Heavy Immerse lets you visualize your data in the format that provides the clearest insight. Charts range from basic to complex, from aggregate to detail level. You can combine your charts together into dashboards to explore your data in different contexts.

Selecting a Chart Type

You can choose the chart type by toggling among the chart icons at the top of the chart editing screen. If you have already chosen dimensions and measures for a chart, Immerse indicates which other chart types are also capable of displaying that data by highlighting the chart icons in green.

Some charts require certain types of dimensions/measures and disallow others. As you switch between charts, you might see dimensions or measures become deactivated (greyed out) if they are not appropriate for that chart type. Deactivated fields are discarded when you save a chart.

A Bubble chart is a variation of the Scatter Plot. Aggregated data is grouped by dimension into circles displayed on an x/y axis. Additional measures change the size or color of the circles. A Bubble chart can represent up to four measures for your chosen dimensions (x, y, size, and color).

Use a Bubble chart to show a correlation between the x measure and the y measure. When you do not expect a correlation, you can use a Bubble chart to understand the distribution and influence of multiple factors.

# Of Groups

Display up to 100 groups of records. You can enter a value or use the slider to visually set the number of groups.

Null Dimensions

Choose whether to show or hide Null values for your chosen dimension.

Color Palette

You can use a custom palette to visually group values in your chart. By default, data points are colored arbitrarily with a spectrum of solid colors. You can choose to arbitrarily color bubbles with 2, 3, or 4 colors. You can also apply colors to individual Dimension values.

If you set the Color measure, you can choose a gradient to visually express relative quantitative values.

Custom Measure Formatting

You can use custom measure formats for the values in your chart. See Customizing Measure and Date Formats.

Bubble Chart Examples

Create a new Bubble chart. Choose a Data Source. This example graphs employment statistics for all 50 United States for the years 1980-2015. The data is available at the University of Kentucky website.

State_name is a handy dimension for this data. Use the average Unemployment_rate as the X Axis, and the average Unemployment total for the Y Axis. Increase the # of Groups to 50 to create an individual bubble for each state.

California has a significantly higher number of unemployed residents compared with the other states. Bubble charts are a good way to show outliers in a dataset. But that figure might be misleading. One reason for a higher average number of unemployed persons might be the fact that California is the most populous state in the country. Use Population as the Size measure to create proportionally sized bubbles, based on total population.

You can add Employment as the Color measure, which casts California in a more favorable light.

The Choropleth lets you compare values by geographic region.

Choropleth comes from the Greek choros (area) and pleth (multitude). Immerse colors the map regions based on the measure you choose.

Use a Choropleth to compare aggregate values across regions. Choropleths are useful for spotting outliers, but are not intended to provide detail on the values within a region. (For detailed, point-level geographic information, consider using a Pointmap.)

The Choropleth is available both as a browser-rendered chart for CPU and distributed systems, and a server-rendered chart for non-distributed GPU systems that provides additional capabilities.

Geo JSON Join

Geo JSON Join is used for browser-rendered Choropleths to link geographic shape information to the geographic names in your dataset.

When you assign a map overlay, the region names in the dataset column you select must match the names in the overlay. The column you choose can spell out the name of the region, or use a standard abbreviation, but cannot use both.

For countries, you can use a dataset column with the two- or three-character abbreviation, or one that spells out the full name. See Country Abbreviations.

For US states, you can choose a column with either the full name or the two character abbreviation. See US State Abbreviations.

The list of US county names can be found in the List of United States Counties and County equivalents.

Color Palette

You can choose one of four quantitative gradients to represent the relative values for each area in the Choropleth.

Custom Measure Formatting

You can use custom measure formats for the values in your chart. See Customizing Measure and Date Formats.

Chart Popup Information

When you hover over a server-rendered Choropleth chart, a popup box appears that contains the column information for the highlighted area. You can copy this information to the clipboard. If the column information includes a URL, you can click the URL to open it in a browser.

Browser-rendered Choropleth Example

Create a new Choropleth. Choose a Data Source. This example graphs employment statistics for all 50 United States for the years 1980-2015. The data is available at the University of Kentucky website.

Set the Dimension to state_name and the Color measure to average Employment. Set GEO JSON JOIN to US State to overlay the defined geojson shapes onto the map.

Server-rendered Choropleth Example

OmniSci running with GPUs provides additional flexibility for the data files and the ability to create a multi-layer chart.

This example uses building footprint data for all of the structures in New York City. The data is available at the NYC Open Data website.

Create a new Choropleth and select the NYC Buildings dataset. Choose mapd_geo as the Geo measure. Choose heightroof as the Color measure. Set the MAP THEME to Dark to make the shapes stand out more clearly. Change the COLOR PALETTE to the blue-to-red spectrum. The tallest buildings now display in red, the shortest in blue.

Click +Add Layer. Set the Geo field to mapd_geo once again, then set the Color measure to cnstrct_yr. Change the COLOR PALETTE to the blue-to-red spectrum. The newest buildings now display in red.

Click the Master layer tab. Adjust LAYER 2 OPACITY to 50. Now, the tallest, newest buildings show bright red.

The Combo chart displays multiple data series on up to two separate axes as line or vertical bar charts.

The optional multi-series capability of the Combo chart can break out values by additional dimensions using up to two Y axes.

You can also choose multiple data sources and display data together on the same chart. There is no limit to the number of data sources you can display on a single chart, up to the capabilities of your chosen browser. You can use the same data source more than once, and choose a different set of columns to display.

Zoom and Pan

You can scroll the mouse wheel or track pad to zoom in and out on the data in your combo chart. This is equivalent to manually brushing over the range chart to choose a zoomed in area.

If you scroll while holding the shift key, the range box pans left and right. This lets you zoom in on a section (normal scrolling) then move left and right to find the exact data you want to see.

You can zoom in or zoom out regardless of whether the range chart is currently displayed. Turning the range chart on or off does not clear a range brush filter.

