Prioritize intervention with suitability analysis

Gwinnett County, Georgia, in the Atlanta metropolitan area, has undergone significant urbanization, population growth, and demographic shifts over the past few decades. These changes have led to increased disparities in socioeconomic conditions, often intertwined with racial and ethnic backgrounds.

In this tutorial, you're a GIS analyst tasked with identifying socioeconomically vulnerable neighborhoods in Gwinnett County. Your results may shape policies and programs aimed at reducing disparities and fostering equity. To accomplish your goal, you'll use the suitability analysis workflow in ArcGIS Business Analyst Pro to show areas that should be prioritized for intervention based on income, housing stability, employment opportunities, and more.

First, you'll add census tract data for Gwinnett County to an ArcGIS Pro project. You'll enrich the tracts with relevant demographic information and use suitability analysis to calculate socioeconomic vulnerability scores for each tract. Lastly, you'll symbolize the results and add contextual information about race and ethnicity. This tutorial will enhance your technical skills in suitability analysis and help you better understand the socioeconomic dynamics of communities, empowering you to make informed, data-driven decisions in business and social contexts.

This tutorial was last tested on November 1, 2024, using ArcGIS Pro 3.4. 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)
• ArcGIS Business Analyst Pro

Enrich the data

Your analysis requires data, so you'll create a layer containing census tracts in Gwinnett County. After that, you'll enrich the tracts with the key socioeconomic vulnerability indicators you'll use in your suitability analysis.

Create a project

First, you'll create a project in ArcGIS Pro. You'll also confirm you have the ArcGIS Business Analyst license necessary to complete the tutorial.

1. Start ArcGIS Pro. If prompted, sign in using your licensed ArcGIS organizational account.

   Note:

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

2. Under New Project, click Map.

   

3. In the New Project window, for Name, type Socioeconomic_vulnerability_in_Gwinnett_County. Leave Location unchanged and confirm that Create a folder for this project is checked.
4. Click OK.

   The project is created with a default map. For now, the only layer is the Topographic basemap, which provides geographic context.

5. On the ribbon, click the Project tab.

   

6. Click Licensing.

   

7. Under ArcGIS Pro Extensions, confirm that you have the ArcGIS Business Analyst license, which is required to complete this tutorial.

   Note:

   To learn more about the ArcGIS Business Analyst license and how to acquire it, visit the ArcGIS Business Analyst product page.

   

8. Click the back button.

   

   You return to your project.

Add census tract boundaries

Next, you'll create a layer of census tract boundaries for Gwinnett County using a geoprocessing tool available with the ArcGIS Business Analyst extension.

Note:

The workflow in this tutorial can be performed for any United States county or county equivalent. If you want, you can use your own county of interest instead of Gwinnett County, but your results will differ from the example images. It's recommended you complete the workflow for Gwinnett County before attempting it with a different county.

Before you continue, you'll confirm you're using the most recent United States data from Esri.

1. On the ribbon, click the Analysis tab.

   

2. In the Workflows group, click Business Analysis.

   

3. At the bottom of the menu, confirm Business Analyst Data Source is set to United States (Esri 2024).

   

   Note:

   If the data source is not set to United States (Esri 2024), click Change data source. In the Business Analyst Data Source window, under Portal, click North America. Expand United States and click Esri 2024. Click OK.

   Next, you'll create the census tract boundaries.

4. In the Geoprocessing group, click Tools.

   

   The Geoprocessing pane appears.

5. In the Geoprocessing pane search bar, type Generate Standard Geography Trade Areas. In the list of results, click Generate Standard Geography Trade Areas.

   

   This tool creates a layer with basic geography boundaries at a level you specify based on the active ArcGIS Business Analyst data source.

6. For Geography Level, choose Census Tracts (US.Tracts).
7. For Output Feature Class, type Gwinnett_County_tracts.

   

   Next, you'll choose the census tracts you want to include in your output layer. You can search for and choose census tracts from any United States county.

8. For Geography IDs List, click the Browse button.

   

9. In the Select Geography: US.Tracts window, click US by Tracts.

   A list of all states appears. Gwinnett County is in Georgia.

10. Click Georgia.

    

11. In the list of Georgia counties, scroll down and click Gwinnett County.

    

    Tip:

    You can also search for the county using the search bar.

