The ArcGIS Business Analyst Web App Results pane displays the results of analysis through data summaries, visualizations, and tables. The data visualizations and tables are interactive. For instance, if you hover over a bar in the histogram or a cell in a table, its corresponding site is highlighted on the map.
You can access the Results pane within the following workflows:
- Color-coded maps
- Smart map search
- Points of interest (POI) search
- Suitability analysis
- Benchmark comparisons
- Nearby analysis
Potential applications
The following scenarios provide examples of organizations using the Results pane in various workflows.
| Workflow | Example |
|---|---|
Color-coded maps | You can create a color-coded map of uninsured adults to guide local health outreach. Create a bivariate map visualizing households below the poverty level and adult population without health insurance. Use the Results pane to interpret your results. For example, do the following:
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Smart map search | You can explore life expectancy across race groups using smart map search. Add county health ranking variables for life expectancy to your map. Use the Results pane to interpret your results. For example, do the following:
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Points of interest (POI) search | You can map the current competitive landscape of cinemas using points of interest (POI) search. Use the Results pane to interpret your results. For example, do the following:
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Suitability analysis | You can rank the top five best locations for an urgent care center using suitability analysis. Determine sites for your analysis, select criteria related to the population's demographics and health, and adjust how your criteria are weighted. Use the Results pane to interpret your results. For example, do the following:
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Benchmark comparisons | You can compare ZIP codes for your television ad campaign using benchmark comparisons. Map the population and income of a variable then use the Above and below benchmark comparison method to represent whether a ZIP code is above or below the median. Use the Results pane to interpret your results. For example, do the following:
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Nearby analysis | You can analyze the distance between your fast casual restaurant location and competitors using nearby analysis. Use the Results pane to interpret your results. For example, do the following:
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Summary calculations
The Summary view 
provides an overview of aggregate-level analysis of the workflow. For instance, it lists the overall number of geographies analyzed and trends in the data.
| Calculation | Description | Workflows |
|---|---|---|
Aggregate-level data | Aggregate-level data is a summarization of data. It can be represented in the form of averages, percentages, or proportionality. |
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Top 5/Bottom 5 | Top 5 and Bottom 5 represent the five highest- and lowest-ranking locations. |
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Trends | Trends represent how the data variable has changed over time, if time-series data is available for the variable. |
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Rank | The rank of a site is how that site's final score compares to other sites in the analysis. The better the final score, the higher the rank of the site. |
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Score | A site's final suitability score is calculated by adding the weighted scores for each of the variables used in the analysis. |
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Weighted score | The weighted score for each variable is calculated as a percent difference between the value for a given site and the target value selected by the user. A site's final suitability score is calculated by adding the weighted scores for each of the variables used in the analysis. |
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Within range | A range sets a minimum and maximum value to limit the scope of the analysis. Values that are within range fall within the defined minimum and maximum. |
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Mean or average | The mean (or average) is calculated by summing all values and dividing that sum by the number of values. It gives a central point of the data. |
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Minimum | The minimum is the smallest value in the data. |
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Maximum | The maximum is the largest value in the data. |
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Median | The median is the middle value when the data is ordered from lowest to highest. If the dataset is skewed, the median might provide a better indication of central tendency than the mean because it is less affected by extreme values or outliers, which can distort the mean. |
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Standard deviation | Standard deviation measures how much variation or dispersion there is in a dataset. A low standard deviation means most data points are close to the mean, while a high standard deviation indicates a wide spread of data. Evaluating the standard deviation helps assess how dispersed the data is compared to the benchmark. |
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IQR | IQR is useful for identifying the central spread of data and is often visualized in box plots. The interquartile range (IQR) measures the spread of the middle 50 percent of the data. It is the range between the first quartile (Q1) and third quartile (Q3). |
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Skewness | Skewness measures the asymmetry of a data distribution. |
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Kurtosis | Kurtosis describes the peakedness and the heaviness of the tails in a data distribution compared to a normal distribution. It indicates the presence of outliers compared to a normal distribution. |
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Count of points | The count of points is the total number of points within each site area. |
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Distance to nearest point | The distance to the nearest point measured as straight-line distance in the project units (miles or kilometers) between a site and its nearest point. |
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Points per 1,000 households | The points per 1,000 households within each site area represents the point density. |
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Histogram calculations
The Histogram view 
provides an interactive histogram visualizing the variables or attributes used for the selected geography. A histogram is a graphical representation, similar to a bar chart, that represents the distribution of the data.
| Calculation | Description | Workflows |
|---|---|---|
Mean | The mean (or average) is calculated by summing all values and dividing that sum by the number of values. It gives a central point of the data. |
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Median | The median is the middle value when the data is ordered from lowest to highest. If the dataset is skewed, the median might provide a better indication of central tendency than the mean because it is less affected by extreme values or outliers, which can distort the mean. |
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Density chart | A density chart shows the distribution of data as a smooth curve. Adjusting the bandwidth changes how detailed (less smooth) or averaged (smoother) the curve appears. |
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Outliers
| Outliers represent data points or values that are in an abnormal range and do not follow the pattern of the rest of the data. |
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IQR | IQR is useful for identifying the central spread of data and is often visualized in box plots. The interquartile range (IQR) measures the spread of the middle 50 percent of the data. It is the range between the first quartile (Q1) and third quartile (Q3). |
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Standard deviation | Standard deviation is the measure of how much variation exists in a variable or attribute, compared to its mean. Increasing the standard deviation (SD) represents an increase in variation to the mean, and therefore a greater range of data points. Decreasing the SD represents a decrease in variation to the mean, which narrows the data points used and may be more accurate. |
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and click Bubble chart and scatterplot calculations
The Bubble chart view 
provides a bubble chart or scatterplot visual representation of the data. A bubble chart and scatterplot display points on an x- and y-axis to represent the distribution of data. In a bubble chart, the size of the plotted point is proportional to the value of the data.
