Explore the mortality rate maps

In a January 2021 article, the Breast Cancer Research Foundation reported that that while the rate of new cases of breast cancer between White and Black women are more similar, the death rate among Black women diagnosed with breast cancer is 40 percent higher than the death rate for White women. Because of lack of health care and preventive measures on top of social, economic, and behavioral factors, the gap between new breast cancer cases and outcomes is complex. The first version of this lesson analyzed data available in 2013. Although the disparity between Black women and other races and ethnicities is narrowing, Black women continue to experience the highest proportion of deaths due to breast cancer.

Chart showing female breast cancer mortality over time

For this lesson, you are a GIS analyst for a research advocacy group whose mission is to close the racial inequity gap of breast cancer mortality rates in the United States. Your first goal in this lesson is to confirm there is a racial disparity gap for breast cancer mortality rates. To do so, you'll map available data on breast cancer mortality rates for Black and White women. In some parts of the United States, mortality data has been suppressed, hidden, or simply unavailable by the data provider. Data can be suppressed due to high rates of unreported numbers or privacy concerns. Your goal is to create a map you can share with partnering advocacy organizations and elected officials to raise awareness and advocate for additional resources to address this important public health need.

Note:

For this lesson, you will only analyze two racial groups. It is considered best practice to consider data for all racial and ethnic groups in a given population when conducting a racial equity analysis. You are encouraged to use the workflow of this lesson to analyze the disparity between other racial and ethnic groups on your own.

Map breast cancer mortality

To view the data, you'll create a map of your area of interest in ArcGIS Pro. Then, you'll add mortality data for Black and White women and compare it.

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

    If you don't have ArcGIS Pro or an ArcGIS account, you can sign up for an ArcGIS free trial.

  2. Under New, click Map.
  3. In the Create a New Project window, name the project Breast Cancer Mortality. Save the project in the location of your choice and make sure the Create a new folder for this project check box is checked, and click OK.

    A blank map project opens in ArcGIS Pro. Next, you'll add data.

  4. On the ribbon, on the Map tab, click Add Data.

    Add Data

    The Add Data window appears.

    Tip:

    In ArcGIS Pro, you can personalize the appearance of the user interface with either a light or a dark theme. In these lessons, the example images will use the dark theme, but you can use the theme that you prefer. If you want to change the theme, click Options. In the Options window, under Application, click General. Expand Application Theme and choose Dark. You'll need to restart ArcGIS Pro for your theme changes to take effect.

  5. In the Add Data window, under Portal, click ArcGIS Online. In the search bar, type breast cancer owner:Learn_ArcGIS.
  6. Click Breast Cancer Mortality Rate by Race 2018, and click OK.

    Search for data layer

  7. Use your mouse wheel to zoom in to the contiguous United States.
    Note:

    It may take a couple minutes for the layer to complete drawing.

    The MortalityRate2018 layer is added to the map. The default symbology shows the overall mortality rate for breast cancer in the United States. Mortality rate is the number of deaths per 100,000, and is calculated using the formula: Mortality Rate = (Cancer Deaths / Population) × 100,000.

    Counties symbolized in darker pinks have higher mortality rates and lighter pinks are lower mortality rates. Counties on this map that aren't filled in show where data has been suppressed by the provider. Data is suppressed when there are too few cases in each county that individual privacy would be violated.

  8. In the Contents pane, right-click the MortalityRate2018 layer. In the menu, click Attribute Table.

    Open Attribute Table

    The attribute table shows all the attribute data contained in the feature layer. There is population data for each county, including breakdowns of Black, White, male, and female populations. Scroll right until you see the mortality rate columns. Notice that there are a lot of zeros; this is the placeholder value for the suppressed data.

    Next, you'll change the symbology to view and compare the available data for Black and White women.

  9. Close the attribute table.
  10. In the Contents pane, right-click the MortalityRate2018 layer and choose Symbology.

    The Symbology pane appears. The primary symbology is already set to graduated colors.

  11. Expand Field and select Black Mortality Rate.

    Field set to Black Mortality Rate

    The symbology draws in the default five classes. Now you'll change the basemap.

    Symbology pane

  12. On the ribbon, on the Map tab, in the Layer group, click Basemap.

    Basemap

  13. Choose Light Gray Canvas.

    Reported county data for Black women

    The counties that have reported data for breast cancer mortality among Black women are shown in darker pink colors. Most of the data is relatively scattered throughout the country. In all other areas, there are large gaps in the data. Counties on this map that aren't filled in show where data is not provided.