    A list of all census tracts in the county appears. Each census tract is designated by a number. You'll select every tract in the county.

12. Check the Gwinnett County box.

    

    An icon indicates that 220 census tracts are selected. This number represents the total number of tracts in Gwinnett County.

    Note:

    The Generate Standard Geography Trade Areas tool can only process a maximum of 1,000 records. For counties with more than 1,000 census tracts, it's recommended to instead run the tool with Geography Level set to Counties (US.Counties) and Geography IDs List set to the county of interest. This creates a layer of the county boundaries. Then, run the Generate Geographies From Overlay tool to create census tracts within the county boundaries. For more information on this tool, see the Generate standard geography, distance, or time-based trade areas documentation page.

13. Click OK.
14. In the Geoprocessing pane, click Run.

    The tool runs. A layer of census tracts in Gwinnett County is created and added to the map.

    

    Note:

    Your default symbology may differ from the example images.

    You'll change the basemap to one with a more minimal design to emphasize your analysis results as the focus of your map.

15. On the ribbon, click the Map tab. In the Layer group, click Basemap.

    

16. Choose the Light Gray Canvas basemap.

    

    The basemap changes.

    
17. On the Quick Access Toolbar, click the Save Project button.

    

    Tip:

    You can also press Ctrl+S to save the project.

Add socioeconomic indicators

Now that you have census tract data for your study area, you'll enrich it with key indicators to assess socioeconomic vulnerability, including income, housing, employment, and health insurance. You'll use the Enrich Layer tool, which adds demographic data to features based on their geographic location.

Note:

In a real-world scenario, before determining what indicators to use to determine socioeconomic vulnerability, it's important to work with a broad range of stakeholders, including residents in affected areas, to define the purpose of your analysis and the question you intend to answer.

1. In the Geoprocessing pane, click the Back button.

   

2. In the search bar, type Enrich Layer. In the list of results, choose Enrich Layer (Business Analyst Tools).

   

3. For Input Features, choose Gwinnett_County_ tracts. For Output Feature Class, type Gwinnett_County_enriched.
4. Next to Variables, click the add button.

   

   The Data Browser window appears. This window categorizes and displays all data variables available for the selected data source. You'll search for and add variables that capture the following components of socioeconomic vulnerability:

   • Income and employment status
   • Poverty and government assistance
   • Housing affordability and burden
   • Health care access
   • Education and digital access
   • Disability and accessibility

   Note:

   A list of each variable, their dimension, and the reason for including them in the analysis is provided at the end of the tutorial in the Rationale for variables section.

5. In the Data Browser window, in the search bar, type Household Income and press Enter.

   

6. In the list of results, check the box for 2024 Median Household Income.

   

   For this variable, there are two options, known as metrics: # and Index. These metrics determine whether income is measured as a raw number or an index that compares it to the national average. You can choose to include either or both metrics, but for this tutorial, you'll only add the number metric, which is chosen by default.

   The Show/Hide details panel icon indicates you have selected one variable.

   

7. In the search bar, type Food Stamps and press Enter.
8. In the list of results, check the 2022 HHs w/Food Stamps/SNAP (ACS 5-Yr) box. Select the percent metric (%) and deselect the count metric (#).

   

   Now, the data for households (HHs) with food stamps will be presented as a percentage of the total number of households, rather than a raw number.

9. Using the search bar, search for and add the following variables with the indicated metric:

   Variable	Metric
   2024 Median Home Value	#
   2024 Owner Occupied HUs	%
   2022 HHs/Gross Rent 50+% of Income (ACS 5-Yr)	%
   2022 HHs: Inc Below Poverty Level (ACS 5-Yr)	%
   2024 Pop Age 25+: Bachelor's Degree	%
   2024 Unemployed Population 16+	%
   2022 HHs w/1+ Persons w/Disability (ACS 5-Yr)	%
   2022 Pop 35-64: No Health Insurance (ACS 5-Yr)	%
   2022 Pop 65+: No Health Insur (ACS 5-Yr)	%
   2022 HHs w/No Internet Access (ACS 5-Yr)	%

   You've selected 12 variables in total.

