Create a social equity index to improve public health

Organizations worldwide are using geographic information systems (GIS) to address social and racial equity issues. The ArcGIS Social Equity Analysis solution provides spatial decision-making tools for governments and organizations to tackle systemic inequities. Developed in collaboration with Race Forward's Government Alliance on Race and Equity (GARE), the tool helps visualize complex decisions and advance racial equity.

In this tutorial, you are a GIS analyst tasked with improving respiratory health in your community. By creating a social equity index map for Lucas County, Ohio, you can identify areas that require increased funding for respiratory health programs. The index ranks census block groups based on indicators identified with local residents, enabling decision makers to allocate resources where they will have the greatest impact.

Your organization has implemented equitable practices, including community engagement and a community advisory board. Through engagements with schools, parents, physicians, neighborhood groups, and a public health commission, key indicators related to air quality and childhood health have been identified. The social equity index will inform planning and prioritization for historically underinvested families and communities.

This tutorial was last tested on March 15, 2024, using ArcGIS Pro 3.2. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

View final result

Requirements

• ArcGIS Pro (see options for software access)
• 84.83 credits (optional)

Deploy the solution and add data

ArcGIS Solutions provides industry-specific configurations for ArcGIS that are designed to meet key requirements and support common workflows in your organization. Each solution includes one or more applications, surveys, maps, feature layers, and ArcGIS Pro projects that can be configured to meet your needs.

You will begin by deploying the Social Equity Analysis solution from ArcGIS Online and opening the ArcGIS Pro project from the solution.

Download Social Equity Analysis solution

First, you’ll sign in to your ArcGIS account and search for the Social Equity Analysis solution.

1. Sign in to your ArcGIS organizational account.

   Note:

   If you don't have an organizational account, see options for software access.

2. At the top of the screen, click the App button and choose Solutions in the app launcher.

   

   The ArcGIS Solutions page appears.

3. In the search bar, type social equity.

   

4. Click the Social Equity Analysis solution card.

   

   The Social Equity Analysis window appears.

5. In the Social Equity Analysis window, review the slides that outline the solution workflow.

   The Social Equity Analysis solution has four key components:

   • Evaluate community conditions and actions—This component involves mapping community conditions, creating composite indices, and identifying demographic patterns, and perform site selection based on equity goals.
   • Measure progress over time—This component includes an Interactive Legend app to share the community characteristics index with the general public.
   • Increase transparency and public trust—This component includes an ArcGIS Hub site template to share about progress on racial equity initiatives or programs with the public and stakeholders. It also includes a Survey123 survey for the public to provide general feedback.

   This tutorial will focus on the first component: evaluate community conditions and actions.

6. When you are finished reviewing the slides, click the Deploy now button.

   The My Solutions page appears, and the Social Equity Analysis solution deploys.

   Next, you will download the desktop application template of the solution to open in ArcGIS Pro.

7. Point to the Social Equity Analysis solution and click Open.

   

8. Scroll down to the Solution Contents section and click SocialEquityAnalysis.

   

   The SocialEquityAnalysis item page appears. This is the item page for the Social Equity Analysis solution ArcGIS Pro package.

9. Click Download.
10. Unzip and open the contents of SocialEquityAnalysis.zip. Double-click the SocialEquityAnalysis project file to open the project in ArcGIS Pro.

    

    The ArcGIS Pro project opens to a blank map.

Add census block groups data

Next, you will add the layers you'll need to create a social equity index map, such as census tracts, school locations, demographic data, and health outcome data.

1. In the ArcGIS Pro project, in the Catalog pane, expand Tasks and double-click Social Equity Analysis.

   Tip:

   If the Catalog pane is not visible, on the ribbon, click the View tab, and in the Windows group, click Catalog Pane.

   

   The Tasks pane appears.

   The Task pane contains a guided process for using tools in ArcGIS Pro that includes identifying a focus area of interest, an evaluation of which communities are disproportionately impacted or burdened by it, and where to target interventions to address disproportionate impacts or outcomes. You'll start by preparing the data on assets and outcomes.

2. In the Tasks pane, expand Identify Study Area, Create and Prepare Variables, and double-click Identify study area and enrich with demographic variables.

   

   The Add Data window appears.

3. In the Add Data window, under Portal, click Living Atlas. On the search bar, type usa census block group boundaries and press Enter.
4. Click USA Census Block Group Boundaries, owned by esri_dm.

   

5. Click OK.

   The USA Census BlockGroups layer is added to your map.

6. In the Tasks pane, click Next Step.

   The next step in the Task pane instructs you to zoom into your reporting area. You will zoom in closer to Toledo, Ohio.

7. On the ribbon, on the Map tab, in the Inquiry group, click Locate.

   

   The Locate pane appears.

8. In the Locate pane, on the search bar, type Toledo, OH and press Enter.

   

   The map zooms to Toledo, Ohio.

   

9. Close the Locate pane. On the map, zoom out until you see most of the state of Ohio.

   You notice that the USA Census BlockGroups layer includes data for the entire United States, but you are only interested in analyzing data in Lucas County, the county which contains the city of Toledo. You will create a definition query to limit the visible census block groups to only the ones within Lucas County. Before you can create the definition query expression for the layer, you will find out the unique identifying number for the county.

