Data Visualization

5月 212019

If you spend any time working with maps and spatial data, having a fundamental understanding of coordinate systems and map projections becomes necessary.  It’s the foundation of how spatial data and maps work.  These areas invariably evoke trepidation and some angst, even in the most seasoned map professional.  And rightfully so, it can get complicated quickly. Fortunately, most of those worries can be set aside when creating maps with SAS Visual Analytics, without requiring a degree in Geodesy.

Visual Analytics includes several different coordinate system definitions configured out-of-the-box.  Like the Predefined geography types (see Fundamental of SAS Visual Analytics geo maps), they are selected from a drop-down list during the geography variable setup.  With the details handled by VA, all you need to know is what coordinate space your data uses and select the appropriate one.

The four Coordinate spaces included with VA are:

  1. World Geodetic System (WGS84)
    Area of coverage: World.  Used by GPS navigation systems and NATO military geodetic surveying.  This is the VA default and should work in most situations.
  2. Web Mercator
    Area of coverage: World.  Format used by Google maps, OpenStreetMap, Bing maps and other web map providers.
  3. British National Grid (OSGB36)
    Area of coverage: United Kingdom – Great Britain, Isle of Man
  4. Singapore Transverse Mercator (SVY21)
    Area of coverage: Singapore onshore/offshore

But what if your data does not use one of these?  For those situations, VA also supports custom coordinate spaces.  With this option, you can specify the definition of your desired coordinate space using industry standard formats for EPSG codes or Proj4 strings.  Before we get into the details of how to use custom coordinate spaces in VA, let’s take a step back and review the basics of coordinate spaces and projections.


A coordinate space is simply a grid designed to cover a specific area of the Earth.  Some have global coverage (WGS84, the default in VA) and others cover relatively small areas (SVY21/Singapore Transverse Mercator).  Each coordinate space is defined by several parameters, including but not limited to:

  • Center coordinates (origin)
  • Coverage area (‘bounds’ or ‘extent’)
  • Unit of measurement (feet or meters)

Comparison of coordinate space definitions included in Visual Analytics -- Source:

The image above compares the four coordinate space definitions included with VA.  The two on the right, BNG and Singapore Transverse Mercator, have a limited extent.  A red rectangle outlines the area of coverage for each region.  The two on the left, WGS84 and Mercator, are both world maps.  At first glance, they may appear to have the same coverage area, but they are not interchangeable.  The origin for both is located at the intersection of the Equator and the Prime Meridian.  However, the similarities end there.  Notice the extent for WGS84 covers the entire latitude range, from -90 to +90.  Mercator on the other hand, covers from -85 to +85 latitude, so the first 5 degrees from each Pole are not included.  Another difference is the unit of measurement.  WGS84 is measured in un-projected degrees, which is indicative of a spherical Geographic Coordinate System (GCS).  Mercator uses meters, which implies a Projected Coordinate System (PCS) used for a flat surface, ie. a screen or paper.

The projection itself is a complex mathematical operation that transforms the spherical surface of the GCS into the flat surface of the PCS.  This transformation introduces distortion in one or more qualities of the map: shape, area, direction, or distance.  The process of map projection compares to peeling an orange. Removing the peel and placing it on a flat surface will cause parts of it to stretch, tear or separate as it flattens. The same thing happens to a map projection.

A flat map will always have some degree of distortion.  The amount of distortion depends on the projection used.  Select a projection that minimizes the distortion in the areas most important to the map.  For example, are you creating a navigation map where direction is critical?  How about a World map to compare land mass of various countries?  Or maybe a local map of Municipality services where all factors are equally important?  These decisions are important if you are collecting and creating your data set from the field.  But, if you are using existing data sets, chances are that decision has already been made for you.  It then becomes a task of understanding what coordinate system was selected and how to use it within VA.

Using a Custom Coordinate Space in VA

When using VA’s custom coordinate space option, it is critical the geography variable and the dataset use the same coordinate space.  This tells VA how to align the grid used by the data with the grid used by the underlying map.  If they align, the data will be placed at the expected location.  If they don’t align, the data will appear in the wrong location or may not be displayed at all.

Illustration of aligning the map and data grids

To illustrate the process of using a custom coordinate space in VA, we will be creating a custom region map of the Oklahoma City School Districts.  The data can be found on the Oklahoma City Open Data Portal.  We will use the Esri shapefile format.  As you may recall from a previous blog post, Creating custom region maps with SAS Visual Analytics, the first step is to import the Esri shapefile data into a SAS dataset.

Once the shapefile has been successfully imported into SAS, we then must determine the coordinate system of the data.  While WGS84 is common and will work in many situations, it should not be assumed.  The first place to look is at the source, the data provider.  Many Open Data portals will have the coordinate system listed along with the metadata and description of the dataset.  But when using an Esri shapefile, there is an easier way to find what we need.

Locate the directory where you unzipped the original shapefile.  Inside of that directory is a file with a .prj extension.  This file defines the projection and coordinate system used by the shapefile.  Below are the contents of our .prj file with the first parameter highlighted.  We are only interested in this value.  Here, you can see the data has been defined in the Oklahoma State Plane coordinate system -- not in VA’s default WGS84.  So, we must use a custom coordinate system when defining the geography variable.

