Map and analyze the urban heat island effect

Heat islands are places that experience sustained elevated temperatures compared to surrounding areas, generally occurring in urban spaces with an abundance of impervious surfaces (such as sidewalks, rooftops, and buildings constructed using concrete, asphalt, and metal) and low tree canopy coverage. The dangerous conditions associated with urban heat islands, known as the urban heat island effect, contribute to higher rates of heat-related illnesses and deaths, compromising the health and quality of life of those living in affected communities.

Urban heat islands are also often associated with areas of historic under-investment, and they incur higher rates of energy consumption for cooling, contributing to accelerated resource consumption and emissions that worsen air quality.

In this scenario, you'll use feature and raster analysis tools to identify where the urban heat island effect impacts districts in Richmond, Virginia, and create a dashboard to monitor conditions throughout the city.

This tutorial was last tested on August 27, 2024.

View final result

Requirements

• The Zonal Statistics as Table tool requires ArcGIS Image for ArcGIS Online
• User, Publisher, or Administrator role in an ArcGIS organization (see options for software access)

Map urban heat island factors

To understand who is affected by urban heat islands, you'll first identify where the conditions that contribute to extreme heat are present. Using evening air temperature, tree canopy coverage, and impervious surface coverage data, you'll use analysis tools to visualize where urban heat island factors intersect across city block groups.

While there are several other factors that can be considered when examining the urban heat island effect, such as vehicle emissions and air conditioner usage, this analysis focuses on how impervious surface and tree canopy coverage contribute to evening air temperature. This information can then be used to identify places within the community that are disproportionately impacted by the conditions associated with urban heat islands

Explore and save a copy of the map

First, you'll open an existing web map, save a copy, and explore the layers in the map.

1. Open the Richmond Urban Heat Islands web map.

   

2. Sign in to your ArcGIS organizational account.

   Note:

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

3. If necessary, on the Contents toolbar, click Layers pane, view the layers included in the web map.

   

   The City Boundary and Census Block Groups layers outline the study area and the block groups that make up the city of Richmond. The Impervious Surfaces, Evening Temperature, and Percent Tree Canopy layers will be used to assess the impact of the urban heat island effect on city block groups. You will explore each of these factors, but before you continue, you will save a copy of the map.

4. On the Contents (dark) toolbar, click Save and open and choose Save as.

   

5. In the Save map window, enter the following:
   • For Title, type Urban heat islands followed by your name or initials.
   • For Tags, type heat islands, raster analysis, and Richmond VA.
   • For Summary, type Map of heat islands in Richmond, VA, for monitoring conditions across city block groups and city districts.
6. Click Save.

   You have saved a copy of the Richmond Urban Heat Islands map to your ArcGIS account.

   Next, you'll explore the first factor for assessing urban heat islands: tree canopy cover. In addition to absorbing carbon dioxide and improving air quality, trees provide natural cooling to surrounding areas by acting like an umbrella. When the sun hits their leaves, they use some of this solar energy and release moisture into the air through a process called transpiration. This moisture cools down the air around the trees.

   When you have many trees in an urban setting, their leaves create a canopy layer above streets and buildings that block some of the sun's heat from reaching the ground. This helps lower the surrounding air temperature, making cities more comfortable places, especially during hotter months of the year.

   The Percent Tree Canopy layer shows the percent coverage of tree canopy for each census block group. In the city of Richmond, some areas have a much higher presence of trees than others.

   Note:

   This layer is sourced from the City of Richmond's GeoHub and provides a way to estimate the area within each census block group that is shaded by trees.

   Using the Visibility options, you'll view the Percent Tree Canopy layer by itself.

7. In the Layers pane, click Visibility on the Percent Tree Canopy layer.

   

   The Percent Tree Canopy layer is shown along with the City Boundary and Census Block Groups layers.

   

   To better understand what the color gradient of the Percent Tree Canopy layer represents, you will open the Legend pane.

8. On the Contents toolbar, click Legend.

   

   The Legend pane appears with the Percent Tree Canopy layer section.

   

   The lighter areas on the map indicate a lower percentage of tree canopy coverage, while darker areas represent a higher percent of tree canopy coverage.

   Note:

   To learn more about how this layer was created, see Build a Heat Risk Index for Local Climate Planning: Part 2 of 3.

9. Return to the Layers pane and click Visibility on the Percent Tree Canopy layer to hide the layer.

   Next, you'll explore another urban heat island effect factor: impervious surfaces. Impervious surfaces absorb and retain heat, keeping the ambient air temperature at elevated levels for longer periods of time.

