Create an environmental justice index map

You are a geospatial technician in a state-level government agency concerned with environmental justice. You have been tasked with creating a screening tool that will allow state and local agencies to make decisions based on equity, ensuring that areas that are experiencing a cumulative burden of social and environmental harms are getting the most support. To accomplish this, you will design and create a composite index.

Environmental justice is the process of addressing the historic and current conditions that cause certain communities to experience a disproportionate burden of environmental hazards. It is a process that involves equity, because it recognizes that some communities—usually under-resourced communities of color—experience higher exposure to environmental hazards and are therefore more likely to develop negative health outcomes due to environmental risks. The process of mapping and analyzing inequity is an important first step in the racial equity and social justice workflow.

An index is a number that measures a subject of interest, often something that is difficult to directly measure or define, such as social vulnerability or business innovation. The Calculate Composite Index tool creates an index by combining multiple variables into a single indicator. The tool follows a three-step workflow to preprocess the variables, combine the variables, and postprocess the index. In this tutorial, you will walk through best practices for creating a composite index in ArcGIS Pro.

This tutorial was last tested on November 21, 2023, using ArcGIS Pro 3.2. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• ArcGIS Pro (see options for software access)

Design an equity index

The method to create indices is not complex, but what is often the most complicated component is acquiring a detailed understanding of each decision throughout the workflow.

The design phase is an important step to define the intended purpose of the index more clearly and to consider and document justification for the selection and purpose of each indicator and weights.

Note:

For a more detailed guide on best practices for creating composite indices in ArcGIS, see the Creating Composite Indices Using ArcGIS: Best Practices (PDF) technical paper.

Determine index design components

In the design phase of developing a composite index, you will define the question your index will answer, select and assign weights to the variables that will make up the index, and determine the study area of the index.

Before determining the variables or indicators you will use for the index, it is important to work with a broad range of stakeholders, including residents in the most affected areas, to define the purpose of the index and the analysis question your index will seek to answer.

1. Determine the stakeholders you will include in the decision-making process for designing the index.

   Since you will be creating an equity index related to environmental justice, the following are stakeholders you can consider including:

   • Community-based organizations with established trust in environmental justice communities
   • Community coalitions that work on environmental issues or health
   • Health providers
   • Domain experts, such as academics and consultants
   • Department of Transportation
   • Public Health Department
   • Department of Planning and Environmental Regulations

   

   The most important stakeholders to include are members of the impacted community. Whenever you are conducting an equity analysis involving a vulnerable population, it is vital to include the people who are experiencing the inequities firsthand in the decision-making process.

   Note:

   Read examples of partnerships that are using GIS to advance racial equity, social justice, and inclusive development in the Equity and Social Justice section of the Esri blog.

2. Identify the purpose of the index and the analysis question the index will answer.

   The following are some questions you can answer to narrow your index purpose and analysis question:

   • How will the index map be used? Will it help you determine resource allocation? Will it help you identify areas experiencing disproportionate social or environmental burdens?
   • Who is this index map for? Will it primarily be for local community members to view and understand their community? Or is it for decision makers to make data-driven policy decisions? Or both?
   • Is it important to understand the difference in inequality for all areas of your project area or are you most concerned about the areas with a much higher burden?

   For this tutorial, the purpose of the index is to create an environmental justice screening tool to determine which areas of the state of Ohio are experiencing the most environmental and social burdens compared to other areas of the state. The analysis question you want to answer is: What are the cumulative environmental and social burdens are communities experiencing across the state?

   Now that you have defined your analysis question, you will work with stakeholders to determine priorities and dimensions that are important to include in the index.

3. Determine the priorities and dimensions to include in the index.

   Since your goal is to determine cumulative environmental and social burdens that communities are experiencing, your stakeholders identified that it will be important to consider exposure to environmental hazards and vulnerability to environmental hazards.