If you select an area of the focus chart by brushing, then change the range chart in a way that makes the selection on the focus chart no longer visible, the filter on the focus chart is cleared.

A Reset button appears on the lower right as you mouseover a chart with a range filter in effect. Clicking the Range button clears out the range filter only — a brush applied to the focus chart remains.

Combo Chart Examples

Create a new Combo chart. Choose a Data Source. This example uses the flights sample database.

Categorize on the Dimension plane_issue_date, the date the plane was first acquired. To see how on time performance compares to the aircraft acquisitions over time, set the measure for Y Axis 1 to depdelay, and the measure for Y Axis 2 to arrdelay. These measures are within the same range, and combine well on the chart.

You might also want to see how frequently flights are cancelled, based on the age of the airplane. Add the cancelled field as Y Axis 3.

The difference in values makes it difficult to see how the trend of cancelled flights compares to flight delays. Click the line next to the cancelled item in the COLOR PALETTE list and choose Secondary Axis.

You can make the distinction clearer still by changing the secondary axis to a vertical bar chart.

You can also change the color and style of the lines in the chart to make them more distinct.

When you use a numerical dimension, you have the option of displaying the chart in Percentage View. The pop-up box displays not only the values for each item on the X axis, but also the percentage of the total values for that group.

Multisource Combo Chart Example

This example uses a dataset of crimes committed in San Francisco and another dataset of crimes committed in Seattle. We can compare them both on the same axis. Create a new combo chart, and select sfcrime as the Data Source. Choose DateEvent as the X Axis and #Records as the Y Axis.

Click Add Another Data Source and choose crimes_seattle. Set the X Axis to At_Scene_Time and the Y Axis to #Records.

The datasets do not overlap completely. You can use the Range Chart to select a range for fair comparison. In this example, 01-01-2014 to 01-01-2015.

Edit the axis labels and title for clarity. Set the X-axis minimum to 0 and maximum to 900 to match the X-axis for Seattle. Click Apply. Your chart is ready for display on your dashboard.

Horizontal Bar charts display values for multiple dimensions, with two measures displayed as the width and color of the bar for each dimension group.

Bar charts are useful for showing relative values based on categories of information, particularly when the category label is long.

Sort By

You can sort by any chosen dimension or measure in ascending or descending order.

# Of Groups

Display up to 100 groups of records. You can enter a value or use the slider to visually set the number of groups.

Null Dimensions

Choose whether to show or hide Null values for your chosen dimension.

Color Palette

You can use a custom palette to visually group values in your chart. By default, data points are colored arbitrarily with the same solid color. You can change the color for all columns, or apply colors to individual Dimension values.

If you set the Color measure, you can choose a gradient to visually express relative quantitative values.

Custom Measure Formatting

Bar Chart Examples

Bar charts require a minimum of one dimension and one measure. For example, this chart categorizes on the Dimension of shape and uses the count of each shape as the Measure.

You can add any number of dimensions to your Bar chart. For example, you can combine the shape with the state where they were seen.

When you use two measures, the first measure (shape) is indicated by the width of the bar, while the second measure (Duration_Seconds) is indicated by the color of the bar. Reversing the Color Palette makes the labels easier to read, in this example.

You can use a custom palette to color by dimension and visually group values in your bar chart. For example, this chart assigns colors to similar shapes (for example, rounded shapes such as disk, circle, sphere, and oval are all colored orange).

The Geo Heatmap displays aggregate values by geographic location.

The Geo Heatmap lets you visually compare information by region.

Geo Heatmap Examples

Create a new Geo Heatmap. This example uses the 1 Million Row NYC Taxi database as the Data Source. Set the dimensions to dropoff_longitude and dropoff_latitude. Set the measure to AVG fare_amount.

NOTE: You can also use POINT data (generated from longitude/latitude) for LON and LAT; for example, column_9 contains point data:

When you select data of type POINT, Lon and Lat are both populated with the values for the point data:

Changing the Map Theme from Light to Streets improves the contrast for the street names.

The values displayed on the Geo Heatmap are binned aggregate values. You can choose to display the Bin Shape as hexagons or as squares. You can adjust the Bin Pixel Size of the markers to a smaller size to increase the granularity of the Geo Heatmap.

You can set the Color Palette to tell a more effective story with your data. In this example, setting the palette to gradations of blue helps to distinguish between the trees in Central Park versus the fare values.

Multi-layer Geospatial Maps

Zoom

You can zoom in and out of a Geo Heatmap chart in the following ways:

• Using the mouse scrolling wheel.

• Selecting an area by holding down the Shift key and using the mouse to select the zoom area.

• Using the Zoom To box in the upper right of the map:

  • Type the name of a geographic location (address, city, state, or country) and optional zoom level. For example, Denver, CO, !8 zooms to Denver, Colorado, with a zoom level of 8.

  • Enter latitude and longitude coordinates, and optional zoom level. For example, 39.26911, -76.54068, !9 takes you to Baltimore, MD, at zoom level 9.

The Contour chart is based on the polygon contour generation function. It takes a point layer and value column, and produces line and polygon output.

The Contour chart generates contour lines from raster data or from a geo point. A common example of this kind of chart shows elevation contour lines on a topographical map.

The ui/enable_contour_chart must be enabled to use the Contour chart.

To create a Contour chart, you input data in the form of longitude and latitude dimensions, and select a contour value measure, which sets a column with which to generate the contour lines. Contour charts have the following available UI settings.

In this topographical map example, the dimensions are raster_lon and raster_lat, and the contour value measure is elevation.

Contour lines are split into two categories, major and minor. You can adjust the interval between the lines, as well as the style. Hovering over the Contour map shows the value for that particular contour.

You can toggle Contour Fill on and off to fill the areas between the contour lines with polygons. You can also select a color scale can be selected for these contour polygons.

You can apply a filter dimensions and measure values. In this example, the contour map only shows average elevation above 2500 feet (z is greater than 2500):

In addition to elevation on a topographical map, you can also use Contour charts for a number of other applications. For example, the following Contour chart uses NOAA data to show average wind speed in the north Atlantic Ocean.

Gauge charts display values for up to four measures in a meter format.

Example: Single Measure

The following dashboard shows a series of four basic Gauge charts (one measure each) that reflect the following values for the transformers data set:

• AVG Strength (Base)

• Minimum Strength (Base)

• Maximum Strength (Base)

• Standard Deviation Strength (Base)

To create the dashboard as shown, add four Gauge charts, using the Transformers source and the measures as defined in the list above. For example, the following image shows the settings for the Average Strength gauge. You define the segment percentages and colors in Segment fields on the right pane. You can also:

• Display absolute values (instead of percentages)

• Set unfiltered values for Min, Max, and Target.

• Set or override Target, Min, and Max values. Here, instead of getting the maximum value from the source information, the Max value has been set to 10.

The dashboard also includes a donut pie chart that indicates the number of records per transformer line, showing the total number of records on which the gauges are based.

Example: Multiple Measures

In this example, multiple measures from the transformers data set are incorporated into one Gauge chart:

• Base - Average Strength (7.00)

• Min - Minimum Strength (1.00)

• Max - Maximum Strength (10.00)

Here, the Target measure has been set to 6.1 and is denoted by the light blue line.

The Heatmap displays information in a two-dimensional grid of cells. Each cell represents a grouping of data.

Cell color indicates the relative value of the cells, from one end of the spectrum to the other.

Heatmaps are ideal for spotting outliers, which show up vividly on the color spectrum. They work best when the number of groupings is not too large, since large numbers of groupings cause the heatmap to exceed the viewport, making comparison harder (for such scenarios, consider using a Scatter Plot).

Heatmap Example

Create a new Heatmap. Choose a Data Source. This example uses NBA Play-by-play Stats from nbastuffer.com.

Set the X Axis dimension to converted_x with Binning On and Y Axis dimension set to converted_y with Binning On. The converted values represent the coordinate location on the basketball court, versus the original values, which represent the distance from the goal (as though all shots were half-court).

You can turn off NULL DIMENSIONS to get a chart that more closely resembles a basketball court.

If you change the COLOR PALETTE to the red-to-green spectrum and reverse the direction, you can discern the highest average scoring locations (in green) from the lowest (in red) using commonly understood visual cues.

You can edit the chart title and axes labels to give them more meaningful names.

Cross-section charts have long been used in architecture, engineering, and the sciences. Now, GPU analytics have the memory capacity and speed to create cross-section charts interactively on big geotemporal datasets.

Multidimensional array data such as the output from weather models or seismic surveys typically contains estimates or observations of properties at multiple vertical elevations. Cross sections provide a way to cut into a three dimensional volume and visualize its internal structure. For example, surface winds can vary from winds aloft, which can cause turbulence or wind shear dangerous for aviation. Subsurface properties also vary significantly, which is important in assessing both aquifers and oil reservoirs.

A cross section is slightly more difficult to specify than some other chart types because there can be a nearly-infinite number of cross sections drawn within a volume. The location of the sections must be specified on a map chart, and the line tool provides a simple and quick way of doing so. The visualization properties of the section are set in the contour chart itself. You can add the charts in any order, but you must have a map chart on your dashboard before you can complete the full specification of an interactive cross-section.

Enabling the "ui/enable_cross_section_chart" flag adds a new cross-section chart type, and also adds a new cross-section line tool to the existing draw tool menu on map charts.

The following figure shows the cross-section line tool on a Pointmap:

Creating a Cross Section

To create a cross section, on any existing map chart, such as a Pointmap, use the line tool to define a transect to be used as the x axis for the cross-section chart. You can draw one two-point line on a chart; click to mark start and endpoints.

You can edit the line, similar to other draw tools, by selecting and dragging.

When creating a Cross-section chart, any lines that were defined appear under the Sections dropdown, listed by chart ID. The following figure shows a Cross-section chart defined using a line in the chart above.

• If a line is selected, the horizontal axis of the Cross-section chart represents the normalized distance along the selected line.

• If no line is selected, the initial default x axis of the chart represents a northward view from the south edge of the map, as if a selection had already been drawn from the bottom left to bottom right of the map view.

• Creating a line also creates read-only latitude/longitude parameters for the start and endpoints; these auto-generated parameters appear in the parameter panel if they are used in a cross section chart.

• The Z axis measure defines the vertical axis.

Data Validation

To render a cross section, the entire volume of data must be equally spaced:

• The entire volume must be rectilinear; see https://en.wikipedia.org/wiki/File:Rectilinear_grid.svg.

• The lat/lon plane must be regular; that is, equal spacing in both dimensions. See https://en.wikipedia.org/wiki/Regular_grid#/media/File:Regular_grid.svg.

• There must be one unique sample point for every voxel in the volume. For example, if you are defining an x/y/z volume with columns lon/lat/isobaric_level, there can be only one row in the query result with the values lon:-100, lat:35, isobaric_level:1000.

If the data fails validation, the chart will fail to render and display an error indicating which requirement failed.

The Histogram displays the distribution of data across a continuous (typically time-based) variable, by aggregating the data into bins of a fixed size. Vertical bars show the count of data within each bin.

Histograms can count occurrences of data other than the binned dimension by assigning a series.

Use a Histogram to understand the distribution of your data, and to see areas of unusually high or low density, which would be masked by an aggregate such as Average.

Once you choose your measure and dimension, you can edit the labels for the X and Y axes. Click the label and enter your custom text.

Show Range Chart

You can change the range of values in your chart by "brushing" over the range chart, or by entering fixed start and end values on the X Axis.

Color Palette

You can use a custom palette to choose the bar color in your chart. By default, data points are colored light blue. You can choose one of 8 colors.

If you set the Color dimension, you can assign colors to individual values, or you can assign the same color to several dimension values to visually group them in the chart.

Custom Measure Formatting

You can also use custom measure formats for the values in your chart. See Customizing Measure and Date Formats.

Histogram Examples

Choose a Data Source. This example uses the official database of UFO sightings

This chart categorizes UFO sightings on the dimension of Sighting_Time and uses the number of sightings as the measure.

Adding the Color dimension of shape visually shows the relative number of sighting types within each time-based bin.

The records go back to 1905, with very few recorded sightings. Selecting a more recent 10-year block presents information in a more digestible form. For an ad hoc report, you can brush across values in the Range Chart to filter a smaller number of bins, or you can enter start and end dates on the x axis for precise values.

When you set the x axis to a date/time value, you have the option of setting the BIN size to month, quarter, or year. Setting the BIN size to 1y lets you compare the aggregate values at a glance.

Once you set the boundaries for the x axis, you can turn off the SHOW RANGE CHART setting. You can also reduce the number of series displayed by removing their entries on the COLOR PALETTE.

You can change the labels on the axes and the chart title to reflect the current state of the information.

The Linemap superimposes LINESTRING or MULTILINESTRING objects onto a geographic map. Uses include tracking shipping and transportation routes in real time, and studying geographic features such as fault lines.

Chart Popup Information

When you hover over a Linemap chart, a popup box appears that contains the column information for the highlighted area. You can copy this information to the clipboard. If the column information includes a URL, you can click the URL to open it in a browser.

Examples

You can use length to determine the width of the lines to make longer faults stand out. You can also use a text label for the color, which provides a legend and a guide to the type of faults displayed.

You can fine tune the graph further with enhancements such as changing the map theme, reducing the number of lines, changing the size domain and range, and adding values to the pop-up box.

Loading Linestring Data for Linemap

You can load your own linestring data to create Linemap charts. Linestring coordinates must be:

• Expressed as longitude latitude

• Between -180 to 180 (longitude), -90 to 90 (latitude)

North and East coordinates are positive numbers, South and West are negative numbers.

For example, you can import this CSV file with coordinates for Los Angeles to New York, San Francisco to Miami.

1. Save the following content as crossCountry.csv.
2. Open Immerse

3. Choose the Data Manager tab.

4. Click Import Data.

5. Click Import data from a local file.

6. Click the + icon to browse for and open your file.

7. Click Preview.

8. Name the table crossCountry.

9. Verify the data is correct and click Import Table.

10. Click the Dashboards tab.

11. Click New Dashboard.

12. Click Add Chart.

13. Choose Linemap.

14. Click + Add Data Source.

15. Choose crossCountry as the data source.

16. In the Geo field, click + Add Measure.

17. Choose Distance.

    Your Linemap links Los Angeles to New York, San Francisco to Miami.

Zoom

You can zoom in and out of a Linemap chart in the following ways:

• Using the mouse scrolling wheel.

• Selecting an area by holding down the Shift key and using the mouse to select the zoom area.

• Using the Zoom To box in the upper right of the map:

  • Type the name of a geographic location (address, city, state, or country) and optional zoom level. For example, Denver, CO, !8 zooms to Denver, Colorado, with a zoom level of 8.

  • Enter latitude and longitude coordinates, and optional zoom level. For example, 39.26911, -76.54068, !9 takes you to Baltimore, MD, at zoom level 9.

The New Combo chart combines the functionality of a number of existing chart types and can display multiple data series in a variety of configurations. This flexibility makes it easier to create, change, and manipulate charts in a variety of formats without having to create a number of different chart types on your dashboard.

The New Combo chart is highly customizable; you can change type and orientation on the fly, switching between a number of different types and views to get the most effective chart for your data. New Combo chart configuration drop downs allow you to sort and filter by data type, making data discover easier and faster. For numerical base dimensions, you can configure measures to display as cumulative and percentage distributions. You can select dimension values dynamically or manually, and configure sorting logic you want to use.

You can pin chart legends to the right, or you can superimpose them on the chart. You can also use the chart legend to toggle on or off the visibility of an individual group-by series.

Chart orientation can be toggled between vertical and horizontal. If you have bar and Combo charts that you have created previously, you can duplicate them as New Combo charts at the Dashboard level.

Example 1

This example shows some of the flexibility of the New Combo chart. You want to create a chart that shows the number of flight arrivals for each selected carrier. Create a New Combo chart based on the flights data source, configure the chart type and granularity, and customize how the Group by dimension displays. Start by opening a new Dashboard, and then click the New Combo chart type.

Select the flights data source, and set the following dimensions:

• Base dimension: arr_timestamp

• Group by dimension: carrier_name

• Base measure: # Records

This results in the following chart.

In Legends and Settings, you can configure the existing chart in a variety of ways:

• Change the type/orientation of the chart to Vertical, Horizontal, Group (Lines for Line/Area charts), Stack, and Percentage.

• Toggle Range charts on and off.

• Adjust data and formatting settings.

• Customize the Group by dimension.

Let's change the chart to a Line/Area chart, and adjust the granularity:

• In Graphical settings, select Line/Area and change the line thickness to 3.

• In the Bin: dropdown, select Quarter.

Say you want to customize the number of carriers displayed. In addition to the dynamic values that already appear, you can manually select other airlines to appear in the chart.

• Open the carrier_name Group by dimension by clicking the settings icon to the right.

• Click + Manual Selection, and then select additional airlines. When you have finished, click Done.

Now, 10 airlines are defined, and those selected manually appear with lock icon to the right.

If you click the color next to the carrier name, you can change it. You can also hide a carrier from view by clicking the eye icon that appears when you hover over the carrier name.

Example 2

Let's create a chart that shows the number of flights based on origin city, and then see how many flights orginated from each city based on airline.

Create a chart using the flights data source, with the following:

• Base dimension: origin_city

• Base measure: # Records

The chart created looks like this. You can scroll horizontally to see values that are not displayed because of space constraints.

Let's add the carrier_name dimension, change the orientation to Horizontal, and select Stack to create a stacked bar chart.