10. In the Layers pane, click the Visibility button for the Impervious Surfaces layer.

    The Impervious Surfaces layer is visible.

    

11. In the Contents pane, click Legend to view the legend.

    The raster pixels with lighter color represent a lower percentage of impervious surface, and the raster pixels with a darker color represent a higher percentage of impervious surface.

    Finally, use what you've learned to view the last urban heat island effect factor for this tutorial: evening air temperature. Air temperature is affected by tree canopy and impervious surface coverage, providing a way to measure the impact of these factors as they contribute to the urban heat island effect.

12. In the Layers pane, click the Visibility button for the Impervious Surfaces layer to turn it off and click the Visibility button for the Evening Temperature layer.

    The Evening Temperature layer is visible on the map.

    

13. In the Contents pane, click Legend to view the legend.

    Analyzing evening air temperature, as opposed to daytime temperature, provides valuable insight into the sustained elevated temperatures associated with urban heat islands. When the temperature is high during the day and into the night, this makes it difficult to escape the heat.

    For this tutorial, these will be the three factors for urban heat island effect you will explore. In a real-world scenario, there may be many other factors to include depending on the specific study area and aims of the spatial analysis.

    Note:

    To learn more about urban heat island factors, see the University Corporation for Atmospheric Research (UCAR) Center for Science Education Urban Heat Islands page.

    The Percent Tree Canopy layer was already prepared with summarized data by the census block group level. The Impervious Surfaces and Evening Temperature layers are currently in raster format. To summarize the data from each of these layers, you will use analysis tools to summarize and visualize the data by census block groups. This allows you to compare block groups and identify patterns of urban heat across the city of Richmond.

    Before you continue, you will save the map.

14. On the Contents toolbar, click Save and open and choose Save.

Analyze air temperature raster data

In this section, you will calculate the maximum evening air temperature for each census block group. The Zonal Statistics as Table tool will allow you to extract the values from the air temperature layer, which contains raster data, and summarize this information to each census block group to understand how air temperature changes from neighborhood to neighborhood.

1. On the Settings (light) toolbar, click Analysis. In the Analysis pane, click Tools.

   

2. In the search bar, type zonal. In the list of results, choose the Zonal Statistics as Table tool.

   

   Note:

   If you do not have a license for ArcGIS Image for ArcGIS Online, you can add the EveningTemp_CBG (Tutorials) and EveningTemp_Mean (Tutorials) tables to the map instead and skip to the next section to continue the tutorial.

   To add the tables, in the Contents pane, click Tables. Click the Add button. Search ArcGIS Online for EveningTemp owner:Esri_Tutorials and click the Add button for the two tables.

3. In the Zonal Statistics as Table tool, enter the following:
   • For Input zone raster or features, choose Census Block Groups.
   • For Zone field, choose GEOID.
   • For Input value raster, choose Evening Temperature.
   • Under Statistical analysis settings, for Statistic type, choose Maximum.

   

4. For Output table name, type EveningTemp_CBG and add your name or initials.

   Note:

   CBG stands for census block groups.

5. Above the Run button, click Estimate credits.

   

   Running this tool requires about 1 credit.

   Note:

   To learn more about credits, see Understand credits.

6. Click Run.

   It may take a few minutes to complete.

7. Click the History tab in the Analysis pane.

   

   As the tool runs, you can view the tool progress on the History tab.

   When the tool has completed running, the table is added to the map in the Tables pane.

8. On the Contents toolbar, click Tables.

   

   The Tables pane appears with the EveningTemp_CBG - ZonalStatisticsTable table listed. This table shows the maximum evening air temperature for each census block group in degrees Fahrenheit. Click the table to open and inspect it, making note of the range of temperatures that you observe across census block groups. Later in this tutorial, you will use the Join Features tool to join this table to the Census Block Groups layer.

   Next, you will determine a baseline value of evening air temperature by calculating the mean value for the whole city.

9. On the Zonal Statistics as Table tool, update the following:
   • For Input zone raster or features, choose City Boundary.
   • For Input value raster, confirm it is set to Evening Temperature.
   • Under the Statistical analysis settings, for Statistic type, choose Mean.

   

10. For Output table name, type EveningTemp_Mean and add your name or initials. Click Run.

    Note:

    Running this tool requires 1 credit.

    The EveningTemp_Mean - ZonalStatisticsTable table is created and added to the Tables pane.

11. Open the Tables pane. Click the Options button for the EveningTemp_Mean- ZonalStatisticsTable table and choose Show table.

    

    The EveningTemp_Mean - ZonalStatisticsTable table contains the evening air mean temperature for a single day of collection across the entire city, which was 87.63 degrees Fahrenheit.