10. On the ribbon, on the Map tab, in the Selection group, click Select.

    

11. On the map, zoom in to Toledo, and click a census tract near the label for the city of Toledo.

    The selected tract highlights in blue, indicating that it is selected.

    

12. Close the Locate pane.
13. At the bottom of the Tasks pane, click the Contents tab.

    

14. In the Contents pane, right-click the USA Census BlockGroups layer and click Attribute Table.
15. In the attribute table, click Show Selected Records.

    

    The table shows only the row for the tract you selected.

16. Locate the STCOFIPS attribute and make note of the value.

    

    FIPS stands for Federal Information Processing System. The STCO in STCOFIPS stands for state and county. FIPS codes are numbers that uniquely identify geographic areas. The first two digits in the STCOFIPS code represent the state code the county is located in, and the remaining three digits represents a specific county code.

    The STCOFIPS for Lucas County, Ohio, is 39095. You will use this information to create a definition query for the layer so that it only shows data for Lucas County.

17. In the Contents pane, double-click the USA Census BlockGroups layer.

    The Layer Properties window appears.

18. In the Layer Properties window, click Definition Query and click New definition query.

    

19. Build the expression Where STCOFIPS is equal to 39095.

    

20. Click Apply and click OK.
21. On the ribbon, on the Map tab, in the Selection group, click Clear.

    The layer now shows only the census block groups in Lucas County, Ohio.

    

Enrich the block groups with demographic data

Now that you have the census block group divisions on your map, you will add demographic information for each of the block groups.

1. Return to the Tasks pane and click Next Step twice.

   

   The Enrich with Demographic Data pane appears.

   The next task uses the Enrich tool to add key focus demographic data. This tool consumes credits.

   Note:

   Geocoding will consume credits. Credits are the currency used across ArcGIS and are consumed for specific transactions and types of storage, such as storing features, performing analytics, and using premium content. Matching addresses when publishing a spreadsheet as a hosted feature layer using ArcGIS Geocoding service consumes credits. Learn more about credits.

   To learn how many remaining credits are in your ArcGIS Online account, at the top of the page, click your username, and click My settings. On the My settings page, click Credits to see how many remaining credits are in your account.

2. In the Enrich with Demographic Data tool pane, for Input Features, choose USA Census BlockGroups, for Output Features, type LucasCounty_Enrich.

   

   Next, you will choose the variables in the Enrich with Demographic Data pane that were determined through your outreach and collaboration efforts with the community.

   In any given equity analysis workflow, variables or indicators selected to create an equity index will vary based on the specific community and particular intervention you are seeking to address. As mentioned earlier, the most crucial step in the Racial Equity workflow is to engage communities impacted by the inequity and who will be receiving the support of the intervention. Such members of the community must be included in the process of determining these indicators before creating the index.

3. Under Variables, click the remove button for the following preconfigured indicators.

   Note:

   If you accidentally remove a variable, you can reset the list of variables by clicking the Back to previous step button at the bottom of the Tasks pane. Then click Skip to return to the Enrich with Demographic Data to reporting areas step. The Enrich with Demographic Data pane appears with the preconfigured list of variables.

   • 2023 Total Population
   • 2021 Population Age 65+ (ACS 5-Yr)
   • 2023 Unemployed Population 16+
   • 2023 Per Capita Income
   • 2023 Median Household Income
   • 2021 Pop Ratio Inc/Poverty: 2.00+ (ACS 5-Yr)
   • 2021 Pop 19-34: No Health Insurance (ACS 5-Yr)
   • 2021 Pop 35-64: No Health Insurance (ACS 5-Yr)
   • 2021 HHs w/ Food Stamps/SNAP (ACS 5-Yr)
   • 2021 Owner HHs with 0 Vehicles (ACS 5-Yr)
   • 2021 Renter HH with 0 Vehicles (ACS 5-Yr)
   • 2023 Owner Occupied HUs

   Five variables remain with percent selected:

   • 2023 Child Population
   • 2021 HHs: Inc Below Poverty Level (ACS 5-Yr)
   • 2021 Pop <19: No Health Insurance (ACS 5-Yr)
   • 2023 Pop Age 25+: High School/ No Diploma
   • 2021 HHs w No Internet Access (ACS 5-Yr)

   

   Note:

   Running this tool will require 23.95 credits.

   If you do not have sufficient credits to complete this step, you can use the provided the LucasCounty_Enrich_Learn_2024 layer to continue the tutorial. To add this layer, on the ribbon, on the Map tab, in the Layer group, click Add Data. Search for LucasCounty_Enrich_Learn owner: Learn_ArcGIS, and in the list of results, choose the LucasCounty_Enrich_Learn 2024 layer. Skip this step to continue the tutorial.

4. In the Enrich with Demographic Data pane, click Run.
5. In the Contents pane, right-click LucasCounty_Enrich and click Attribute Table.

   

   The indicators you selected have been added to the block group layer.

   

6. Close the table.

Add and filter health outcome data

Next, you want to add health outcome data so you can better understand the current rate of asthma in the county. You will add a layer owned by the Centers of Disease Control and Prevention that includes data on asthma prevalence at the census tract level.

1. On the ribbon, on the Map tab, in the Layer group, click Add Data.
2. Ensure you are searching Living Atlas, and in the search bar, type places cdc and press Enter.
3. Double-click the group layer PLACES: Local Data for Better Health by data_cdc.