| Calculation | Description | Workflows |
|---|---|---|
Bubble chart | A bubble chart displays points on an x- and y- axis to represent the distribution of data. In a bubble chart, the size of the plotted point is proportional to the value of the data. |
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Scatterplot | A scatterplot displays points on an x- and y-axis to represent the distribution of data. In a scatterplot, the size of each plotted point is standardized. |
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X-axis | The x-axis in a chart is horizontal, or east-to-west oriented. |
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Y-axis | The y-axis in a chart is vertical, or north-to-south oriented. |
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Regression line | In statistics, a regression line is a straight line that is used in a data visualization (like a scatterplot) to represent how variables correspond with each other. A regression line is calculated with a formula, in which y = mx + b. In this formula, the m variable represents the slope of the regression line, and the b variable represents the y-intercept. Data analysts use a regression line to understand the trends in the data and estimate or predict what a value could be. To measure how close data is to the regression line, use the R-squared (R2) value. |
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Correlation matrix calculations
The Correlation matrix view 
provides a visualization of how variables correlate to one another and the final score.
Note:
This view is only available in the suitability analysis workflow. For more information on the underlying calculations, see Variable correlation reference.
| Calculation | Description |
|---|---|
Correlation coefficient
| Pearson's r is a coefficient ranging between -1 and 1 that measures both the strength and direction of a linear relationship. Values closer to +1 indicate a strong positive relationship whereas values closer to -1 indicate a strong negative relationship. Values close to 0 indicate little or no linear relationship. |
Statistical significance
| A p-value shows how likely it is to observe a correlation this strong purely by chance if no real correlation exists. When the p-value is below 0.05 (which equals a 95 percent confidence level), the result is considered statistically significant, meaning the correlation is unlikely to be due to random variation. |
Sample size
| The sample size is the number of locations used for the analysis. For example, a sample size of 16 means that there were 16 locations (which could be counties if you're using standard geographies or number of hexagons if you're using hexagons) used in the analysis. |
Correlation
| The correlation matrix provides a visual overview of how variables relate to one another and the final score using color-coding. By default, dark green cells show strong positive correlations (r close to +1), while dark red cells show strong negative correlations (r close to -1). Lighter shades—light green, light red, or near-white—indicate weak or no linear relationship (r near 0). |
Scatterplots
| Each cell in the correlation matrix displays a scatterplot of one variable against another—one on the x-axis and one on the y-axis. Hovering over the chart reveals a tooltip with the exact variable names and allows you to examine how the data points are distributed. For example, in the Population and income variable list, you can instantly see how median household income relates to growth rate, total daytime population, or the overall suitability score, with any outliers or clustering patterns immediately apparent. |
Histograms
| A histogram is a graphical representation, similar to a bar chart, that represents the distribution of the data. Hover over a variable's histogram to see its mean (which is the central point of the data, known as its average). This provides a visual overview of how the variable's data is distributed. For example, you can see if a variable's data distribution is relatively evenly distributed or has extreme outliers. The data distribution of each variable impacts how the variable correlates to the other variables and the overall suitability analysis. |
Table calculations
The Table view 
provides the data results in a tabular and downloadable format.
| Calculation | Description | Workflows |
|---|---|---|
Score | A site's final suitability score is calculated by adding the weighted scores for each of the variables used in the analysis. |
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Weighted score | The weighted score for each variable is calculated as a percent difference between the value for a given site and the target value selected by the user. A site's final suitability score is calculated by adding the weighted scores for each of the variables used in the analysis. |
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Benchmark | A benchmark is a value set for comparison. |
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Point details table calculations
The Point details table view 
lists each location near your site along with its attribute details, such as square foot minimum and employee count.
Note:
This view is only available in the nearby analysis workflow.
| Calculation | Description |
|---|---|
Distance (in miles or kilometers) | The distance (in miles or kilometers) is measured as straight-line distance between the site and the listed point. |
Square foot minimum | The square foot minimum is the estimated minimum square footage of the point's building location. |
Employee count | The employee count is the number of employees for a business location. |
Sales volume | The sales volume represents estimated sales revenue or assets in dollars. |
Site summary table calculations
The Site summary table view lists your location and nearby analysis calculations, including the distance to the nearest point, the total count of points, and point density.
Note:
This view is only available in the nearby analysis workflow.
| Calculation | Description |
|---|---|
Distance to nearest point | The distance to the nearest point measures the straight line distance (in miles or kilometers) between a site and its nearest point. |
Count of points | The count of points is the total number of points within each site area. |
Point density | The point density is calculated by dividing the total number of points within each site area by 1,000 households. |
Resources
To learn more about the workflows that generate Results panes, see the following: