Where Are the Benefits of Trees Needed Most? A Comparison of Equity-Based Mapping Tools in Austin, Texas

Austin, Texas

Austin, Texas, USA, was selected as the study area for this paper because of its history of redlining and the availability of a locally developed mapping tool. Located in the heart of central Texas, Austin’s population was estimated to be 974,447 people as of 2022 July 1 (United States Census Bureau [date unknown]). Patterns begin to emerge when looking at the geographic distribution of certain demographics, specifically related to income, race, and ethnicity within the city. Higher concentrations of people experiencing poverty and people of color tend to reside on the east side of the city, while the west side of the city is home to higher concentrations of white residents (Esri 2023a; Esri 2023b).

In 2022 Austin’s urban tree canopy cover was estimated to be 41%, however canopy cover was not evenly distributed across the city (City of Austin 2023). The west side of the city has more tree cover where whiter and wealthier neighborhoods reside compared to the east side of the city where historically lower-income residents and communities of color reside (Halter 2022). The City of Austin set a goal to “achieve at least 50% citywide tree canopy coverage by 2050, focusing on increasing canopy cover equitably” (City of Austin 2020).

Climate and Economic Justice Screening Tool

The Climate and Economic Justice Screening Tool (CEJST) was developed in response to Executive Order 14008, which directed the agency to create the tool in January of 2021 (Council on Environmental Quality 2022a). CEJST was designed for federal agencies to identify disadvantaged communities, aligning with the goal of the Justice 40 initiative (Council on Environmental Quality 2022a). The initiative’s goal was for “40 percent of the overall benefits of certain Federal climate, clean energy, affordable and sustainable housing, and other investments flow to disadvantaged communities that are marginalized by underinvestment and overburdened by pollution” (The White House [date unknown]). One such investment was the USDA Forest Service’s Urban and Community Forestry Inflation Reduction Act Grants, which awarded over $1 billion in grant funds to projects that “provide equitable access to trees and nature, and the benefits they provide, especially in disadvantaged urban communities” (USDA Forest Service [date unknown]). Grantees were encouraged to utilize CEJST to identify disadvantaged communities in their proposals.

CEJST identifies disadvantaged communities nationwide, including the District of Columbia and all United States territories (The White House [date unknown]). Communities are considered disadvantaged “if they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden” or “if they are on land within the boundaries of Federally Recognized Tribes” (Council on Environmental Quality 2022b). Additionally, a community is considered disadvantaged if it “is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income” (Council on Environmental Quality 2022b). The tool highlights disadvantaged communities at the census tract level and categorizes burdens related to climate change, energy, health, housing, legacy pollution, transportation, water and wastewater, and workforce development (Council on Environmental Quality 2022b). Table 1 summarizes the categories of burden, thresholds, and data used in CEJST to identify disadvantaged communities. Version 1.0 of CEJST, released on 2022 November 22, was utilized for this comparison (Council on Environmental Quality 2022b).

To use CEJST, users input an address, city, state, or zip code into the search bar and are directed to the corresponding area on the map. Disadvantaged tracts are highlighted in blue, and users can explore tract data and categories of burden by selecting a tract. Overall, CEJST is a user-friendly tool with simple navigation. Data from CEJST were retrieved on 2024 January 14 from the tool’s website (Council on Environmental Quality 2022c).

American Forests’ Tree Equity Score

American Forests is a national nonprofit headquartered in Washington, DC, USA. The organization was originally established in 1875 as the American Forestry Association and advocated for the creation of the United States Forest Service (American Forests 2025a). American Forests works to “continue to be a leader for forest conservation and believe[s] trees are a solution to two major crises: climate change and social inequality” (American Forests 2025a). American Forests launched Tree Equity Score in 2020 (American Forests 2020). The tool was developed to “make the case for planting trees in the neighborhoods that need them the most, and allocating the resources needed to do so” (American Forests 2020). It displays a tree equity score for all urban localities within the United States and census block groups that make up those areas. Beyond using the tool to view tree equity scores, filters can be used to further explore underlying data, and reports can be generated.