   

   The group layer contents appear. You will only need the Tracts layer.

4. Click the Tracts layer and click OK.

   

   The Tracts layer adds to your map. The default symbology shows the census tracts by the percent of the population in those tracts who lack health insurance coverage. You are interested in the data on asthma prevalence in this layer, but you do not need to change the symbology for now.

   The Tracts layer also shows data for the entire country. You only need data for Lucas County. Before you can create the definition query expression for the layer, like you did earlier in the tutorial, you want to find out the unique identifying number for the county.

5. On the ribbon, on the Map tab, in the Selection group, click Select, and click a census tract near the label for the city of Toledo.

   The selected tract highlights in blue, indicating that it is selected.

   

6. In the Contents pane, right-click the Tracts layer and click Attribute Table.
7. In the attribute table, click Show Selected Records.

   

   The table shows only the row for the tract you selected.

8. Scroll to see the County FIPS attribute and make note of the value.

   

   The County FIPS value for Lucas County is 39095. You will use this information to create a definition query for the layer so that it only shows data for Lucas County.

9. Close the table.
10. In the Contents pane, double-click the Tracts layer. In the Layer Properties pane, click Definition Query.
11. Click New definition query and build the expression Where County FIPS is equal to 39095. Click Apply and click OK.
12. On the ribbon, on the Map tab, in the Selection group, click Clear.

    The Tracts layer now shows Lucas County.

    

    The remaining steps in the Tasks pane are related to summarizing and calculating the asset, condition, or outcome data and rate. Since your outcome data of interest is already provided as a rate in the layer by the CDC, you will not need to use the remaining steps. However, the CDC data is provided at a tract level, so you will use the Spatial Join tool to apply the asthma prevalence rate values from the tract level data to the smaller block group level data.

13. At the top of the Task pane, click the back arrow. In the Tasks window that appears, click Yes.
14. On the Quick Access Toolbar, click Save to save your project.

    

Use spatial join

Now that you have added and filtered health outcome data to your project, you will use the Spatial Join tool to add the asthma prevalence field from the Tracts layer to the LucasCounty_Enrich layer.

1. Click the Contents tab to view the Contents pane.
2. Right-click the Tracts layer, point to Data Design, and choose Fields.

   

   The Fields view appears for the Tracts layer.

   The layer contains several fields for various health outcomes that the CDC reports on, but you are only interested in data related to asthma. By specifying which field you want to be visible, it will make it easier to find your field of interest when you need to set tool parameters for the remainder of the workflow.

3. At the top of the Fields view, uncheck the box for Visible.

   

   All the fields are unchecked.

   Next, you will locate the data related to asthma prevalence and make it the only visible field in the layer.

4. Locate the field Current asthma crude prevalence (%) and check the Visible box for the field.

   

5. On the ribbon, on the Fields tab, in the Changes group, click Save and close the Fields view.

   Next, you will run the Spatial Join tool to create a new layer that includes the asthma prevalence data and the LucasCounty_Enrich demographic data.

6. In the Geoprocessing pane, click the back arrow.
7. In the search bar, type spatial join, and in the list of results, choose Spatial Join.

   

8. In the Spatial Join tool pane, provide the following parameters:
   • For Target Features, choose LucasCounty_Enrich.
   • For Join Features, choose Tracts.
   • For Output Feature Class, type LucasCounty_Enrich_AsthmaP.
   • For Join Operation, choose Join one to many.
   • For Match Option, choose Have their center in.

   You are joining the Tracts layer, which is data at the census tract level, to the LucasCounty_Enrich layer, which contains data at the census block group level. Block groups are smaller than the tract level. By choosing Join one to many for Join Operation and Have their center in for Match Option, you are assigning the values at the tract level to apply to all the block groups within the tract.

   

9. Click Run.

   The LucasCounty_Enrich_AsthmaP layer adds to your map and the Contents pane.

10. In the Contents pane, right-click the LucasCounty_Enrich_AsthmaP layer and choose Attribute Table. Explore the fields in the layer.

    The social and demographic indicators and the asthma prevalence data are now in the same layer with the block groups.

11. Close the table and save the project.

You have deployed the Social Equity Analysis solution and used the guided steps to evaluate community conditions and actions of interest. You added social characteristics data and health outcome data for asthma prevalence. Next, you will continue to use the Social Equity Analysis solution to create analysis and index maps.

Create a composite equity index

Now that you have all the data prepared, it is time to create maps to understand what the data can tell you about the community's socioeconomic and health outcomes are distributed across the community.

Note:

For this tutorial, race and ethnicity variables will not be included to create the index because later in the tutorial we will disaggregate the index results by race and ethnicity to better understand the disparity in experiences and needs for each group. Every index should be specific to the jurisdiction and intended use case for the index, so there may be situations where including race and ethnicity categories are important to include in this step of the index creation process.

The most important component of the Racial Equity workflow is to engage the communities. It is crucial that any social equity GIS analysis meaningfully engages and includes community members the analysis is seeking to serve. At the beginning of this tutorial scenario, your organization has already begun the work of engaging communities to identify the socioeconomic and health outcome indicators you used to enrich the block group layer.

