Customize a climate resilience index

Extreme heat is just one of many hazards exacerbated by climate change. High temperatures can cause or worsen health conditions, including heat exhaustion, and the hazard is especially intensified in urban areas due to the urban heat island effect. The urban heat island effect is created when human-built materials, such as asphalt, concrete, and rooftops absorb and hold heat from sunlight, making ambient air temperatures higher. The heat island effect has been shown to raise urban daytime temperatures higher than suburban and outlying areas with less built area.

One of many ways that communities can take action to mitigate extreme heat and reduce the urban heat island effect is by planting more trees. Trees not only provide shade to people in their immediate vicinity, but also can cool ambient temperatures through the process of evapotranspiration, releasing water from the ground into the air. To decide where tree planting as a mitigation strategy is most appropriate, communities first need to know where the worst effects of extreme heat are being felt and where tree cover already exists.

In this tutorial, you'll create a heat risk index showing where to prioritize tree planting in Sevilla, Spain, with three variables: high average summer surface temperature, percent of area without tree cover, and population density.

This process can be adapted for your own area of interest and additional demographic and environmental variables.

This tutorial was last tested on March 12, 2024, using ArcGIS Pro 3.2. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• ArcGIS organizational account with a Publisher, Facilitator, or Administrator role in an ArcGIS organization (see options for software access)
• ArcGIS Pro

Prepare index variables

The first step to creating a heat risk index is to prepare the input data. You'll use three variables for your index: high average summer surface temperature, percent of area without tree cover, and population density. Each of these inputs is derived from ArcGIS Living Atlas of the World data, and can be repeated or customized for your own neighborhood or other area of interest.

Add data for the study area

The example heat risk index (HRI) will be calculated for Sevilla, a city in southern Spain. Before processing the data that will comprise the risk index, you'll find and prepare census data for Sevilla. This layer will be used to filter and clip global raster services, and will allow you to create the index within neighborhood-level geometries meaningful to local planning and intervention.

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 account (for ArcGIS Online or ArcGIS Enterprise), see options for software access.

2. Under New Project, click Map.

   

3. In the Create a New Project window, for Name, type Sevilla Heat Resilience Index. Click OK.

   The project is created.

   The first variable for the HRI is land surface temperature in degrees Celsius. You'll derive this from the Multispectral Landsat imagery service in ArcGIS Living Atlas.

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

   

5. In the side menu of the Add Data window, under Portal, click Living Atlas.
6. Search for Spain census sections owner:esri_dm. Click the Spain Census Section Boundaries feature layer to select it.

   Tip:

   Adding owner: and the owner name to a search filters search results by a specific owner.

   

7. Click OK.

   The layer is added to the map and the map zooms to Spain. You'll filter the census sections to only show Sevilla.

8. In the Contents pane, right-click ESP_CensusSection and choose Attribute Table.

   The attribute table opens. The city where each census section is located is listed in the Name field.

   Note:

   If you don't see the Name field, click the Options button in the attribute table ribbon and click Show All Fields.

9. In the attribute table, for Selection, click Select By Attributes.

   

   The Select By Attributes tool opens.

10. In the Select By Attributes tool, build the query Where Name is equal to Sevilla and click OK.

    The bottom of the attribute table indicates that 524 sections are selected. You'll save a copy of this filtered layer to your project so that you can work with the data.

11. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Tools.
12. Search for and open Export Features (Conversion Tools).
13. For Input Features, choose ESP_CensusSection. For Output Feature Class, type Sevilla_Census_Sections.

    

14. Click Run.

    When the tool is finished running, the Sevilla_Census_Sections layer is added to the Contents pane. You can now remove the original census sections layer.

15. In the Contents pane, right-click ESP_CensusSection and choose Remove.

    Next, you'll symbolize the Sevilla_Census_Sections layer so that you can see it on top of the layers you'll add later.

16. In the Contents pane, right-click Sevilla_Census_Sections and choose Symbology.
17. For Symbol, click the current symbol swatch. On the Gallery tab, under ArcGIS 2D, click Black Outline (1 pt).