   Environmental hazards consider potential exposure to air pollutants and toxic chemicals, as well as mitigating factors like distance to green space. The dimension of vulnerability to environmental hazards considers social factors that may prevent people from having resources to respond to environmental hazard exposures, such as those who are very young or old, those with low incomes, and those with pre-existing health conditions that made more sensitive to environmental pollution.

   

4. Choose variables.

   It is recommended that you seek the advice of subject matter experts who are familiar with the factors that contribute to your dimensions. Consider consulting with the following types of resources:

   • Domain-specific literature, such as peer-reviewed academic journal articles and studies, subject matter books, and conference proceedings
   • Domain experts, such as community and advocacy practitioners, academic researchers and professors, and consultants
   • Members of the community and groups that represent their interests, especially those who live in areas that have historically been affected by environmental justice issues

   Note:

   See the Special considerations for variable selection section on page 11 of the Creating Composite Indices Using ArcGIS: Best Practices (PDF)technical paper.

   Also see the Handbook on Constructing Composite Indicators: Methodology and User Guide (online PDF) by the Organisation for Economic Co-operation and Development (OECD), an international organization that develops data and research for best practices for public policies.

   For this tutorial, you will use the following variables:

   • Asthma prevalence
   • Percent unemployed
   • Percent with income below 200 percent poverty level in the past year
   • Percent over 25 years old with education less than high school
   • Percent under severe housing burden (over 50 percent income spent on rent or mortgage)
   • Child lead risk (percent children below the poverty level and housing built 1949 or earlier)
   • Distance to nearest park
   • Average PM 2.5 from 2014 to 2016
   • Traffic counts at major intersections
   • Amount of toxic chemicals released within one mile

   This list of indicators is not meant to be prescriptive or comprehensive for an environmental justice index. These 10 indicators will be used for this tutorial to serve as a learning resource on how to design and create a composite index. When creating an environmental justice index, ensure you are following the steps specific to your own jurisdictional experiences, needs, and priorities.

   Note:

   The indicators selected for this tutorial are a sample of indicators derived from the CalEnviroScreen 4.0. To learn more about the indicators and methods CalEnviroScreen used for their indicators, see CalEnviroScreen 4.0 (PDF).

5. Set variable weights.

   Variable weights represent the relative importance of each variable as it contributes to the index. Whether you choose to keep equal weights or add weights, the decision should be backed by a strong rationale.

   For this tutorial, you will not add any weights. You are interested in the cumulative burden of all these variables when combined, so adding a weight in this case will not be necessary.

   Note:

   Later in the tutorial, you will reconsider weighting due to correlation among the social vulnerability variables. More will be discussed about the rationale for weighting subindices in that section of the tutorial.

6. Choose the study area and spatial units.

   The spatial unit corresponds to each location in the index, and the study area is the area covered by all the spatial units in the study area. Generally, it is recommended that you use the smallest area (or highest resolution) possible to ensure each geographical unit has little variation within it. This will maximize the possibility that the variable values are reflective of the lived experiences of the people in that unit.

   For this tutorial, you are interested in the state of Ohio for your study area. You will use census tracts as the spatial units.

In this section, you have completed the design step of the composite index workflow. You determined your list of stakeholders, defined the index analysis question, selected variables using no weights, and determined your study area and spatial units.

Create a composite index

In the previous section, you walked through the steps of designing a composite index. The next step in the workflow is to create and prepare the variables before creating the index. For the sake of brevity, the variables have been prepared for you to use in an ArcGIS Pro package.

Explore indicators

First, you will download the ArcGIS Pro package and use tools in ArcGIS Pro to explore each of the indicators before creating the composite index.

1. Download and open the ArcGIS Pro package for this tutorial.

   First, you will explore the indicators that have been prepared in the ArcGIS Pro package.

2. In the Contents pane, right-click the OhioEJIndicators layer and choose Attribute Table.

   

   The attribute table appears for the OhioEJIndicators layer.

3. In the attribute table, scroll to the right to view the indicators.

   Although you can view more details about the data in the feature layer, it is difficult to fully understand the range of information for each field. You will use the Data Engineering view to generate and investigate statistics on each of the indicator fields.