PROJCS["NAD_1983_StatePlane_Oklahoma_North_FIPS_3501_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101004]],PRIMEM["Greenwich",0],UNIT["Degree",0.0174532925199433]], PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500],PARAMETER["False_Northing",0],PARAMETER["Central_Meridian",-98],PARAMETER["Standard_Parallel_1",35.5666666666667],PARAMETER["Standard_Parallel_2",36.7666666666667],PARAMETER["Scale_Factor",1],PARAMETER["Latitude_Of_Origin",35],UNIT["Foot_US",0.304800609601219]]

Next, we need to look up the Oklahoma State Plane coordinate system to find a definition VA understands.  From the main page of the website, type ‘Oklahoma State Plane’ into the search box. Four results are returned.  Compare the results with the string highlighted above.  You can see the third option is what we are looking for: NAD 1983 StatePlane Oklahoma North FIPS 3501 Feet.

Selecting the appropriate definition based on the .prj file contents

To get the definitions we need for VA, click the third link for the option NAD 1983 StatePlane Oklahoma North FIPS 3501 Feet.  Here you will see a grey box with a bulleted list of links.  Each of these links represent a definition for the Oklahoma StatePlane coordinate space.

Visual Analytics supports two of the listed formats, EPSG and Proj4.  EPSG stands for European Petroleum Survey Group, an organization that publishes a database of coordinate system and projection information.  The syntax of this format is epsg:<number> or esri:<number>, where <number> is a 4-6 digit for the desired coordinate system.  In our cases, the format we need is the title of the page:


The second format supported by VA is Proj4, the third link in the image above.  This format consists of a string of space-delimited name value pairs.  The Oklahoma StatePlane proj4 definition we are interested in is:

+proj=lcc +lat_1=35.56666666666667 +lat_2=36.76666666666667 +lat_0=35 +lon_0=-98 +x_0=600000.0000000001 +y_0=0 +ellps=GRS80 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs

Now we have identified the coordinate system used by our data set and looked up its definition, we are ready to configure VA to use it.

Using a Projected Coordinate System definition in VA

The following section assumes you are familiar with custom region maps and setting up a polygon provider.  If not, see my previous post on that process, Creating custom region maps with SAS Visual Analytics.  The first step in setting up a geography variable for a custom region map is to start with the polygon provider.  At the bottom of the ‘Edit Polygon Provider’ window, there is an ‘Advanced’ section that is collapsed by default.  Expand it to see the Coordinate Space option.  By default, it is populated with the value EPSG:4326, which is the EPSG code for WGS84.  Since our Oklahoma City School District code data does not use WGS84, we need to replace this value with the EPSG code that we looked up from (ESRI:102724).

Using the same Custom Coordinate definition for Polygon provider and geography variable

Next, we must make sure to configure the geography variable itself with the same coordinate space as the polygon provider.  On the ‘Edit Geography Item’ window, the Coordinate Space option is the last item.  Again, we must change this from the default WGS84 to ESRI:102724.  From the dropdown list, select the option ‘Custom’.  A new entry box appears where we can enter the custom coordinate space definition.  If configured correctly, you should see your map in the preview thumbnail and a 100% mapped indicator.

Congratulations!  The setup was successful.  Now, simply click OK and drag the geography variable to the canvas.  VA’s auto-map feature will recognize it and display the custom region map.

In this post, I showed how to identify the coordinate system of your Esri shapefile data, lookup its epsg and proj4 definitions, and configure VA to use it via the Custom Coordinate space option.  While the focus was on a custom region map, the technique also applies to Custom Coordinate maps, minus the polygon provider setup.  The support of custom coordinate spaces in VA allow the mapping of practically any spatial dataset, giving you a new level of power and flexibility in your mapping efforts.

Essentials of Map Coordinate Systems and Projections in Visual Analytics was published on SAS Users.

5月 062019

App security is at the top of mind for just about everybody – users, IT folks, business executives. Rightfully so. Mobile apps and the devices on which they reside tend to travel around, without any physical boundaries that encompass the traditional desktop computers.

In chatting with folks who are evaluating the SAS Visual Analytics app for their mobile devices, the conversation eventually winds up with a focus on security and the big question comes up:

How is this app secure?

Great question! Here’s a whirlwind tour of the security features that have been built into the SAS Visual Analytics app for Windows 10, Android, and iOS devices. The app is now a young kid and not a toddler anymore, it has been around for about six years. And during its growth journey, the app has been beefed up with rock-solid features to address security for Visual Analytics reports viewed from mobile devices.

Before we take a look at the security features in the app, here are a few things you should know:

    • The app is free.
    • No license is needed to use the app.
    • You can download it anytime from the app store, and try out the sample reports in the app.
    • If you already have SAS Visual Analytics deployed in your organization, you can connect to your server, add reports to the app, and start interacting with your reports from your smartphone or tablet. The Help available in the app walks you through these steps.

Now, let’s get back to security for Visual Analytics reports on mobile devices. Here are five things that make the Visual Analytics app robust and secure on mobile devices.

    1. Device Whitelisting: If you want to connect to your SAS Visual Analytics server from the app, your administrator will “whitelist” your mobile device. Your device is first registered as a valid device that can connect to the Visual Analytics server. The whitelist affects devices, not users. If you happen to lose your mobile device, your administrator can remove the device from the whitelist and prevent access to the reports and data. The option to “blacklist” devices is also available.
    2. Cached Reports: After you add Visual Analytics reports to your app, if you don’t want the report data to remain with the report in the app, your administrator can enable the cached report feature. Data is downloaded only when you open and view the report on your mobile device. When you close the report, that data is removed from the device. For enhanced security, thumbnail images for report tiles in your app will not display for cached reports.
    3. Passcode: To prevent anyone other than yourself from opening the Visual Analytics app, you can set a 4-digit passcode for the app. There are two kinds of passcodes: required and optional. A required passcode is mandated by the server – when you connect to the server, you will create a passcode. Then, whenever you open the app or view a report from that server, you must enter the passcode. An optional passcode, on the other hand, is a passcode that you choose to use to lock up the app – it is not required to access the server, it is needed only to open the app. In addition, there are several features for passcode use that solidify security and access to the app: time-out, lock-out and so forth. I’ll go over these features in an upcoming blog.
    4. SSL/HTTPS: If the Visual Analytics server is set up with SSL/HTTPS, the data viewed in the reports on your mobile device is encrypted.
    5. Offline: If you were offline for a specified number of days, you must sign into the server again. If you don’t, the app does not download reports, update reports, or open reports for viewing.