10. Click OK.

    The variables are added to the Enrich Layer tool pane. You'll save this list of variables in case you want to use them again to analyze a different county later.

11. Click Save List.

    

12. In the Save Variable List window, for Name, type Socioeconomic and Demographic Variables. Click OK.

    You can access this list of variables later by opening the Data Browser window and clicking the Variable Lists tab.

    Before you run the tool, you'll estimate its credit usage. The number of credits this tool consumes depends on the number of variables and the size of the geographic area. Because you're using a small study area, the number of credits should be relatively small. If you were to run the tool on a larger county with more census tracts, the number of credits might be much higher. It's recommended that you check credit usage before running a tool that consumes credits.

13. At the top of the Enrich Layer tool pane, click the estimate credits link.

    

    The tool will consume 26.4 credits. The number of credits in your account is listed for comparison.

    Note:

    If you do not have sufficient credits to complete this step, or if you do not want to spend 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. Search for Gwinnett_County_enriched owner:Learn_ArcGIS. In the list of results, choose the Gwinnett_County_enriched layer. Then, close the Geoprocessing pane instead of running the tool.

14. Click Run.

    The Gwinnett_County_enriched layer is added to the Contents pane and appears on the map. The layer doesn't look different from the original layer but has new information in its attribute table.

15. In the Contents pane, right-click Gwinnett_County_enriched and choose Attribute Table. In the table, scroll to the right.

    The variables you chose have been added as attribute fields. Each variable is represented by a column in the table.

    

16. Close the table. Save the project.

Assess correlation among variables

Before using the variables you added for analysis, you'll investigate correlations among them. Highly correlated variables can disproportionately impact and skew the results. If two variables are correlated, you may need to remove one.

To determine correlation, you'll create a scatter plot matrix, which displays patterns and relationships in your data.

1. In the Contents pane, confirm that the Gwinnett_County_enriched layer is selected.
2. On the ribbon, click the Data tab. In the Visualize group, click Create Chart and choose Scatter Plot Matrix.

   

   An empty chart and the Chart Properties pane appear.

3. In the Chart Properties pane, under Numeric Fields, click Select.

   

4. On the Select menu, check the box for the following fields:
   • 2024 Median Household Income
   • 2022 HHs w/Food Stamps/SNAP (ACS 5-Yr): Percent
   • 2024 Median Home Value
   • 2024 Owner Occupied HUs: Percent
   • 2022 HHs/Gross Rent 50+% of Income (ACS 5-Yr): Percent
   • 2022 HHs: Inc Below Poverty Level (ACS 5-Yr): Percent
   • 2024 Pop Age 25+: Bachelor's Degree: Percent
   • 2024 Unemployed Population 16+: Percent
   • 2022 HHs w/1+ Persons w/Disability (ACS 5-Yr): Percent
   • 2022 Pop 35-64: No Health Insurance (ACS 5-Yr): Percent
   • 2022 Pop 65+: No Health Insur (ACS 5-Yr): Percent
   • 2022 HHs w/No Internet Access (ACS 5-Yr): Percent
5. Click Apply.

   The chart updates, showing a scatter plot matrix for the selected fields. You'll set the chart to use Pearson's r, a statistical method for assessing the degree of correlation between variables.

6. Under Matrix Layout, set the following parameters:
   • For Lower left, choose Pearson's r.
   • For Upper right, choose Scatterplots.
   • For Sort by, choose Pearson's r.

   

   The chart updates.

   

   Note:

   To better see the variable names, you can resize the chart. You can also point to a rectangle to see the names of the variables being compared.

   The colored rectangles indicate the degree of correlation between the corresponding variables on the x- and y-axis. Dark pink rectangles indicate negative correlation (when one variable increases, the other decreases), while dark green rectangles indicate positive correlation (when one variable increases, the other increases). The number in each rectangle is its Pearson's r value, which ranges from -1 to 1. A value of 0 indicates no correlation. Values below -.8 indicate a very strong negative correlation, while values above .8 indicate a very strong positive correlation.

   Overall, there isn't much correlation between most variables. The strongest correlation is between median household income and owner occupied housing units. This rectangle has a Pearson's r value of 0.73.

