Alteryx Designer Desktop Knowledge Base

Alteryx users who have purchased the TomTom Alteryx Maps data set have the ability to extract layers into various formats using the Alteryx Street Analytic App. Installs from TomTom 2011 Q3 Alteryx Maps forward, include the Street App within the install.  The App replaces what was previously known as the StreetWare product.  The Alteryx Street App is an extraction tool which allows the user to extract TomTom layers into various file formats such as .tab, .shp, .yxdb to name a few. The App is specific to the data install vintage of TomTom Alteryx Maps Data and is located in the, AlteryxDataProductsAlteryxMapTomTom_US_xxxx_Qx directory.  To run the Street App (TomTom_US_yyyy_Qx.yxwz) you will need to browse to the location above and select it. Under the Geography Selection tab, select the Geographies you would like data for. You may select all, single or multiple Geographies as shown below: Under the Layer Selection tab, you may select the individual layers you would like to install for the Geographies that you selected. The Output Options tab allows you to select the file format you wish to create and the Destination directory you wish to install the data to.  There is also an option to merge the geographies.   One feature of the wizard that was not present in the StreetWare product is the ability to create seamless .tab files.  This feature allows the user the ability to bypass the 2 GB limitation that .tab files have. Please note .shp files also have a 2 GB file size limitation but do not offer the same seamless ability that is incorporate in the extraction of .tab files When .tab file format is selected a folder is created containing the referenced .tab tables, the seamless .tab files are visible below this folder.  As Shown Below:   The seamless .tab files will be opened and viewed in MapInfo as normal.  The individual .tab files that are referenced by the Seamless files can be found in the SeamlessTabFiles folder. We hope our users find that the Street App will be an easy-to-use utility to extract the desired map layers for use in their preferred applications.

Often, we get support requests asking if we can do a quick session on best practices for a particular tool or set of tools.  The Spatial Match Tool is certainly no stranger to this request.  Even though Alteryx is extremely fast and efficient with spatial processing, there are instances where a slight change in settings could speed up your total module runtime. For example, let us assume that we have 10,000 records that we are trying to perform a spatial match against.  Upon looking at our data, we notice that the points were derived from the centroid of another polygon, but the previous polygon field was still in the data stream.  Removing the unnecessary spatial field is one of the first steps in optimizing your data stream.  Why?  If you don't need the data, why are you pushing it all the way through your module?  That extra data consumes memory, so removing it from your data stream reduces that consumption, increasing the available resources on your computer.   Ultimately, this practice can be used in any aspect of your data, but it is especially relevant with spatial processing.  For more tips and tricks regarding your spatial match process, download the included zip package.  It contains information that can help you optimize your modules that utilize the Spatial Match tool.   Special thanks to Paul Treece for helping out with the module! Until next time! - Chad  Follow me on Twitter! @AlteryxChad    

I have no doubts that many of you have attempted to geocode a set of records, only to find that your records show up in the middle of the ocean on the other side of the world.  I certainly have, and the result is always that I accidentally reversed my non-descriptive latitude and longitude fields just before the Alteryx Create Points Tool.  If I had taken a quick look at my data, I would have known that my field with a non-zero hundreds digit (100+) is my longitude field, and setting it properly would have saved me valuable processing time.   While the above example is common, there are many others that are equally as common that can be fixed or avoided completely with a bit of additional knowledge.  The main question of the article (How accurate does my data need to be?) all depends on your use case.  For example, in many point-in-polygon cases you may only need 5 digits of precision rather than 6.  Why?  5 decimal places are accurate at 1.1 meters, while 6 decimals are accurate to 0.11 meters.  This may seem like a big difference, but when you are trying to search for entire buildings based on a trade area, 5 decimals should do the trick. If you are interested in learning more, please take a look at the StackExchange article How to Measure Accuracy of Latitude and Longitude.  This breaks down the difference between accuracy and precision, and goes into detail about each decimal in a latitude and longitude point (with examples!).   Big thanks goes out to our very own Paul Treece for his help with the article.  Until next time! Chad Follow me on Twitter! @AlteryxChad