The variable, tree equity score, is at the heart of the tool. Tree equity scores (TES) were calculated by multiplying 2 variables: the baseline gap score and the priority index (American Forests 2025b). $\text{TES}=100\left(1-{\text{GAP}}_{\text{Score}}\times E\right)$

The canopy gap score (GAP_Score) was normalized from 0 to 100 for each urban area and resulted from dividing tree canopy cover gap by the maximum tree canopy cover gap across each urban area. The priority index (E) was calculated by first combining equally weighted indicators comprising age, employment, health burden, income, linguistic isolation, race, and heat extremity. The criteria were normalized following min-max normalization and then combined to create the priority index. The resulting TES ranges from 0 to 100, where a lower score indicates a higher priority. Table 2 summarizes variables and data used to calculate TESs and prioritize block groups within the tool.

To use Tree Equity Score, users first enter a location or address into a search bar. Users will then see a composite score for an entire locality. Composite scores are an “overall assessment of Tree Equity in a locality (city, town, village or other)” (American Forests 2025b). Zooming in allows users to view TESs for each block group within the area. TESs range from 0 to 100 with lower scores shown in orange, indicating a higher priority, while higher scores are shown in green, indicating a lower priority. By selecting an individual block group, users can further explore data used to calculate the TES. Filters and layers can also be used to explore other trends within datasets utilized in the tool. Reports can also be generated and shared at various map scales. Overall, Tree Equity Score is a user-friendly and easy-to-navigate tool. It allows users to explore multiple datasets and apply filters to look beyond TESs. Data were retrieved from Tree Equity Score on 2024 January 13 from the tool’s website (American Forests 2025b).

i-Tree Landscape

i-Tree is a suite of peer reviewed tools used to assess the benefits of trees and forests. The suite of tools was and continues to be developed through a collaborative partnership between the USDA Forest Service, Davey Tree Expert Company, Arbor Day Foundation, Urban and Community Forestry Society, International Society of Arboriculture, and Casey Trees (i-Tree 2006a). i-Tree’s vision is “to improve forest and human health through freely available, user friendly technology that engages people throughout the world in enhancing forest management and resiliency” (Nowak 2021). The i-Tree suite was originally released in 2006 with a limited number of tools and has grown over the years with tools designed for a wide range of users from the general public to natural resource professionals (Nowak 2021).

i-Tree Landscape is one of the core tools within the larger i-Tree suite that allows users to identify and prioritize areas for climate or social justice, like planting or protecting trees, using multiple datasets (i-Tree 2006b; Nowak 2021). It can also be used to explore datasets, assess the benefits and value of trees, and understand risks to people and forests (Nowak 2021). The tool is customizable, allowing users to determine both the geographic area and datasets used for prioritization. It also has 3 common scenarios for prioritization comprising Population, Minorities, and Poverty. The boundary area and scale used to generate results can be user-defined. The smallest unit of area for prioritization is the census block group, which was used in this comparison. For the purposes of this comparison, 2 of the common scenarios were selected, Minorities and Poverty, since they include variables related to urban forest equity. i-Tree Landscape calculates a Priority Index (PI) from 0 to 100, where a higher score indicates a higher priority (i-Tree 2006c). The indices and weights used are determined by the user or defined by the common scenario. $\text{PI}=\left(\text{index}\_1\times \text{weight}\_1\right)+\left(\text{index}\_2\times \text{weight}\_2\right)+\left(\text{index}\_3\times \text{weight}\_3\right)$

Table 3 summarizes the indices, weights, data sources, and datasets used by the Minorities and Poverty scenarios in i-Tree Landscape to prioritize areas.

To use i-Tree Landscape, users first enter a location of interest in the search bar. Units of area are then selected, such as block groups or places. After the desired areas and scale for prioritization are selected, data is then processed by the tool. Users have the option to explore additional location-based datasets and tree benefits related to the defined area. Next, a prioritization scenario is defined based on a user-defined or common scenario. Finally, results in the form of a report can be generated. Compared to the other tools in this report, i-Tree Landscape was more technical and not as user-friendly. Data from i-Tree Landscape were generated on 2024 January 28 by utilizing the Minorities and Poverty scenarios in Austin.

Texas A&M Forest Service’s My City’s Trees

My City’s Trees is a web-based application developed by Texas A&M Forest Service in partnership with the USDA Forest Service. Texas A&M Forest Service is Texas’ state forestry agency and one of the lead agencies in the state for incident management (Texas A&M Forest Service 2025). Founded in 1915 and headquartered in College Station, Texas, USA, the agency is part of the Texas A&M University System. The agency has a history of developing a diverse array of tools related to forestry and natural resource management, all of which are freely available on their Texas Forest Information Portal.