4. Close the table.
5. In the Contents pane, right-click the OhioEJIndicators layer and choose Data Engineering.

   

   The Data Engineering view appears.

6. Drag the Current asthma crude prevalence (%) field into the statistics panel.

   

   The Current asthma crude prevalence (%) field adds to the statistics panel. Next, you will add the remaining nine indicators into the statistics panel.

7. Drag the following nine indicators to the statistics panel:
   • Percent Unemployed
   • Percent income in past year below 2x poverty level
   • Percent 25+ Education Less Than High School
   • PerSevHousingBurden
   • ChildLeadRisk - Mean
   • Distance to nearest park (miles)
   • Avg PM2.5 2014--2016
   • Sum Traffic
   • Toxic Release Chemicals (lb/km2) within 1 mile

   Tip:

   To select multiple fields, you can click the first field you want to add, press Shift, and click the last field you want to add. All the fields in between them will be selected.

8. At the top of the Data Engineering view, click Calculate.

   

   Statistics appear for each of the fields. Before you explore the data, you will freeze the Alias column so it continues to be visible as you scroll through the statistics panel.

9. Right-click Alias and click Freeze/Unfreeze.

   

   The Alias column is now locked to the first column in the statistics panel.

   

   The Chart Preview column shows a histogram of each indicator's values. This is a helpful way to notice the data distribution and to consider what methods would be best to use for your composite index.

10. Scroll to the Outliers column.

    In the Outliers column, you can view the number of data values that are outliers. You can also use the Data Engineering view to highlight them on the map so you can visualize where they are located.

11. For the Percent income in past year below 2x poverty level row, right-click the Outlier value and choose Select Outliers.

    

    The census tracts with outlier values for the Percent income in past year below 2x poverty level indicator highlight on the map.

    

12. On the ribbon, click the Map tab, and in the Selection group, click Clear Selection to clear the selection.

    

    You can also use the Data Engineering view to select values by quartiles for each indicator.

13. In the Data Engineering view, in the statistics panel, scroll to the Q3 column.
14. For the Percent Unemployed row, right-click the Q3 value, point to Select, and click Above Quartile.

    

    Values above the third quartile represent values that are in the top 25 percent of all the values for a field. The map selects the census tracts with the highest 25 percent of unemployment rates.

    The Data Engineering view also identifies any null values.

15. In the Data Engineering view, in the statistics panel, scroll to the Nulls column. For the Percent 25+ Education Less Than High School row, right-click the Nulls value, and click Select Nulls.

    The tracts with null values for the Percent 25+ Education Less Than High School indicator highlight on the map. These tracts represent areas that do not have any population, so they have null values. The Calculate Composite Index tool will automatically ignore these records and exclude them from the index calculation.

16. Continue exploring the indicators. When you are finished, clear any selection and close the Data Engineering view.

In this section, you explored the 10 indicators that will be used to create a composite index for environmental justice using Data Engineering. Next, you will use the Calculate Composite Index tool to preprocess, create, and postprocess a composite index.

Use the Calculate Composite Index tool

You will use the Calculate Composite Index tool to create a composite index. You will explore each of the tool parameters for preprocessing, combining, and postprocessing the indicators.

1. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Tools.

   

2. In the Geoprocessing pane, on the search bar, type calculate composite index.
3. In the list of results, click the Calculate Composite Index tool.

   

4. In the Calculate Composite Index tool pane, for Input Table, choose OhioEJIndicators. For Output Features or Table, type Ohio_EJIndex.

   

5. Next to Input Variables, click the Add Many button.

   

6. On the Add Many menu, check the 10 indicator fields and click Add.

   

   Next, you will choose the parameters for preprocessing and combining the indicators. You can use the Preset Method to Scale and Combine Variables parameter to choose common methods of scaling and combining the indicators. You can also manually choose a Method to Scale Input Variables option and a Method to Combine Scaled Variables option.