  14. If necessary, in the Contents pane, click the arrow next to the MortalityRate2018 layer to expand the legend.

    The legend indicates that from 2014-2018, the breast cancer mortality rates for Black women were as high as 58.5 average annual deaths per 100,000 Black women in a single county (Adams County, Mississippi).

    Before you change the symbology to show the breast cancer mortality rate for White women, you'll create a copy of the layer that has been symbolized for the mortality rate for Black women so that you can more easily compare the two layers.

  15. Right-click MortalityRate2018 and choose Copy.
  16. Right-click Map and choose Paste.

    An identical layer is added to the Contents pane.

  17. Click the copy layer's name twice to edit it. Type Black Mortality Rate, and uncheck the layer to turn it off.
    Note:

    The copy is not a new file that's been saved to your computer, but a layer that exists only in the map document. When you close the project, the layer data won't be saved.

  18. Right-click MortalityRate2018 and choose Symbology. In the Symbology pane, expand Field and choose White Mortality Rate.

    Reported county data for White women

    While both maps for breast cancer mortality rates for White and Black women do not have available data for every county in the United States, more data is available for White women. The legend also indicates that the range of mortality rates for White women is lower, with rates up to 49.8, than the range for Black women, with rates up to 58.5.

    By comparing the rate of breast cancer deaths for White and Black women, there is strong indication that a higher proportion of Black women are affected by breast cancer mortality compared to White women.

  19. On the Quick Access Toolbar, click the Save button to save your project.

    Save on the Quick Access Toolbar

Classify data

Next, you'll look at the classification statistics for both Black and White women to see if the information proves that there is a gap in mortality rates. The default classification is Natural Breaks (Jenks). Jenks breaks the data into classes using a combination of statistical measures (mean, median, quantiles) and gaps that exist naturally in the data. Since no two datasets are the same, this creates unique classes. To better compare the mortality rates, you'll want to use a more uniform classification technique and exclude all the counties with suppressed data.

  1. If necessary, in the Contents pane, right-click MortalityRate2018 and choose Symbology. Confirm that Field is set to White Mortality Rate.
  2. For Method, expand the list and choose Quantile.

    Quantile method

    Quantile is a data classification method that separates data values so that each category has the same number of data values. Because the null values are shown as 0, they are making the statistics skew low. To more permanently remove the null values from the map so you can see more accurate statistics, you'll use data exclusion. While the null values will still exist in your data, you will write a query to remove them from the symbology.

  3. Click the Advanced symbology options tab.

    Advanced symbology

  4. Expand Data exclusion and click New Expression.
  5. Use the query wizard to build the expression White Mortality Rate is equal to 0.

    Data exclusion properties window

    All the 0 values that you just specified will now be excluded from the data you show.

  6. Click Apply.

    The null values are shown on the map in a default color. You'll make them transparent again.

  7. Click the Primary symbology tab.

    Primary symbology tab

    A new category for excluded values has been added to the symbology pane, but the zero class is still shown.

  8. For Classes, choose 4.
  9. Right-click the symbol box for <excluded>.

    The Color selector window appears.

  10. At the top of the Color window, choose No color.

    Color Properties

    Now that the suppressed data has been removed, the available data is accurately categorized. To view it, you'll look at the layer's data distribution using a histogram.

  11. Click the Histogram tab.

    Histogram view

    The list of classes changes to a histogram of the data for White Mortality Rate.

    Histogram view of White Mortality Rate

  12. Click More and choose Show Statistics.

    The classification statistics for White Mortality Rate are shown in the pane. The Count statistic indicates that there are 1,581 counties with data for White women, which is about 49 percent of all 3,220 United States counties and county equivalents. Additionally, the statistics indicate that the average (mean) breast cancer mortality rate for White women is 21.15.

    Now you'll repeat the process to see the distribution histogram for Black Mortality Rate.

  13. In the Contents pane, click the MortalityRate2018 layer two times. Type White Mortality Rate and press Enter.
  14. In the Contents pane, uncheck White Mortality Rate and check Black Mortality Rate. Click the Black Mortality Rate layer to make it active.

    For comparison, you'll change the symbology to the same method as for White mortality.

  15. In the Symbology pane, for Method, choose Quantiles.
  16. Click the Advanced Symbology Options tab and, if necessary, expand the Data exclusion group.
  17. Click New Expression and build the expression Black Mortality Rate is equal to 0. Click Apply.
  18. Click the Primary Symbology tab to return and set Classes to 4.
  19. Click the Classes tab, and right-click the symbol for <excluded> and choose No color.
  20. Click the Histogram tab and open the attribute statistics.