You apply filters in the same way as you would with other chart types. Here, you decide you want to see only those flights for year 2005. Click + Add filter, and select flight_year Equals 2005. You can toggle this filter on and off.

Duplicating and Upgrading Other Chart Types to New Combo Charts

You can copy or upgrade compatible chart types (Combo, Bar, Histogram, and Stacked Bar) as New Combo charts at the Dashboard level. Copying a chart preserves the original chart and makes a duplicate New Combo chart; upgrading converts that chart to a New Combo chart.

To duplicate an individual chart or convert it as a New Combo chart, click the More Options icon at the far right, and then click Duplicate as New Combo or Upgrade to New Combo.

If you select Duplicate as New Combo, an identical chart of New Combo type is created and added to the dashboard.

If you select Upgrade to New Combo, that chart is converted to a New Combo chart and the original chart is no longer available.

You can also convert all upgradable charts on a dashboard at once by clicking the More option menu on the dashboard and then clicking Upgrade charts.

In the Upgrade Charts on Page dialog box, you can select the charts you want to upgrade or cancel the upgrade.

If you do not save the dashboard after the conversion, the charts revert back to their original format and the conversion is lost.

The Line chart represents a series of data as a line or multiple lines, plotted across time or another numerical dimension.

The optional multi-series capability of the Line chart can break out values by an additional dimension.

Line Chart Examples

Create a new Line chart. Choose a Data Source. This example uses the official database of UFO sightings

Categorize UFO sightings on the Dimension sighting_time and set the Measure to the number of records (the number of sightings).

Adding the Dimension shape displays a separate line for each sighting type across time.

The records go back to 1905, with very few recorded sightings. Selecting a more recent 10-year block presents information in a more digestible form. For an ad hoc report, you can brush across values in the Range Chart to filter a smaller number of data points, or you can enter start and end dates on the x axis for precise values.

When you set the x axis to a date/time value, you have the option of setting the intervals to month, quarter, or year. Setting the BIN to 1y can make the values easier to discern.

You can change from a line chart to an area chart, adding more contrast to make the differences between the values more apparent.

You can also reduce the number of series displayed by removing their entries from the COLOR PALETTE.

After you reduce the scope of the chart, you can change the labels on the axes and the chart title to reflect the current state of the information.

A Skew-T diagram, also known as a sounding, is plotted from data measured by weather balloons. National Weather Service observation sites usually release weather balloons twice a day. When the weather is expected to be severe, some sites may release them more often. The data plotted on the Skew-T chart includes temperature, dew point, and winds at various levels in the atmosphere.

Skew-T charts can be used to forecast a wide variety of phenomena, including thunderstorms, hail, heavy rainfall, or tornadoes. During the winter, Skew-T charts are useful for determining the type of likely precipitation, whether it's snow, sleet, or freezing rain.

This web page provides a basic overview of Skew-T charts. For more in-depth information, see the Skew-T tutorials at weather.gov.

Pressure is plotted on the y axis of a Skew-T diagram, and temperature is plotted on the x axis. Pressure decreases as you go up the y axis, just like it does in the atmosphere. Pressure is plotted on a logarithmic scale to approximate the way it decreases with height. The following chart uses data from NOAA; data includes pressure , height, temperature, dew point, wind direction, and wind speed. The green line is the dew point profile; the red line is the temperature profile. Just to the right of the diagram are wind barbs plotted with increasing height.

The Pie chart lets you compare the relative sizes of groups of data as segments of a circle.

Use a Pie chart to show the relative proportions of a small number categories. If you have many categories, or the values are very similar in size, a Bar chart is often a more effective way to compare the categories. Note also that you should not create Pie charts with a mix of positive and negative values.

Chart Style

You can style your chart as a Donut or a Pie, depending on your dessert preference. The default Donut style tends to make it easier to see the relative sizes of the chart divisions.

Sort By

SORT BY determines the order of the sections starting at the 12 o'clock position and proceeding around the chart in clockwise fashion.

# of Groups

You can divide your chart into up to 100 values. A smaller number of values often results in a more readable chart.

Null Dimensions

You have the option of hiding null dimensions if they are not significant in your data sample.

Color Palette

You can choose 1, 2, 3, 4, or 9 colors for your chart. You can also use a gradient color to show relative values within a dimension.

Labels

You can toggle off or on the absolute value labels on the pie sections. This toggle is always enabled and available.

When the measure values for your chart represent the number of records or a sum aggregate value, you can show percentage labels of each slice relative to the other slices of the pie. This value always appears in the popup when you hover over a slice (when the feature is available). In the chart editor, you can toggle the percentages on or off (default) for each slice.

If you create the chart using Number of Records or the Sum size measure, then change to a different kind of value, the Percentage and "All Others" settings toggles turn off, with a ToolTip explanation when you hover over them.

Custom Measure Formatting

You can also use custom measure formats for the value in your chart. See Customizing Measure and Date Formats.

"All Others" Value

When you select "All Others," you add an extra section to the pie representing all dimension groups that are not already represented by a slice. The section is grey and cannot be selected to crossfilter. Otherwise, it acts like any other slice. You can toggle the All Others value on (default) or off in the chart editor.

Pie Chart Examples

Create a new Pie chart. Choose a Data Source. This example graphs employment statistics for all 50 United States for the year 1980. The data is available at the University of Kentucky website.

Use the Dimension state_name and the Size measure AVG Employment.

You can use the Color measure to reflect the Unemployment_rate, providing more nuanced understanding of the relative segments.

There are 50 states, so it might be tempting to show all 50 in the chart. The problem becomes obvious when there are too many segments with too little variation to provide meaningful analysis. 10 or fewer is probably a reasonable setting for # of Groups.

Changing the Color Palette might make an outlier such as Michigan stand out more effectively.

Where the segments do not have sufficient width to display the measures, you can mouse over to see a pop-up with the information for your chosen segment.