   For now, you will keep the default selection, which is to combine values by mean of scaled values using the minimum to maximum method of scaling.

7. Ensure Preset Method to Scale and Combine Variables is set to Combine values (Mean of scaled values).

   

   Next, you will review the Variable Weights settings.

8. Expand the Variable Weights section.

   

   At this point, you want to treat each indicator with the same weight, because you are concerned about how they will combine and reflect the cumulative burden communities face. You will not change the weights.

   Finally, you will configure the Output Settings options and postprocessing parameters.

9. Expand the Output Settings section.
10. For Output Index Name, type Ohio_EJIndex. For Minimum, type 0, and for Maximum, type 100.

    

    Setting the Output Index Minimum and Maximum Values range between 0 and 100 is one way to make the resulting index score quickly understandable.

    You have completed setting the preprocessing, combining, and postprocessing parameters in the Calculate Composite Index tool.

11. Click Run.

    The resulting composite index map appears.

    

    Note:

    Notice the tracts that had a null value for any of the indicators were excluded from the composite index.

    In the Contents pane, the Ohio_EJIndex Layers group layer contains all of the Calculate Composite Index tool outputs.

    

    Two maps were created. Ohio_EJIndex - Mean is styled by the raw index values scaled between 0 to 100. It also contains a number of charts for investigating the index output in more detail. Ohio_EJIndex - Percentile styles the index mean values by percentile values.

    Before you continue, you will save the project.

12. On the Quick Access Toolbar, click Save.

    

In the next section, you will explore the charts created by the Calculate Composite Index tool and evaluate your index results.

Examine the relationship of scaled variables

The Calculate Composite Index tool produces resulting maps of the composite index as well as charts to help you evaluate and assess your index results. You will focus on the matrix relationship chart.

1. In the Contents pane, double-click Relationship of Scaled Variables and Index Variables.

   

   The matrix chart appears. The most critical row to pay attention to is the bottom row, which shows any correlations between the index score and each indicator.

   

   The green cells denote high correlations. In other words, as one variable goes up, so does the other. For example, the Percent Unemployed and Percent below 2x the poverty level indicators are 55 percent correlated.

   The pink cells donate negative correlations. In other words, as one variable increases the other decreases. For example, the Distance to toxic releases indicator is negatively correlated with the Percent severe housing burden indicator. This could be because housing burden is more correlated with urban areas and toxic releases may be more correlated with rural areas.

   Another finding that stands out in this matrix chart is that there are some very high values—0.85 for income, which means is that the final index is almost 90 percent correlated with this indicator. There are also some very low values—0.03 for distance to toxic releases. Although you weighted each input equally, the correlations are different from each other. This could be due to the ranges of the variables or the correlation among the inputs.

   

   Many of the indicators related to proximity to environmental factors have a much lower correlation with the index scores. This is expected as many of the social vulnerability indicators likely correlate strongly with populated areas and therefore correlate strongly to each other, increasing the correlation effect.

   

   One way to gain more control of the indicators and the correlations among them is to group them into subindices. You determine that you will create two subindices and combine them again in the next iteration of the index.

2. Locate column for the Toxic Release Chemicals indicator.

   

   The distribution of the Toxic Release Chemicals indicator values is especially interesting because most of the values are very low values and only a few are very large. This means that the minimum-maximum method of preprocessing may not be the best choice given the intense skew for this indicator. You determine that it would be better to use a percentile preprocessing method in your next iteration.

3. Close the chart.

You used the Data Engineering view to explore the indicators for the index. You used the Calculate Composite Index tool to preprocess, combine, and postprocess the index. The tool created an index map and charts that you investigated, and you determined that you will need to create subindices to produce a more accurate environmental justice index map.

Create subindices for the final index

You have created an environmental justice index by combining 10 indicators and preprocessed it using the minimum-maximum preprocessing method. Upon examining the resulting composite index, you determined that it would be better to use the percentile preprocessing method and to create subindices for each of the dimensions of the index.

