One of the great features of SAS Viya jobs is the ability to define user input forms to let end users without programming experience run programs and customize reports. JSON-based step prompt forms make it even easier for non-programmers to create user interfaces for jobs, as it is possible to use to the custom step Designer within SAS Studio to create interactive forms with no knowledge of HTML, JSON, or XML code required.   However, JSON-based step prompt forms can currently only be used in SAS Visual Analytics and SAS for Microsoft 365, meaning that you can’t easily share a job submit URL with end users to let them run the job. In this article, I’ll discuss how to incorporate a JSON-based step prompt form in a SAS Visual Analytics report to allow end users to access job results.   Start with a Working Program   Like all job development, it’s a good idea to begin with a program that you know works. In this post, I’ll use a simple program that allows a user to select a make of car from SASHELP.CARS and generate a report of the five most expense cars and a graph of the MSRP by car type. The code looks like this:   %let selectMake=Honda; proc sql; create table work.cars as select Make, Model, Type, MSRP from sashelp.cars where Make = "&selectMake" order by MSRP desc; quit; title "Top Five Most Expensive: &selectMake"; proc print data=work.cars(obs=5); run; title; title "MSRP by Type: &selectMake"; proc sgplot data=work.cars; vbar type / response=MSRP stat=mean; run; title;   To turn this code into a job definition, I only need to swap out the %LET statement for a %GLOBAL statement, creating the macro variable selectMake.   %global selectMake; proc sql; …   Create a Step Prompt Form   As of SAS Viya 2023.06, it is possible to define prompt forms using JSON syntax. Unlike HTML and XML forms which require you to write the code yourself, JSON- based forms can be created using the point-and-click custom step designer. All I need to do is create a new custom step:     Then I can select the controls I want from the control library on the left side and arrange them in the canvas.     In this case, I want to create a drop-down list that automatically populates with the values of the Make columns from SASHELP.CARS. I’ve created three prompts:   An input table selector to specify the SASHELP.CARS input table. I’ve made this read-only and hidden, so the end user will not see it. A column selector to specify the Make column from SASHELP.CARS. I’ve also made this read-only and hidden, so the end user won’t see it. The input table selector and column selector will be used to dynamically populate the drop-down list. A drop-down list that dynamically populates with the Make values from SASHELP.CARS. I also need to make sure that the ID value associated with the drop-down selector matches the macro variable used in the job definition.   Behind the scenes, this tool generates JSON code that defines the interface I’ve created. The Prompt UI tab contains JSON syntax:     To use this interface in my job, all I need to do is copy it and paste it in the Step Prompt (JSON) tab in the job definition.     However, I won’t be able to preview this job from within SAS Studio. To test it and to share it with end users, I’ll need to use SAS Visual Analytics.   Add a Job to a SAS Visual Analytics Report   There are a variety of ways that you can incorporate jobs in a SAS Visual Analytics report. For example, both jobs with HTML and XML-based forms can be added using either the web content object or the data-driven content object. However, to add a job a that uses a JSON-based prompt, you must use the job content object.     The job content object allows you to select a job definition to use in the report. Once you select a job definition, the input form associated with it will be displayed:     I can use this form to select the parameters I want to use to run the job, and then click OK to run it. The results of the job will be displayed directly in the object:     To re-submit the job using different parameters, you can click on the Object menu and select Set job parameters. You can also view the log of the submitted job.     Share the Job with End Users   One common use of jobs is to allow end users who are not programmers to control parameters, run programs, and create reports without having to modify the underlying code. Once the job content object has been added to a Visual Analytics report, the report can be shared with end users, and anyone with access to the report can set the parameters, run the job, and generate the reports that need, without having to touch a single line of code.   To share a SAS Visual Analytics report, you can use the Share report option to send it to a specified user or group, or you can use the Copy link option to generate a link to share.     Conclusion   SAS Viya jobs provide a great way to automate code execution and to simplify reporting for end users who are not programmers. By creating JSON-based step prompts, developers don’t have to worry about XML or HTML syntax, and they can still easily distribute the job to end users by using the job content object in SAS Visual Analytics. Stay tuned for a future article, where I’ll dive deeper into some of the other ways to integrate jobs and SAS Visual Analytics!   Tips   Make sure that job definitions are stored in a location that report viewers have access to. You can convert an XML-based task prompt to a JSON-based step prompt. However, be aware that the conversion may not always result in one-to-one equivalence. You need to save a report before you can share it with others. You can hide controls from the user by setting the visible value to false in the JSON form definition. You can also make controls read-only by setting the readonly value to true.   Other Resources                                Create a SAS Viya Job with a prompt using HTML Prompts Create a SAS Viya Job with a prompt using Task Prompts     Find more articles from SAS Global Enablement and Learning here. ... View more

If you work with geospatial data, then you know that are a wide variety of data sources available: shapefiles from the US Census Bureau, built-in map data sets included with SAS, and any number of options available from third-party providers. However, you may often find that to create the exact visualization you want or to perform the specific type of analysis that you want, available spatial data sources don’t quite match your needs. In this article, I explain how to dissolve internal boundaries in polygon data to construct the mapping data sets that perfectly meet your needs.     Overview   Imagine that you have a map data set of US states, but your data is at the region level. For example, you might want to map a variable like population at the level of regions, but all you have is a map data set of states. To make this work, you’d need to remove the boundaries between states and replace them with region boundaries.    Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.     Using the GREMOVE Procedure   In SAS, you can do this with the GREMOVE procedure. The GREMOVE procedure aggregates individual units in a map data set to combine them, removing internal boundaries and creating a larger unit. It does this based on a common ID value. For example, in the image below, there are four square polygons. Three of them are gray, and the fourth is blue.  By using the color as an ID value, I could dissolve the boundaries between the three gray polygons, creating a resulting map data set with just two polygons: a large gray L-shape, and a blue square.       PROC GREMOVE Syntax   The GREMOVE procedure looks like this:   PROC GREMOVE DATA=input-map-data-set <OUT=output-map-data-set>; BY variable(s); ID id-variables(s); RUN;   The DATA= option specifies the input data set, which must have X and Y variables that contain the latitude and the longitude. The OUT= option specifies the new map data set that will be created. You’ll also need to identify both the current polygon units and the desired polygon units. The ID statement specifies the variable identifying the current units, and the BY statement specifies the variable identifying the new units.     Putting It All Together   Let’s look at an example of how this might work in practice. Imagine I have a map data set of the continental United States. It has X and Y coordinates, as well as a column that identifies the state name and code.       I eventually want to use this map data set to create a map using data about the different US Court of Appeals circuits. There are 11 different circuit courts, each covering a different geographic area of the United States. However, this map data set only contains state boundaries. In order to use PROC GREMOVE to dissolve the borders between states and create a map of circuit court boundaries, I’ll need to modify my data. PROC GREMOVE requires that the input data has two ID variables: one that identifies the current unit areas, and one that identifies the desired unit areas. This data has a variable that identifies the current unit of states, but I need to add a second variable that indicates which circuit each state belongs to. To do this, I can simply join the map data with a lookup data containing the state/circuit information.       I can do this with a simple SQL join. I make sure to order the resulting table by the CIRCUIT variable and the _SEQ_ variable, which is the order the points should be connected in to form a map.   proc sql; create table state_circuit as select states.*, appeals_court.circuit from work.states left join work.appeals_court on states.stusps=appeals_court.state_abr order by circuit, _seq_; run;   The resulting table looks like this, where each row now has the corresponding court of appeals circuit:       Now, I’m finally ready to use the GREMOVE procedure. The input data has a column that identifies the current unit area (or state), and the CIRCUIT column identifies the desired unit areas. All that’s left is to run the following code:   proc gremove data=state_circuit out=circuit; by circuit; id name; run;   The output table will have less rows because rows that make up state boundaries within an appeals court circuit have been removed.       Now when I create a map using the output data, I can clearly see the different circuit count boundaries, appropriate for creating visualizations or doing further analysis using data at the circuit court level. Conclusion   Spatial data may not always come perfectly prepared in the format that you need to create visualizations or perform a particular analysis. However, with the use of tools like the GREMOVE procedure in SAS, it is easy to manipulate and customize map data to meet your needs. Stay tuned for future articles about other ways to manipulate and prepare spatial data!     Find more articles from SAS Global Enablement and Learning here. ... View more