   

   You have the option to remove one of these variables from your analysis due to their strong correlation. For this tutorial, though, you'll keep both, because it's acceptable to have some correlation between variables.

7. Close the chart and the Chart Properties pane.
8. Save the project.

So far, you've created a layer of census tracts for Gwinnett County, Georgia. You enriched the layer with socioeconomic data and assessed the correlation between variables with a scatter plot matrix. You're ready to perform suitability analysis.

Calculate priority scores

Suitability analysis using ArcGIS Business Analyst Pro is used to rank and score sites based on multiple weighted criteria. In this case, your criteria are the variables you used to enrich your census tracts layer. You'll use suitability analysis to identify areas with higher socioeconomic vulnerability, which can be prioritized for intervention based on their need.

Perform suitability analysis

First, you'll create a layer for suitability analysis. Then, you'll specify the fields to use as the suitability criteria and adjust the influence of each criterion appropriately. The result of your analysis will be a suitability score for each census tract, which indicates a higher or lower priority level for intervention.

1. On the ribbon, click the Analysis tab. In the Workflows group, click Business Analysis and choose Suitability Analysis.

   

   The Make Suitability Analysis Layer tool appears. This tool creates a new layer in the Contents pane to store your analysis results.

2. For Input Features, choose Gwinnett_County_enriched. For Layer Name, type Gwinnett County Priority Levels.

   

3. Click Run.

   The tool runs and the layer is created. Next, you'll choose the criteria to be analyzed.

4. On the ribbon, click the Suitability Analysis tab. In the Criteria group, click the Add Criteria drop-down menu and choose Add Fields from Input Layer.

   

   The Add Field Based Suitability Criteria tool appears.

5. For Input Suitability Analysis Layer, confirm that Gwinnett County Priority Levels is chosen. For Fields, click the Add Many button.

   

   A list of fields in the layer appears, including the variables you added by enriching.

6. In the list of fields, check the box for the following fields:
   • 2024 Median Household Income
   • 2021 HHs w/Food Stamps/SNAP (ACS 5-Yr): Percent
   • 2024 Median Home Value
   • 2024 Owner Occupied HUs: Percent
   • 2021 HHs/Gross Rent 50+% of Income (ACS 5-Yr): Percent
   • 2021 HHs: Inc Below Poverty Level (ACS 5-Yr): Percent
   • 2024 Pop Age 25+: Bachelor's Degree: Percent
   • 2024 Unemployed Population 16+: Percent
   • 2021 HHs w/1+ Persons w/Disability (ACS 5-Yr): Percent
   • 2021 Pop 35-64: No Health Insurance (ACS 5-Yr): Percent
   • 2021 Pop 65+: No Health Insur (ACS 5-Yr): Percent
   • 2021 HHs w/No Internet Access (ACS 5-Yr): Percent
7. Click Add.

   The 12 fields are added.

   

8. Click Run.

   The tool runs. It calculates a suitability score for each census tract based on the fields you chose. The Gwinnett County Priority Levels layer symbology updates to show the range of the suitability score values.

   

   These results consider all 12 fields, or criteria, to have the same weight and influence. However, not all criteria influence socioeconomic vulnerability the same way. For example, a higher median household income indicates less vulnerability, while a higher unemployed population indicates more vulnerability. You'll adjust the variables so they influence the analysis appropriately.

   By default, when you make a change to the criteria, the results are automatically calculated and applied on the map. You'll turn off automatic calculation until you've made all of the changes.

9. On the Suitability Analysis tab, in the Suitability Score group, turn off Auto Calculate.

   

10. In the Criteria group, click Suitability Analysis pane.

    

    The Suitability Analysis pane appears. It lists the criteria used in the analysis. Each criterion has several parameters associated with it.

    By default, all criteria have the same Weight value. Weight represents how important a criterion is to the analysis relative to the other criteria. It is expressed as a percentage, and all weights combined must equal 100. Because you have 12 variables, the default weight is 100 divided by 12, or 8.33. You can adjust the weights to make certain variables more or less important to the analysis. For instance, you could make median household income more important, while making households with food stamps less important.

    The Influence parameter determines whether a criterion has a positive or negative influence on the analysis. By default, all criteria have a positive influence, meaning higher values indicate higher priority for intervention. While this influence is correct for most criteria, some (like median household income) should have a negative influence, meaning lower values indicate higher priority.