The primary function of My City’s Trees is to display Urban Forest Inventory and Analysis data for cities across the United States (Texas A&M Forest Service 2024). It also allows users the ability to explore various themes and generate custom reports. Themes are geospatial layers that vary by city and convey data related to local resource issues (Texas A&M Forest Service 2024). In Austin, 6 themes were available comprising land cover, city growth, watershed, ecoregion, social vulnerability, and heat island. For this investigation, the social vulnerability theme was selected for comparison since it utilizes a variable related to equity. The social vulnerability theme in My City’s Trees is taken from the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry (ATSDR) social vulnerability index (SVI). Social vulnerability is defined as “the degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, may affect that community’s ability to prevent human suffering and financial loss in the event of disaster” (ATSDR 2022). SVI ranks census tracts across the United States on 16 social factors across 4 themes including socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation (ATSDR 2022). Table 4 summarizes the themes, social factors, data sources, and datasets from the SVI used in My City’s Trees’ social vulnerability theme to identify areas. The social vulnerability theme used in My City’s Trees combined census tracts to form 4 classes and truncated data at the city boundary (Texas A&M Forest Service [date unknown]). Classes for social vulnerability include highest vulnerability, moderate to high vulnerability, low to moderate vulnerability, and lowest vulnerability.

To access the social vulnerability theme in My City’s Trees, users first select a city from a panel on the left-hand side of the tool. Then, users use the panel to select the social vulnerability theme, with the option to filter on classes within the theme. Overall, My City’s Trees is an easy-to-use tool. Although its core functionality is not related to equity-based mapping, the social vulnerability theme allows users to explore a useful metric for urban forest equity-based planning. Data were retrieved from Texas A&M Forest Service on 2024 February 5 by contacting My City’s Trees.

City of Austin’s Community Tree Priority Map

The Community Tree Priority Map was developed by the Community Tree Preservation Division’s Urban Forestry Program at the City of Austin. Located within the Development Services Department, the Community Tree Preservation Division is responsible for administering the city’s tree protection and replanting regulations and implementing Austin’s Urban Forest Plan (A Halter, personal communication). The Community Tree Priority Map was released in 2020 and “serves as a decision support tool to determine where to focus forestry activities in Austin, Texas” (Halter 2015). The tool is used internally within the City of Austin to prioritize programing and reporting (A Halter, personal communication). Externally the tool is used by several local organizations including Austin Parks Foundation, TreeFolks, The Trail Conservancy, Fruitful Commons, and Austin Independent School District (A Halter, personal communication). In 2015 an earlier version of the tool was released called the Planting Prioritization Map, which was designed for tree planting prioritization and did not include an emphasis on equity (A Halter, personal communication).

The tool displays areas of high and low priority based on a calculated priority score at the census tract level. The priority score “combines nine measures normalized and summarized into four broad categories” comprising natural environment, social vulnerability, community investment, and health and well-being using the min-max feature scaling method for normalization (Halter 2020). The formula used can be seen below: $\text{Priority}\text{Score}=\left(\sum \text{Natural}\text{Environment}+\sum \text{Social}\text{Vulnerability}+\sum \text{Community}\text{Investment}+\sum \text{Health}\text{\&}\text{Well-Being}\right)/4$

Table 5 summarizes the categories, measures, data sources, and datasets used by the Community Tree Priority Map to calculate priority scores. Scores range from 0 to 100, where higher values indicate a higher priority and lower values indicate a lower priority (Halter 2020). Tracts with a priority score greater than 50 are categorized as high priority, while tracts with a priority score less than or equal to 50 are categorized as low priority (Halter 2020). Although the tool distinguishes visually between high and low priority areas, priority scores for each tract can be viewed along with additional data when the user selects a tract.

To use the Community Tree Priority Map users can either search for a specific location using the search bar or zoom to an area of interest. Higher priority areas were shown in red, while lower priority areas were shown in green. Users can select individual tracts to view priority scores and data used to calculate the priority scores. Overall, the tool was easy to use and navigate. Data were retrieved from the Community Tree Priority Map on 2024 January 14 from the City of Austin’s website (City of Austin 2022).