A SAS Viya job is an excellent way to automate SAS programming and allow users who are not programming experts to interact with SAS programs, generate results, and create reports using custom point-and-click prompts and forms. However, there might be times when one job alone doesn’t accomplish everything you need. In that case, you may need to use multiple jobs in conjunction with one another. By linking two jobs together, you can allow a user to “drill down” from a general result to a more specific result. In this article, I discuss how to link jobs to create drill-down functionality.   Overview   Imagine that I have data about sales across several different product lines. I want to create a job that displays general sales numbers for each product line, as shown below: Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   However, I also want to allow users of this job to drill further down and view information about the profitability of each of the individual product categories within each product line, as shown here:     Rather than having two separate jobs that run independently, I’d like to create a more cohesive experience for the user by linking them. I’d like to turn the Product Line value in the first result into a hyperlink that executes the second job, with the appropriate parameters passed along.     I need to build a column that includes URLs that will call a particular job for the corresponding Product_Line value. For example, I want the Children product line to link to the following URL:   https://server.demo.sas.com/SASJobExecution/?_program=/Programs/DrillDown_Specific&_action=wait&product_line=Children    This will involve two steps:   Defining the base URL Passing the necessary parameters   Defining the Base URL   The code used to generate the first, general report is a simple PROC PRINT step, using a table called work.sales_summary.   proc print data=work.sales_summary noobs label; var product_link profit; sum profit; label product_link = 'Product Line' profit = 'Profit'; format profit dollar11.; run;   The hyperlink in the product_link variable displayed in the results needs to point to the job submit URL of the second job, with a parameter containing the value of Product_Line that will be used in the second job to filter the results. In order to create the hyperlink values in the results of the first job, we need to first create a base URL that contains the job submit URL, including the Product_Line parameter, but without a value. I can do that with the following code:   %let BASE_URL=&_URL.?_program=/Programs/DrillDown_Specific%nrstr(&_action)=wait%nrstr(&product_line=);   &_URL is a reserved macro variable that contains the URL of the web server. Next, the program= value contains the path to where the second job is stored. In this case, it’s stored in a SAS Content folder called Programs, and the job itself is called DrillDown_Specific.   Finally, I need to pass some additional parameters to the second job in the URL: the _action parameter and the product_line parameter. The value of wait for the ­_action parameter means that a loading screen will display while the job executes. The product_line parameter supplies a value for a macro variable used in a WHERE statement in the second job to filter the results to just the selected product line. Both of these need to be enclosed in the %nrstr() macro function so that macro triggers are not resolved, and the string is treated as plain text.   Notice I’m not specifying any value for the product_line parameter.   Passing the Necessary Parameters   Once the base URL macro variable has been constructed, I need to create the product_link variable and pass the correct parameters to the second job. In this step, I will build the work.sales_summary table used in the final general report.   To do this, I’ll use ODS inline formatting, which means that I’ll need to define an ODS escape character:   ods escapechar='^';   Next, within a DATA step, I’ll create the product_link variable and assign it an appropriate length.   length product_link varchar(1024);   Finally, I need to build the complete job submit URL for the second job, containing the value of product_line. I need to display this as a hyperlink, with the product_line value as the text.   The general ODS inline formatting syntax for creating hyperlinks is:   ^{style [url='URL-of-second-job']link-text}   In this case, I want the URL to be the BASE_URL macro variable, followed by the value of Product_Line, and I want the link text to be the value of Product_Line.   product_link = "^{style [url='&BASE_URL" || urlencode(strip(Product_Line)) || "']" || strip(Product_Line) || '}';   Here, the STRIP function removes any trailing blanks in the Product_Line, and the URLENCODE function formats the parameter values. The value of Product_Line is appended to the end of the BASE_URL.   Altogether, the DATA step looks like this:   data work.sales_summary; set work.sales_summary; length product_link varchar($1024); product_link = "^{style [url='&BASE_URL" || urlencode(strip(Product_Line)) || "']" || strip(Product_Line) || '}'; run;   Because I’m doing this in an assignment statement in a DATA step, the product_link variable will be generated for each row in the table, creating a unique hyperlink for each value of the product_line parameter. This hyperlink is then what I display to the user in the final PROC PRINT report.   Putting It All Together   When I put it all together, the result should look like this. When I run the first job, I’m shown a table displaying the total profit for each of the four product lines. If I click on Clothes & Shoes, the second job will execute with the value of Clothes & Shoes for the product_line parameter. That value then gets used in a WHERE statement in the second job, filtering the detailed results to just the Clothes & Shoes product line. In the end, I get the following result:       Conclusion   Linking jobs to provide drill-down functionality allows you to use the results of one job to drive another. These techniques can be combined with other common job enhancements, such as prompt forms or HTML forms, to allow further customizable and flexibility. For more information about some of the other ways you can customize SAS Viya jobs, see the following articles:   Create a SAS Viya Job with a prompt using Task Prompts How-to display results from a SAS Viya Job in the same page? Create a SAS Viya Job with a prompt using HTML Prompts   ... View more