    Histogram view for Black women

    Notice that the range of mortality rates for Black women and White women are different. The upper value for breast cancer mortality rates among Black women are as high as 58.5, but for White women, the highest value is 49.8.

    For Black Mortality Rate, there is data for 341 counties (just over 10 percent of all counties and county equivalents). The mean mortality rate for Black women is 28.88, a rate nearly 8 deaths higher than for White women. Compared to the available data for 2015, the average breast cancer mortality rate disparity between Black and White women was nearly 10 deaths per 100,000. The disparity in 2012 was about 9 deaths per 100,000. While the disparity has been decreasing, it continues to persist.

  21. Save the map.

Although there is less available data for Black women, using the mortality rate data and accounting for unreported counties still indicates that there is a higher average rate of breast cancer mortality among Black women compared to White women.


Map the mortality rate difference

Previously, you looked at maps that showed breast cancer mortality rates are generally higher for Black women across the country compared to the breast cancer mortality rates for White women. But because of the large amount of unreported data, you could not accurately compare where the variation occurs. Next, you'll map the difference in the breast cancer mortality rates for Black and White women for counties that have data for both groups.

Identify dual-data counties

To quantitatively compare breast cancer mortality rates for Black and White women, you need to know what counties have data for both groups. You'll find these counties by creating an SQL query to select counties that meet the criteria.

  1. If necessary, open your Breast Cancer Mortality project.
  2. Uncheck the Black Mortality Rate layer and check the White Mortality Rate layer.
  3. Right-click White Mortality Rate and choose Attribute Table.

    The attribute table opens to show all the records for the layer. To find counties that have data for both Black and White women, you'll create a new field.

  4. On the ribbon, on the Table contextual tab, click the View tab, and in the Selection group, click Select By Attributes.

    Select By Attributes
    Note:

    Contextual tabs appear when the application is in a particular state. The Table tab will only be visible when an attribute table is open.

    The Select By Attributes window appears.

    The Expression section is similar to the Data exclusion section in the Symbology pane you used earlier in the lesson. You will use the Expression builder in the Select By Attributes window to select counties that have mortality data for both Black and White women.

  5. In the Select By Attributes window, click New Expression. Build the query White Mortality Rate is greater than 0 and click Add Clause.
  6. Build the query And Black Mortality Rate is greater than 0. Click OK.

    Select By Attributes query

    By using the And operator in this query, you've specified that the selected records have to meet the criteria in both queries. The records that meet both queries are highlighted in the attribute table and on the map.

  7. In the attribute table, click Show selected records.

    Show selected records

    There are 318 counties and county equivalents that have provided data for both White and Black breast cancer mortality rate. Next, you'll export the data for these counties into a new layer that will keep all of the associated attributes.

  8. On the ribbon, click the Analysis tab, and click Tools.

    Tools on Analysis tab

    The Geoprocessing pane appears.

  9. In the Geoprocessing pane, search and select the tool Feature Class To Feature Class.

    Feature Class To Feature Class tool

  10. In the Feature Class To Feature Class tool, enter the following:
    • For Input Features, choose White Mortality Rate.
    • For Output Name, type Dual_Data_Counties.

    Feature Class To Feature Class parameters

  11. Click Run.

    The new layer, Dual_Data_Counties, is added to the Contents pane.

  12. In the Contents pane, turn off the White Mortality Rate layer.

    The counties in the Dual_Data_Counties layer are the counties for which you can run comparisons.

    Dual-data counties

    It is currently symbolized by White Mortality Rate. Next, you will change the symbology to compare the rate of breast cancer mortality between Black and White women.

  13. On the ribbon, click the Map tab, and in the Selection group, click Clear.

    Clear selection

  14. Close the attribute table for White Mortality Rate and save the project.

Calculate mortality rate difference

Now that you have a layer that only displays counties with available data for both Black and White breast cancer mortality rates, you will calculate the difference between the rates to visualize the distribution of racial disparity throughout the United States.

  1. Right-click Dual_Data_Counties and open the attribute table.

    To calculate the difference in mortality rates, you'll create a new column in the table.

  2. In the attribute table, on the Field ribbon, click Add.

    Add Field

    The Fields view appears.

    The blank field is added as the last row and is populated with default values.

    Setting the field parameters determines how the information is stored and displayed. The Field Name requires underscores because spaces between words are not readable by the software. But you can provide a more readable Alias, which will appear in the attribute table.

    Data Type specifies how many decimal points of each data record will be saved. Choosing Short allocates the least amount of storage to the field and only accepts integer values up to five digits. To store larger numbers or numbers with fractional values, use Long Integer, Float, or Double as the type.

  3. In the new row, for Field Name, type Mortality_Rate_Difference.
  4. For Alias, type Mortality Rate Difference. For Data Type, choose Short.

    Edit new field

  5. On the ribbon, on the Fields tab, click Save.

    Save field edits

  6. Close the Fields view.
  7. In the Dual_Data_Counties attribute table, scroll to the end of the table to confirm that the Mortality Rate Difference field has been added.

    Mortality Rate Difference field

    Because Mortality Rate Difference is a new field, the data values are null, or not yet defined. Next, you'll calculate data values based on the data in other columns. For this analysis, you're interested in the difference between Black female mortality rates and White female mortality rates. To find the difference, you'll subtract White female mortality rates from Black female mortality rates using the Field Calculator.

  8. In the attribute table for Dual_Data_Counties, right-click Mortality Rate Difference and choose Calculate Field.

    Calculate Field

    The Calculate Field window appears.

  9. Under Expression, for the Fields box, double-click Black Mortality Rate.

    The field is added to the = expression box.

  10. Click the subtract button and double-click White Mortality Rate to build the expression !Black_Mortality! - !White_Mortality!.

    Calculate Field expression

  11. Click OK.

    Data values are added to the Mortality Rate Difference column in your table. Most of the numbers in this column are positive, meaning that the mortality rate is higher for Black women than for White women. Scattered throughout the data, though, are counties with zeros and negative values, indicating that the mortality rate for White women is equal to or higher than Black women.

    Mortality Rate Difference attributes

    Now that you have calculated the differences, you'll map them.

  12. Close the attribute table and save the map.

Symbolize the values

Next, you'll symbolize the differences you just calculated to visualize them on the map. You'll display the features using the Mortality Rate Difference field to show where racial disparities in breast cancer mortality exist in the United States.

  1. In the Contents pane, right-click the Dual_Data_Counties layer and choose Symbology.
  2. For Field, choose the Mortality Rate Difference field.

    When you calculated this field, you determined that negative numbers belonged to counties that had higher mortality rates for White women, zeros had an equal mortality rate, and positive numbers were counties that had higher mortality rates for Black women. You'll reduce the number of classes to three to show these categories on the map.

  3. For Classes, choose 3.

    Field set to Mortality Rate Difference

  4. On the Classes tab, in the Upper value column, double-click the first row value and type -1, and press Enter. Change the second row value to 0, press Enter, and leave the third row unchanged.

    Upper value updated

    The values for upper value set the break value for each symbology category. By changing the breaks, you now have three categories that represent your three categories of interest. The default color ramp makes the categories difficult to distinguish on the map.

  5. In the Classes tab, right-click the symbol for ≤ -1 and choose Blue Gray Dust. For ≤ 0, choose Yucca Yellow, and for ≤ 27, choose Tudor Rose Dust.

    Point to a color to reveal its name.

  6. In the Label column, double-click the label for ≤ -1 type Mortality Rate higher for White women. Change the label for ≤ 0 to Mortality Rate equal between White and Black women, and change the label for ≤ 27 to Mortality Rate higher for Black women.

    Symbol range labels

    The map now shows the counties symbolized with the new colors.

    Mortality Rate Difference map

    The majority of counties that have reported data have higher breast cancer mortality rates for Black women compared to White women. This map shows where the variation in mortality rates occurs, demonstrating that the racial disparity in breast cancer mortality rates is a national problem.

    Variation

    The areas with some variation of racial disparities for breast cancer mortality rates are in Florida and parts of the New England states.

  7. Save the project.

You've analyzed data about mortality rates for Black and White women and symbolized it to show the large scope of the problem. Next, you will calculate the rate ratio between mortality rates for White women compared to the mortality rates for Black women. The results from the rate ratio analysis can then be used to perform a hot spot analysis to visualize areas with statistically significant clusters of high and low rate ratios.


Map the mortality rate ratio

Previously, you mapped the differences in breast cancer mortality rates for Black and White women. Next, you'll determine how wide the mortality rate gap is in each county. You'll calculate this gap using a rate ratio, which compares rates (of mortality, in this case) in two groups that differ by demographic characteristics (race, in this case). The rate for the primary group of interest (Black women) is divided by the rate for a comparison group (White women). Using this statistic, you'll perform a hot spot analysis to map where the mortality rates are highest. Based on your results, your cancer research advocacy group will know in which areas of the country to focus its campaign.

