Tree canopy cover, AREA_trees, is derived from pre-aggregated Google high-resolution tree canopy sourced from Google Environmental Insights Explorer (34,349 LSOAs). Imagery for tree canopy data was not available for parts of Scotland (left), Northern Ireland (middle) and Wales (right) for a total of 526 LSOAs due to weather conditions affecting satellite imagery (mapped below in the darker green). The Woodland Trust used iTree Landscape to manually estimate tree canopy for all of the missing LSOAs.

Tree canopy cover percent is calculated as follows, where:

• AREA_land is the land area of the neighbourhood, not including water area.

TREE CANOPY COVER % = AREA_trees / AREA_land * 100

Compute the percent area of a neighbourhood that could be planted to reach its tree canopy goal.

The neighbourhood tree canopy gap, GAP, is calculated by subtracting the percent existing neighbourhood canopy, EC, from the density adjusted tree canopy goal, GOAL (%):

GAP = GOAL - EC

Surface temperature is a good estimate of where excess heat is generated in urban areas.

All Landsat 8 Collection 2 Level 2 Surface Temperature scenes from the summers of 2020-2023 that intersect urban areas were compiled. Scenes were filtered using Landsat's Quality Assessment Raster to remove pixels that were clouds, cloud shadows, and water.

All scenes that fell within the same path and row in the Worldwide Reference System were merged and the maximum temperature value for each pixel selected. Maximum pixel values were extracted to represent the highest extremities of surface heat for all urban areas in the UK.

Neighbourhood (urban areas alone) and local authority averages were calculated from the heat extremity dataset, excluding water area. If not enough data were available for a neighbourhood, the neighbourhood was set to the local authority average.

For each neighbourhood, the difference from the local authority average measures how much each neighbourhood fell above or below the local authority average. This produces a measure of heat disparity, TEMP_diff, where:

• TEMP_n,ave is the neighbourhood's urban area average of all maximum pixel values for summer surface temperature
• TEMP_la,ave is the local authority average of all maximum pixel values for summer surface temperature

TEMP_diff = TEMP_n,ave - TEMP_la,ave

• Positive values indicate hotter than average neighbourhoods, and negative values indicate cooler than average neighbourhoods. Neighbourhoods set to the local authority average due to lack of data result in a heat disparity value of zero.

The Priority Index helps prioritise the need for planting to achieve Tree Equity based on climate, health and socio-economic variables.

The index comprises six equally weighted indicators:

• Air pollution index: The concentration of NO2 and PM2.5 pollution from annual 2021 emissions, normalised independently by country then averaged.
• Age: Ratio of older people (age 65+) and children (0-17) as a proportion of working age adults (18-64).
• Employment: A neighbourhood's relative deprivation in its country based on several indicators of employment.
• Health: A neighbourhood's relative deprivation in its country based on several indicators of health.
• Income: A neighbourhood's relative deprivation in its country based on several indicators of income.
• Heat disparity: Compares average neighbourhood heat extremity with the local authority average to measure variance in heat severity across neighbourhoods.

The indices, N, are normalised as follows, where for each indicator, N_i:

• x_i is the value for that neighbourhood for that indicator, i;
• x_i,max is the maximum value for that indicator across each country, i; and
• x_i,min is the minimum value for that indicator across each country, i.

N_i = (x_i - x_i,min) / (x_i,max - x_i,min)

The indices are then combined to create a simple Priority Index from 0.1 to 1, where 1 indicates greater priority. The Priority Index, E, is calculated as follows, where N_i refers to each indicator value:

E = 0.1 + (1 - 0.1) * (N₁ + N₂ + N₃ + N₄ + N₅ + N₆) / 6

Tree Equity Scores range from 0 to 100. A lower Tree Equity Score indicates a greater priority for tree planting and protection. A score of 100 means the neighbourhood meets or surpasses the canopy goal for that neighbourhood.

The canopy gap is normalised to a score from 0-100 for each country as follows, where:

• GAP_max is the maximum GAP value for that indicator across each country
• If the GAP is negative (i.e., existing canopy is greater than the neighbourhood goal), it is adjusted to 0 before normalising to calculate Gap_score.
• If Gap_max = 0, then Gap_score is set to 0 as well.

GAP_score = GAP / GAP_max

Tree Equity Score, TES, is calculated by multiplying the Gap_score by the Priority Index, E:

TES = 100 (1 - Gap_score * E)

Composite scores provide an overall assessment of Tree Equity in a local authority. A local authority's score depends on (1) the average of Tree Equity Scores in neighbourhoods scoring below 100, and (2) the Priority Index of neighbourhoods that already score 100. For neighbourhoods scoring 100, the higher the priority of the neighbourhood, the higher the composite score—an indicator that Tree Equity has been achieved in areas with greater need. Local authorities can raise their composite score faster by working first in areas with low scores and greater need.

Compute composite scores, where:

• TES_<100 is a Tree Equity Score below 100.
• E₁₀₀ is a priority index for a Tree Equity Score of 100.

Composite Score = (Sum(TES_<100) + (Sum(E₁₀₀) * 100)) / (# of TES_<100 + SUM(E₁₀₀))