    You can now compare this mean value to the maximum temperatures within each block group and determine if a particular census block group is a certain number of degrees warmer or cooler than the rest of the city.

12. Save the map.

Map impervious surfaces

Impervious surfaces, including sidewalks, rooftops, buildings, and parking lots, that are present in developed spaces absorb and retain heat, contributing to the urban heat island effect. Using the Zonal Statistics as Table tool, you calculate mean percent of impervious surfaces within each census block group. You can later calculate the percentage of impervious surfaces in each census block group.

Note:

If you do not have a license for ArcGIS Image for ArcGIS Online, you can add the ImperviousSurface_CBG (Tutorials) table to the map instead and skip to the next section to continue the tutorial.

To add the tables, in the Contents pane, click Tables. Click the Add button. Search ArcGIS Online for ImperviousSurface owner:Esri_Tutorials and click the Add button for the two tables.

1. In the Zonal Statistics as Table tool pane, enter the following:
   • For Input zone raster or features, choose Census Block Groups.
   • For Zone field, choose GEOID.
   • For Input value raster, choose Impervious Surfaces.

   

2. Under the Statistical analysis settings section, for Statistic type, choose Mean.

   

3. For Output table name, type ImperviousSurfaces_CBG, and add your name or initials.
4. Click Run.

   Note:

   Running this tool requires 1 credit.

   The table adds to the Tables pane and appears.

5. In the table, scroll until you see the MEAN fields.

   

   The MEAN field represents the mean percent of impervious surfaces in the block group.

6. Save the map.

Summarize the analysis

Now that you've summarized air temperature and impervious surfaces by census block group, you'll join the analysis results into a single layer showing where potential heat islands are located across the city.

1. In the Zonal Statistics as Table tool pane, click the back arrow to return to the Tools pane.
2. Search for and open the Join Features tool.
3. In the Join Features tool pane, for Target layer, choose Census Block Groups. For Join layer, choose Evening Temp CBG.

   

4. Under the Join settings section, for Target field and Join field, choose GEOID.

   

5. Under the Result layer section, for Output name, type CBG_Temp and add your name or initials.
6. Click Run.

   Note:

   Running this tool requires 0.38 credits.

   Next, you will join the data from the impervious surface table to the CBG_Temp layer.

7. In the Join Features tool pane, enter the following:
   • For Target layer, choose CBG_Temp.
   • For Join layer, choose ImperviousSurfaces CBG.
   • Under the Join settings section, for Target field and Join field, choose GEOID.
   • For Output name, type CBG_Temp_Surfaces, and add your name or initials.
8. Click Run.

   Note:

   Running this tool requires 0.38 credits.

   The CBG_Temp_Surface layer adds to the Layers pane. The CBG_Temp_Surface now contains data for the maximum evening temperature and mean impervious surface value for each census block group.

   Finally, you will join the tree canopy data to the CBG_Temp_Surface layer.

9. In the Join Features tool pane, for Target layer, choose CBG_Temp_Surface. For Join layer, choose Percent Tree Canopy.

   

   Since this will be the final Join Features tool you will run, you will name the final layer something that will reflect it containing all the heat island related factors.

10. For Output name, type Urban heat islands factors and add your name or initials.
11. Click Run.

    Note:

    Running this tool will require 0.406 credits.

    The Urban heat island factors layer adds to the map and the Layers pane.

    You now have a layer that can visualize the urban heat island effect factors by census block groups. In the next section, you will style duplicates of this layer to show each of the factors by census block group and group them in a group layer.

Create a group layer

Next, you will organize the layers with data related to the urban heat island effect into a group layer.

Group layers are collections of layers and tables organized as a group, and they can be made up of the same item type or different item types. For example, a raster layer and feature layer can exist together in a group layer. Creating groups layers makes it easier to manage the layers that make up a map, especially when working with many individual layers.

Before you continue, you will remove layers you will no longer need to use.

1. In the Layers pane, for the CBG_Temp_Surfaces layer, click the Options button and click Remove.

   

   The CBG_Temp_Surfaces layer is removed from the Layers pane.

2. Use what you have learned to remove the following layers:
   • CBG_Temp
   • Impervious Surfaces
   • Evening Temperature
   • Census Block Groups

   The only layers left in the Layers pane should be the City Boundary, Urban heat island factors, and Percent Tree Canopy layers.

   

   Next, you will start a group layer using the Percent Tree Canopy layer.

3. For the Percent Tree Canopy layer, click the Options button and click Group.

   

   A group layer is created.

   Next, you'll rename the group layer that you just created.