Create subindices

You will prepare two subindices—one for the social vulnerability indicators and one for the environmental indicators. You will use the Calculate Composite Index tool to create each of the subindices.

1. If necessary, open the Geoprocessing pane, search for and open the Calculate Composite Index tool pane.
2. In the Calculate Composite Index tool pane, enter the following:
   • For Input Table, choose OhioEJIndicators.
   • Check the box for Append Fields to Input Table.

   

   Since you will eventually combine the subindex scores together, it is recommended that you append the tool output to the input table instead of starting a new feature layer and table.

   Next, you will add the indicators related to social vulnerability and outcomes.

3. For Input Variables, click the Add Many button. Check the boxes for the following fields:
   • Current asthma crude prevalence (%)
   • Percent Unemployed
   • Percent 25+ Education Less Than High School
   • PerSevHousingBurden
   • Percent income in past year below 2x poverty level

   

4. Click Add.

   The five indicators related to social vulnerability are added to the Calculate Composite Index tool pane.

5. For Preset Method to Scale and Combine Variables, choose Combine ranks (Mean of percentiles).

   

6. Expand Output Settings and enter the following:
   • For Output Index Name, type SV subindex.
   • Under Output Index Minimum and Maximum Values, for Minimum, type 0.
   • For Maximum, type 100.

   

7. Click Run.

   The SV subindex fields have been created and added to the attribute table in the OhioEJIndicators layer.

8. In the Contents pane, right-click the OhioEJIndicators layer and click Attribute Table. In the table, scroll until you see the SV subindex fields.

   The fields that were added include the preprocessed values of the five social vulnerability indicators and the subindex mean values.

   Next, you will use the Calculate Composite Index tool to create the environmental subindex.

9. In the Calculate Composite Index tool pane, under Input Variables, point to each field and click the Remove button.

   

10. Next to Input Variables, click the Add Many button, and check the fields related to environmental indicators:
    • ChildLeadRisk Score
    • Distance to nearest park
    • Avg PM2.5 2014-2016
    • Sum Traffic
    • Toxic Release Chemicals
11. Click Add.
12. Confirm that Preset Method to Scale and Combine Variables is set to Combine ranks (Mean of percentiles).
13. Under Output Settings, for Output Index Name, type ENV subindex.

    

14. Click Run.

    The fields for the ENV subindex are added to the OhioEJIndicators attribute table.

15. Save the project.

    Tip:

    You can save the project by pressing Ctrl+S.

You have created two subindex values and appended them to the OhioEJIndicators attribute table.

Combine the subindices and examine results

In this section, you will use the Calculate Composite Index tool to combine the two subindices, adding a weight to the environmental subindex.

You no longer need to append the output to the OhioEJIndicators attribute table, so you will uncheck the Append Fields to Input Table option.

1. In the Calculate Composite Index tool pane, update the following parameters:
   • Under Input Table, uncheck the box for Append Fields to Input Table.
   • For Output Features or Table, type Ohio_EJIndex_Final.
   • Under Input Variables, remove the existing fields and add SV subindex - Mean and ENV subindex - Mean.

   

   Earlier in the tutorial, you observed that there was a high level of correlation between the social vulnerability indicators. Their strong correlation impacted the overall index score and caused the environmental related indicators to not contribute very much to the resulting index score.

   By creating the subindices, you corrected for this. In addition to the use of subindices, your expert panel advising the development of this environmental justice screening tool recommended an additional 50 percent weight to the environmental factors.

2. Expand the Variable Weights section. For the ENV subindex - Mean field, type 1.5.

   

3. Under Output Settings, for Output Index Name, type Ohio_EJIndex_Final. Under Additional Classified Outputs, check the box for Equal interval, Quantile, and Standard deviation.

   

4. Click Run.

   The Ohio_EJIndex_Final layer appears on the map.

   

   Next, you will review the matrix chart to better understand the correlations between the two subindices in the final index.