Next, you will visualize the community characteristics and share the resulting map with the community and ask for feedback. After engaging with the target communities and gathering feedback, revisions may be necessary to ensure the data you're using is contextual to the local experience and most accurately reflects the needs of the communities.

Identify community characteristics

You will create a map with a feature layer and chart outputs to display how the six indicators you specified earlier in the tutorial are distributed. The Social Equity Analysis solution uses the Calculate Composite Index tool to run this calculation.

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 variable. The tool follows a three-step workflow to preprocess the variables, combine the variables, and postprocess the index.

Note:

See Calculate Composite Index tool to learn more about the Calculate Composite Index tool. See the Create a composite index using ArcGIS Pro tutorial series for more articles, guidance documents, videos, and tutorials using the tool.

1. In the Tasks pane, click the back arrow. In the Tasks window, click Yes.
2. In the Tasks pane, expand Create a Composite Index, double-click Create a community characteristics data.

   

   The Calculate Composite Index tool pane appears.

3. In the Calculate Composite Index tool pane, for Input Table, choose LucasCounty_Enrich_AsthmaP. For Output Features or Table, type ChildAsthmaIndex.

   

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

   

5. Check the following attributes and click Add:
   • 2023 Child Population: Percent
   • 2021 HHs: Inc Below Poverty Level (ACS 5-Yr): Percent
   • 2021 Pop <19: No Health Insurance (ACS 5-Yr): Percent
   • 2023 Pop Age 25+: High School/No Diploma: Percent
   • 2021 HHs w/No Internet Access (ACS 5-Yr): Percent
   • Current asthma crude prevalence (%)

   

   The six variables appear in the Calculate Composite Index tool pane.

   

   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.

   Note:

   To learn more about the methods to scale and combine variables, see How Calculate Composite Index works.

   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.

6. Expand Output Settings. For Output Index Name, type ChildAsthmaIndex.

   

7. Under Output Index Minimum and Maximum Values, for Minimum, type 0. For Maximum, type 100.

   

8. Under Additional Classified Outputs, check Equal interval, Quantile, and Standard deviation. For Output Index Number of Classes, type 10.

   

   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.

9. Click Run.

   The resulting composite index map appears.

   

10. In the Tasks pane, click Next Step.

    The Interpret results page appears.

    It is important to explore the resulting map and charts to view the distribution of the index, determine if the preprocessing steps achieved the intended result, and review any correlations among the input variables and the index. Other questions to consider include the following:

    • Does the resulting index address the index question and dimensions?
    • How are the input variables affecting the output index?
    • Do all the input variables belong or can some be removed?
    • Does the output index unintentionally weight one particular dimension or variable?
    • Is the spatial unit appropriate based on the input variables?

    Note:

    Review the Creating Composite Indices Using ArcGIS: Best Practices (PDF) technical paper for more guidance, tips, and best practices.

11. At the bottom of the Tasks pane, click Contents.

    In the Contents pane, the ChildAsthmaIndex Layers group layer contains the ChildAsthmaIndex layer as well as the other additional outputs you selected in the Calculate Composite Index tool. The ChildAsthmaIndex layer also includes charts that were automatically created by the tool.

12. For the ChildAsthmaIndex layer, double-click the Relationships of Scaled Variables and Index chart.

    

    The chart appears.

    

    The most important row to investigate is the bottom row. This shows you the correlation between the ChildAsthmaIndex values and each of the variables. The results show that none of them are too highly correlated at 0.90 or higher. None of them are too low (0.10 or less) or a negative value. This means there is not likely to be unintentional weighting or uneven variable contributions in the index.

    You can also use this chart to assess the correlation between the variables used to create the composite index. For assessing correlation between variables, you mainly want to ensure none of them have high values of correlation. In this example, the variables are no higher than 0.53, meaning they do not correlate too much with one another.

13. Close the chart.
14. In the Contents pane, uncheck the ChildAsthmaIndex layer and check the ChildAsthmaIndex - Quantile Classes layer.

    

    The ChildAsthmaIndex - Quantile Classes layer is now visible on the map.

    

    This map shows the block groups divided into 10 equal groups by their composite index score value. The block groups in Class 10 are the block groups in the top 10th percentile of block groups with the highest index score, meaning they should be most prioritized for intervention programs.

15. In the Contents pane, turn off the ChildAsthmaIndex - Quantile Classes layer and turn on the ChildAsthmaIndex - Standard Deviation Classes

    The ChildAsthmaIndex - Standard Deviation Classes layer is now visible on the map.

    

    This layer calculates how many standard deviations an index score is above or below the mean of the block groups. This map can help you interpret which areas are greater magnitude of the impacts of the composite index variables. The quantile and standard deviation layers show different methods for prioritization.

16. On your own, explore the other index output layers.
17. When you are finished, return to the Tasks pane and click Next Step.

    The Load output index into hosted feature layer page appears. This step is not necessary at this time, so you will click Finish to close the task.

18. Click Finish.
19. Press Ctrl+S to save the project.

Share the community index map

It is important to share the resulting community characteristic data with community stakeholders to verify and ensure that it accurately represents the community and identify any needed adjustments so that the map is more reflective of the community. You will share the map as a web map.

1. On the ribbon, click the Share tab. In the Share As group, click Web Map.

   

   The Share As Web Layer pane appears.