Compariing spatial objects for equality is very subtle, because you have to define what is meant by equal.  In the case of polygons, you can have 2 different polygons that describe the same shape, but have a different start and end point.  Most people would say these are the same, but if you compare them exactly, they are in fact different.  For instance:   [ -104, 39], [ -105, 39 ], [ -104, 38 ], [ -104, 39 ] [ -105, 39 ], [ -104, 38 ], [ -104, 39], [ -105, 39 ]   These 2 sets of points describe the same shape just from different starting points.  The same problem exists with lines - the same line specified backwards and forwards looks the same on a map, but would not be superficially equals.   The easiest way to test for equality is to test: does object A contain object B and also, does object B contain object A.  If they both contain each other, they must cover the exact same area on a map.   In a formula or filter tool, you can say:  ST_Contains([A],[B]) AND ST_Contains([B],[A)   In a SpatialMatch tool, you have to use a custom DE-9IM string to describe the match you want.  In this case, use the string "T*F**FFF*".  I won't try to document that string - see https://en.wikipedia.org/wiki/DE-9IM for a reference of how that works.   Attached is a module (for Alteryx 10) that demonstrates both techniques:

Those of you who have used the Report Map tool to create thematic maps have likely been unimpressed with the way Alteryx outputs the thematic legend text. Alteryx added two little known/used tools: the Map Legend Splitter and Map Legend Builder. With a little finesse, you can get the legend to go from completely unformatted to fully customized.   Before After       Not only does this allow for an easier to read the legend, but it also can save valuable space on your map or document. The example above simply involves taking the default thematic output legend text and replacing it with user-defined text for those layers.   Here's How You Do It    The entire workflow is illustrated after all of the steps below. In the Report Map tool on the Legend tab, change Position to "Separate Field". This will output the map and legend as separate objects, allowing you to work with just the legend. Add two Select tools after the map. In the first Select tool, select only the legend. In the second, only the map (and BoundingRect, if needed). Add the Map Legend Splitter tool after the Select tool that selects the legend, and select "Legend". Add a Record ID tool which will be used later to re-sort the legend back to its original order. Add a Filter tool using the [ThemeName] field in order to extract just the records which make up the thematic part of the legend. For this example: [ThemeName] = “Block Groups”. Create a Lookup table containing the Record IDs and the new text for the legend rows that you want to replace. Join the lookup table to the legend stream using RecordID. Deselect the original “Text” field and rename the “NewText” field to “Text”. Deselect the second RecordID. Union the new modified legend rows back with the non-modified legend rows using “Auto Config by Name”. Sort the records back to their original position. Use the Map Legend Builder to rebuild the new legend. The default configuration is all that is necessary. From this point, you can choose to either overlay the legend on the map (using the Overlay tool), or join the legend back to the map (using “Join by Record Position” in the Join tool) and position the legend adjacent to the map as desired using the Layout tool.   Below is the entire workflow numbered by the steps above. Attached is a sample workflow created in 10.0.  

  Often in spatial analytics, you’ll need to find the closest spatial object to another. The most intuitive way to do that is through the Find Nearest Tool, which specifically captures the ability to find the shortest distance between spatial objects in one file (targets) and a user-specified number of objects in another file (universe objects). This tool does an amazing job of simplifying the process of finding the nearest object to another but it can also add significant time to your workflow.   I often elect for an alternative method that has trimmed significant run time off of many of my spatial workflows. That is, using the Append Fields Tool to duplicate your target spatial objects for each universe and using the Distance Tool to calculate DriveTime. After that’s done, simply add on a Summarize Tool, group by the target and take the “Min” DriveTime for each. You could also sort ascending by DriveTime and sample for the first target by grouping with that field. There is a caveat, however, as the Append Fields Tool drastically increases the number of records in your input and will only speed up the process if there are significantly more targets than universes.   These methods are distinct in that the Find Nearest Tool must do a DriveTime run from each target spatial object to each universe spatial object (200 DriveTime passes in Example 1) whereas the Distance Tool approach already has all the points available to it and recognizes that there are many more targets than universes. As a result, it runs the reverse-direction DriveTime calculation starting from each universe to all target spatial objects at once (5 DriveTime passes in Example 1). If it is quicker for you to use the Find Nearest Tool, be sure to shed the spatial objects you no longer need in your workflow as soon as possible, even inside the Find Nearest Tool’s configuration if possible. That could also reduce your run time due to the sheer size of the spatial object datatype. Below are some examples of the methods. They can also be seen in the attached workflow, AppendAlternative.yxzp.   Example 1 Targets: 200 Universe Objects: 5   Attempt 1: Find Nearest Tool     Run Time: 8 minutes 13 seconds   Attempt 2: Append Fields Tool and Summarize     Run Time: 11.9 seconds   Example 2 Targets: 100 Universe Objects: 52   Attempt 1: Find Nearest Tool     Run Time: 49.7 seconds   Attempt 2: Append Fields Tool and Summarize     Run Time: 12.6 seconds