    The Minimum Value and Maximum Value parameters can be used to exclude features outside the minimum or maximum range from the analysis.

    For this tutorial, you won't change the weights or minimum and maximum values. For some parameters, however, you'll change the influence.

11. On the Criteria tab, scroll and find the 2024 Median Household Income variable.For Influence, choose Inverse.

    

12. For the following variables, change Influence to Inverse:
    • 2024 Median Home Value
    • 2024 Owner Occupied HUs: Precent
    • 2024 Pop Age 25+: Bachelor's Degree: Percent

    Now, all of the criteria have an appropriate influence on the analysis. You'll recalculate the suitability score.

13. On the ribbon, in the Suitability Score group, check the box next to Auto Calculate.

    The Gwinnett County Priority Level layer updates to reflect the changes you made. Tracts with lower priority are yellow and tracts with higher priority are red.

    

    The map shows many high priority census tracts in the central western part of the county, with a few high priority tracts in the north and south.

14. Save the project.

Symbolize the tracts

Now that your suitability analysis is complete, you'll symbolize the results. Currently, there are five symbol classes, defined by statistical patterns in the dataset. Your symbology will classify the data into quartiles, which divide the data into four equal parts.

1. In the Contents pane, right-click Gwinnett County Priority Levels and choose Symbology.

   The Symbology pane appears. The layer already uses graduated colors for its symbology, so you only need to change the method and number of classes.

2. For Method, choose Quantile. For Classes, choose 4.

   

   You'll also change the color scheme.

3. For Color scheme, choose Purples (4 Classes).

   

   Tip:

   To see the name of a color scheme, either point to it or check the box next to Show names.

   The symbology updates on the map. Now, the layer has four symbol classes. Each symbol class represents 25 percent of the total number of census tracts, organized by priority level.

   

   The labels for the symbol classes use the suitability score cutoffs for each quartile. The suitability scores might not mean much to users who don't understand what they mean. You'll change the symbol class labels to instead describe the priority level of each quartile.

4. Under Classes, double-click the label of the first symbol class to edit it. Type Class 1: Least Priority and press Enter.

   

5. Change the label of the second symbol class to Class 2: Low Priority, the third to Class 3: Moderate Priority, and the fourth to Class 4: High Priority.

   The labels update in the Contents pane.

   

6. Close the Symbology pane. Save the project.

You've performed suitability analysis in ArcGIS Business Analyst Pro to assess which census tracts should be prioritized for interventions to improve socioeconomic vulnerabilities. Next, you'll contextualize your results with charts and demographic data.

Contextualize the results

The layer you created prioritizes census tracts in Gwinnett County for intervention, based on indicators of socioeconomic vulnerability. Though your analysis is complete, you can still improve your map with context that gives better insight into the county's demographics.

First, you'll create a chart to show specific demographic variables, such as income, for each priority level. Then, you'll add another layer to the map, showing the predominant race or ethnicity in each tract. This context can help policymakers make more informed decisions about distributing resources in an equitable way based on need.

Classify tracts by priority level

When you symbolized the priority levels layer, you classified the data into quartiles. These quartiles served as the basis of your priority levels, ranging from least priority to high priority. Currently, this classification exists only in the layer's symbology. To create a chart or perform further analysis using this classification, you'll need to create a field in the layer's attribute table to show the priority level of each tract. You can do this using the Reclassify Field tool.

1. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Tools.
2. In the Geoprocessing pane, search for and open the Reclassify Field tool.

   This tool reclassifies a field based on a specified statistical method and creates a new field with the results.

3. For Input Table, choose Gwinnett County Priority Levels.

   Next, you'll choose the field to reclassify. The output field created by your suitability analysis is called Final Score.

4. For Field to Reclassify, choose Final Score.

   

5. For Reclassification Method, choose Quantile. For Number of Classes, type 4.
6. For Output Field Name, type Priority_Level_by_Quartile.

   

7. Click Run.

   The tool runs. Because the analysis only affected the attribute table, the map doesn't change.

