxiliary datasets to enrich analysis:

• Local land use data for contextual insights.
• Climate models to assess heat event severity.
• Open-source geographic datasets to validate findings.

Outputs and Applications

HeatView generates actionable outputs to support:

• Urban Heat Island Mitigation: Highlighting areas for green infrastructure implementation.
• Energy Efficiency Planning: Identifying overheating risks in housing stock.
• Climate Resilience Strategies: Informing adaptation plans for long-term temperature increases.