The Pointmap plots geographic latitude/longitude data to visualize the location of data on a map.

The Pointmap, by default, presents each record as an individual point on the map.

Size Domain

Size Domain sets the minimum and maximum bounds for the size measure. The size domain does not exclude values outside of those bounds from the dataset. The minimum value sets the smallest point size: any values lower than the minimum value uses the same point size. Maximum value sets the size of the largest point. For example, if you set the maximum Size Domain to 5,000, any value 5,000 or greater is shown the same size.

The practical effect of Size Domain is to reduce the impact of outliers and create a more informative map for the most meaningful range of values.

Size Range

Size Range represents sizes of the smallest and largest points on the map, measured in pixels. Points can range in size from 1 to 20 pixels. Note that if you set very large pixel values for the top of the range (for example, 20), the largest points might cover smaller points beneath them. Setting the size range is a balance between making it easy to spot large values while still displaying all significant information.

Mark Shape

Mark Shape lets you choose from a variety of shapes to use as data point markers in your Pointmap. Choosing the correct shape can make data values stand out more clearly, and help to differentiate values on layered charts.

Density Gradient

Use Density Gradient to toggle density accumulation on and off. Density accumulation performs a count aggregation by pixel and allows you to color a pixel by normalizing the count and applying a color to it. For more information about density accumulation, see Density Mode in Example: Vega Accumulator.

Chart Popup Information

When you hover over a Pointmap chart, a popup box appears that contains the column information for the highlighted area. You can copy this information to the clipboard. If the column information includes a URL, you can click the URL to open it in a browser.

Pointmap Example

Create a new Pointmap. For the Data Source, use the official database of UFO sightings.

Set the Lon measure to longitude and Lat measure to latitude. Set the Size measure to duration_seconds.

You can also use POINT data (generated from longitude/latitude) for LON and LAT; for example, column_9 contains point data:

When you select data of type POINT, Lon and Lat are both populated with the values for the point data:

Keep the defaults for THEME, # OF POINTS, SIZE DOMAIN, SIZE RANGE, and MARK SHAPE . Change the LAYER OPACITY to 75, and under POPUP BOX, click + ADD COLUMN and choose shape. Then, click Apply.

On the dashboard, you can compare the Pointmap, which can display detailed information for each sighting, versus a Choropleth, which displays only aggregate values (for example, total sightings) for a geographic region (for example, a state).

Multilayer Geospatial Maps

Pointmap and Geo Heatmap charts can be layered on top of one another to allow visual comparison of datasets. See Creating Multi-layer Geospatial Charts.

Using Dimensions to Aggregate Results

Instead of plotting every individual point in a dataset, you can aggregate your results using a dimension setting.

For example, the Pointmap chart below shows the location of hundreds of tweets in Santa Clara County, California.

When you add the dimension county_state, the map displays a single point representing the average of all the points in Santa Clara County and the total number of “tweets”. When you hover over the point, a pop-up box shows the average longitude and latitude, with a summary of the results. In the POPUP BOX section on the right side, you can adjust the popup box contents to change the formatting, and you can change the order that the measures appear by dragging them to the desired location in the measures list.

You can also filter the results of your aggregation. When you add a Dimension to your chart, the Filter On Aggregate field displays. Choose a field on which to filter your data (or create a custom dimension), then add the filter criteria. Only the records that meet your criteria are plotted on the chart.

For example, this Pointmap shows the origin points of flights that experienced a weather delay.

If you are not concerned with trivial delays, you can filter the aggregated results to show only the delays greater than 30 minutes.

Zoom and Select

Pointmap charts have additional features for zooming in and selecting details.

You can zoom in and out of a Pointmap chart in the following ways:

• Using the mouse scrolling wheel.

• Selecting an area by holding down the Shift key and using the mouse to select the zoom area.

• Using the Zoom To box in the upper right of the map.

You can type the name of a geographic location (address, city, state, or country) and optional zoom level. For example, Denver, CO, !8 zooms to Denver, Colorado, with a zoom level of 8.

You can also enter latitude and longitude coordinates, and optional zoom level. For example, 39.26911, -76.54068, !9 takes you to Baltimore, MD, at zoom level 9.

Select

You can select geographic regions based on proximity or defined boundaries using the Circle, Polygon, and Lasso selection tools.

Circle Selector

Use the Circle tool to select an area around a specific central point. Click the Circle tool icon, then click anywhere on the map to create a circular selection.

To move the circle, click anywhere inside the selected area to select the circle. Drag the circle to the new location.

To resize the circle, click anywhere inside the selected area, then drag any of the white squares to scale the circle up or down.

Due to the distortion inherent in Mercator map projections, the circumference of the selection is reduced as you get closer to the equator, and increased as you approach the poles. In the example below, all of the areas are the same number of meters in diameter, with the size of the selection circle adjusted to allow for Mercator distortion.

Polygon Selector

Use the Polygon tool to select an area with noncomplex angles. Click to set each point. As you draw the selection, you can hold the Shift key to contrain each line to 45° angle increments relative to the previous line.

To complete the selection, do one of the following:

• Double-click

• Click the starting point a second time

• Press Enter

To change the size of the selection, click anywhere in the selected region. Drag any of the white corner dots to resize the selection. Hold the Shift key to constrain the relative proportions of the selection. Hold the Alt key to scale the selection from the center.

To rotate the selection, mouse hover over any corner to display a curved arrow. Click and drag to rotate. Hold the Shift key to rotate in 45° increments.

To edit the endpoints in the selection, double-click anywhere in the region. Drag the white endpoints to new locations. To add an endpoint, click one of the small orange dots in the middle of a line segment; it becomes a new endpoint that you can drag to your desired position. To delete an endpoint, hold the Alt key and click the endpoint.

Note that if you create a selection where lines intersect the behavior is undefined and has unpredictable results.

Lasso Selector

Use the Lasso tool to trace around an area with curves or complex angles. Click to start, drag to draw the outline of your desired area, then release the mouse at any time to complete the selection with a straight line.