    

    The layer also has a transparency applied that makes the boundaries difficult to see against the basemap.

18. On the ribbon, click the Feature Layer contextual tab. In the Effects group, change Transparency to 0 percent.
19. In the Contents pane, right-click Sevilla_Census_Sections and choose Zoom To Layer.

    

    Your area of interest layer is now symbolized and centered on the map. You'll use this extent later to clip the raster data for use.

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

    

    The project is saved.

Prepare Landsat data

The first variable in your index is high average summer surface temperature, which can be derived from global Landsat imagery available in ArcGIS Living Atlas. To prepare this input, you'll add the image service to your map and find scenes available for your area of interest. Then, you'll copy the raster locally and use the Zonal Statistics as Table tool to determine the maximum value within each Sevilla census section.

1. On the ribbon, click the Map tab. In the Layer group, click the Add Data button.
2. In the Add Data window, ensure Living Atlas is selected. Search for and add the Multispectral Landsat imagery layer owned by Esri.

   

   The Multispectral Landsat imagery layer is added to your project. It contains decades' worth of data organized in scenes. You'll adjust the Landsat service properties to get only the temperature data you're interested in.

3. In the Contents pane, double-click Multispectral Landsat.

   The Layer Properties window appears.

4. In the Layer Properties window, click the Processing Templates tab.
5. For Processing Template, choose Band 10 Surface Temperature in Celsius.

   

6. Click the Mosaic tab. For Mosaic operator, choose Mean.

   This service uses a mosaic dataset to manage the decades' worth of scenes. By default, it displays the first scene. Selecting the mean operator will calculate an average temperature value from all the scenes available for your area of interest and based on the filters you'll apply. Next, you'll add a definition query with three arguments: Cloud Cover is less than or equal to 10 percent, scenes are classified as primary, and scenes were acquired during summer months.

7. Click the Definition Query tab. Delete the existing query.
8. Click New definition query.

   

9. Build the expression Where Cloud Cover is less than or equal to 0.10. Click Add Clause.

   

   This query will filter out all scenes with more than 10 percent cloud cover. Clouds and cloud shadows in Landsat scenes adversely impact the results of any analysis.

10. Build the expression And Category is equal to 1 – Primary.

    For performance reasons, this dataset contains lower-resolution scenes not suitable for analysis. Using only primary scenes will ensure the results are derived from accurate input data.

11. Click Add Clause and build the expression And Month includes the value(s) 6,7,8.

    This clause will include only months that are considered summer months in the northern hemisphere. Your query now has three clauses.

    

12. For Query 1, click Apply. In the Layer Properties window, click OK.

    The service may take a few minutes to update. When it's finished, the scenes may show as a gray rectangle. To visualize the average summer temperatures in the layer, you'll symbolize the raster.

13. In the Contents pane, right-click Multispectral Landsat and choose Symbology.
14. In the Symbology pane, for Statistics, click Dataset and choose DRA.

    

    DRA is short for dynamic range adjustment, which automatically adjusts your active stretch type as you navigate around your image based on the pixel values in your current display.

15. For Color scheme, choose a graduated color ramp such as Inferno.

    Tip:

    Point to color ramps to see their name.

    

Calculate the high average summer surface temperature

Now that you've set the processing template and filters on the Landsat imagery, you'll copy just these scenes of interest to your project.

1. In the Contents pane, right-click Multispectral Landsat and choose Attribute Table.
2. At the bottom of the attribute table, click the Filter by Extent button.

   

   The table is filtered to show only the scenes available in the map's current extent, the Sevilla region.

3. Close the attribute table. In the Contents pane, right-click Sevilla_Census_Sections and choose Zoom To Layer.
4. In the Geoprocessing pane, open the Copy Raster tool.
5. For Input Raster, choose Multispectral Landsat.

   Note:

   A red X may appear next to the Input Raster parameter, indicating the parameter is invalid. The Landsat Imagery service only allows exports of 4000x4000 pixels at a time. Before running this tool, you'll set a processing extent to ensure that the raster export is within these limits.

6. For Output Raster Dataset, click the Browse button.

   You'll save this raster as a TIFF file, which can't be stored in a geodatabase.

7. In the Output Raster Dataset window, under Project, click Folders. Double-click the Sevilla Heat Resilience Index project folder.

   Note:

   If you gave your project a different name, the project folder's name will also be different.

8. For Name, type Avg_SurfaceTemp_Sevilla.tif.

   

9. Click Save
10. In the Geoprocessing pane, click the Environments tab. For Extent, click the Extent of a Layer button and choose SevillaCensus_Sections.

    

    Setting a processing extent resolves the error condition you saw when you set the input raster.

11. Click Run.

    When it's finished processing, the Avg_SurfaceTemp_Sevilla.tif raster is added to the Contents pane and the map.

    Note:

    You may receive a warning message after the tool completes, stating WARNING 003485: Processing templates will not be saved to the output raster dataset because the input layer already has an active processing template. This warning is expected because only the imagery data gets copied and not any of the service processing templates.

12. In the Contents pane, right-click Multispectral Landsat and choose Remove.

    

    Now that your area of interest's surface temperate raster is copied to a local file, you can use the Zonal Statistics as Table tool to summarize all the temperature values within each census polygon to determine the maximum value.

13. In the Geoprocessing pane, open the Zonal Statistics as Table (Spatial Analyst Tools) tool.

    The Zonal Statistics as Table tool calculates statistics of the raster cells within the zones of another dataset. In this case, you'll calculate the Maximum statistic to find the highest average temperature within each census section.

14. Enter the following parameters:
    • For Input Raster or Feature Zone Data, choose Sevilla_Census_Sections.
    • For Zone Field, choose ID.
    • For Input Value Raster, choose Avg_SurfaceTemp_Sevilla.tif.
    • For Output Table, type High_Avg_Surface_Temp_Sevilla.
    • For Statistics Type, choose Maximum.

    

15. Click Run.

    The High_Avg_Surface_Temp_Sevilla table is added to the Contents pane under Standalone Tables.

16. In the Contents pane, right-click High_Avg_Surface_Temp_Sevilla and choose Open.

    The MAX field shows the maximum statistic. You'll rename this field for clarity.

17. On the ribbon, click the Standalone Table contextual tab. In the Data Design group, click Fields.

    

18. In the Fields table, in the Alias column, double-click MAX to edit the record. Type High Avg Temp (C).

    

19. On the ribbon, in the Manage Edits group, click Save to save your edits to the table.
20. Close both tables and save the project.

    You have now completed the workflow for preparing the first input in the heat resilience index. First, using ArcGIS Living Atlas data, you derived land surface temperature for an area of interest using processing templates on the Multispectral Landsat image service. Then, you adjusted properties on the service to filter the scenes by attribute and calculate average values across the filtered scenes. You also applied a spatial filter to limit the scenes to an area around the census boundaries.

Derive lack of tree canopy

The second input for the heat risk index is the lack of tree canopy. This input is derived from the European Space Agency WorldCover 2020 Land Cover imagery service in ArcGIS Living Atlas.

1. On the ribbon, click the Map tab and click the Add Data button. From the Living Atlas portal, add the European Space Agency WorldCover 2020 Land Cover layer owned by esri_environment.

   

   This layer is a global land cover dataset using 11 land cover classes. Out of these classified pixels, you'll only need those showing tree cover. You'll use the Reclassify tool to isolate only the tree cover pixels.

2. In the Geoprocessing pane, open the Reclassify (Spatial Analyst Tools) tool.
3. For Input raster, choose European Space Agency WorldCover 2020 Land Cover. Ensure that Reclass field is set to ClassName.
4. In the Reclassification table, leave the New value for Tree Cover set to 1. Change all the other New values except for NODATA to 0.

   

5. For Output raster, click Browse. Browse to the Sevilla Heat Resilience Index project folder.
6. For Name, type Tree_Canopy_Sevilla.tif. Click Save.

   To process only the pixels relevant to your area of interest, you'll use the processing extent to clip the raster.