Geocoding is the process of converting addresses into latitude and longitude coordinates. Doing so allows you to take address data and then easily visualize it on a map. If you want to geocode address data using SAS, you can use the GEOCODE procedure (see this article for more information).   However, sometimes you may need to do this reverse: you may have latitude and longitude data and need to figure out the address, or components of the address. This process is known as reverse geocoding, and it consists of converting geographic coordinates into a human readable address or place name. While there is no REVERSE GEOCODE procedure, SAS still makes it easy to derive place names and address components from latitude and longitude values. In this article, I discuss how to use SAS procedures and easily available lookup data to do so.   How (Reverse) Geocoding Works   Both geocoding and reverse geocoding work in basically the same way. At a high level, both processes require input data and lookup data. For reverse geocoding, the input data contains latitude and longitude coordinates, and the lookup data contains some kind of reference range of latitude and longitude values that correspond to certain address values or place names.   One common source of coordinate range lookup data is polygon data. For example, if you have coordinate location information and you need to determine what US state a particular location is in, you can use a data set containing US state polygons. Then you simply need to determine a coordinate point is within a particular polygon or not. The image below demonstrates this: you can see that one coordinate point is located in the state of North Carolina, and the other is within Virginia.    Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   In SAS, you can do this with the GINSIDE procedure.     PROC GINSIDE Basics   The GINSIDE procedure compares a data set of X and Y coordinates to a map data set containing polygons and determines if the X and Y coordinates are inside or outside of the polygon. It requires two input data sets. The syntax of PROC GINSIDE is as follows:   PROC GINSIDE DATA=points-data-set                                                MAP=map-data-set                                                <OUT=output-data-set>                                                <DROPMAPVARS>                                                <INCLUDEBORDER>;                 ID id-variable(s);   RUN;   The DATA= option is where you can specify the X-Y coordinate data set being reverse geocoded, and the MAP= option is where you specify the reference polygon data set. In SAS, a polygon data set is a normal SAS table where each row represents a single point. To create a polygon, the points are connected together in order.   The ID statement is where you specify the ID variable in the map data set that should be checked against the points from the input data set – for example, the column containing the US state name. Additional options include INCLUDEBORDER, which includes points that are on the border of a polygon, rather than only those fully inside it, and DROPMAPVARS, which only keeps whatever the ID variable is from the map data set in the output data set and drops everything else.   In both the input data sets, the longitude and latitude values must be in columns with the names X and Y.   Let’s look at an example.   Deriving County Names   In this example, I have latitude and longitude coordinates that represent traffic violations in Montgomery County, Maryland, recorded using a GPS device. While I can easily plot those coordinates on a map, I also want to be able to aggregate them by Census tract to see which areas are most incident-prone, as well as to compare other county-level demographic statistics. This means that I need to use reverse geocoding techniques to figure out which Census tract a particular set of geographic coordinates is within.     Since I want to determine the Census tract of each point, I need to use a lookup data set that contains US Census tract polygons. Luckily, the US Census Bureau publishes a comprehensive set of cartographic boundary files that can be used to reverse geocode states, counties, Census tracts, and other administrative boundaries in the US. I will download the Census tract boundary file for the state of Maryland and import it into SAS as a SAS table.     Note these boundary files provided by the Census Bureau (and other polygon lookup data set more generally) come in the shapefile file format, which is a common file structure used to store geographic data. In order to import a shapefile into SAS, you must use the MAPIMPORT procedure.   To do so, I’ll run the following code:   %let tract_lookup=C:\Documents\cb_2022_24_tract_500k.shp; proc mapimport out=lookup datafile="&tract_lookup"; id name; run;   Note that the ID statement in the MAPIMPORT procedure must identify the column that uniquely identifies each polygon in the shapefile. For example, in this shapefile containing US Census tract boundaries, the ID statement must specify the column containing the tract name.   Projections and Coordinate Systems   One important requirement is that in order for PROC GINSIDE to work accurately, both input data sets must be in the same projection.   While projections and coordinate space can seem overwhelming, it simply refers a mathematical transformation for taking the curved surface of the earth and representing on something flat, like a paper map or a computer screen. Because there are many different possible projections that rely on different underlying mathematical models, the same coordinates can be displayed in wildly different locations if the wrong projection or coordinate space is specified.      