8. Open the attribute table for the Gwinnett County Priority Levels layer. Scroll to the end of the table.

   

   The tool added two fields to the end of the table: Priority_Level_by_Quartile_CLASS and Priority_Level_by_Quartile_RANGE. The first shows whether a tract is in the first, second, third, or fourth quartile, while the second shows the range of values in the tract's quartile. Because you used the same classification method for the layer symbology, the classes in the table correspond to the symbols.

9. Close the table.

Create a chart to compare classes

The value of creating a field with the quartile classes is that you can use these classes for further analysis. For instance, what if you wanted to know how a specific socioeconomic indicator, such as median income, differs between classes? You'll create a chart that shows the average median income for each class.

1. In the Contents pane, right-click Gwinnett County Priority Levels, point to Create Chart, and choose Bar Chart.

   

   A chart and the Chart Properties pane appear.

2. In the Chart Properties pane, for Category or Date, choose Priority_Level_by_Quartile_CLASS.

   You'll set the y-axis to show mean household income.

3. For Aggregation, choose Mean.
4. For Numeric fields, click Select. Check the box for 2024 Median Household Income and click Apply.

   

   The chart updates to show the mean 2024 median household income for each priority level. Before you look at the chart, you'll change the chart's title and axis labels to be more understandable.

5. In the Chart Properties pane, click the General tab.

   

6. Set the following parameters:
   • For Chart title, type Priority class by 2024 median household income.
   • For X axis title, type Priority class.
   • For Y axis title, type Mean of 2024 median household income.

   

   The chart is now complete.

   

   The chart shows a clear trend of higher priority classes having lower median household income. The income in class 1 (about $118,000) is over twice that as class 4 (about $53,000), which gives an idea of the economic disparity across the county. In 2022, the median household income for Georgia was $71,355. Class 3 is slightly above that mark, while class 4 is below it.

   Tip:

   You can repeat the workflow in this section to create charts for any demographic variable you used for your suitability analysis. You can even use the Enrich Layer tool to add other variables for comparison. For instance, you could enrich your data with information about race and ethnicity and chart it by priority level to better understand how race and ethnicity differ across socioeconomic groups.

7. Close the chart and the Chart Properties pane.

Add race and ethnicity data

So far, your analysis hasn't considered race and ethnicity. Race and ethnicity are often intertwined with socioeconomic vulnerability and can be important factors to consider when determining how to prioritize resources for intervention. To contextualize your analysis results, you'll add an ArcGIS Living Atlas layer showing the predominant race in each census tract.

1. On the ribbon, click the Map tab. In the Layer group, click the Add Data button.

   

2. In the Add Data window, under Portal, click Living Atlas. Search for ACS Race and Hispanic Origin Variables.
3. Double-click ACS Race and Hispanic Origin Variables - Centroids.

   

   The layer has three sublayers, corresponding to different geographies: county, state, and tract. You'll add the tract layer, because your analysis involves census tracts.

4. Double-click Tract.

   

   The layer is added to the map.

   

   The layer shows centroids, or point symbols that represent the center of a polygon (in this case, census tract polygons). The size of the circle corresponds to population, while the color corresponds to the predominant race or ethnicity in each tract. The transparency of each symbol indicates the strength of predominance.

   You'll create a definition query to only show Gwinnett County.

5. In the Contents pane, double-click Tract.

   

6. In the Layer Properties window, click the Definition Query tab. Click New definition query.

   

7. Create the clause Where State is equal to Georgia. Add a clause that reads And County is equal to Gwinnett County.

   

8. Click OK.

   The Layer Properties window closes and the query is applied. Now, only centroids for Gwinnett County are shown.

   Lastly, you'll adjust the symbology so the centroids appear more clearly over the priority levels.

9. In the Contents pane, right-click Tract and choose Symbology.

   You'll give each symbol a halo, so they show up more strongly over a variety of background colors.

10. Under Classes, click More and choose Format all symbols.

    

11. Click the Properties tab. Expand Halo and change Halo symbol to Black fill.

    

12. Change Halo size to 0.5 pt.

    

13. At the bottom of the Symbology pane, click Apply.

    The halo is applied to the map. You'll also adjust the size and transparency of the symbols.

