Methods & Data
Tree Equity Score measures how well the benefits of trees are reaching communities living on low-incomes, communities of color and others disproportionately impacted by extreme heat and other environmental hazards.
The priority index helps prioritize the need for planting to achieve Tree Equity based on seven equally-weighted climate, health and socioeconomic variables that are then integrated into Tree Equity Score. A higher priority index indicates greater potential for residents to be disproportionality affected by extreme heat, pollution and other environmental hazards which could be reduced with the benefits of trees.
Seniors (age 65+) and children (0-17) as a proportion of working age adults (18-64).
Data Source: American Community Survey 2017-2021
Percentage of the labor force that do not have a job, are available, and looking for one.
Data Source: American Community Survey 2017-2021
Self-reported prevalence of poor mental health, poor physical health, asthma and heart disease in an equally weighted index. A higher index indicates higher reporting of poor health factors. Not available for Puerto Rico or the U.S. Virgin Islands.
Data Source: Center for Disease Control CDC PLACES 2022
Surface temperature is a good estimate of where excess heat is generated in urban areas. Average surface temperatures for the hottest days were estimated by extracting maximum pixel values from all Landsat 8 Surface Temperature scenes for the summer of 2022 (may include summers from earlier years for certain locations). Maximum pixel values representing the highest extremities of summer surface heat were compiled for all urban areas in the United States, then averaged by block group to create a heat extremity dataset. Heat disparity is measured by comparing average block group heat extremity with the urban area average to measure variance in heat severity across an urban area.
Data Source: USGS Earth Explorer - Landsat 8 Collection 2 Level 2 Surface Temperature
Percentage of people living below 200% of the federally-designated poverty line.
Data Source: American Community Survey 2017-2021
Percentage of households where no person age 14+ speaks only English, or no person age 14+ who speaks a language other than English speaks English "very well."
Data Source: American Community Survey 2017-2021
Percentage of people that are Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, and includes all people classified as Hispanic by the Census Bureau.
Data Source: American Community Survey 2017-2021
Achieving Tree Equity in cities provides numerous benefits to public health, water, air quality, climate and community wellbeing. The tree canopy benefits measures utilize the power of i-Tree Landscape and research funded by American Forests on job creation.
Tree Equity is a job producer and sustainer. American Forests supported research that used the U.S. Bureau of Economic Analysis' Regional Input-Output Modeling System II (RIMS) to estimate the direct, indirect and induced jobs from investing in urban forestry. These calculations rely on the following estimates and calculations:
Neighborhood (block group) tree canopy goals estimate the percent tree canopy required to deliver a minimum standard of tree cover to a block group. Goals are canopy targets based on natural biome baselines then adjusted based on building density, as buildings limit plantable space.
Natural biome baseline canopy targets were selected with guidance from the USDA Forest Service and feedback from stakeholders:
Building density (%) was calculated from Microsoft Building footprints. OpenStreetMap Buildings footprints were used to fill in gaps for just ~150 block groups that were missing data.
Baseline canopy targets were adjusted according to the slope of the trend line between building density and tree canopy in each biome. Adjustment factors are in increments of 0.25 and were applied as follows:
The formula for each neighborhood goal, GOAL, is as follows:
GOAL = Baseline target * Building density adjustment factor
The adjusted goals, GOAL, are shown in tree canopy (%) below:
Building density (%) | Forest (% canopy) | Grassland (% canopy) | Mediterranean (% canopy) | Desert (% canopy) |
---|---|---|---|---|
<14% | 50% [1.25] | 30% [1.5] | 30% [1.5] | 15% |
14-22% | 40% | 30% [1.5] | 25% [1.25] | 15% |
22-30% | 30% [0.75] | 25% [1.25] | 20% | 15% |
>30% | 20% [0.5] | 20% | 15% [0.75] | 15% |
*Goals are in percent tree canopy. Adjustment factor in brackets
Tree canopy cover, AREAtrees, is derived from pre-aggregated Google high-resolution tree canopy sourced from Google Environmental Insights Explorer. This is substituted by local high-resolution tree canopy in cities that have Tree Equity Score Analyzers. Tree canopy cover percent is calculated as follows, where:
TREE CANOPY COVER % = AREAtrees / AREAland * 100
Compute the percent area of a block group that could be planted to reach the neighborhood tree canopy goal.
The neighborhood tree canopy gap, GAP, is calculated by subtracting the percent existing neighborhood 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 summer of 2022 that intersect urban areas were compiled (may include summers from earlier years for locations with less data).
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 United States.
Block group and urban area averages were calculated from the heat extremity dataset, excluding water area. If not enough data were available for a block group, the block group was set to the urban area average.
For each block group, the difference from the urban area average measures how much each block group fell above and below the urban area mean. This produces a measure of heat disparity, TEMPdiff, where:
TEMPdiff = TEMPbg,ave - TEMPua,ave
The Priority Index helps prioritize the need for planting to achieve Tree Equity based on climate, health and socio-economic variables.
The index comprises seven equally-weighted indicators:
The indices, N, are normalized as follows, where, for each indicator, Ni:
Ni = (xi - xi,min ) / (xi,max - xi,min)
*All urban areas <10,000 were grouped within each state and treated as a single urban area. Any urban areas crossing state lines were split and treated as separate urban areas in each state.
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 Ni refers to each indicator value:
E = 0.1 + (1 - 0.1) * (N1 + N2 + N3 + N4 + N5 + N6 + N7) / 7
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 block group meets or surpasses the canopy goal for that block group.
The canopy gap is normalized to a score from 0-100 for each urban area as follows, where:
GAPscore = GAP / GAPmax
*All urban areas <10,000 were grouped within each state and treated as a single urban area. Any urban areas crossing state lines were split and treated as separate urban areas in each state.
Tree Equity Score, TES, is calculated by multiplying the Gapscore by the Priority Index, E:
TES = 100 (1 - GAPscore * E)
Composite scores provide an overall assessment of Tree Equity in a locality (city, town, village or other). A city's score depends on (1) the average of Tree Equity Scores in neighborhoods scoring below 100, and (2) the priority index of neighborhoods that already score 100. For neighborhoods scoring 100, the higher the priority of the neighborhood, the higher the composite score—an indicator that Tree Equity has been achieved in areas with greater need. Localities can raise their composite score faster by working first in areas with low scores and greater need.
Compute composite scores, where:
Composite Score = (Sum(TES<100) + (Sum(E100) * 100)) / (# of TES<100 + Sum(E100))
Made possible with tree canopy provided by
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