To use PROC GINSIDE, both input data sets must use the same project so that two data sets “line up” with each other spatially. Luckily, it’s easy to convert coordinates from one projection to another. In SAS, you can accomplish this with the GPROJECT procedure.   For the purposes of the GINSIDE procedure, it doesn’t actually matter what projection is used – all that matters is that it is the same projection. However, in many cases this step will not be necessary, as the lookup data and the data being reverse geocoded may already be in the same projection. For example, in this example, the traffic violation data and the lookup data set are both unprojected, and so nothing else needs to be done. The projection/coordinate space of shapefile data can be verified by examining the .PRJ file.   Putting It All Together   Once I’ve loaded the lookup data set containing Census tract polygons and confirmed that both the lookup data set and the point data set to be reverse geocoded are using the same projection, I need to run the PROC GINSIDE step. I’ll submit the following code:   proc ginside data=violations map=lookup out=violations_tract includeborder dropmapvars; id name; run;   In the ID statement, I’ve specified which column identifies the Census tract polygons. For each X and Y point in the input data set, the procedure will determine if the point is within a polygon in the reference data set. If it is, the ID value of that polygon will be written to the output data set. I’m using the INCLUDEBORDER option to keep any points that may fall exactly on a polygon border. I’ve also added the DROPMAPVARS option because the only column I want to keep from the reference data is NAME, which contains the Census tract name.   Then, I can examine the results. The output table, TV_TRACT, has two new columns: _ONBORDER_ and NAME. The _ONBORDER_ column comes from adding the INCLUDEBORDER option, and it contains either a 1 or a 0. A 0 means that a particular point is not on a polygon border, whereas a 1 means that it is. The NAME column indicates which Census tract each point is located within – this is added from the reference data.     Now that I have this output data set, I could aggregate traffic violations by Census tract to see which areas have the highest number of violations, or I could join the results with other demographic information to add additional context – neither of which would be possible without first reverse geocoding the coordinate data.   Conclusion   While SAS doesn’t have an explicit reverse geocoding procedure, it is easy to use the GINSIDE procedure to add address components or place names to geographic coordinate data. By simply changing the reference data set, you can easily reverse geocode coordinate data at many different levels, from Census tract to ZIP code to county and more.   Tips and Tricks   Boundaries for many administrative units change over time. The US Census Bureau provides historical boundary information for many geographies, including Census tracts and ZIP code tabulation areas. If your data needs to be reprojected, you can use the resource SpatialReference.org to lookup up projection codes that PROC GPROJECT understands. Filtering your reference data can improve performance and save space. For example, if you are reverse geocoding counties in Colorado but your reference data set is all counties in the US, you can subset it to include only Colorado counties. Your results will only be as good as your reference data. Be sure to verify that your reference data is from a reliable, up-to-date source. Be careful with column names. Longitude and latitude values must be in columns named X and Y.     Find more articles from SAS Global Enablement and Learning here. ... View more

Export as CSV. Import. Load table. Save. If you’ve ever worked on a project that involved multiple analytical platforms, you may have resorted to those exact steps while trying to figure out the best way to move data back and forth between locations. However, if your workflow involves using both SAS and ArcGIS Pro, you don’t need to worry about this!   ArcGIS Pro is a GIS (geographic information system) software developed by Esri. It is widely used by geospatial professional to create, manage, manipulate, and visualize spatial data. The SAS-ArcGIS Bridge, a partnership between Esri and SAS, includes two key features: the ability to move data back and forth between ArcGIS Pro and SAS Viya, and the ability to run SAS code directly within ArcGIS Pro. I’ll discuss both of these in detail in this post.   Requirements   In order to use these features, you must have access to ArcGIS Pro 2.9 or later, your CAS server must be configured to allow external connections (such as using the SWAT package), and you must know the CAS connection information (the host and port, and an authorization method).   The SAS Toolset   In ArcGIS Pro, geoprocessing tools are grouped together in toolboxes.   Select any image to see a larger version. Mobile users: If you do not see this image, scroll to the bottom of the page and select the "Full" version of this post.   There is a Conversion toolbox that contains a number of tools that allow you to easily convert data back and forth between different formats. One of the toolsets in that toolbox is called the SAS Toolset, and it allows you to move data back and forth between ArcGIS Pro and SAS Viya. This means that you can take an Esri table and load it to memory in CAS, or take a CAS table in memory and view it in ArcGIS. The integrity of your data is maintained in both directions: lengths, types, labels, and other attributes are respected in a way that makes sense on both platforms.   Let’s look at an example.   Note: For more information about getting started with ArcGIS Pro, Esri provides a set of quick-start tutorials.   