7. Click the Environments tab. Under Processing Extent, for Extent, click the Extent of a Layer button and choose SevillaCensus_Sections.
8. Click Run.

   When the raster is finished processing, it is added to the Contents pane and drawn on the map.

9. In the Contents pane, right-click European Space Agency WorldCover 2020 Land Cover and choose Remove.

   

   Note:

   Your raster may have different symbology than the example image.

   The Tree_Canopy_Sevilla.tif layer has two classes: Tree Cover and everything else. You can use this raster to calculate the lack of tree cover variable that will be an input to your index.

   The lack of tree canopy is calculated using the formula 100 – Percent Tree Canopy.

10. In the Geoprocessing pane, open the Zonal Statistics as Table (Spatial Analyst Tools) tool.

    This time, you'll use the tool to summarize the number of tree cover pixels within each census polygon. The tool also counts the total number of pixels within each zone (polygon), so you can calculate the percentage of the polygon pixels covered with trees.

11. Enter the following parameters:
    • For Input Raster or Feature Zone Data, choose Sevilla_Census_Sections.
    • For Zone Field, choose ID.
    • For Input Value Raster, choose Tree_Canopy_Sevilla.tif.
    • For Output Table, type Tree_Pixels.
    • For Statistics Type, choose Sum.
12. Click Run

    The Tree_Pixels table is added to the Contents pane under Standalone Tables.

13. Open the Tree_Pixels table.

    The table contains two columns of interest: COUNT, which is the total number of pixels within each polygon zone, and SUM, which is the sum of tree cover pixels. You'll calculate the percent tree cover and percent lacking tree cover for each census polygon using the following formulas:

    • PCT_Tree_Cover = (Sum / Count) * 100
    • PCT_Lacking = 100 - PCT_Tree_Cover
14. In the attribute table, click Calculate.

    

15. In the Calculate Field tool, for Field Name (Existing or New), type Pct_Tree_Cover. For Field Type, choose Float (32-bit floating point).
16. Under Expression, for Pct_Tree_Cover =, build the expression (!SUM! / !COUNT!) * 100 and click OK.

    The new field is added to the end of the attribute table.

17. Click Calculate. For Field Name (Existing or New), type Pct_Lacking, and for Field Type, choose Float (32-bit floating point).
18. For Pct_Lacking =, build the expression 100 - !Pct_Tree_Cover! and click OK.

    The Tree_Pixels table has two new fields, Pct_Tree_Cover and Pct_Lacking. The Pct_Lacking attribute represents the percentage of the census section lacking tree cover and is the second input to the heat resilience index.

    

19. Close the Tree_Pixels table and save the project.

Calculate population density

The final input to the heat risk index is population density. This component of the index prioritizes populated areas where the most people may benefit from the intervention. You'll derive the population density input from Spanish census data in the Sevilla_Census_Sections layer.

1. Open the attribute table for the Sevilla_Census_Sections layer.

   The layer contains attributes for both total population and area in square kilometers. You'll calculate a new field by dividing population by area.

2. In the attribute table, for Field, click Calculate. In the Calculate Field window, for Field Name (Existing or New), type PopDensity, and for the Field Type, choose Float (32-bit floating point).
3. Under Expression, for PopDensity =, copy and paste the expression !TOTPOP_CY! / !AREA!.

   

   Note:

   Though the attributes are shown by their aliases, or readable names, in the Fields pane, they populate in the expression using their field name.

4. Click OK.

   The PopDensity field is added to the table. The three derived inputs are now ready to be combined into the heat risk index and symbolized on a map. You'll first transfer all the inputs to the Sevilla_Census_Sections layer for processing.

5. In the Geoprocessing pane, open the Join Field tool.
6. Enter the following parameters:
   • For Input Table, choose Sevilla_Census_Sections.
   • For Input Join Field, choose ID.
   • For Join Table, choose High_Avg_Surface_Temp_Sevilla.
   • For Join Table Field, choose ID.
   • For Transfer Fields, choose High Avg Temp (C).

   

7. Click Run.

   When the tool finishes running, the High Avg Temp (C) field is added to the Sevilla_Census_Sections table.