Cached Reports

One of the security features we just talked about was the cached report feature. Here’s how cached report thumbnails are displayed in the Visual Analytics app on Windows 10, without any images.

When you tap the thumbnail for the cached report, data is immediately downloaded and the report opens in the app for viewing and interaction:

When you close this cached report in the app, the data is removed from the device and the cached report thumbnail displays in the app without any images.

Thanks for joining me on this whirlwind security tour of the SAS Visual Analytics app. Now you know the many different security mechanisms that are in place to protect your organization’s data and reports accessed from the mobile app.

Five key security features in the SAS Visual Analytics app was published on SAS Users.

4月 112019

What's the impact of using data governance and analytics for the business side of education? It's an interesting question, and during a video interview, Dale Pietrzak, Ed.D., Director of Institutional Effectiveness and Accreditation (IEA) at the University of Idaho shared details on the results they're realizing from using SAS for data [...]

The impact of data governance and analytics: An interview with the U. of Idaho was published on SAS Voices by Georgia Mariani

4月 082019
The catch phrase “everything happens somewhere” is increasingly common these days.  That “somewhere” translates into a location on the Earth; a latitude and longitude.  When one of these “somewhere’s” is combined with many other “somewhere’s”, you quickly have a robust spatial data set that becomes actionable with the right analytic tools.

Opportunities for Spatial Analytics are increasing

In today’s modern world, GPS-enabled devices are ubiquitous, and their use continues to increase daily.  Cell phones, cars, fitness trackers, and cameras are all able to locate and track our position.  As a result, the location analytics market is expected to grow to over USD 16 Billion by 2021, up 17.6% from 2016 [1].

Waldo Tobler, an American-Swiss geographer and cartographer, developed his First Law of Geography based on this concept of everything happening somewhere.  He stated, “Everything is related to everything else, but near things are more related than distant things”[2].  As analytic professionals, we are accustomed to working with these correlations using scatterplots, heatmaps, or clustering models.  But what happens when we add a geographic map into the analysis?

Maps offer the ability to unlock a new level of insight into our data that traditional graphs do not offer: personal connection.  As humans, we naturally relate to our surroundings on a spatial level.   It helps build our perspective and frame of reference through which we view and navigate the world.  We feel a sense of loss when a physical landmark from our childhood – a building, tree, park, or route we used to walk to school – is destroyed or changed from the memories we have of it.  In this sense, we are connected, spatially and emotionally, to our surroundings.

We inherently understand how data relates to the world around us, at some level, just by viewing it on a map.  Whether it is a body of water or a mountain affecting a driving route or maybe a trendy area of a city causing housing prices to increase faster than the local average, a map connects us with these facts intuitively.  We come to these basic conclusions based solely on our experiences in the world and knowledge of the physical landmarks in the map.

One of the best examples of this is the 1854 Cholera outbreak in London.  Dr. John Snow was one of the first to use a map for understanding the origin of an epidemiological outbreak.  He created a map of the affected London neighborhood by plotting the location of all known Cholera deaths.  In addition to the deaths, he also plotted the location of 13 community wells that served as the public water supply.  Using this data, he was able to see a clustering of deaths around a single pump.  Armed with this information, Dr. Snow was able to convince local officials to remove the handle from the Broad Street pump.  Once removed, new cases of Cholera quickly began to diminish.  This helped prove his theory the outbreak’s origin was not air-borne as commonly believed during that time, but rather of a water-borne origin. [3]

1854 London Cholera deaths: Tabular data vs. Coordinate map [3]

Let’s look at how Dr. Snow’s map helped mitigate the outbreak and prove his theory.  The image above compares the data of the recorded deaths and community wells in tabular form to a Coordinate map.  It is obvious from the coordinate map that there is a clustering of points.  Town officials and those familiar with the neighborhood could easily get a sense of where the outbreak was concentrated.  The map told a better story by connecting their personal experience of the area to the locations of the deaths and ultimately to the wells.  Something a data table or traditional graph could not do.

Maps of London Cholera deaths with modern analytic overlays [3]

Today, with the computing power and modern analytic methods available to us, we can take the analysis even further.  The examples above show the same coordinate map with added Voronoi polygon and cluster analysis overlays.  The concentration around the Broad Street pump becomes even clearer, showing why Geographic Maps are an important tool to have in your analytic toolbox.

SAS Global Forum 2019 is being held April 28-May 1, 2019 in Dallas, Texas.  If you are planning to go to this year’s event, be sure to attend one of our presentations on the latest mapping features included in SAS Visual Analytics and BASE SAS.  While you’re there, don’t forget to stop by the SAS Mapping booth located in the QUAD to say ‘Hi!’ and let us help with your spatial data needs.  See you in Dallas!