Map the breast cancer mortality rate ratio

Now that you've seen the difference in mortality rates, you'll calculate another statistic, the rate ratio. The ratio gives the likelihood of an outcome for a specific group. In other words, a rate ratio of 5 in a specific county would mean that a Black woman with breast cancer in that county is five times more likely to die of the disease than a White woman.

First, you'll calculate the ratio of deaths due to breast cancer between Black women and White women using the following formula:

Rate ratio = Rate for Black women / Rate for White women

Then, you'll use this calculation in a hot spot analysis. Using the rate ratio identifies counties in which the mortality rate for Black women is significantly higher, instead of counties where the mortality rate is high for both Black and White women.

  1. If necessary, open your Breast Cancer Mortality project.
  2. Copy the Dual_Data_Counties layer and paste it in the Contents pane on top of Map.
  3. Turn off the original Dual_Data_Counties layer. Rename the copy Mortality Rate Ratio.

    For this map, the mortality rates for Black women are divided by the rates for White women. This value will tell you how much more likely Black women are to die from breast cancer than White women. You'll add a new field to the attribute table and calculate the values in the field to show the rate ratio.

  4. Right-click Mortality Rate Ratio and choose Attribute Table.
  5. In the attribute table on the Field ribbon, click Add Field.
  6. In the Fields view, click the last record in the Field Name column and name your new field Mortality_Rate_Ratio. Change the Data Type setting to Double.
  7. For Alias, type Mortality Rate Ratio, and on the ribbon, click Save. Close the Fields view.
  8. Right-click the new Mortality Rate Ratio field and choose Calculate Field.

    Earlier in this lesson, you calculated the difference in breast cancer mortality rates by subtracting mortality rates from Black women from mortality rates for White women. To find the rate ratio, you'll divide mortality rates for Black women by mortality rates for White women.

  9. In the tool window, double-click field names and operators to create the expression !Black_Mortality! / !White_Mortality!.

    Calculate rate ratio

  10. Click OK.

    The rate ratio calculations are added to the new field.

  11. Right-click the Mortality Rate Ratio header and choose Sort Acending.

    By dividing the value of Black Mortality Rate by the White Mortality Rate, the resulting value indicates the degree to which there is a higher rate of breast cancer mortality for Black women. For example, a ratio value of 2 indicates that in that county, the breast cancer mortality rate for Black women is two times that of the rate for White women. Ratio values that are less than or equal to 1 are counties in which the mortality rate is the same or higher for White women compared to the mortality rate for Black women.

  12. Close the attribute table.

    Next, you'll symbolize the values you calculated.

Symbolize the values

To symbolize the difference, you'll use graduated symbology in which the size of a county's circle relates to the magnitude of the value you're mapping. Graduated symbols on a map change sizes according to the value of the attribute they represent. For instance, a county with a higher rate ratio would be symbolized with a larger shape than a county with a smaller rate ratio. You want to make sure that the counties in which Black women have lower mortality rates are clearly distinguished from all others. To symbolize this distinction, you'll use a different color.

  1. In the Contents pane, right-click Mortality Rate Ratio and choose Symbology.
  2. In the Symbology pane, for Primary symbology, choose Graduated Symbols.

    Graduated Symbols

  3. For Fields, choose Mortality Rate Ratio, and confirm Classes is set to 5.
  4. Change Minimum size to 5 pt, and change Maximum size to 25 pt.

    In the previous lesson, you had three categories because you symbolized the distribution of data into the three kinds of mortality rates: higher for Black women, equal, and higher for White women. This time, you'll symbolize the range of mortality rates, so you'll break the data into five classes. Remember that values less than or equal to 1 are counties in which the mortality rate is the same or higher for White women compared to the mortality rate for Black women.

  5. Click the Histogram tab.
  6. Click 2.628205 at the bottom of the histogram, type 3.5, and press Enter.

    Upper value for highest class set on histogram

  7. Change the values on the histogram so the upper values are 1.0, 1.5, 2.0, 2.5, and 3.5.

    Upper values

    The symbols on the map redraw to show the classes you specified. Now you'll symbolize the ratios. Based on the rate ratios you calculated, the first symbol will show where mortality rates for White women are higher. Following the same color scheme you used earlier, you'll make this symbol green to differentiate it from the rest of the data.