2. In the Share As Web Layer pane, type a descriptive sentence for Summary, and for Tags, type a few relevant words, pressing Enter after each.
3. Under Share with, check the box for Everyone.
4. Click Analyze.

   An error appears requiring the layer to allow unique numeric IDs before it can be shared as a web layer.

5. Double-click the error.

   The Map Properties window appears.

6. In the Map Properties window, check the box for Allow assignment of unique numeric IDs for sharing web layers and click OK.

   

7. In the Share As Web Layer pane, click Analyze.

   There are no more errors.

8. Click Publish.

   The layer publishes as a web layer.

9. Click Manage the web layer.

   

   The item page for the community characteristics index layer appears in your browser.

10. Click Open in Map Viewer.

    

    The layer opens in Map Viewer. In Map Viewer, you can configure which indicators to show on your map and share the map as a link so community members can review the data.

    In addition to sharing the map showing the ChildAsthmaIndex results, consider creating maps that show key indicators, such as the percent of population by race and ethnicity, by internet access, or by poverty level.

    Here are some questions you can consider asking the community:

    • Does the distribution of this indicator align with your experience in the community or in your area of the city?
    • Are there areas the ChildAsthmaIndex layer results did not prioritize that are surprising to you, based on your experience and knowledge of the city?
    • Are there other indicators that need to be added to better reflect areas that should be prioritized?

    The process of community engagement is a dialogue. It may require multiple iterations to have these discussions and to develop solutions. But it is valuable because it ensures the analysis reflects lived experiences that the data may not be able to capture. With more accurate results, more accurate solutions can be identified.

    Note:

    To learn more about how to use Map Viewer, consider exploring the ArcGIS tutorials Make a map of China and Create a policy map to address health conditions.

The map is shared at a community town hall where the analysis methodology and maps were presented. Stakeholders, such as parents from local schools, physicians from local clinics, neighborhood groups, and a community-appointed public health commission, reviewed the map and provided feedback. Next, you will integrate what you have learned from the feedback and adjust your map to more accurately represent the community.

Adjust your community characteristics data

After sharing your map with the community, residents shared an additional experience that may have been left out in the community characteristics map. It was identified that another cumulative burden families are facing is the lack of access to home ownership opportunities and accessible transportation, which are largely due to systemic barriers like economic and housing policies. Residents shared that it would be valuable to add consideration and priority for those who are currently renting their housing unit and do not have access to a personal vehicle. Such neighbors are especially vulnerable to pollution and traffic exposure because of extended time spent walking and using public transit to get to school. This is particularly important for those who live closer to the center of the city where there is a higher percentage of renters without personal vehicles.

You will use what you have learned to include an additional indicator and create an updated community characteristics index map.

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

   

   The History pane shows all the geoprocessing tools you have already run.

2. In the History pane, double-click Enrich with Demographic Data.

   The Enrich with Demographic Data tool pane appears, populated with the parameters you entered earlier in the tutorial.

   The LucasCounty_Enrich_AsthmaP layer already contains the other demographic indicators you added earlier in the tutorial as well as the health outcome data. You will add one more indicator to this layer using the Enrich with Demographic Data tool.

3. In the Enrich with Demographic Data tool pane, for Input Features, choose LucasCounty_Enrich_AsthmaP. For Output Features, type LucasCounty_Enrich2.

   

4. Under the list of variables, click Remove all.

   

5. Next to Variables, click the add button.

   

   The Data Browser window appears.

6. In the search bar, type renter vehicles and press Enter.

   

   A list of related variables appear.

7. For the variable 2021 Renter HHs by Vehicles Avail: 0 (ACS 5-Yr), click the number button to deselect it and click percent to select it, and then check the box for the variable.

   

8. Click OK.
9. In the Add Demographics Data tool pane, click Run.

   Note:

   Since you are using the Enrich tool again, this process will require credits. Adding this single indicator requires 4.79 credits. If you do not have sufficient credits to complete this step, you can use a provided enriched layer to continue the tutorial.

   To add this layer, on the ribbon, on the Map tab, in the Layer group, click Add Data. Under Portal, click ArcGIS Online, and in the search bar, type LucasCounty_Enrich2 owner: Learn_ArcGIS and press Enter. Add the layer LucasCounty_Enrich2_Learn_2024. Skip this step to continue the tutorial.

   The LucasCounty_Enrich2 layer is added to your map and the Contents pane. It now contains all the indicators identified by the community to include in the community characteristics map.

10. In the History pane, double-click Calculate Composite Index.
11. In the Calculate Composite Index tool pane, update the following:
    • For Input Table, choose LucasCounty_Enrich2.
    • For Output Features or Table, type ChildAsthmaIndex2.
    • At the bottom of the Input Variables list, add 2021 Renter HHs by Vehicles Avail: 0 (ACS 5-Yr): Percent.

    

12. Under Output Settings, for Output Index Name, type ChildAsthmaIndex2.

    

13. Click Run.

    The ChildAsthmaIndex2 layer adds to your map and Contents pane.

    In the Contents pane, you will see that the legend for the ChildAsthmaIndex and ChildAsthmaIndex2 layers are the same. The difference between them is that the ChildAsthmaIndex2 layer includes calculations for one additional indicator, the percent of residents who are renters without a vehicle.

    Next, you will compare the original ChildAsthmaIndex layer to the updated one you just created.