Converting an SAS dataset to an Esri table   In the SAS Toolset, there are two tools: SAS to Table and Table to SAS.     The SAS to Table to tool converts a SAS dataset to an Esri table, and the Table to SAS tool converts an Esri table to a SAS dataset.   The tool uses a point-and-click interface. First, you must specify the name and library of the input SAS dataset and the name of the Esri table to create. ArcGIS Pro can access both datasets in a local SAS 9 installation, and CAS tables in SAS Viya. CAS tables must be loaded to memory.   In order to access a CAS table, you must also check the Download SAS Dataset from SAS Cloud Analytic Services (CAS) option. Clicking this add additional fields to specify how to connect to CAS. You must provide the CAS Hostname URL and Port of your environment, and you must also authenticate to CAS, either by providing a CAS Authorization File, or by specifying your CAS username and password.   In this example, I want to convert a CAS table called OK_POST_LOC in the PUBLIC caslib to an Esri table. This table contains latitude and longitude coordinates for post office locations in the state of Oklahoma, and I want to convert it to an Esri dataset to take advantage of ArcGIS’s spatial visualization capabilities. I’ve provided the connection information for my deployment of SAS Viya and supplied my credentials to authenticate to CAS.     Once the tool has finished running, the new table will appear in the content pane on the left-hand side of the window.     Once a SAS dataset has been converted to an Esri table in ArcGIS Pro, I can open it and examine the attribute table:     Or I can use one of the many other geoprocessing tools available in ArcGIS Pro to create a spatial visualization, such as the XY Table to Point tool:       Converting an Esri table to a SAS dataset   The Table to SAS tool takes an Esri table as an input and creates a SAS table. Like the reverse, it can create both a .sas7bdat dataset in a local SAS 9 installation, and it can create an in-memory CAS table in SAS Viya.   The parameters for the Table to SAS tool are very similar to the parameters for the SAS to Table tool. You must specify the input table in ArcGIS Pro, which can be either a stand-alone table or the attribute table of a feature class, and library and table name of the SAS table you want to create. Like before, you also need to choose whether to upload the table to CAS or not. If you do, you’ll need to provide the same CAS connection and authentication information. You can also select whether you’d like to replace the dataset if it already exists.   In this example, I want to convert a feature class containing population information for counties in North Carolina to a CAS table so I can do additional analytics and processing. I’ve selected the attribute table of the feature class as the input table, and I’m creating a CAS table called NC_COUNTY in the PUBLIC caslib. I’ve provided the connection information for my deployment of SAS Viya and supplied my credentials to authenticate to CAS.     Once I’ve run the Table to SAS tool, I can confirm that the new table appears in SAS Viya:     Note: Both the Table to SAS and the SAS to Table can also be scripted in Python using the ArcPy package.   Running SAS code from ArcGIS Pro   Once you have converted an Esri table to a SAS dataset, you can then interact with it and analyze it using SAS code, directly from ArcGIS Pro, using the built-in Python editor. There are two ways to do this. If you are working with a local installation of SAS 9, you can use the SASPy package. If you are using SAS Viya, you can use the SWAT package to connect to CAS and use CAS actions analyze your data.   To run SAS code in ArcGIS, I’ll start by creating a Python notebook. The first step to accessing data in CAS is to import the SWAT package and create a CAS connection. I can do so with the following code (Note that you will need to supply your own CAS credentials or other authorization method):   import swat conn = swat.CAS("server.demo.sas.com", 30570, 'christine', 'Student1')   Note: For demonstration purposes, I’ve put my authentication information in plain text. In the real world, never do this! Instead, use an authinfo file, set up Python environment variables, or use a hashed version.   Once you’ve connected to CAS, you can use all of the different CAS actions available as part of the SWAT package to manipulate and analyze your data.   For example, I can reference the table I converted from Esri in the last section and store it in a variable called tbl with the following code:   tbl = conn.CASTable('nc_county', caslib = 'public') Then, I can use the Summary action from the Simple action set to calculate summary statistics about my CAS table:   tbl.simple.summary()   Notice the syntax used to call CAS actions with the SWAT package: object.CAS-Action-Set.CAS-Action. It isn’t necessary to qualify the CAS action with the action set name, but it is a good practice.   Finally, I can also run regular, CAS-enabled DATA step code using the SWAT package. For example, I can run the following code to filter my CAS table and calculate a new column:   conn.runcode(code = ''' data public.nc_pop_centers; set public.nc_county; where Total > 100000; Pop_Density = Total/Land_km; run; ''')   For more information about all of the powerful ways to use the SWAT package to manipulate CAS data using Python, see Peter Styliadis’ blog series on the topic.   Conclusion   ArcGIS Pro is a commonly used tool by geospatial professionals in a wide variety of industries. Thanks to the SAS-ArcGIS Bridge, it’s possible for ArcGIS users to quickly and easily perform robust analysis using SAS code, and it’s possible for SAS users to move data quickly and easily to ArcGIS to create visualizations and perform advanced spatial analysis.     Find more articles from SAS Global Enablement and Learning here. ... View more