8. In the Join Field pane, change Join Table to Tree_Pixels and change MAX to Pct_Lacking. Click Run.

   Now, all three inputs are in the same table.

9. Close the table. Save the project.

You've prepared the variables you'll use to create your heat index. You're ready to create the index.

Create a heat risk index

With all the index inputs prepared, you'll create your index. There are many ways to create, combine, and interpret indices based on the purpose; in this tutorial, you'll use the Calculate Composite Index tool. This tool incorporates some data preprocessing and data combination steps to help you choose the best index methods for your data. If you're working with different study areas or variables, adjust your index processing accordingly using the Calculate Composite Index tool documentation and best practices guide.

Test heat risk index methods

To create an effective index, you'll need to choose what methods to use to preprocess and combine the data. To choose a good preprocessing method for this data, you'll check the distribution of input variables. Variables exhibiting skew, for example, can be rescaled for better comparison. Once you've chosen a preprocessing method, you'll use a template based on commonly used approaches for creating indices and evaluate the results.

1. In the Contents pane, uncheck Tree_Canopy_Sevilla.tif and Avg_SurfaceTemp_Sevilla.tif to turn them off.
2. Right-click Sevilla_Census_Sections and choose Symbology.
3. Under Primary symbology, click Single Symbol and choose Graduated Colors. For Field, choose PopDensity.

   The map updates to display tracts based on their population densities.

   

   The map of population density shows higher density values in smaller census areas near the center of the city, while larger census areas and areas on the outskirts of the city have lower densities.

4. In the Symbology pane, click the Histogram tab.

   

   The distribution shows a slight positive skew, tail points to the right.

5. Change the Field parameter to view the histograms for High Avg Temp (C) and Pct_Lacking.

   

   The High Avg Temp (C) variable is also positively skewed, while the Pct_Lacking variable is strongly negatively skewed. You'll use this understanding of your index inputs to choose a preprocessing method, and later to validate the results of the index.

6. In the Geoprocessing pane, search for and open the Calculate Composite Index tool.

   While there are many ways to create indices, you'll use this tool because it combines multiple data processing steps into a single tool and creates a series of charts to help you validate the results of the index tool.

   First, you'll add the three inputs you've prepared and set the preprocessing parameters you want to use. You'll run the tool twice, so you'll name the tool outputs after the scaling and combination methods used.

7. For Input Table, choose Sevilla_Census_Sections. For Output Features or Table, type Sevilla_HRI_MeanofPercentiles.
8. For Input Variables, click the Add Many button.

   

9. Choose PopDensity, High Avg Temp, and Pct_Lacking. Click Add.

   

   Note:

   If your Input Variables are listed using field names instead of aliases, click the Field list settings button and choose Show Field Aliases.

   Each input has a check box to reverse direction. Depending on how you defined your index and prepared your input variables, you may need to reverse a variable's direction. In this case, you defined increasing index values to mean extreme heat conditions for the tract are worse and the benefit from tree planting would be greater. The goal of the index is to identify census areas with higher population density, higher maximum average summer temperature, and more area without trees. During your data preparation, you derived input variables in such a way that increasing values match that of the index. Therefore, you don't have to reverse any of them.

   Next, you'll choose how to preprocess the inputs. Preprocessing is the method used to standardize all the inputs to a common scale. Because you're working with variables with different units and scales, you need to standardize them before you can combine them. The first method you'll use is the mean of percentiles.

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

    Mean of percentiles standardizes values by translating them into percentiles between 0 and 1. In this case, you're using percentiles for two main reasons. First, this method can be useful when the ranks of each variable are more important than their actual values. For example, it's not meaningful to say that one census area should be prioritized because it's five degrees hotter than its neighbor. You only care that the census area is hotter relative to other areas you're measuring. With population density, an extra one or two people moving to a census area aren't going to cause a meaningful shift in your prioritization. You care where the population density is higher in comparison to other neighborhoods.

    Second, this method works well for skewed data, like the Pct_Lacking attribute, because all the variables are transformed to a uniform distribution.