Introduction to Esri Integration in SAS Visual Analytics

  • Monday, April 29, 4:30-5:30p, Room: Level 1, D162

There’s a Map for That! What’s New and Coming Soon in SAS Mapping Technologies

  • Tuesday April 30, 4:00-4:30p, Room: Level 1, D162

Creating Great Maps in ODS Graphics Using the SGMAP Procedure

  • Wednesday May 01, 11:30a-12:30p, Room: Level 1, D162




How the 1854 Cholera outbreak showed us the importance of spatial analysis was published on SAS Users.

3月 272019

SAS Visual Analytics supports region maps for Country, US states, and provinces out-of-the-box.  These work well for small scale maps covering the world, a continent, or a single country.  However, other regions are often needed.  Beginning in version 8.3, VA supports custom polygons to display regions such as sales territories, counties, or zip codes.

Region (choropleth) maps use a fill color to show relationships between the regions based upon a response value from your data.  Using custom polygons in VA follows the same steps outlined in previous posts for predefined or custom coordinate geography items, with just a few additional steps.  Here’s the basic flow:

  • Identify your data
  • Import polygon shapefile into SAS dataset
  • Import the shape dataset into VA
  • Create a Custom polygon provider
  • Create the geography item
  • Create and customize the map

Before we begin

VA supports two sources for creating custom polygons: Esri shapefiles and Esri Feature Services.  The goal for this post is to show how to create custom polygons using an Esri shapefile.

Typically, when working with custom polygons, you will have two datasets: the first defines the custom regions (shape data) and the second contains the data you wish to map (business data).  The shape data is derived from an Esri shapefile or feature service.  The business data can be in a shapefile or any format supported by VA (.sas7bdat, .csv, .xls, etc). It contains the information you want to analyze distributed across the regions defined by the shape data.

It is recommended that you verify the imported shape data before using it in your final map.  This will confirm the data is valid and make debugging an issue easier should you encounter any errors.  To verify, use the same dataset for both the shape and business data.  The example below will use this approach.

Access to a GIS application such as Esri’s ArcGIS or QGIS is recommended.  There are two areas where they can help you prepare to use custom polygons in your VA map:

  • Creating a shapefile to define polygons specific to your business need or application
  • Viewing the attribute table of existing shapefiles to determine its unique identifier column

For this example, we will be creating a map of registered Neighborhood Associations in Boise, Idaho. To follow along, download the data from the City of Boise open data site: Boise Neighborhood Associations

1. Identify your data

Shape data

The shape data defining the custom regions needs to be in an Esri shapefile format. These files can be created in a GIS application or obtained from a wide variety of online sources such as: the US Census Bureau (; local and state municipalities; state agencies such as the Department of Transportation; and university GIS departments.  Most municipalities now have Open Data portals that provide a wealth of reliable data for public use.  These sources are maintained by dedicated staff and are updated regularly.

Business data

The business data can be specific to your company’s operation or customer base.  Or it can be broad and general using census or demographic information.  It answers the question of What you want to analyze on the map.  The business data must contain a column that aligns with your shape data.  For example: If you want to map the age distribution and spending habits of your target customers across zip codes, then your business data must have a column for zip codes that allows it to be joined to a zip code region in the shape data.

2. Import polygon data into a SAS dataset

VA 8.3 does not support the native shapefile format. To use a shapefile in VA, you must first import it into SAS.  Included with Viya3.4, the %shpimprt macro will convert a shapefile into a SAS dataset and load it into CAS.  You can find the documentation for it here: %shpimprt documentation.

Alternatively, the shapefile can be manually imported with these basic steps:

  • Import the shapefile into SAS
  • Add a sequence column to the dataset
  • Reduce the density of the dataset
  • Limit the dataset based on the density value

Additional details and sample code for each of these steps can be found in the text file linked here: Manual shapefile import steps.

3. Import the shape dataset into VA

Next, we must import the dataset into VA, if using the manual shapefile import process.  To do this, locate the data pane on the left of VA.  From the ‘Open Data Source’ window, select Import > Local File.  Navigate to the location of the SAS dataset created from Step 2 and click the Open button.

Adjust the target location as needed, based on your VA installation, and make note of the location selected.  This path will be required to configure the custom polygon provider. Review and adjust the other options as needed.  Click the blue ‘Import Item’ button at the top of the window to start the import process.  A message will appear indicating the import status. Upon successful import, click the 'OK' button to open the dataset.

Since we are using the same dataset for the shape and business data, we need to make a copy of the category variable that will be used for our map. Right click on ‘ASSOCIATIO’ and select ‘Duplicate’.  Next, let’s change the names of both variables to better distinguish them from one another:

  • Change ‘ASSOCIATIO’ to ‘Business data’
  • Change ‘ASSOCIATIO (1)’ to ‘Shape data’

4. Create the geography item

We are now ready to start creating the geography item.  With Custom polygons, an additional step is required beyond what was described in previous posts with predefined and custom coordinates geography items.  We must define a Custom Polygon provider so VA knows how to locate and display the Boise Neighborhood Associations.  This is needed only once and is part of the geography item setup you are familiar with.

Our goal is to map the regions of the Boise Neighborhood Associations, so we will use ‘Shape data’ for our geography item.  Locate it in the VA data panel and change its Classification type to ‘Geography’.  From the ‘Geography data type’ dropdown, select ‘Custom polygonal shapes’. Several new fields will be displayed.  In the ‘Custom polygon provider’ dropdown, click the ‘Define new polygon provider’ button.

A ‘New Polygon Provider’ window will appear.  All fields shown are required.  The Advanced section has additional options, but they are not needed for this example.

Configure the fields based on the following:

  • Name / Label – Enter ‘Boise Neighborhoods’ for both (these values do not have to be the same)
  • Type – The default CAS Table is the correct option for this example.
  • Server / Library – These values must match those used for the data upload in Step 3.
  • Table – Select the name of the table uploaded in Step 3 (Boise_Neighborhoods)
  • ID Column – The unique identifier column of the dataset. Used to join the shape and business data together. (Select OBJECTID)
  • Sequence Column – This column is created during the import process from Step 2. Needed by VA to display the custom regions. (Select SEQUENCE)

The custom polygon provider is now configured.  All that is needed to finish the geography item setup, is to identify the Region ID.  This is the crucial step that will join the shape data to the business data.  The Region ID column must match the ID Column chosen when the custom polygon provider was setup.  Since we are using the same dataset in this example, that value is the same: OBJECTID.

In cases where different datasets are used for the shape and business data, the name of Region ID and ID Column may be different.  The column labels are not important, but their content must match for the join to occur.

Notice that once you select the correct RegionID value, the preview window will display the custom regions from the imported shape data.  The Latitude and Longitude columns are not required in this example.  Click the ‘OK’ button, to finish the setup.

5. Create and customize the map

You are now ready to create your map.  Drag the Boise Neighborhoods geography item to the report canvas.  Let’s enhance the appearance of our map by making a few style changes:

  • Set a Color role to shade the Neighborhood Association regions (Roles > Color > Business data)
  • Position the legend on the left of the map (Options > Legend)
  • Adjust the transparency of the fill color to 45% (Options > Map Transparency)
  • Change the map service to Esri World Street Map (Options > Map service)

Final map with custom polygons.

Congratulations!  You have just created your first custom region map.  In this post we discussed how to use the Custom Polygon provider to define your own regions using an Esri shapefile.  Compared to the Predefined and Custom Coordinate options, custom polygons give you additional flexibility and control over how your spatial data is analyzed.

Creating custom region maps with SAS Visual Analytics was published on SAS Users.

3月 152019
SAS makes it easy for you to create a large amount of procedure output with very few statements. However, when you create a large amount of procedure output with the Output Delivery System (ODS), your SAS session might stop responding or run slowly. In some cases, SAS generates a “Not Responding” message. Beginning with SAS® 9.3, the SAS windowing environment creates HTML output by default and enables ODS Graphics by default. If your code creates a large amount of either HTML output or ODS Graphics output, you can experience performance issues in SAS. This blog article discusses how to work around this issue.

Option 1: Enable the Output window instead of the Results Viewer window

By default, the SAS windowing environment with SAS 9.3 and SAS® 9.4 creates procedure output in HTML format and displays that HTML output in the Results Viewer window. However, when a large amount of HTML output is displayed in the Results Viewer window, performance might suffer. To display HTML output in the Results Viewer window, SAS uses an embedded version of Internet Explorer within the SAS environment. And because Internet Explorer does not process large amounts of HTML output well, it can slow down your results.

If you do not need to create HTML output, you can display procedure output in the Output window instead. To do so, add the following statements to the top of your code before the procedure step:

   ods _all_ close; 
   ods listing;

The Output window can show results faster than HTML output that is displayed in the Results Viewer window.

If you want to enable the Output window via the SAS windowing environment, take these steps:

    1. Choose Tools ► Options ► Preferences.
    2. Click the Results tab.
    3. In this window, select Create listing and clear the Create HTML check box.
    4. Click OK.

A large amount of output in the Output window, which typically does not cause a performance issue, might still generate an “Output window is full” message. In that case, you can route your LISTING output to a disk file. Use either the PRINTTO procedure or the ODS LISTING statement with the FILE= option. Here is an example:

   ods _all_ close; 
   ods listing file="sasoutput.lst"; 

Option 2: Disable ODS Graphics

Beginning with SAS 9.3, the SAS windowing environment enables ODS Graphics by default. Therefore, most SAS/STAT® procedures now create graphics output automatically. Naturally, graphics output can take longer to create than regular text output. If you are running a SAS/STAT procedure but you do not need to create graphics output, add the following statement to the code before the procedure step:

   ods graphics off; 

If you want to set this option via the SAS windowing environment, take these steps:

    1. Choose Tools ► Options ► Preferences.
    2. Click the Results tab.
    3. In this window, clear the Use ODS Graphics check box.
    4. Click OK.

For maximum efficiency, you can combine the ODS GRAPHICS OFF statement with the statements listed in the previous section, as shown here:

   ods _all_ close;
   ods listing;
   ods graphics off; 

Option 3: Write ODS output to disk

You can ask SAS to write ODS output to disk but not to create output in the Results Viewer window. To do so, add the following statement to your code before your procedure step:

   ods results off;

Later in your SAS session, if you decide that you want to see output in the Results Viewer window, submit this statement:

   ods results on;

If you want to disable the Results Viewer window via the SAS windowing environment, take these steps:

    1. Choose Tools ► Options ► Preferences.
    2. Click the Results tab.
    3. In this window, clear the View results as they are generated check box.
    4. Click OK.

The ODS RESULTS OFF statement is a valuable debugging tool because it enables you to write ODS output to disk without viewing it in the Results Viewer window. You can then inspect the ODS output file on disk to check the size of it (before you open it).

Option 4: Suppress specific procedure output from the ODS results

In certain situations, you might use multiple procedure steps to send output to ODS. However, if you want to exclude certain procedure output from being written to ODS, use the following statement:

   ods exclude all;

Ensure that you place the statement right before the procedure step that contains the output that you want to suppress.

If necessary, use the following statement when you want to resume sending subsequent procedure output to ODS:

   ods exclude none;

Five reasons to use ODS EXCLUDE to suppress SAS output discusses the ODS EXCLUDE statement in more detail.


Certain web browsers display large HTML files better than others. When you use SAS to create large HTML files, you might try using a web browser such as Chrome, Firefox, or Edge instead of Internet Explorer. However, even browsers such as Chrome, Firefox, and Edge might run slowly when processing a very large HTML file.

Instead, as a substitute for HTML, you might consider creating PDF output (with the ODS PDF destination) or RTF output (with the ODS RTF destination). However, if you end up creating a very large PDF or RTF file, then Adobe (for PDF output) and Microsoft Word (for RTF output) might also experience performance issues.

The information in this blog mainly pertains to the SAS windowing environment. For information about how to resolve ODS issues in SAS® Enterprise Guide®, refer to Take control of ODS results in SAS Enterprise Guide.

How to view or create ODS output without causing SAS® to stop responding or run slowly was published on SAS Users.

2月 282019

Across organizations of all types, massive amounts of information are stored in unstructured formats such as video, images, audio, and of course, text. Let’s talk more about text and natural language processing. We know that there is tremendous value buried in call center and chat dialogues, survey comments, product reviews, technical notes, legal contracts, and other sources where context is captured in words versus numbers. But how can we extract the signal we want amidst all the noise?

In this post, we will examine this problem using publicly available descriptions of side effects or adverse events that patients have reported following a vaccination. This Vaccine Adverse Event Reporting System (VAERS) is managed by the CDC and FDA. Among other objectives, these agencies use it to:

* Monitor increases in known adverse events and detect new or unusual vaccine adverse events

* Identify potential patient risk factors, including temporal, demographic, or geographic reporting clusters

Below is a view of the raw data. It contains a text field which holds freeform case notes, along with structured fields which contain the patient’s location, age, sex, date, vaccination details, and flags for serious outcomes such as hospitalization or death.

In this dashboard, notice how we easily can do a search for a keyword “seizure” to filter to patients who have reported this symptom in the comments. However, analysts need much more than just Search. They need to be able to not only investigate all the symptoms an individual patient is experiencing, but also see what patterns are emerging in aggregate so they can detect systemic safety or process issues. To do this, we need to harvest the insights from the freeform text field, and for that we’ll use SAS Visual Text Analytics.

In this solution, we can do many types of text analysis – which you choose depends on the nature of the data and your goals. When we load the data into the solution, it first displays all the variables in the table and detects their types. We could profile the structured fields further to see summary statistics and determine if any data cleansing is appropriate, but for now let’s just build a quick text model for the SYMPTOM_TEXT variable.

After assigning this variable to the “Text” role, SAS Visual Text Analytics automatically builds a pipeline which we can use to string together analytic tasks. In this default pipeline, first we parse the data and identify key entities, and then the solution assigns a sentiment label to each document, discovers topics (i.e. themes) of interest, and categorizes the collection in a meaningful way. Each of these nodes is interactive.

In this post, we’ll show just a tiny piece of overall functionality – how to automatically extract custom entities and relationships using a combination of machine learning and linguistic rules. In the Concepts node, we provide several standard entities to use out of the box. For example, here are the automatic matches to the pre-defined “DATE” concept:

However, for this data, we’re interested in extracting something different – patient symptoms, and where on the body they occurred. Since neither open source Named Entity Recognition (NER) models nor SAS Pre-defined Concepts will do something as domain-specific as this out of the box, it’s up to us to define what we mean by a symptom or a body part under Custom Concepts.

For Body Parts, we started with a list of expected parts from medical dictionaries and subject matter experts. As I iterate through and inspect the results, I might see a keyword or phrase that I missed. In the upcoming version of SAS Visual Text Analytics, I will be able to simply highlight it and right click to add it to the rule set.

We also will be adding a powerful new feature that applies machine learning to suggest additional rules for us. Note that this isn’t a simple thesaurus lookup! Instead, an algorithm is using the matches you’ve already told it are good, combined with the data itself, to learn the pattern you’re interested in. The suggested rules are placed in a new Sandbox area where you can test and evaluate them before adding them to your final definition.

We will also be able to auto-generate fact rules. This will help us pull out meaningful relationships between two entities and suggest a generalized pattern for modeling it. Here, we’ll have the machine determine the best relationship between Body Parts and Localized Symptoms, so that we can answer questions like, “where does it hurt?”, or “what body part was red (or itchy or swollen or tingly, etc.)?”. For this data, the tool suggested a rule which looks for a body part within 6 terms of a symptom, regardless of order, so long as both are contained in the same sentence.

Let’s apply just these few simple rules to our entire dataset and go back to the dashboard view. If we look at the results, we can see now much richer potential for finding insights the data. I can easily select a single patient and see an entire list of his/her side effects alongside key details about the vaccination. I can also compare the most commonly reported symptoms by age group, gender, or geography, or which body parts and symptoms may be predictors of a severe outcome like hospitalization or death.

Of course, there is much more we could do with this data. We could extract the name of the vaccine that was administered, the time to symptom onset, duration period of the symptoms, and other important information. However, even this simple example illustrates the technique and power of contextual extraction, and how it can enhance our ability to analyze large collections of complex data. Currently, concept rule generation is on the forefront of our research efforts in its experimental first stages. This, along with the sandbox testing environment, will make it even faster and easier for analysts to do this work in SAS Visual Text Analytics. Here are a few other resources to check out if you want to dig in further.