Once you have created your selection, the selection points are simplified, and the selection becomes effectively the same as a selection made with the Polygon tool.

The Scatter Plot displays unaggregated, row-level data as points, plotting the points along an x and y axis. Each axis represents a quantitative measure. You can use additional measures to change the size or color points, making the scatter plot capable of representing up to four measures for each group (x, y, size, and color).

Scatter plots resemble Bubble charts, but are used to view unaggregated data, while Bubble charts aggregate data.

Use a scatter plot chart to study the correlation between two measures, or to spot outliers or clusters in the distribution of data. You can use a Scatter Plot to visualize any dataset, but they are most useful for exploring large amounts of data.

Dimensions are optional for Scatter Plot charts. When you add a dimension, Immerse represents binned values by averaging X and Y coordinates.

# of Points

While you normally would show as many points as possible, you have the option of decreasing the number of points displayed if that makes the most important values stand out more prominently. You can enter a value for the maximum number of points, or use the slider to make adjustments visually.

Size Domain

Size Domain sets the minimum and maximum bounds for the size measure. The size domain does not exclude values outside of those bounds from the dataset. The minimum value sets the smallest point point size: any values lower than the minimum value uses the same point size. Maximum value sets the size of the largest point. For example, if you set the maximum Size Domain to 5,000, any value 5,000 or greater is shown the same size.

The practical effect of Size Domain is to reduce the impact of outliers and create a more informative map for the most meaningful range of values.

Size Range

Size Range represents sizes of the smallest and largest points on the map, measured in pixels. Points can range in size from 1 to 30 pixels. Note that if you set very large pixel values for the top of the range (for example, 30), the largest points might cover smaller points beneath them. Setting the size range is a balance between making it easy to spot large values while still displaying all significant information.

Mark Shape

Mark Shape lets you choose from a variety of shapes to use as data point markers in your Scatter Plot chart. Choosing the correct shape can make data values stand out more clearly, and help to differentiate values on layered charts.

If you use an orientation measure, you can select a shape that indicates direction, either a wedge or an arrow. Here, a wedge shows flight track angles.

Density Gradient

Use Density Gradient to toggle density accumulation on and off. Density accumulation performs a count aggregation by pixel and allows you to color a pixel by normalizing the count and applying a color to it. For more information about density accumulation, see Density Mode in Example: Vega Accumulator.

Custom Measure Formatting

You can also use custom measure formats for the values in your chart. See Customizing Measure and Date Formats.

Scatter Plot Example

Create a new Scatter Plot chart. For the Data Source, this example uses NBA Play-by-play Stats from nbastuffer.com.

Set the X Axis set to converted_x and Y Axis set to converted_y. These represent the places on the basketball court where shots were recorded. The color indicates how frequently a shot was taken at a particular location.

You can make the highest scoring points more obvious by assigning the points to the Size measure.

You can add a pop-up box to annotate the points on the chart. Under POPUP BOX, click + Add Column and choose points. Click + Add Column again, and choose shot_distance.

You can make the chart communicate more effectively at a glance by changing the color palette to green-to-red and reversing the order. This shows the lowest scoring points in red, highest scoring points in green.

The final chart is similar to the Heatmap chart of the same data, but provides much more detail and precision for analysis of the highest scoring locations on the basketball court.

Zoom and Selection

You can zoom in on specific areas of a Scatter plot chart and select arbitrary sets of data points using the selection tools.

Zoom

You can select and zoom a specific region of a Scatter plot chart. Hold the shift key, then drag a rectangular selection around the area on which you want to zoom. You can also zoom in and out using a mouse scrolling button.

Selection

You can select a subset of the points in a Scatter plot using the Circle, Polygon, and Lasso selection tools.

Circle Selector

Use the Circle tool to select an area around a specific central point. Click the Circle tool icon, then click anywhere on the map to create a circular selection.

To move the circle, click anywhere inside the selected area to select the circle. Drag the circle to the new location.

To resize the circle, click anywhere inside the selected area, then drag any of the white squares to scale the circle up or down. Drag the white squares at the midpoints to scale in only one direction. Hold the Shift key to maintain the relative proportions of the selection. Hold the Alt key to scale the selection from the center outward.

Polygon Selector

Use the Polygon tool to select an area with noncomplex angles. Click to set each point. As you draw the selection, you can hold the Shift key to contrain each line to 45° angle increments relative to the previous line.

To complete the selection, do one of the following:

• Double-click

• Click the starting point a second time

• Press Enter

To change the size of the selection, click anywhere in the selected region. Drag any of the white corner dots to resize the selection. Hold the Shift key to constrain the relative proportions of the selection. Hold the Alt key to scale the selection from the center.

To rotate the selection, mouse hover over any corner to display a curved arrow. Click and drag to rotate. Hold the Shift key to rotate in 45° increments.

To edit the endpoints in the selection, double-click anywhere in the region. Drag the white endpoints to new locations. To add an endpoint, click one of the small orange dots in the middle of a line segment; it becomes a new endpoint that you can drag to your desired position. To delete an endpoint, hold the Alt key and click the endpoint.

Note that if you create a selection where lines intersect the behavior is undefined and has unpredictable results.

Lasso Selector

Use the Lasso tool to trace around an area with curves or complex angles. Click to start, drag to draw the outline of your desired area, then release the mouse at any time to complete the selection with a straight line.

Once you have created your selection, the selection points are simplified, and you can edit or update the selection as you would a selection made with the Polygon tool.

Table charts display raw data in rows and columns.

You can display individual data rows, or use grouping or aggregation. There is no limit for dimensions or measures on Table charts. You can view more aggregated measures in a table chart than most other chart types.

You can also use custom measure formats for the value in your chart. See Customizing Measure and Date Formats.

Table Chart Examples

Create a new Table chart. Choose a Data Source. This example uses the official database of UFO sightings. You can create a Table chart using only measures, to display all sightings individually. Here, display of null values is selected.