  We recently had a user that was looking to distinguish polygons between each other using dashed lines, a style not currently available in the Report Map Tool. But that’s alright, we can use the opportunity to showcase how you can be creative in Alteryx by using a few tools.       In cifically filter out the record IDs you want to change or use the Sample tool to pull random records, or 1 of every N Records. (FYI – if you use the record ID, you will want to remove that column after you have split the records, due to the record ID used later in the mapping process.)   Once you have selected the polygons you want to use, you will need to break those polygons into individual points using the Poly-Split tool. Here, choose Polygon field and Split to Points. Splitting the polygon into points will allow you to adjust the polygon by each point.   Then, you'll want to remove some of the points to create the “dotted line” effect by using the Sample tool. This tool's settings will want 1 of every N Record selected (you can change the N=3 to any number you like that will have the spacing effect you want).   Now that you have removed some points, you'll want to do a few things to give the points a grouping effect. In order to do this, add another Record ID tool, then filter the record ID by odd and even numbers. You can do this using the Filter tool and using the expression mod([Record ID],2)>0. Then add Record ID tools to the T and F anchors to complete the grouping effect when you add them both to a Union tool.   Grouping the points allows you to build your Polylines. After the Union tool, add a Poly-Build tool. The Build method will be Sequence Polyline using the SpatialObj and the RecordID, as the Source and Group fields, respectively.   Your final step is to add a Map tool and pull in the data from your Poly-Build tool, as well as the original centroid points of the polygons with which you created the split lines, and finally the remaining polygons you want to be represented as full lines around the radius. When configuring the Map tool, remember that the points coming from the Poly-Build are actually lines and not Polygons. Your layering will need to have Points, Lines, and Polygons to complete the map.

  One of the great things about Alteryx is the ability to have multiple geography types (points, lines, regions) all contained within the same record.     However, when exporting map layers to MapInfo Tab file format, a decision has to be made of which spatial field to keep.  What if you want all of them?!     Not to worry! The Transpose tool will save the day!     To configure the Transpose tool all fields will need to be selected under the Key Fields except for the spatial fields, they will be selected under the Data Fields.       Just note that there are now 2 fields for the radius. RadiusSize will correspond to the first trade area (row 2) and RadiusSize2 will correspond to the second trade area (row 3).   Now, more importantly all the spatial objects exist in one field (the Value field) and can now easily be exported to MapInfo.      Note: this methodology will also work for ESRI shapefiles, however, all the spatial objects must be of the same type. All points, all polygons...   (An example workflow is attached. This workflow was created in Alteryx 10.0)

How do we do this? Read on.

To create an inset map, follow these steps:

Multi-Line labels can be useful when you want to display multiple data points. Instead of using one long concatenated string, you can tell Alteryx to create a new line once it encounters a specific character specified by the user. To create a multi-line label simply follow these steps:

Use the Summarize tool to perform spatial functions, such as Combine and Intersect, and create spatial objects

A common use case related to mapping is creating a map of customers that are color-coded, for example, according to which sister store they purchased from or which branch they bank at. Typically, a thematic map is thought of as a map of polygons shaded by a range of values. But in this case, we are shading points according to unique values.

Improving processing speed up to drive time trade areas

The difference between a heat map and a thematic map, explained.

Alteryx’s spatial functions are not limited to the tools found in the Spatial Palette.  The Formula Tool, for example, has a variety of spatial functions that expand the Designer’s spatial capabilities beyond those in the Spatial Tool Palette.  This article highlights the Formula Tool’s ability to create spatial objects, particularly points and lines, and calculate distance using the built-in expression library.

This article describes how to create an indexed map

using the “Sequence Polyline” Build Method to visualize and analyze data

The example below will provide the distance between every store in a dataset to every store in that same dataset. Here's how we did it.

locating the nearest retail location to a customer using Designer tools

If you are unlucky while rendering a map in Alteryx with a Carto base map, you may encounter the error message Error: AGG error loading font (C:\WINDOWS\fonts\C:\Program). This article explains the cause of this error, and how to resolve it.