Article: Reduce the cost-barrier of generating labeled text data for machine learning algorithms

Paper: Analyzing Text In-Stream and at the Edge

Automatically extracting key information from textual data was published on SAS Users.

2月 272019

In this post, we continue our discussion of geography variables, the foundation of Visual Analytics Geo maps. This time we will look at Custom Coordinates.  As with any statistical graph, understanding your data is key.  But when using Custom Coordinates for geographic maps, this understanding becomes even more important.

Use the Custom Coordinate geography variable when your data does not match one of VA’s predefined geography types (see previous post, Fundamentals of SAS Visual Analytics geo maps).  For Custom coordinates, your data set must include latitude and longitude values as separate variables.   These values should be sourced from trustworthy providers and validated for accuracy prior to loading into VA.

When using Custom Coordinates, the Coordinate Space must also be considered.  The coordinate space defines the grid used to plot your data.  The underlying map is also based on a grid.  In order for your data to display correctly on a map, these grids must match.  Visual Analytics uses the World Geodetic System (WGS84) as the default coordinate space (grid).  This will work for most scenarios, including the example below.

Once you have selected a dataset and confirmed it contains the required spatial information, you can now create a Custom Geography variable.  In this example, I am using the variable Business Address from the dataset Wake_Co_Pizza.  Let’s get started.

  1. Begin by opening VA and navigate to the Data panel on the left of the application.
  2. Select the dataset and locate the variable that you wish to map. Click the down arrow to the right of the variable and chose ‘Geography’ from the Classification dropdown menu.
  3. The ‘Edit Geography Item’ window appears. Select Custom coordinates in the ‘Geography data type’ dropdown.   Three new dropdown lists appear that are specific to the Custom coordinates data type: ‘Latitude (y)’, ‘Longitude (x)’ and ‘Coordinate Space’.

When using the Custom coordinates data type, we must tell VA where to find the spatial data in our dataset.  We do this using the Latitude (y) and Longitude (x) dropdown lists.  They contain all measures from your dataset.  In this example, the variable ‘Latitude World Geodetic System’ contains our latitude values and the variable  ‘Longitude World Geodetic System’ contains our longitude values.   The ‘Coordinate Space’ dropdown defaults to World Geodetic System (WGS84) and is the correct choice for this example.

  1. Click the OK button to complete the setup once the latitude and longitude variables have been selected from their respective dropdown lists. You should see a new ‘Geography’ section in the Data panel.  The name of the variable (or its edited value) will be displayed beside a globe icon to indicate it is a geography variable.  In this case we see the variable Business Address.


Congratulations!  You have now created a custom geography variable and are ready to display it on a map.  To do this, simply drag it from the Data panel and drop it on the report canvas.  The auto-map feature of VA will recognize it as a geography variable and display the data as a bubble map with an OpenStreetMap background.

In this post, we created a custom geography variable using the default Coordinate Space.  Using a custom geography variable gives you the flexibility of mapping data sets that contain valid latitude and longitude values.  Next time, we will take our exploration of the geography variable one step further and explore using custom polygons in your maps.

Using Custom Coordinates for map creation in SAS Visual Analytics was published on SAS Users.

2月 152019
Beginning with SAS® 9.4, you can embed graphics output within HTML output using the ODS HTML5 destination. This technique works with SAS/GRAPH® procedures (such as GPLOT and GCHART), SG procedures (such as SGPLOT and SGRENDER), and when you create graphics output with ODS Graphics enabled. Most (if not all) existing web browsers support graphics output embedded in HTML5 output.

Note: The default graphics output format for the ODS HTML5 destination is Scalable Vector Graphics (SVG). SVG documents display clearly at any size in any viewer or browser that supports SVG. So, SVG files are ideal for display on a computer monitor, PDA, or cell phone; or printed documents. Because it's a vector graphic, a single SVG document can be transformed to any screen resolution without compromising the clarity of the document. Here's an example:

The same SVG graph, scaled at 90% and then at 200%. But 100% crisp!

SAS/GRAPH procedures

When you use the ODS HTML5 destination with a SAS/GRAPH procedure, specify a value of SVG, PNG, or JPEG for the DEVICE option in the GOPTIONS statement. The following sample PROC GPLOT code embeds SVG graphics inside the resulting HTML output:

goptions device=svg;
ods _all_ close;  
ods html5 path="c:\temp" file="svg_graph.html"; 
symbol1 i=none v=squarefilled; 
proc gplot; 
  plot mpg_city * horsepower;   
  where make="Honda"; 
ods html5 close; 
ods preferences;

Note that the ODS PREFERENCES statement above resets the ODS environment back to its default settings when you use the SAS windowing environment.