You can also use Table charts to group information by a dimension, similar to other chart types. For example, add the shape dimension, and you can display the average duration in seconds for UFOs of that shape. In a grouped chart, you can sort on any column in ascending or descending order. You can format the measures you use to make them easier to scan.

On a dashboard, you can click a dimension in a separate chart to show detailed records for that dimension in a Table chart.

You can use the Text Widget to include descriptive text, code samples, and graphics to help your OmniSci charts communicate more effectively. The Text Widget is an implementation of the Quill rich text editor.

The Text Widget Toolbar

Use the toolbar to apply formatting to your content.

Font

There are three font choices: Serif, Sans Serif, and Monospace.

Style

There are four styles: Heading 1, Heading 2, Heading 3, and Normal. You can apply a style to a paragraph before you type or apply a style to an existing paragraph.

Bold, Italic, Underline, Strikethrough

Format selected text with Bold, Italic, Underline, or Strikethrough.

Text Color

Change the color of the text.

Highlight

Change the color of the text background.

Number List

Create an enumerated list. Type tab to indent subordinate lists, shift-tab to move up one level in the hierarchy.

Bullet List

Create a bullet list. Type tab to indent subordinate lists, shift-tab to move up one level in the hierarchy.

Alignment

Align text left, center, right, or fully justified.

Link

Create a link by applying a URL to selected text. Note that you must include the http:// prefix in the URL.

Image

Insert a web compatible image. Click the Image icon, then use the file selector to upload your image to OmniSci.

Quotation

Indent text and display a vertical bar at the left margin to indicate the section is a quotation.

Code

Format the block with white text on a black background in monospace font.

Clear Formats

Clear all formats applied to selected text.

Text Widget Example

You can use the Text Widget to add explanatory text to your dashboard.

Stacked bar charts display values for one or two dimensions, one of which must be defined on the x-axis. Measures are displayed as the height and color of the bar for each dimension group. With stacked bar charts, you can display multiple measure/dimension combinations on each bar. This makes it easy to visually compare measures for the defined dimensions, as well as see the cumulative value for the defined measure.

Stacked Bar Chart Examples

The following examples use the flights sample database.

Stacked Bar charts use two dimensions (X axis and color) and one measure.

1. Create a new Stacked Bar chart.

2. Choose flights as the Data Source.

3. For Dimensions, select destination state for X Axis and origin state for Color. To remove the null dimension on the X Axis, toggle Null Dimensions to off. Note:

   • If you omit the Color dimension, you create a single-color vertical bar chart.

   • When you use the Color dimension, Immerse selects the top five categories for the dimension and measure. When the All others toggle is on, all categories not in the top five are shown as Other.

4. For Measures, select the sum of airtime.

5. Sort by descending airtime, and define the colors for origin states. Note: You can enable and disable All Others Color Palette, but you cannot add or remove categories.

This creates a chart like the following, showing the cumulative airtime for flights, based on origin state and destination state. The origin state airtimes are stacked on the same bar, making it easy to see total airtime by destination as well as total destination airtime by individual origin.

Now, adjust the chart dimensions and measures to see information about cancelled flights for particular origination cities for individual airlines.

1) For Dimensions, select origin city for X Axis and select carrier name for Color.

2) For Measures, select the sum of cancelled flights.

This creates a chart that shows the total cancelled flights for particular origin cities, broken out by air carrier.

If you prefer, you can set the Percentage View toggle to compare relative values within a dimension and side-by-side.

You can reduce the number of cities on the X Axis by sliding the # of Groups down (15 in the next example), and show the value for all other airlines by toggling All Others to On in the Color Palette:

In some cases, if you disable All Others from the color menu, the # of Groups does not match what is shown on the x axis. For example, in the following chart, the number of X axis items for dest_state is five, but the # of Groups is set to 6.

This occurs because the query that is returned has "undefined" for "key1" values, and these values are filtered out by Immerse. When the All Others toggle is enabled, these values are referred to as "other" instead and are grouped together, making the number of values on the X axis match the number defined in # of Groups.

A Wind barb chart uses wind barb shapes to indicate wind speed and direction.

Speed is indicated using "flags" on the end of the barb:

• Each half of a flag depicts 5 knots (5.8 mph).

• Each full flag depicts 10 knots (12 mph).

• Each pennant (filled triangle) depicts 50 knots (58 mph).

Direction is indicated by orientation of the flags on the barb; the flags point in the direction of the source of the wind. For more information, see from the National Weather Service.

For example, the following Wind barb chart has these measures, using data set noaa_gfs_v from :

• Lon - AVG longitude

• Lat - AVG latitude

• Speed - AVG a80m_Wind_Speed

• Direction - AVG a_80m_Wind_Direction

You can increase or decrease the number of barbs in the right panel. Here, barb number is increased to the maximum to provide a more contoured visual.

Using the optional Color measure, you can add more information to your Wind barb chart. For example, the following figure shows a Color value of a AVG a2m_Relative_Humidity to provide humidity information for the air, with yellow indicating areas of higher relative humidity. Adjust the Color Palette to change how the Color measure is represented.

The Number chart displays a single aggregate measure with no dimensions.

Specify a measure and aggregation to create your Number chart.

Color Palette

Choose a color for your number that sets the correct tone. Red typically means danger or negative information, but it can also make the viewer more likely to agree with the value. Green is positive, but can be easier to overlook. Choosing the right color to match the information and message is both an art and a science.

Custom Measure Formatting

You can also use custom measure formats for the value in your chart. See Customizing Measure and Date Formats.

Number Chart Examples

Choose a Data Source. This example uses the official database of UFO sightings.

Choose an aggregate measure to display, and give the chart a title.

You can use a Number chart for dramatic impact, or to monitor a specific, important aggregate value from a dataset. You can use it to add emphasis to an important value in another chart on the dashboard.

You can use multiple Number charts on the same dashboard to show several aggregate values at once.