14. Under the ChildAsthmaIndex2 Layers group layer, press Ctrl and collapse the ChildAsthmaIndex2 layer.

    

    All layers in the group layer collapse.

    

15. Under the ChildAsthmaIndex Layers group layer, press Ctrl and collapse the ChildAsthmaIndex layer and all the other layers in the group layer. Ensure the only two layers that are checked are the ChildAsthmaIndex layer and the ChildAsthmaIndex2 layer. Click the ChildAsthmaIndex2 layer to select it.

    Now the layers in both group layers are collapsed, which will make it easier to use the Swipe tool to compare the two results.

    

16. On the ribbon, click the Feature Layer tab. In the Compare group, click Swipe.

    

17. On the map, click and drag across the map to compare the ChildAsthmaIndex2 layer and the ChildAsthmaIndex layer.

    The updated ChildAsthmaIndex2 layer brought more focus to a couple of block groups. It is likely that these were areas that experience a higher proportion of residents who have the added exposure risk of increased time outdoors, exposed to air pollutants, because there were more renters without vehicles in these areas.

    

18. On the ribbon, click the Map tab. In the Navigate group, click Explore to deactivate the Swipe tool.
19. Use what you have learned to share the layer as a web map and share the resulting map with community members and stakeholders.
20. Save your project.

Because you took the important step of engaging the community in your first round of the composite index map, you integrated an important and locally relevant indicator to your analysis. You added the indicator to your analysis and created a map that better fits the community's lived experiences and needs.

Next, you will disaggregate the index results by race and ethnicity and prioritize which schools in the county are in the best location to host the public health education program.

Evaluate the equity index and propose program locations

When creating a composite index, it is important to evaluate the index in order to better understand how the index impacts different subpopulations of your target project area. In this section, you will also use network analysis to determine which five public schools are in the best location to reach the block groups with the highest priority index scores.

Disaggregate demographic data

Disaggregated data is data that has been broken down by subcategories, such as race and ethnicity groups, gender, language, and more. Disaggregating data can reveal the benefits and burdens experienced by each subcategory that may not be evident in the aggregated data. In this tutorial, you have created an equity index that considered several socioeconomic and health outcome data. In this section, you will disaggregate the mean index score values by race and ethnicity to determine which groups are experiencing a disproportionately high burden of childhood asthma indicators.

1. In the Tasks pane, click the back arrow. Click Yes to continue.
2. Expand the Optional: Disaggregate and Visualize Demographic Data folder and double-click Disaggregate and visualize demographic data.

   

3. Click Next Step.
4. In the Enrich with Disaggregated Demographic Data pane, for Input Features, choose ChildAsthmaIndex2 - Quantile Classes. For Output Features, type ChildAsthmaIndex_Disaggregated.

   

5. Under Variables, for all the preloaded variables except 2023 Hispanic Pop, click the remove button.

   

   The only variable that remains in the Enrich with Disaggregated Demographic Data tool pane is 2023 Hispanic Population.

   

6. Next to Variables, click the add button.

   

   The Data Browser window appears.

7. In the Data Browser window, double-click Race.

   

8. Double-click Non-Hispanic Origin.
9. Expand 2023 Race and Hispanic Origin (Esri).
10. For the following variables, click the percent button to select it, deselect the number button, and check the variable:
    • 2023 Non-Hispanic White Pop
    • 2023 Non-Hispanic Black Pop
    • 2023 Non-Hispanic American Indian Pop
    • 2023 Non-Hispanic Asian Pop
    • 2023 Non-Hispanic Pacific Islander Pop
    • 2023 Non-Hispanic Other Race Pop
    • 2023 Non-Hispanic Multiple Race Pop

    

    The seven percentage variables are added to the list of selected variables, bringing the total to eight selected variables.

    Note:

    In the United States, the Census Bureau collects demographic data for several categories of race and further distinguishes the categories by the Hispanic ethnicity. Although these categories are limited in capturing the diverse and complex range of people groups, experiences, and cultures, it remains a reliable data source for better understanding how race and ethnicity correlates to other experiences of equity in the U.S.

11. Click OK.
12. In the Enrich with Disaggregated Demographic Data tool pane, click Run.

    Note:

    Running this tool will require 38.32 credits. If you do not have sufficient credits, you can use a provided enriched layer to continue the tutorial.

    To add this layer, on the ribbon, on the Map tab, in the Layer group, click Add Data. Under Portal, click ArcGIS Online, and in the search bar, type ChildAsthmaIndex_Disaggregated_Learn owner: Learn_ArcGIS and press Enter. Add the layer ChildAsthmaIndex_Disaggregated_Learn. Skip this step to continue the tutorial.

    The ChildAsthmaIndex_Disaggregated layer is added to the project.

    

    It is styled by 10 classes, with the highest class, Class 10, representing census block groups with the highest composite index score or areas of priority for childhood asthma supportive programming.

    Next, you will create a chart to visualize which racial and ethnic groups are most represented in the census block groups by their index score.

13. In the Tasks pane, click Next Step twice.
14. Click Run.
15. In the Chart Properties pane, for Category or Date, choose ChildAsthmaIndex2 - Mean (Quartile Classes). For Aggregation, choose Mean.