4. Click Options on the group layer and click Rename.
5. For Title, type Urban heat island indicators and click OK.

   The group layer has been renamed. Next, you'll duplicate the Urban heat island factors layer and style it by impervious surfaces values.

6. For the Urban heat island factors layer, click the Options button and click Duplicate.
7. Rename the copied layer Impervious Surfaces.
8. Expand the Urban heat island indicators group layer.

   

9. Drag Impervious Surfaces into the Urban heat island indicators group layer.

   

10. Use what you have learned to duplicate the Urban heat island factors layer and rename the copied layer Evening Temperature.
11. Drag the Evening Temperature layer into the Urban heat island indicators group layer.

    Next, you will style the Impervious Surfaces and Evening Temperature layers.

Style layers in the group layer

Next, you will style the Impervious Surfaces and Evening Temperature layers.

1. In the Layers pane, click the Impervious Surfaces layer. On the Settings toolbar, click Styles.
2. In the Styles pane, under Choose attributes, click Field. From the list of fields available, choose MEAN and click Add.

   The Impervious Surfaces layer is styled.

   

3. In the Layers pane, click the Evening Temperature layer. If necessary, ensure that the layer is visible.
4. In the Styles pane, for Choose attributes, click Field, click MAX_ and click Add.

   The default color ramp is a blue color ramp. To match the theme of temperature, you will choose a different color ramp with shades of red.

5. Under Pick a style, for Counts and Amounts (color), click Style options. In the Style options pane, click the color ramp under Symbol style to open the Symbol style pane.
6. In the Symbol style window, click Colors.
7. In the Ramp window, for Category, choose Reds and yellows. Click the Orange 4 color ramp and click Done.

   

   Tip:

   To see the name of a color ramp, point to a color ramp.

   The Evening Temperature layer style updates.

   

   Next, you'll configure the visibility settings of the Urban heat island indicators group layer.

8. In the Layers pane, click the Urban heat island indicators group layer to select it.

   A layer or group layer is selected when there is a blue indicator next to the layer name.

9. In the Properties pane, under Visibility, turn on Exclusive visibility.

   

   The Enable visibility toggle button updates the group layer in the Layers pane to show one layer at a time within the group layer.

   

   By turning on Enable visibility, you can view each of the layers in the group layer. This allows you to visually inspect the trends of each layer, such as which areas have fewer trees, more impervious surfaces, and higher temperatures.

10. Save the map.

In this section, you opened an existing web map and created a copy to explore and analyze. You also calculated the maximum high evening air temperature and total impervious surface for each census block group in Richmond using analysis tools in Map Viewer.

Now that you have visualized each of the heat island factors across the city, you will bring in contextual data to understand who is affected by these dangerous environmental conditions.

Assess community impact

Previously, you mapped how heat island factors are distributed throughout the city. In this section, you will investigate who is affected by these conditions. You'll add demographic data to learn more about the communities where these conditions are present and how these align with long-standing patterns of systemic disinvestment.

Enrich block groups with demographic data

Using the Enrich Layer tool, you'll add demographic data to the potential heat islands layer to better understand who lives in these communities and how these characteristics can compound vulnerability to extreme temperatures.

1. If necessary, open the Richmond Urban Heat Island web map.
2. On the Settings (light) toolbar, click Analysis. In the Analysis pane, click Tools. Search for and choose the Enrich Layer tool.

   

3. In the Enrich Layer tool pane, for Input features, choose Urban heat island factors.
4. Under Enrichment data, click Variable.

   

   The Data Browser window appears.

5. Click Race.

   

6. Click Non Hispanic Origin. In the list of results, expand 2024 Race and Hispanic Origin (Esri).

   Note:

   Demographic data in the Data Browser window is regularly updated. Use the most recently available data.

7. Check the following variables:
   • 2024 White Non-Hispanic Population (Esri)
   • 2024 Black/African American Non-Hispanic Population (Esri)
   • 2024 American Indian/Alaska Native Non-Hispanic Population (Esri)
   • 2024 Asian Non-Hispanic Population (Esri)
   • 2024 Pacific Islander Non-Hispanic Population (Esri)
   • 2024 Other Race Non-Hispanic Population (Esri)
   • 2024 Multiple Races Non-Hispanic Population (Esri)

   

   Selected Variables shows that seven variables have been added so far.

8. Click the Back button once.
9. Under Race Variables, type Hispanic and press Enter.
10. If necessary, expand 2024 Race and Hispanic Origin (Esri) and check the box for 2024 Hispanic Population (Esri).