Watch this Ask the Expert session to learn how to create rich, customizable maps in SAS Visual Analytics.    Watch the Webinar   You will learn how to: Import shapefiles into CAS in SAS® Viya®. Define a custom polygon provider in SAS Visual Analytics. Create a geo map using an Esri feature service. Create region maps for custom areas.   The questions from the Q&A segment held at the end of the webinar are listed below and the slides from the webinar are attached.   Q&A Any suggestions on how to manually add the sequence variable (or a macro similar to SHPIMPRT but for SAS EG)?  If you have SAS9.4 (with EG), there are a different set of steps to follow to import the shapefile and use it in Visual Analytics (https://documentation.sas.com/doc/en/bicdc/9.4/vaag/n1oaashorywocun10c5epp8z9r97.htm). But to answer your question, you can create the sequence variable by adding an assignment statement like this:   __seq__ = _n_;    We have both VA on LASR, as well as VA on Viya. Could we use a geography data item created in Viya by means of the macro also in VA on LASR?  There are other steps for creating a geographic data provider in SAS 9 (https://documentation.sas.com/doc/en/bicdc/9.4/vaag/n1oaashorywocun10c5epp8z9r97.htm)    Is there anything that we can do if we are *not* admins of our SAS product?  You just need admin privileges to create the geographic data provider. If you don't have them, you can ask your admin to do it. Once it's created, you can use it however you wish (even without admin privileges).    What predefined geographic regions/boundaries are built into SAS Visual Analytics?  There are a couple of different ways to create geography data items. If you are creating a coordinate map and you have latitude and longitude in your data, you can use X and Y coordinates in your data to create a geography data item. You can also create geography data items using a geographic name or code look up. Out of the box, SAS Visual Analytics supports a number of different geographies: country names or country codes, state or province names, ZIP codes, and US state names or abbreviations. For example, in the previous demo, I created a geography data item based on US state names and then used that to create a region map of US states - without having to worry about Esri feature services or shape files.     Can you show us how to find the feature maps on the ESRI site?  If you Google ArcGIS Online or go to arcgis.com, you'll see that there's a search field. Once you open that, you can search not just feature layers, but many different kinds of data and information that's present on ArcGIS. Because SAS Visual Analytics supports creating geographic data providers from feature services, you can limit your search to just that: select Layers and then Feature layers. There are thousands of possibilities. You can simply enter a search term and look for whatever particular feature service you might be interested in using in SAS Visual Analytics.    Can we overlay features using ESRI feature service in the same map?  You can use SAS Graph Builder to create multi-layer geo map templates – for example, a geo map with three different region map layers. Then you could create geographic data providers based on three different Esri feature services and display them all on the same map. (See https://documentation.sas.com/doc/en/vacdc/v_019/grbldrug/p1wjt3d8a05thmn14gkcug4ft5wu.htm for more information)  You can also display custom Esri ArcGIS web maps using the Data-driven content object in SAS Visual Analytics. Doing so will allow you to display as many feature services layers as you want. See the following blog series for more information about this: (https://communities.sas.com/t5/SAS-Communities-Library/Everything-you-need-to-know-about-web-maps-in-SAS-Visual/ta-p/516042)    Recommended Resources SAS® Visual Analytics 1 for SAS® Viya®: Basics course, available as Live Web and as self-paced e-learning SAS® Visual Analytics 2 for SAS® Viya®: Advanced course, available as Live Web and as self-paced e-learning SAS® Visual Analytics: “Where” Can Tell You “Why” Introduction to Esri Integration in SAS® Visual Analytics Moving from SAS®9 to SAS® Viya® Move to Viya Board Please see additional resources in the attached slide deck.   Want more tips? Be sure to subscribe to the Ask the Expert board to receive follow up Q&A, slides and recordings from other SAS Ask the Expert webinars.     ... View more

In SAS Visual Analytics, you can quickly create geography data items based on a number of predefined geographies and regions, such as country and region names, state and province names, US state names and abbreviations, and ZIP codes. As long as your CAS table has one of those data items, you can create a geo map. However, there may be times when you need to create a geo map of a region not defined automatically in SAS Visual Analytics. For example, you may need to create geo maps showing custom regions such as sales areas, counties, school districts, or many other possibilities.   In many cases, polygons representing the areas you would like to visualize already exist in some other format, whether it is a shapefile, a geodatabase, or an Esri feature service – it's just a question of using them in SAS Visual Analytics. This article will cover creating custom polygons using an Esri feature service.   What is an Esri feature service? Esri feature services are geospatial layers that have been created in ArcGIS software and then published to ArcGIS online or hosted on an organization’s Esri server. Many organizations, such as government agencies and Esri itself, maintain large collections of feature services representing many different possible geographies. These can easily be used to create custom region maps in Visual Analytics.     