    

16. Under Numeric field(s), click the Select button.
17. Check the eight race and ethnicity variables and click Apply.

    

18. Click the General tab and enter the following:
    • For Chart title, type Mean child asthma index scores disaggregated by race and ethnicity.
    • For X axis title, type Child asthma index scores by deciles.
    • For Y axis title, type Percent of race/ethnicity group.

    

    The chart is configured.

    

    The chart shows that the block groups with the highest mean index scores, in Classes 8 to 10, have proportionally larger Hispanic and Non-Hispanic Black populations. The block groups with the lowest mean index scores, Classes 1 to 4, have proportionally larger Non-Hispanic White populations. This chart helps you better understanding which racial and ethnic groups may be experiencing more of the burdens of childhood asthma in Lucas County.

Use equitable site selection tasks

While historic policy decisions are a major reason some areas in a community lack resources and opportunity, every community also has assets it can leverage to strengthen its residents. In this scenario, public schools are an important asset as a meeting space where parents and students can learn about protecting and managing respiratory health.

1. In the Catalog pane, expand Tasks and double-click Equitable Site Selection.

   

2. In the Tasks pane, expand Identify Study Area, Create and Prepare Variables and double-click Identify study area and enrich with demographic variables.

   The Add study area to map pane appears. You have already defined your study area and your map is already zoomed into the area, so you will skip this step and the next step.

3. Close the Add Data window that appears. In the Tasks pane, click Skip twice.

   The Enrich with Demographics Data tool pane appears.

   To run the Solve Location Allocation with Index tool later in the tutorial, you will need to add the 2023 Total Population variable.

4. In the Enrich with Demographics Data tool pane, enter the following:
   • For Input Features, choose ChildAsthmaIndex2 - Quantile Classes.
   • For Output Features, type Priority_Schools_Selection.
   • Under Variables, remove all the variables except 2023 Total Population.

   

5. Click Run.

   Note:

   Running this tool will require 4.79 credits.

   The Priority_Schools_Selection layer adds to the Contents pane and map.

6. In the Tasks pane, click Finish.

   Next, you will use the next steps in the Tasks pane to create an asset layer showing the location of public schools in Lucas County.

7. In the Tasks pane, expand Identify Study Area, Create and Prepare Variables and double-click Create and prepare additional variables.

   The Geocode Addresses pane appears.

   The next step is to import assets, condition, or outcome data. You will import asset data, which are the public schools where the health education workshops will be hosted.

8. Download the file LucasCounty _Schools_List_data.csv and save it to a folder you can easily access.

   The .csv file contains information on the public schools within the two school districts located in Lucas County.

   Note:

   The data was acquired from the Ohio Educational Directory System online tool for School and District Directory Information on the Ohio Department of Education website.

9. In the Geocode Addresses pane, for Input Table, click the browse button.
10. In the Input Table window that appears, browse to the folder where you saved the .csv file, click LucasCounty_Schools_List_data.csv, and click OK.
11. For Input Address Locator, click the arrow and choose ArcGIS World Geocoding Service.

    Note:

    Geocoding will consume credits. This step will require 4.28 credits.

    

12. Set the following parameters:
    • For Output Feature Class, type LucasCounty_Schools.
    • For Country, check United States.
    • For Category, check Address.

    

13. Click Run.

    Note:

    If you do not have sufficient credits to complete this step, you can use a provided geocoded layer to continue the tutorial.

    To add this layer, on the ribbon, on the Map tab, in the Layer group, click Add Data. Under Portal, click ArcGIS Online and in the search bar, type LucasCounty_Schools owner: Learn_ArcGIS and press Enter. Add the layer LucasCounty_Schools. Skip this step to continue the tutorial.

    The LucasCounty_Schools layer is added to your map.

Propose program locations with equity priorities

There are over 100 schools in Lucas County. Your organization will not have the capacity to run a program at every school, so you will need to prioritize which school locations are the most strategic to host the program and will also meet your organization's goals of equity and inclusion. You will use the Social Equity Analysis solution to evaluate the school locations and use spatial analysis to locate the schools that best fit your organizational needs.

1. In the Tasks pane, click the back arrow.
2. Expand Evaluate Coverage and Perform Site Selection and double-click Identify candidate sites.

   You already added the school data, so you will skip the first step.

3. Click Next Step.

   The Add Candidate Sites pane appears. You will use this tool to add the school locations as candidate asset locations. This will set all the schools in the county as potential sites to host the program.

4. In the Add Candidate Sites pane, for Candidate Sites, choose LucasCounty_Schools.

   

5. Click Run.
6. If necessary, in the Contents pane, drag the SiteSelection group to the top of the Contents pane.

   

   The orange flag symbols on the map indicate that the candidate asset locations are set to the school locations.

   

   For this tutorial, you will only consider the locations of schools. But it is important to consider other potential assets in a community, such as public parks, community centers, libraries, and other spaces that might serve as a suitable location for your intervention. Consider soliciting community input on potential assets.

7. In the Tasks pane, click Next step and Finish.
8. In the Tasks pane, double-click Perform site selection.

   The Convert Equity Analysis Index to Demand Points tool pane appears.

   A demand point is typically a location that represents the people or things requiring the goods and services your facilities provide. In this case, it will be the census block group they live in. You will use this tool to convert the block group layer from a polygon feature layer to a point feature layer. This will enable the analysis to calculate how close the school locations, or asset locations, are to the center of each block group. The block group center points will represent the demand points that need to access the asset locations.