    

11. Click the Back button two times.
12. Click Population and check the box for 2024 Total Population (Esri).

    Including this variable allows you to convert race and ethnicity counts into percentages of the population for each block group.

    

    Next, you will add variables related to vulnerable populations. People who do not have access to a vehicle or have incomes below the poverty level will have fewer resources to cope with potential extreme heat events.

13. Click the Back button. In the search bar, type no vehicle and press Enter.

    

14. Expand 2018-2022 Vehicles Available (ACS) and check the box for 2022 Renter Households with No Vehicles (ACS 5-Yr).

    Not having access to a vehicle can make it more challenging to get to places that provide refuge from extreme temperatures, such as cooling centers.

15. Click the Back button. Click Poverty and check the box for 2022 Households Below the Poverty Level (ACS 5-Yr).

    

    You should have 11 selected variables.

    Note:

    For this tutorial, you will use these variables to provide context for the population impacted by urban heat island factors. In a real-world scenario, there are many additional relevant variables you could add to your analysis. Variables for any equity workflow should be decided upon in collaboration with the impacted community and key stakeholders and be specific to the study area and intervention to address equity goals.

16. Click Select to close the Data Browser window and save your selection.

    The variables add to the Enrich Layer tool pane.

    

17. In the Enrich Layer tool pane, for Output name, type Urban heat island factors enriched.
18. Click Run.

    Note:

    Running this tool requires 20.9 credits.

    If you do not have enough credits, you can add a prepared layer with the enriched data. To add the Urban heat islands enriched (Tutorials) layer, in the Layers pane, click the Add button. Search ArcGIS Online for urban heat island enriched owner:Esri_Tutorials.

    The Urban heat island factors enriched layer is added to the map and the Layers pane.

    Enriching the geographic boundaries with demographic information helps us better understand the vulnerable communities potentially affected by urban heat island conditions.

    Now that you have an enriched version of the urban heat island factor layer, you no longer need the Urban heat island factor layer.

19. In the Layers pane, remove the Urban heat island factor layer.
20. Save the map.

Add historic context

Next, you'll bring in a layer showing historical redlining grades for neighborhoods throughout Richmond as outlined by the Home Owners' Loan Corporation, allowing you to visualize how these patterns overlap with areas that are disproportionately impacted by extreme temperatures.

Between 1935 and 1940, the Home Owners' Loan Corporation (HOLC), a federal agency established in 1933 as part of the New Deal, created maps outlining the risk to mortgage lending institutions associated with different neighborhoods across major U.S. cities. These maps are also referred to as redlining maps, as the areas identified as hazardous during this process are outlined in red.

Many of these redlined areas are now characterized as low-to-moderate income, predominantly minority communities, illustrating how these maps effectively solidified decades-long patterns of systemic inequity by restricting access to economic opportunity on the basis of race.

You will add Home Owners' Loan Corporation (HOLC) data from ArcGIS Living Atlas of the World.

1. In the Layers pane, click the Add button.
2. Click My content and choose Living Atlas.
3. On the search bar, type redlining. In the list of results, for the Mapping Inequality Redlining Areas layer, click Add.

   

4. At the top of the Add layer pane, click the back arrow.
5. In the Layers pane, drag the Mapping Inequality Redlining Areas layer into the group layer.

   

6. Select the Mapping Inequality Redlining Areas layer so it is visible.

   You can now see the Mapping Inequality Redlining Areas layer over the city of Richmond.

   

   The layer currently shows areas that do not have a rating in a gray color. These areas are industrial or commercial areas. You will turn off the visibility for these areas so the residential areas with ratings are the only areas that are visible.

7. In the Settings toolbar, click Styles. For Types (unique symbols), click the Style options button.
8. In the Style options pane, uncheck Other.

   

   The non-residential areas are no longer visible.

9. In the Layers pane, select each of the urban heat island factor layers and see if you notice any possible connections with the redlining data.

   Areas that were historically graded hazardous are more likely to have more impervious surfaces, higher evening temperatures, and less tree canopy compared to areas that were historically rated A for best.

   By considering the decisions and practices of historic land use policies, you are taking a root cause approach to understanding the cumulative burdens some communities are experiencing which will in turn inform your decision to achieve greater equity.

10. Save the map.

    You will also need to set share settings for the map and the layers you've created so that it will appear when you share the dashboard.

11. On the Contents toolbar, click Share map.
12. In the Share window, click Everyone (public) and click Save.
13. In the Item sharing successfully updated window, click Review sharing. In the Review sharing window that appears, click Update sharing.

Prepare web map for creating a dashboard

In this section, you will configure and prepare the web map for creating an ArcGIS Dashboards web app. You will rename layers, ensure the layers you need to be visible are on, and customize the display names for key fields you will use as indicators in the dashboard.

1. In the Layers pane, rename the Urban heat island factors enriched layer to Urban heat island factors and enriched data.

   This layer contains the summarized data for the urban heat island factors and demographic data. You want it to be selectable, but you want to be able to see the layers beneath it. You will configure the style so that the fill color is transparent and there is only an outline color.

2. In the Layers pane, ensure the Urban heat island factors and enriched data layer is visible and selected.
3. On the Settings toolbar, click Styles. In the Styles pane, under Pick a style, for Location (single symbol), click Style options.
4. In the Style options pane, click the symbol under Symbol style.
5. In the Symbol style window, for Fill color, click No color. For Outline color, choose a gray color and adjust the Outline width value to 1.

   

   Now the Urban heat island factors and enriched data layer only shows an outline, and the layers beneath it are visible.

   Next, you will ensure the Urban heat island factors group layer is on and set to the layer you want to first appear when someone opens the dashboard.

6. Ensure the Urban heat island factors group layer and that the Mapping Inequality Redlining Areas layer is selected.

   

   Next, you will review the field name. This will be helpful when you set up and calculate indicators in the dashboard.

7. In the Layers pane, for the Urban heat islands factors layer, click Options and click Show table.
8. In the table for Urban heat island factors, locate the fields that were joined: MAX, MEAN, and Tree Canopy.

   

   • The MAX field contains the values that represent the max Evening Temperature values in each census block group.
   • The MEAN field is the average percent of impervious surfaces in each census block group.
   • The TreeCanopy field contains the percent of tree canopy coverage in each census block group.

   Next, you will turn off pop-ups for all the layers. You will use the dashboard indicators to show details about each block group, so showing pop-ups will not be necessary.

9. In the Layers pane, click the Percent Tree Canopy layer so it is selected. On the Settings toolbar, click Pop-ups.
10. In the Pop-ups pane, turn off Enable pop-ups.

    

    Pop-ups will not appear for the Percent Tree Canopy layer.

11. Use what you have learned to turn off pop-ups for the remaining layers in the map.

    Finally, you will adjust the map extent, which will be the extent of the map when the dashboard opens.

12. In the map, zoom and pan the map so that the city of Richmond is centered and fills the map.

    

13. Save the map.

In this section, you enriched census block groups with demographic information to better understand the community potentially affected by the heat island effect. You have developed an awareness of where high temperatures and differences in land cover type exist as well as who is exposed to these conditions. You better understand the historical context associated with the heat island effect after adding historical redlining information to the map.

Now you're ready to create an engaging dashboard to summarize all of this information and share the results with the community.

Share the results in a dashboard

Now that you've identified block groups impacted by the heat island effect and added contextual data, you'll create a dashboard to summarize the results and monitor these areas. Moving from Map Viewer to ArcGIS Dashboards, you'll create a dashboard that will help summarize the heat island effect in the Richmond area.

Create and configure the first element

You will start by creating a dashboard from the web map and add the first element: a serial chart.

1. If necessary, open the Urban heat island effect web map.
2. On the Contents (dark) toolbar, click Create app and choose Dashboards.

   

3. In the Create new dashboard window, for Title, add Dashboard to the end of the title and add your name or initials. Optionally, enter tags and a summary.

   

4. Click Create dashboard.

   The map appears as a map indicator in the dashboard. First, you will set a theme for the dashboard.

5. On the dashboard toolbar, click Theme. In the Theme pane, under Layout, for Theme, choose Dark.

   

   Tip:

   To see the toolbar labels, at the bottom of the toolbar, click Expand.

   The dashboard theme updates.

6. Close the Theme pane.

   Now you're going to create an indicator element to better understand the maximum temperatures within each census block group.

7. On the dashboard toolbar, click Add element.

   

8. Point to the right side of the map and click the dock button on the right side of the map.

   

9. In the list of indicators, click Serial chart.

   

10. In the Select a layer window, choose Urban heat island factors and enriched data.
11. In the Serial chart window, for Categories from, choose Fields.
12. Click Add fields and choose 2024 White Non-Hispanic Pop.
13. Continue to add the following fields:
    • 2024 Non-Hispanic Black Pop
    • 2024 Hispanic Population
    • 2024 Non-Hispanic Asian Pop
    • 2024 Non-Hispanic Pacific Islander Pop
    • 2024 Non-Hispanic American Indian Pop
    • 2024 Non-Hispanic Other Race Pop
    • 2024 Non-Hispanic Multiple Races Pop

    The fields add to the Data options pane.

    

14. Click the Series tab. For Bar colors, choose By category.

    

15. Click the Category axis tab, expand the Labels section. For Placement, choose Wrapped.

    

    The serial chart indicator is configured.

    

16. Click Done.

    Before you continue, you will save the dashboard.

17. On the dashboard toolbar, click Save and choose Save.

    

You have configured and added the first dashboard element: a serial chart showing the race and ethnicity data.

Add an indicator element

Next, you will add an indicator element to show the Evening Temperature variable.

1. On the dashboard toolbar, click Add element. Click the top dock on the serial chart to add the indicator above the chart and click Indicator.

   

2. In the Select layer window, choose Urban heat island factors and enriched data.
3. In the Indicator window, for Value type, choose Feature. For Value field, choose MAX_.

   

4. Click the Indicator tab. In the Indicator options pane, for Bottom text, type Maximum Temperature (F).

   

5. For Middle text, set the font color to red. In the color palette pane, for Saved, click the add button.

   

   By saving the red color, you can use the exact same red color later.

6. Click Add icon. In the Select an icon pane, expand Solutions and choose the flame icon.

   

7. Click OK.
8. For Fill, choose the saved red color.

   

   The indicator is configured.

   

9. Click Done.
10. Save the dashboard.

    Next, you will duplicate the indicator element and configure it to show the percent of impervious surfaces in each block group.

Configure additional indicator elements

To simplify creating a second indicator to show the percent of impervious surfaces, you will start by duplicating the indicator you just configured.

1. Point to the corner of the indicator element and click Duplicate.

   

2. Point to the corner of the duplicated indicator element and click Configure.

   To configure the percent of impervious surfaces, you will set Value field to the MEAN field, which represents the mean percentage of impervious surface coverage in each block group.

3. In the Indicator window, in the Data options pane, for Value field, choose MEAN.

   

4. Click the Indicator tab. For Middle text, add % to the end of the existing text. Choose an orange-brown color and save the color.

   

5. For Bottom text, clear the existing text and type Impervious surface coverage.
6. For Icon, click Change. Expand Solutions and choose the road and bridge icon. Click OK.
7. For Fill, choose the saved orange-brown color.

   The indicator is configured.

   

8. Click Done.
9. Use what you have learned to duplicate the indicator element to show the percent of tree canopy coverage.
   • Duplicate the temperature indicator element.
   • Click configure for the duplicated indicator element.
   • In the Data options pane, set Value field to TreeCanopy.
   • On the Indicator tab, for Bottom text, change the text to Tree canopy coverage.
   • Change the icon to a tree
   • Optionally, change the Middle text font color and icon fill color to a green color.
   • Click Done.

   The tree canopy coverage indicator is configured.

   

10. Use what you have learned to duplicate the indicator element to show the difference between the maximum temperature of each census block group and the mean temperature associated with the city boundary.
    • Duplicate the temperature indicator element.
    • Click configure for the duplicated indicator element.
    • In the Data options pane, turn on Value conversion, and for Offset, type -87.63.
    • On the Indicator tab, for Top text, type Evening Temperature Difference (F), and for Bottom text, type from the city average.
    • Change the icon to the thermometer.
    • Update the Middle text font color and icon fill color to a yellow color.
    • Click Done.

    The evening temperature indicator is configured.

    

    Now you have four indicators on the dashboard. Next, you will rearrange them so they are all visible and evenly sized.

11. For the second indicator listed, point to the indicator and click the Drag item button.

    

12. Drag the evening temperature difference indicator to the right of the indicator at the top.

    

    You now have two indicators listed side by side.

    

13. Drag the bottom indicator to the right of the second row of indicators.
14. Drag the dividers so the indicators are visible and relatively even.

    You now have the four indicators arranged in a grid above the bar chart.

    

15. Save the dashboard.

Add indicators for demographic data (optional)

Optionally, you can add two more indicators to show additional variables for social vulnerability.

1. Duplicate the impervious surfaces indicator.
2. For the duplicated impervious surfaces indicator, click the Configure button.
3. In the Indicator window that appears, on the Data tab, choose the following parameters:
   • For Value field, choose 2022 HHS: Inc Below Poverty Level (ACS 5-Yr).
   • Expand the Reference section and for Reference type, choose Feature.
   • For Reference field, choose 2024 Total Population.

   

4. Click the Indicator tab. For Middle text, clear the existing text and click the Add field button. Choose 100 * value / reference.