Requirements and Steps To use custom polygons in SAS Visual Analytics, you must create a new geographic data provider. There are a few requirements that must be met to do this. You must have administrative privileges in SAS Viya to be able to define and edit geographic data providers. Specifically, you must have Create, Delete, and Update permission for the /maps/providers URI. For more information, see here. If you’re using an Esri feature layer hosted on your organization's own Esri server, then you will need to specify the credentials for that server in the Esri Custom Services Credentials setting. For more information, see here.   Once you have confirmed that you have the necessary permissions and authenticated to your organization’s Esri service (if necessary), then creating a geographic data provider using an Esri feature service involves three steps: Find and copy the URL of the Esri feature service you want to use in Visual Analytics.  Create a new geographic data provider, specifying the Esri feature service URL. Identify the column in the feature service that uniquely identifies each feature in the data being mapped. I will walk through each of these steps in more detail below.   Find and copy the URL In this example, I'll use a table of housing units per block group from the US Census Bureau (available here) to create a geo region map of the number of housing units per Census Block Group in Wake County, North Carolina. Since Census Block Groups are not one of the built-in geographies in Visual Analytics, I'll need to create a new geographic data provider. The Wake County government has published an Esri feature service of the block group boundaries, which can be found here.     The URL that we need to copy is not the URL of the webpage, but of the feature service itself. This can be found on the bottom right-hand side of the page, underneath the Credits section.     Create a new Geographic data provider Next, I’ll create a new geographic data provider using the Esri feature service. I’ll create a new geographic data item based on the category I want to map (in this case, the Block Group).     Then, in the Edit Geography Item window, I'll select Geographic data provider for the Geography data source and click New Provider. I’ll enter a name and select Esri feature service for the Type.     In the New Geographic Data Provider window, I’ll enter a name and select Esri feature service for the Type. In the URL field, I’ll paste the URL I copied from the Esri feature service webpage. I’ll also specify the type of geometry: point or polygon. In this example, I’ll choose Polygon.     Identify the ID column The geographic data provider doesn’t contain the data being mapped. It just contains information about how to draw polygons on a geo map. I need to tell SAS which polygon in the geographic data provider corresponds with which row in the CAS table being mapped (in this example, the table of housing units in Wake County). I can do this by specifying a common ID column in both the CAS table and the Esri feature service. The ID value needs to uniquely identify each feature (in this case, each Census Block Group), and it needs to be common between both the Esri feature service and the CAS table. For example, Census Block Group 1 needs to have the same ID value in both the Esri feature service and the CAS table.   On the Esri feature service webpage, I can examine the attribute table by clicking the Data tab.     It looks like the GEOID column in the feature service corresponds to the ID column in the CAS table. Note that the names of these columns don’t have to match – just the values. For example, the ID values all begin with 37 for the state of North Carolina, followed by 183 for Wake County, and so on.     In the New Geographic Data Provider window, I’ll select the ID column from the Esri feature service – GEOID.     Then in the Edit Geography Item window, I’ll select the ID column I just identified from the CAS table – ID, in this example. Remember, specifying the ID column in both the geographic data provider and the geography data item allows Visual Analytics to associate individual rows in the CAS table to individual polygons from the Esri feature service. In this example, each polygon in the geographic data provider is one Census Block Group, and each row in the CAS table contains information about the number of housing units in a single Census Block Group.     Results Finally, I’ll create a geo region map using this new geography data item.     Tips You can use a List Table object in Visual Analytics to examine your CAS table and identify the ID column. If your CAS table doesn't have an ID column, you can calculate one. For example, in this example, I could have calculated the ID column by concatenating the State, County, Block, and Block Group columns to match the structure of the GEOID from the Esri feature service. You can also use Esri feature services to create coordinate maps. This can be useful if you have a dataset that doesn't contain latitude and longitude, but for which an Esri feature service already exists. Some Esri feature services may have multiple layers. Make sure you pick the right one in the Feature Service Layer field. Visual Analytics can render a maximum of 250,000 vertices. If you have a large number of polygons, consider filtering the map to show a smaller subset of the data and improve performance. Once you've created a geographic data provider, it can be reused in other reports with other CAS tables, as long as the CAS table has an ID column that corresponds with the Esri feature service.   ... View more