Comparisons

In total, 4 comparisons were conducted which focused on the methodologies, data, boundaries, and results of the tools. The first comparison was of each tool’s methodology. For the purposes of this comparison, the term “identify” was used when a tool identified areas but did not calculate a score for each individual unit of area that could be compared to others. The term “prioritize” was used when a tool calculated a score such that each individual unit of area could be compared to others. The information for this comparison was obtained from the tools’ technical documentation.

Next, the data sources and dataset utilized by the tools to identify or prioritize areas were compared for similarities. For this comparison the term “data source” refers to an entity that produced or published data, like the United States Census Bureau, while the term “dataset” refers to the specific dataset produced or published by the source, like the American Community Survey 2017–2021. For some of the variables used in the mapping tools, only data sources were able to be retrieved while others referenced the specific dataset from each source. Additionally, only the data sources and datasets used to prioritize or identify areas were compared as opposed to all the potential data available in the tools. For example, while i-Tree Landscape has a long list of data that it can utilize, only the data sources and datasets used by the Minorities and Poverty scenarios to prioritize block groups were included in this comparison. The information for this comparison was obtained from the tools’ technical documentation.

Each tool was then applied to the study area. The resulting data from each tool were acquired directly from the tool or developing organization. The data from CEJST, Tree Equity Score, My City’s Trees, and the Community Tree Priority Map were acquired as shapefiles or file geodatabases and imported into ArcGIS Pro (Esri, Redlands, CA, USA) to conduct the boundary and results comparisons. Data from i-Tree Landscape were generated in tabular form. The data was joined to the corresponding TIGER/ Line shapefile for use in ArcGIS Pro.

The boundaries of the tools were compared by overlaying the results from each tool in ArcGIS Pro. For this comparison, some adjustments were made to resulting data to more accurately reflect what each tool considered Austin in the online interface. For Tree Equity Score, a filter was applied to ensure only results for Austin were displayed and not other localities, as the original shapefile contained results for all of Texas. The data from CEJST matched the results in the online interface, therefore no filters or adjustments were made. For the Community Tree Priority Map, there was a difference in the boundary of the data displayed in the online tool compared to the boundary of the data downloaded. A filter was used in the online version of the Community Tree Priority Map, while the downloadable file covered a larger geographic area in and around Austin. For the purposes of this comparison the boundary of the downloaded file was used to avoid truncating census tracts. i-Tree Landscape differed from other tools in this comparison as it allowed users to define the area of coverage. A larger area could have been selected for this comparison, however, only the block groups that were contained in or intersected with Austin were selected using the “swap” tool within i-Tree Landscape. The “swap” tool was the quickest and easiest way to select all the block groups in and around a place, therefore, it was utilized in this comparison. These boundaries were used in subsequent comparisons. It is important to note that although each tool prioritizes or identifies areas in Austin, the geographic representation of Austin differed amongst the tools.

Finally, the results of the mapping tools were compared. First, the areas identified as most in need of the benefits of trees were compared by overlaying the results of the tools in ArcGIS Pro. For each tool, determination of what the tool identified as an area of need was made based on how the tool classified or conveyed results to the user. First, if a tool used the words “high” or “highest” to classify an area, then that was considered an area of identified need. If a tool did not include “high” or “highest” descriptors, then a change in color was used. For example, in Tree Equity Score the tool defines a range of scores for highest and high priority areas, therefore those ranges were used for this comparison. Whereas in i-Tree Landscape, the tool does not define a similar range of scores, however the legend on the resulting prioritization map changes color at a score 50, therefore that score was used to delineate an area of identified need for this comparison. Areas of overlap were identified and counted by utilizing the count overlapping features tool in ArcGIS Pro. Next, the 5 highest ranked block groups were selected from Tree Equity Score and i-Tree Landscape and compared to CEJST, My City’s Trees social vulnerability theme, and the Community Tree Priority Map. For each block group, the same geographic area was analyzed amongst each tool to determine how it was prioritized or identified by all mapping tools in ArcGIS Pro. Areas of alignment or misalignment were then identified. For example, if Tree Equity Score identified a block group as high priority, but CEJST did not consider that same area as disadvantaged, then it was considered a misalignment. If all tools identified the area as most in need of the benefits of trees, then it was considered an alignment. If the area was out of a tool’s boundary or if no data existed for the area, then it was not considered in the alignment determination.

Methodology Comparison

Each mapping tool utilized a different methodology to prioritize or identify areas most in need of the benefits of trees. The unit of areas prioritized or identified also differed for each tool with primarily census tracts or block groups used. A summary of methodologies can be seen in Table 6.

Data Comparison

Not only was each mapping tools’ methodology different, but additional differences were found in both the sources of data used by the tools and the datasets utilized. Surprisingly, only a few of the datasets were used by more than one tool. The most commonly used dataset was the 2010 Census from the United States Census Bureau. This dataset was primarily used for geographic boundaries of block groups or tracts, but i-Tree Landscape also used the 2010 Census for demographic variables. The United States Census Bureau’s American Community Survey was most commonly used for demographic variables amongst most of the tools, however the timeframe of the survey varied. Both Tree Equity Score and the Community Tree Priority Map utilized the United States Geological Survey’s Landsat 8 data for heat-related variables. The only other shared dataset was the Centers for Disease Control and Prevention’s PLACES data. Both CEJST and Tree Equity Score used the data for health-related variables, although each tool used datasets from different years. Similarly, the Community Tree Priority Map used data from the Centers for Disease Control and Prevention’s 500 Cities Project for health-related variables, which was replaced by PLACES.

Boundary Comparison

The boundaries of each mapping tool also differed. Although each tool generated results for Austin, Texas, USA, the geographic boundary of Austin was different for each tool. Figure 1 highlights the differences in boundaries. CEJST was the only tool without a defined boundary for Austin. The tool’s results span the entire United States, including the District of Columbia and all territories. CEJST covered the largest amount of area in and around Austin due to this lack of boundaries. The Community Tree Priority Map covered the second largest geographic area in and around Austin. i-Tree Landscape covered the third largest geographic area in and around Austin. Finally, Tree Equity Score and My City’s Trees resulted in the smallest areas of coverage compared to the other tools.

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Figure 1.

Boundary and coverage comparison of mapping tools in Austin, TX, and surrounding areas.

Results Comparison

The differences in functionality, methodologies, data, and boundaries led to different results from each mapping tool in this comparison. Figure 2 emphasizes the differences in results for each tool when the areas identified as most in need of the benefits of trees are combined into a single map. It includes:

• CEJST’s disadvantaged areas

• The Community Tree Priority Map’s high priority areas

• Tree Equity Score’s highest (0 to 69) and high (70 to 79) priority areas

• i-Tree Landscape’s Minorities scenario priority areas (greater than or equal to 50)

• i-Tree Landscape’s Poverty scenario priority areas (greater than or equal to 50)

• My City’s Trees’ social vulnerability theme moderate to high and highest vulnerability areas

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Figure 2.

Areas determined to be most in need of the benefits of trees from each tool compared to an overlay in Austin, TX, and surrounding areas with areas of overlap between multiple tools counted and the area covered by all tools outlined.

These parameters were overlaid such that darker areas on the map indicate those determined to be an area of identified need by more than one tool. The area covered by all tools is also outlined.

After comparing the outputs of the tools via Figure 2, it becomes clear that each tool answered the question where are the benefits of trees needed most? differently, although there were some areas of overlap. Each tool identified or prioritized areas predominantly on the east side of Austin, which was expected given the low percentage of tree cover and higher percentages of people experiencing poverty and people of color. However, differences were found in the areas identified or prioritized within the east side.

Further, differences exist within the alignment of the highest ranked block groups from Tree Equity Score and i-Tree Landscape across the tools. Table 7 displays the results of this comparison. Within Tree Equity Score’s 5 highest ranked block groups, 4 out of 5 block groups were found to be in alignment with the other mapping tools. Within i-Tree Landscape Minorities scenario’s 5 highest ranked block groups, which included 7 block groups due to tied ranks, 3 out of 7 block groups were found to be in alignment with the other mapping tools. Within i-Tree Landscape Poverty scenario’s 5 highest ranked block groups, which included 7 block groups due to tied ranks, only one was found to be in alignment with the other mapping tools.