  8. Under Primary symbology, click the symbol for Background.

    Background symbol

  9. For Outline color, choose No color, and click Apply.
  10. Click the back arrow and click the Classes tab. Click More, and click Format all symbols.
  11. For Color, choose Tudor Rose Dust.
  12. For Color, click the symbol again and click Color Properties.

    Color Properties

    The Color Editor window appears.

  13. For Transparency, type 40, and press Enter. Click OK.

    Set transparency level

  14. In the Format Multiple Point Symbols pane, click Apply.
  15. Click the back arrow, and in the Classes tab, double-click the symbol for the first class.
  16. For Color, choose Blue Gray Dust, and click Apply.

    The symbols for counties where breast cancer mortality rates are equal or higher among White women are now drawn on the map using similar symbology to the Mortality Rate Difference layer.

    Rate ratio map

    This map shows the rate ratio between average mortality rates for Black and White women for the years 2014 to 2018 for each county in the United States. Larger circles are counties where the breast cancer mortality rate among Black women is higher than the mortality rate for White women. Values under 1 (shown in blue) are counties where the breast cancer mortality rate among White women is equal to or greater than the mortality rate among Black women.

    This part of your investigation shows that the vast majority of counties with available breast cancer mortality rate data have higher mortality rates for Black women compared to White women. Calculating rate ratios was not only informative, it was a necessary step to finding hot spots. Next, you'll perform hot spot analysis to find statistically significant clusters of rate ratio values. Knowing where the clusters are concentrated can aid in your advocacy organization's efforts to raise awareness and increase resources to address racial disparity.

Perform hot spot analysis

Based on your rate ratio map, it appears that most counties with the highest rate are located in the south central and eastern coastal parts of the United States. To confirm this, you'll run a hot spot analysis.

Hot spot analysis finds statistically significant clusters of high and low values. By running the hot spot analysis on the rate ratio values, you will identify hot spots where the mortality rate ratios are high and spatially near one another, and any cold spots that show spatial clusters of low mortality rate ratios. The output from the Hot Spot Analysis tool provides a statistically confident measurement that there is clustering of high or low values. A hot spot with a 99 percent confidence level, for example, means there is only one chance out of 100 that a tight spatial cluster of high values is due to random chance. Identifying a spatial cluster of high values that is statistically significant provides confidence that the clustering is not the result of some random process, but rather suggests other factors may be contributing to the higher concentration of disparities (such as differences in environment, local land use policy, access to quality health care, genetics, life style choices, and so on).

First, you'll ensure there are no null values in your data. Null values can make the analysis results inaccurate.

  1. In the Contents pane, double-click Mortality Rate Ratio.

    The Layer Properties window appears.

  2. In the Layer Properties window, click Definition Query.

    Definition Query

    In an earlier part of the lesson, you used a Definition Query to remove null values for symbology. You will repeat a similar process to remove any null values from the layer.

  3. Click New definition query, and build the expression Mortality Rate Ratio is not null.

    Set the definition query

  4. Click Apply, and click OK.

    Any null values are removed from the data.

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

    Tools

    The Geoprocessing pane appears.

  6. In the Geoprocessing pane, search for and choose Optimized Hot Spot Analysis (Spatial Statistics Tools).
  7. In the Optimized Hot Spot Analysis tool pane, enter the following parameters:
    • For Input Features, choose Mortality Rate Ratio.
    • For Output Features , type Mortality_Hot_Spots.
    • For Analysis Field, choose Mortality Rate Ratio.

    Optimized Hot Spot Analysis parameters

  8. Click Run.
  9. In the Contents pane, uncheck the Mortality Rate Ratio layer so the Mortality_Hot_Spots layer is visible.

    Hot spot analysis

    The Hot Spot Analysis tool finds statistically significant clusters of high and low values. The tool uses the Getis-Ord Gi* statistic (pronounced G-i-star) to identify statistically significant hot and cold spot areas. The hot spots indicating clusters of counties where the breast cancer mortality rate for Black women is higher than the mortality rate for White women are shown in bright red. Cold spots, which are clusters of counties where breast cancer death rates for both Black and White women are similar, are shown in blue.

For the counties with available data on breast cancer mortality, your analysis identified clusters of disproportionately high mortality rates among Black women compared to the mortality rate of White women. There is a hot spot clustered in the south central region of the United States, similar to the patterns identified in the Mortality Rate Ratio map. But the cluster around North Carolina and South Carolina was not easily visible in previous maps. There is also a distinct cold spot cluster in the northeast, indicating there was less of a racial disparity in breast cancer mortality rate between Black women and White women in this region.

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