14. Click the options button and choose Vary symbology by attribute.

    

15. Expand Transparency. For Low values, type 60%.

    

16. Expand Size. Change Minimum to 5 pt and Maximum to 35 pt.

    

    The changes are applied to the map. Now, it's easier to read the color of each symbol.

    

    The map makes clear the racial and ethnic diversity of Gwinnett County. Many high priority census tracts in the western part of the county have green (Hispanic) or yellow (Black) centroids, suggesting an element of racial disparity to socioeconomic vulnerability. With this context, policymakers can better understand how race and ethnicity intersect your results.

17. Save the project.

Rationale for variables

Each of the variables or indicators used in this example analysis reflects a different dimension of socioeconomic vulnerability. It is recommended that you choose and develop the dimensions and indicators in collaboration with a group of stakeholders that includes members or representatives of the impacted community. To learn more about selecting indicators for creating a composite index, see the Esri technical paper Creating Composite Indices Using ArcGIS: Best Practices.

Dimension	Variables or Indicators	Rationale
Income and Employment Status	2024 Median Household Income 2024 Unemployed Population 16+: Percent	Income and employment status can indicate the overall economic health and job market strength of an area.
Poverty and Government Assistance	2021 HHs: Inc Below Poverty Level (ACS 5-Yr): Percent 2021 HHs w/Food Stamps/SNAP (ACS 5-Yr): Percent	High poverty rate is one indicator of a community's overall economic health and the proportion of the population likely struggling to meet basic needs like food, shelter, and health care. Poverty is also often associated with a range of other vulnerabilities, including limited access to quality education, exposure to areas with higher crime rates, and increased health risks. Children growing up in poverty are especially at risk, with long-term effects on their education, health, and future economic opportunities.
Housing Affordability and Burden	2021 HHs/Gross Rent 50+% of Income (ACS 5-Yr): Percent 2024 Median Home Value 2024 Owner Occupied HUs: Percent	Housing affordability reveals the financial burden on households. Housing expenses should ideally not exceed 28-33 percent of a household's income. Households that are heavily burdened by mortgage payments are more vulnerable to economic instability in scenarios of income reduction or job loss. Median home value is one indicator that can reflect the overall economic prosperity of an area, while the percentage of owner-occupied housing units offers valuable information about community stability and long-term investment.
Education and Digital Access	2024 Pop Age 25+: Bachelor's Degree: Percent 2021 HHs w/No Internet Access (ACS 5-Yr): Percent	Higher education levels generally lead to better job prospects, higher incomes, and improved quality of life. Internet access is crucial for education, job searching, and accessing services. Lack of digital access can exacerbate educational and economic disparities.
Healthcare Access	2021 Pop 35-64: No Health Insurance (ACS 5-Yr): Percent 2021 Pop 65+: No Health Insurance (ACS 5-Yr): Percent	Individuals without health insurance may be burdened with high medical costs. This can lead to significant economic strain, especially in cases of unexpected and significant illness or injury. People without health insurance are less likely to access preventive care services, such as regular check-ups, vaccinations, and early disease screening. This can result in delayed diagnosis and treatment, leading to more severe health conditions that are costly to treat.
Disability and Accessibility	2021 HHs w/1+ Persons w/Disability (ACS 5-Yr): Percent	This component acknowledges the unique challenges faced by households with disabled members. Disabilities can affect employment opportunities, income levels, access to health care, and overall quality of life.

In this tutorial, you performed suitability analysis to determine socioeconomic vulnerability. You enriched census tract data with socioeconomic indicators, calculated vulnerability scores for each tract, and contextualized the results with race and ethnicity data, revealing significant insights and potential policy implications. This analysis allowed you to highlight the census tracts in Gwinnett County that should be prioritized for targeted interventions, empowering policymakers with data-driven insights for positive community change.

This workflow can be performed for any county in the United States. To view a national-level analysis, see the Socioeconomic Vulnerability web map. To learn more about the suitability analysis workflow, see Perform a suitability analysis.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• Demographic variables provided by Esri. Data Vintage: Esri 2024, Esri 2022 (ACS 5-Yr). Retrieved from Esri Data Browser on November 4, 2024.
• ACS Race and Hispanic Origin Variables - Centroids from United States Census Bureau and Esri.
• Topographic map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• Light Gray Canvas map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community