When you use the PNG or JPEG device driver with the ODS HTML5 destination, add the BITMAP_MODE="INLINE" option to the ODS HTML5 statement. Here is an example:

goptions device=png;
ods _all_ close; 
ods html5 path="c:\temp" file="png_graph.html"     options(bitmap_mode="inline");
symbol1 i=none v=squarefilled; 
proc gplot; 
  plot mpg_city * horsepower;   
  where make="Honda"; 
ods html5 close; 
ods preferences;

ODS Graphics and SG procedures

When you use SG procedures and ODS Graphics, specify a value of SVG, PNG, or JPEG for the OUTPUTFMT option in the ODS GRAPHICS statement. The following sample code uses PROC SGPLOT to embed SVG graphics inside the HTML output with the ODS HTML5 destination:

ods _all_ close; 
ods html5 path="c:\temp" file="svg_graph.html"; 
ods graphics on / reset=all outputfmt=svg;
proc sgplot; 
  scatter y=mpg_city x=horsepower / markerattrs=(size=9PT symbol=squarefilled);   
  where make="Honda"; 
ods html5 close; 
ods preferences;  

The following sample code uses PROC SGPLOT to embed PNG graphics inside the HTML output with the ODS HTML5 destination:

ods _all_ close; 
ods html5 path="c:\temp" file="png_graph.html" options(bitmap_mode="inline");   
      ods graphics on / reset=all outputfmt=png;
proc sgplot; 
  scatter y=mpg_city x=horsepower / markerattrs=(size=9PT symbol=squarefilled);   
  where make="Honda"; 
      ods html5 close; 
      ods preferences; 

The technique above also works when you use the ODS GRAPHICS ON statement with other procedures that produce graphics output (such as the LIFETEST procedure).

Note that the ODS HTML5 destination supports the SAS Graphics Accelerator. The SAS Graphics Accelerator enables users with visual impairments or blindness to create, explore, and share data visualizations. It supports alternative presentations of data visualizations that include enhanced visual rendering, text descriptions, tabular data, and interactive sonification. Sonification uses non-speech audio to convey important information about the graph.

You can use the ODS HTML5 destination in most situations where you need to embed all of your output into a single HTML output location. For example, when you email HTML output as an attachment or when you create graphics output via a SAS stored process. If you currently use the ODS HTML destination, you might want to experiment with the ODS HTML5 destination to see whether it meets your needs even if you cannot completely switch to it yet.

Embed scalable graphics using the ODS HTML5 destination was published on SAS Users.

2月 082019

Creating a map with SAS Visual Analytics begins with the geographic variable.  The geographic variable is a special type of data variable where each item has a latitude and longitude value.  For maximum flexibility, VA supports three types of geography variables:

  1. Predefined
  2. Custom coordinates
  3. Custom polygons

This is the first in a series of posts that will discuss each type of geography variable and their creation. The predefined geography variable is the easiest and quickest way to begin and will be the focus of this post.

SAS Visual Analytics comes with nine (9) predefined geographic lookup types.  This lookup method requires that your data contains a variable matching one of these nine data types:

  • Country or Region Names – Full proper name of a country or region (ISO 3166-1)
  • Country or Region ISO 2-Letter Codes – Alpha-2 country code (ISO 3166-1)
  • Country or Region ISO Numeric Codes – Numeric-3 country code (ISO 3166-1)
  • Country or Region SAS Map ID Values – SAS ID values from MPASGFK continent data sets
  • Subdivision (State, Province) Names – Full proper name for level 2 admin regions (ISO 3166-2)
  • Subdivision (State, Province) SAS Map ID Values – SAS ID values from MAPSGFK continent data sets (Level 1)
  • US State Names – Full proper name for US State
  • US State Abbreviations – Two letter US State abbreviation
  • US Zip Codes – A 5-digit US zip code (no regions)

Once you have identified a variable in your dataset matching one of these types, you are ready to begin.  For our example map, the dataset 'Crime' and variable 'State name' will be used.  Let’s get started.

Creating a predefined geography variable in SAS Visual Analytics

  1. Begin by opening VA and navigate to the Data panel on the left of the application.
  2. Select the desired dataset and locate a variable that matches one of the predefined lookup types discussed above. Click the down arrow to the right of the variable and select ‘Geography’ from the Classification dropdown menu.
  3. The ‘Edit Geography Item’ window will open. Depending upon the type of geography variable selected, some of the options on this dialog will vary.  The 'Name' textbox is common for all types and will contain the variable selected from your dataset.  Edit this label as needed to make it more user friendly for your intended audience.
  4. The ‘Geography data type’ drop down list is where you select the desired type of geography variable.  In this example, we are using the default predefined option.
  5. Locate the 'Name or code context' dropdown list.  Select the type of predefined variable that matches the data type of the variable chosen from your data.  Once selected, VA scans your data and does an internal lookup on each data item.  This process identifies latitude and longitude values for each item of your dataset.  Lookup results are shown on the right of the window as a percentage and a thumbnail size map.  The thumbnail map displays the the first 100 matches.
  6. If there are any unmatched data items, the first 5 will be displayed.  This may provide a better understanding of your data.  In this example, it is clear from variable name as to what type should be selected (US State Names).  However, in most cases that choice will not be this obvious.  The lesson here, know your data!

Unmatched data items indicators

Once you are satisfied with the matched results, click the OK button to continue.  You should see a new section in the Data panel labeled ‘Geography’.  The name of the variable will be displayed beside a globe icon. This icon represents the geography variable and provides confirmation it was created successfully.

Icon change for geography variable

Now that the geography variable has been created, we are ready to create a map.  To do this, simply drag it from the Data panel and drop it on the VA report canvas.  The auto-map feature of VA will recognize the geography variable and create a bubble map with an OpenStreetMap background.  Congratulations!  You have just created your first map in VA.

Bubble map created with predefined geography variable

The concept of a geography variable was introduced in this post as the foundation for creating all maps in VA.  Using the predefined geography variable is the quickest way to get started with Geo maps.  In situations when the predefined type is not possible, using one of VA's custom geography types becomes necessary.  These scenarios will be discussed in future blog posts.

Fundamentals of SAS Visual Analytics geo maps was published on SAS Users.