   

5. Continue configuring the Indicator tab with the following parameters:
   • For Middle text, set the text color to a blue color.
   • For Bottom text, clear the existing text and type Income below poverty level.
   • For Icon, choose an icon that might represent care, like the hands that make the shape of a heart.
   • For Fill, choose the same blue color.

   The poverty indicator is configured.

   

6. Click Done.
7. Use what you have learned to create an indicator showing the percent of renters with no access to vehicles:
   • Duplicate the poverty level indicator.
   • For the duplicated indicator, click the Configure button.
   • On the Data tab, for Field value, choose 2022 Renter HHs with 0 Vehicles (ACS 5-Yr).
   • On the Indicator tab, for Middle text, choose a purple text color.
   • For Bottom text, update the text to Renters with no vehicles.
   • For Icon, choose the car symbol.
   • For Fill, choose the same purple color.

   The renters with no vehicles indicator is configured.

   

8. Drag and resize the two social vulnerability indicators to the right side of the serial chart.

   

9. Save the dashboard.

Configure map actions

To make the dashboard interactive, you will configure layer actions for the map indicator. When layer actions are configured, dashboard users can select features from a layer by clicking the features on the map. You can use the When map is clicked options to determine whether selecting features on the map updates dashboard indicator values for the selected features.

1. Point to the corner of the map element and click Configure.
2. In the Settings pane, turn on Legend and Layer visibility.
3. Click the General tab. For Title, click Edit.
4. In the text editor, type or copy and paste the following text:

   Urban heat island effect factors - Richmond, VA

   Click a Census block group to view heat island factor and demographic data.

5. Highlight the first line of the text. Click Paragraph and choose Heading 2.

   

6. Click the Layer actions tab. Expand Urban heat island factors and enriched data and expand Filter.

   

7. Turn on all the Indicator buttons and the Serial chart elements.
8. For each of them, check the Render only when selected box.

   

9. Under the When map is clicked section, turn off Show pop-up and turn on Select feature.

   

10. Click Done.
11. On the dashboard toolbar, click the Save button.
12. If necessary, refresh the browser page.
13. Test the dashboard by clicking the map to select a block group. Choose a block group that was historically rated D for hazardous.

    

    What do you notice about the urban heat island factor and demographic data for this area?

14. Click a block group that was historically rated A for excellent.

    

    What do you notice about the urban heat island factor and demographic data for this area?

    Next, you will explore the map tools to view other layers in the map.

15. On the map indicator, click the layer list tool. Expand the Urban heat island factors group layer and choose Evening Temperature.

    

    The Evening Temperature layer is now visible on the map.

16. Save the dashboard.

    Next, you will set the dashboard to share with others.

17. Click the dashboard menu and click Dashboard item details.

    

    The item page for the dashboard appears.

18. On the item page for the dashboard, click Share.
19. In the Share window, choose Everyone (public) and click Save.

    You have successfully created a dashboard to summarize the heat island effect in this study area. Now you can share this dashboard with stakeholders, including community members and local elected officials, to help them better understand temperature differences from neighborhood to neighborhood, the land cover within each neighborhood, and the demographics of the communities that may be disproportionately affected by heat islands.

This tutorial helped you analyze the heat island effect in the Richmond, Virginia, area. You examined temperature data across the city to assess the maximum temperature from neighborhood to neighborhood. You analyzed the amount of tree canopy and impervious surface coverage, increasing your understanding of the land cover factors that contribute to the heat island effect.

After understanding which neighborhoods are hotter than others and how they compare to city-wide temperatures, you then enriched the study area with demographic information to understand who lives in the communities that are potentially impacted by these extreme heat events and which characteristics make some individuals more vulnerable than others. You also explored the historical decisions and practices that have contributed to communities being disproportionately impacted by extreme heat.

Finally, you created a dashboard to summarize the results of your analysis and share the results with the stakeholders who can use this information to implement solutions that will lead to improved quality of life throughout the community, such as incorporating more green space and improving access to public transit in neighborhoods where gaps in these resources exist. This tutorial successfully combines the spatial lens and equity lens to examine an environmental equity topic of the heat island effect.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• All Adobe Stock images are © 2024 Adobe Stock. All rights reserved.
• City boundary from the Richmond GeoHub.
• Census block groups are from Census TIGER boundaries.
• Evening temperature data from Evening Air Temperature in Cities - Urban Heat Islands: National Oceanic and Atmospheric Administration and CAPA Strategies.
• Impervious surface tiled imagery layer derived from USA NLCD Tree Canopy Cover.
• Mapping Inequality Redlining Areas from the University of Richmond Digital Scholarship Lab.
• Enriched data from Data Browser.
• Light Gray Canvas map sources: Esri, HERE, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community