    

    Note:

    The Preset Method to Scale and Combine Variables parameter provides templates that set the preprocessing (Method to Scale Input Variables) and combination methods (Method to Combine Scaled Variables) based on commonly used approaches for creating indices.

11. For Method to Scale Input Variables, confirm that Percentile is chosen.
12. For Method to Combine Scaled Variables, confirm that Mean is chosen.

    The next parameter sets the weight of each variable. Variables can be weighted to represent the relative importance of each factor as it contributes to the index.

13. Expand the Variable Weights section.

    By default, all weights are set to 1, meaning each variable is equally weighted. Because you don't necessarily want one variable to have more or less influence over the index, you'll accept this default.

    The Output Settings parameters allow you to choose what to name the index attribute field. You can also choose additional symbolized layers for the tool to generate.

14. Expand Output Settings. For Output Index Name, type HRI.

    

15. Click Run.

    When the tool is finished, the Sevilla_HRI_MeanofPercentiles Layers group layer is added to the Contents pane. The group layer contains two layers, one showing the index values using an unclassified color ramp, and the other showing the index values as percentiles. The HRI layer also contains several charts.

16. In the Contents pane, under HRI, right-click Distribution of Index and choose Open.

    

    In the chart, the highest index values are around 0.9, and the lowest values are around 0.06.

    Note:

    Your values may vary slightly due to differences in processing the data.

    The high index values can be interpreted as showing areas that would benefit most from tree planting as a heat resilience intervention. Likewise, the low index values can be interpreted as benefiting least from tree planting as an intervention. This doesn't mean that these areas don't have high summer temperatures or need resilience measures implemented, but that interventions other than tree planting should be considered.

17. In the chart pane, click the bar representing the highest index values to select them on the map.

    

18. Click one of the highlighted census sections on the map to open its pop-up.

    The pop-up lists several fields that you should consider when validating the index, including the scaled values for each input.

19. In the pop-up, check the PopDensity (Percentile), High Avg Temp (C) (Percentile), and Pct_Lacking (Percentile) fields.

    The highest index values tend to have correspondingly high input attributes and also tend to be in the top percentiles. As you keep exploring the areas with high index values, you may notice that some variables with higher percentile values compensate for areas with lower percentile values to create higher overall index values.

    

    This offsetting effect is called compensability. In the case of this index, it appears that most often, higher percentile values for population density and lack of tree cover are compensating for lower percentile values in temperature. While this isn't necessarily a negative outcome, considering there are a large number of people who could benefit from intervention in these areas, you'll create a second index to compare results.

20. Close any open pop-ups and the chart. On the ribbon, on the Map tab, in the Selection group, click Clear.

Create a multiplicative index

Compensability is an effect that can be addressed by the method you use to combine variables—the Mean method that you used as part of the Combine ranks (Mean of percentiles) method was additive, which allowed the high values to compensate for low values. Two of the other available methods, Multiply and Geometric Mean, are multiplicative, which will cause higher index values in areas where all input values are high. In this section, you'll build off the previous index by using the same Percentiles method to preprocess the variables but using the Geometric Mean method to combine variables.

1. In the Geoprocessing pane, in the Calculate Composite Index tool, change the Output Features or Table name to Sevilla_HRI_Percentiles_GeomMean.
2. Confirm that Method to Scale Input Variables is set to Percentile. Change Method to Combine Scaled Variables to Geometric mean.

   Note:

   The Preset Method to Scale and Combine Variables parameter will change to Custom because you're not using a template input anymore.

   This time, when you run the tool, you'll also choose an additional output layer to give you an additional way to assess the results.

3. Under Output Settings, for Additional Classified Outputs, check the box next to Standard deviation.

   

4. Click Run.

   When the tool is finished, the Sevilla_HRI_Percentiles_GeomMean Layers group layer is added to the Contents pane.

5. In the Contents pane, confirm that only the HRI layers in each group layer are turned on.

   

6. Click the Sevilla_HRI_Percentiles_GeomMean group layer to select it. On the ribbon, click the Group Layer contextual tab.
7. In the Compare group, click Swipe.

   

   Your cursor changes to the swipe tool.