5. In the Contents pane, under the Ohio_EJIndex_Final layer, double-click the Relationships of Scaled Variables and Index chart.

   The chart appears.

   

   Earlier in the tutorial, the matrix chart for the first index you created showed each variables' correlations to one another and to the index score. In this chart, you see each subindex's correlations.

   The environmental subindex is more correlated with the final index than the social vulnerability subindex. This is expected because you added the weight to the environmental subindex.

   The blue bars are histograms of each subindex and the final index score. For the subindices, the distribution forms a flat line. This is the effect of using percentiles as opposed to minimum-maximum preprocessing. Using the percentile method does not preserve the original distribution.

6. Close the chart.

   Next, you will compare this resulting index with the initial index you created that did not use subindices and preprocessed the indicators by minimum-maximum.

7. In the Contents pane, expand the Ohio_EJIndex Layers group layer. Right-click Ohio_EJIndex and click Copy.

   

   You will also turn off all the layers in this map except for the final index layer.

8. In the Contents pane, uncheck the Ohio_EJIndex Layers group layer and the OhioEJIndicators layer so they are no longer visible.
9. On the ribbon, click the Insert tab. In the Project group, click New Map.

   

10. If necessary, click the Map1 view tab. In the Contents pane, right-click Map1 and click Paste.

    

    Next, you will update the basemap on the new map so that it matches the original map.

11. On the ribbon, click the Map tab. In the Layers group, click Basemap and choose Light Gray Canvas.

    

    Next, you will dock Map1 to the side of the first map so you can view them at the same time side by side.

12. Drag Map1 tab and drop it in the right dock of the Map tab.

    

    You can now view the two maps side by side. Next, you will also link the view of the two maps so that their extent matches when you navigate either map.

13. On the ribbon, click the View tab. In the Link group, click the bottom half of the Link Views button and choose Center And Scale.

    

    Now the extent of the maps will match one another when you zoom or pan one of the maps.

    Next, you will explore specific areas in Ohio to compare the two index results.

14. On either map, zoom in to the city of Cleveland, located in the northeast edge of Ohio.

    

    Seeing the two maps side by side, you can see how the results of the first index differ from the second iteration where you combined subindices.

    

    Cleveland is one of the largest urban centers in the state. The initial index results, shown on the right, emphasize the social vulnerabilities. They correlated strongly with populated areas but do not appear to account as much for the potential environmental hazards and risks.

    The second index, which provided more control over the environmental and social dimensions, appears to have captured more of the impacts of environmental exposure in the first map. There are many toxic release facilities along the border of the lake. It is also important to note that the revised index did not simply emphasize environmental hazards; it still maintained priority for areas that had high social vulnerability indicator values.

15. Zoom to the less populated city of Lima, Ohio.

    

    The city of Lima contains the toxic release facility responsible for the most amount of chemicals released in the entire state. In the initial index results, shown in the second image, the index primarily emphasized areas of social vulnerability. The use of the subindex in the resulting index in the first image not only emphasized the substantial environmental impact that a single facility might contribute to the area, but it also still maintains the areas of social vulnerability in the dark purple areas.

    

16. Save the project.

In this section, you used the Calculate Composite Index tool to combine the two subindices into a final environmental justice index map. You examined the correlations between the two subindices in the final index values and compared the results from your index without the use of subindices to the final index.

Explore the index further

The final step of the index creation process involves evaluation, consulting with stakeholders, and refinement. While you will not complete any of these steps for this tutorial, on your own time, consider the following steps to further explore the index.

1. Ensure that those who will be impacted by the index are included in reviewing the index.

   You can share the map to ArcGIS Online and create an app to encourage stakeholder engagement and understanding for the index analysis process.

   

   You can also create a survey with ArcGIS Survey123 and include your final index map so stakeholders can provide specific feedback on a map.