   Before you create the demand points, you will use the Select by Attribute tool to only create demand points for block groups in the top 3 quantile classes of the index score.

9. In the Contents pane, turn off the ChildAsthmaIndex2 Layers group layer.
10. On the ribbon, on the Map tab, in the Selection group, click Select By Attributes.

    

11. In the Select by Attributes window, for Input Rows, choose Priority_Schools_Selection.
12. Under Expression, build the expression Where ChildAsthmaIndex2 - Mean (Quantile Classes) is greater than or equal to 8.

    

13. Click OK.

    

14. In the Convert Equity Analysis Index to Demand Points tool pane, for Input Equity Analysis Index, choose Priority_Schools_Selection. For Output Layer, type Demand_points.

    

    The tool provides a note that 143 records will be processed. This aligns with the number of records you were expecting based on the query you used in the Select by attribute tool.

15. Click Run.
16. On the ribbon, on the Map tab, in the Selection group, click Clear to deselect the block groups.

    The Demand_points layer appears on the map with points in the center of the block groups that are in the top three classes of the index score.

    

17. In the Tasks pane, click Next Step.
18. In the Solve Location Allocation with Index tool pane, for Input Site Selection Layer, choose SiteSelection. For Input Demand Points Layer, choose Demand_points.

    

    You will set the travel time parameters to choose school locations within 30 minutes walking time of the priority block groups.

19. For Number of Sites to Find, type 5. For Travel Mode Cutoff (time or distance), type 30. For Travel Mode, choose Walking Time.

    

20. For Output Allocation Lines Layer, type Allocation_lines. For Output Chosen Sites Layer, type Priority_schools.
21. Click Run.

    Note:

    Running this tool requires 8.7 credits.

    If you do not have sufficient credits to complete this step, you can use the following provided layers to continue the tutorial. To add the provided layers, search for and add the Priority_Schools_Learn and Allocation_Lines_Learn layers owned by Learn_ArcGIS. Skip this step to continue the tutorial.

    The analysis shows that there are four schools within 5 miles driving distance of the prioritized block groups. These locations are ideal for hosting the health education programs because they are most accessible to the block groups in the top 25 percentile of equity index map analysis.

    

    In a real-world scenario, you may need to run this tool multiple times with different distances. Consider creating multiple maps showing options to share with the community. Having maps with different options can serve as a conversation tool to help the community better understand the trade-offs and decide which options are available and should be prioritized.

22. Save the project.

    You can use what you have learned to share this map with the community and discuss if there are any parameters that should be adjusted to better reflect the community's experiences in your analysis.

When conducting a racial and social equity analysis, it is vital for understanding to precede action. In this tutorial scenario, you experienced an example of the value of collaborating with community to identify the key indicators to address childhood asthma. By inviting their feedback throughout the analysis milestones, the maps and solutions were more relevant and accurate to the local need.

"Maps and data—when combined with robust community engagement—help decision-makers and communities develop this shared understanding of the distribution of benefits and burdens in their communities and address barriers to equity. ... Social equity is inherently spatial. It is achieved when social identity (race, ethnicity, gender, disability, etc.) no longer determines one's life outcomes; when everyone has what they need to thrive, no matter where they live. Geographic information is critical for understanding, planning, and acting to achieve social equity while engaging the people that matter most—community members—along the way."

—The Power of Partnership: The Story Behind the Social Equity Analysis Solution

In this tutorial, you deployed the Social Equity Analysis solution, added demographic and health outcome data to evaluate and understand community characteristics. You validated and revised your community characteristics map based on community feedback and created an equity index. You were able to use the results from the equity index to optimize the intervention of starting a health program at local schools by analyzing which school location was most centrally located to serve the census block groups where residents were experiencing the highest risk of childhood asthma.

Consider how this equity index workflow can be applied to a variety of other scenarios for health and public policy, such as where to place a new public park, where to place cooling centers in the summer, or where to improve infrastructure for high-injury areas due to traffic collisions. Additionally, consider other methodologies for developing an index map, such as calculating priorities based on standard deviation or quantile calculations. To learn more, see Methods for creating an index map for social equity.

Although it will not be covered in this tutorial, the next step in the Racial Equity workflow is to manage performance by monitoring and analyzing the performance of your initiative that you have implemented to work toward equity within your community. This step allows you to evaluate what is working (and what may not be working) and adjust your strategy, if necessary. To learn more about the Racial Equity workflow, see Applying the Racial Equity Workflow Using ArcGIS.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• All Adobe Stock images are © 2024 Adobe Stock. All rights reserved.
• Government Alliance on Race and Equity (GARE) and their five member jurisdictions (City and County of Durham, Fairfax County, Milwaukee County, City and County of Denver, and the City of San Antonio) who collaborated with Esri to develop the ArcGIS Social Equity Analysis solution.
• Demographic data from Esri demographic data.
• USA Census Block Groups from the U.S. Census Bureau.
• Asthma data used is from PLACES: Local Data for Better Health provided by the Centers for Disease Control (CDC).
• LucasCounty_Schools created from Ohio Department of Education.
• Light Gray Canvas map sources: Esri, HERE, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community