8. Click the map and drag the mouse to compare the two layers.

   The Percentiles_GeomMean layer is on top; you're swiping it away to reveal the Meanof_Percentiles layer.

   

   Based on a visual comparison, many census areas have lower index values in the Percentiles_GeomMean layer.

9. On the ribbon, click the Map tab. In the Navigate group, click Explore.

   The Swipe tool is deactivated.

10. In the Contents pane, for the HRI layer in the Sevilla_HRI_Percentiles_GeomMean Layers group layer, right-click Distribution of Index and choose Open.

    Use the chart to explore areas on the map with high index scores and compare the percentile scores of the scaled inputs.

11. When finished, close the chart.

    Now you've produced two indices that show census areas that will likely experience the greatest benefit from tree planting campaigns as a heat resilience measure.

Map and interpret the index

Once you're comfortable with the results of your index, you'll style it in a way that's easier for others to interpret. The index values you've been looking at are meaningful to you, but likely won't be to others without additional context.

Additionally, taking action based on this index will require more local input and knowledge. For example, what is the budget for the tree planting intervention? What kind of permitting is required, and do communities want trees planted in their neighborhood? Without this kind of direct understanding, it's potentially misleading to produce maps that show interpretations of your index. Instead, you'll use the standard deviation output you produced earlier to classify the census areas as receiving greatest benefit, moderate benefit, and least benefit.

1. In the Contents pane, under Sevilla_HRI_Percentiles_GeomMean Layers, turn off the HRI layer and turn on the HRI - Standard Deviation Classes layer. Open the Counts by HRI – Geometric mean (Standard deviation classes) chart.

   

   This layer shows the same index data, but it's symbolized by standard deviation, with the highest index values three standard deviations away from the mean.

2. Close the chart. In the Contents pane, right-click HRI - Standard Deviation Classes and choose Symbology.

   The layer is currently symbolized with six classes.

3. For Primary symbology, click Unique Values and choose Graduated Colors.

   

4. For Field, choose HRI – Geometric mean (Standard Deviation Classes).
5. For Classes, choose 3.

   These three classes will represent the census areas receiving greatest benefit, moderate benefit, and least benefit.

6. Click Color scheme and check Show all. Choose the Brown-Green (3 classes) color ramp.

   

   Tip:

   Check the Show names box to see the name of each color ramp.

   Next, you'll decide which census areas belong in the three categories you've decided to use. This decision is as subjective as the index creation process and can be adjusted to meet local needs and guidelines.

7. In the Classes tab, for the first class, confirm that Upper value is set to ≤ –1. For Label, type Least Benefit.
8. For the second class, confirm that Upper value is set to ≤ 1. For Label, type Moderate Benefit.
9. For the third class, confirm that Upper value is set to ≤ 3. For Label, type Greatest Benefit.

   

   Depending on your preferred methods for sharing and communicating the results of this index, you could now publish it as a web map or create a map layout to share a printed version. Either way, this map should be viewed as a midpoint, a way to gather community input and perspective before taking action on any tree planting.

10. Save the project.

In this tutorial, you created a heat risk index showing where tree planting would be most beneficial at mitigating extreme heat based on population density, lack of existing tree canopy, and high average summer temperatures.

The resilience index methodology can be replicated for other areas of interest around the world and for different climate hazards, and can include index inputs specific to your community, such as at-risk populations. When creating your own index, make sure to use data preprocessing and index creation methods specific to your data. For more guidance on the Calculate Composite Index tool, use the tool's documentation page and the Creating Composite Indices Using ArcGIS: Best Practices technical paper.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• The Spain Boundaries layer © Michael Bauer Research GmbH 2022 based on own compilation with data taken from the INE website: www.ine.es, Eurostat.
• World Topographic Map sources: Esri, HERE, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• World Hillshade layer sources: Esri, Airbus DS, USGS, NGA, NASA, CGIAR, N Robinson, NCEAS, NLS, OS, NMA, Geodatastyrelsen, Rijkswaterstaat, GSA, Geoland, FEMA, Intermap, and the GIS user community