   Note:

   To learn more about creating an app with ArcGIS Instant Apps, see the ArcGIS tutorial Map and analyze food access. To learn more about using ArcGIS Survey123, see the ArcGIS tutorial Get started with ArcGIS Survey123.

2. Expect to iterate and create more versions of the index.

   The index creation process is often a cycle where you create the index and learn something new, resulting in the need to add or remove a variable, adjust weights, or change the combining methodology.

3. Use additional tools in ArcGIS Pro for further analysis of index results.

   Consider using the following tools to conduct spatial clustering and regression analysis:

   • Hot Spot Analysis
   • Cluster and Outlier Analysis
   • Multivariate Clustering
   • Generalized Linear Regression

   These tools can help you identify regions with statistically significant clustering of high and low index values, reveal common patterns that drive index results across the study area, and help justify the design and effectiveness of the index you created.

   

4. Communicate your index creation methodology transparently.

   It is important to include clear documentation for the methods used in creating the index, any assumptions, intended uses, and limitations of the index.

   See the following examples of index methodology documentation:

   • Human Development Index (HDI) by the United Nations Development Programme
   • Social Vulnerability Index by the U.S. Centers for Disease Control
   • European innovation scoreboard by the European Commission

   You have created the final environmental justice index by using two subindices to better control for the dimensions of the index and their correlations. You examined the results and compared the updated index to the original index that did not use subindices.

Creating an equity index is essentially an attempt to quantify a phenomenon that does not have a straight-forward variable that defines it, like environmental justice. The process of designing the index, asking the right questions, and selecting the variables and weights can feel like both an art and a science. It is recommended that you ensure you are including a broad range of stakeholders to support the process, are intentional with each decision in the design, and are prepared to iterate and evaluate results with your stakeholders.

It is important to understand that the process of creating an index is not simply a button-pressing process but is one that requires intentional decision-making and collaboration. Index maps are also not meant to produce the answer to the abstract phenomenon you are seeking to understand but should be treated as one of many tools to help work toward addressing a specific need in a specific study area.

The Calculate Composite Index tool is one part of the entire index design workflow that streamlines the analysis steps in ArcGIS Pro to preprocess, combine, postprocess, and evaluate an index. In this tutorial, you created indices using various preprocessing methods and compared the results of creating an index with and without subindices.

Note:

To learn more about the Calculate Composite Index tool, see the Create a composite index using ArcGIS Pro resource page.

It is important to note that this tutorial only covered -the map and analyze inequities step in the Racial Equity and Social Justice workflow. There are two more important steps to the workflow: operationalize positive practices, which involves determining and applying an intervention to address the inequities, and manage progress towards equity goals, which in involves monitoring the process of the intervention in addressing the inequities.

Note:

To learn more about operationalizing positive practices, see the Operationalize an environmental equity plan tutorial. To see examples of monitoring equity goals, see the Maps Help Ensure Equity in Philadelphia's Journey to Curb Blight article.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• The prepared indicator data in the tutorial Pro project was prepared from the following sources:
  • PLACES: Local Data for Better Health—Centers for Disease Control and Prevention, Robert Wood Johnson Foundation, and the CDC Foundation
  • ACS Employment Status Variables - Boundaries—United States Census Bureau's American Community Survey
  • ACS Poverty Status Variables - Boundaries—United States Census Bureau's American Community Survey
  • ACS Educational Attainment Variables - Boundaries—United States Census Bureau's American Community Survey
  • ACS Housing Costs Variables - Boundaries—United States Census Bureau's American Community Survey
  • ACS Housing Units by Year Built Variables - Boundaries—United States Census Bureau's American Community Survey
  • USA Parks—Esri, TomTom
  • USA Particulate Matter (PM) 2.5 between 1998-2016—NASA Socioeconomic Data and Applications Center (SEDAC) and Esri
  • 2020 USA Traffic Counts—Esri, Kalibrate
  • EPA Facility Registry Service - Toxic Release Inventory (TRI)—U.S. Environmental Protection Agency
• Light Gray Canvas map sources: Esri, HERE, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community