Location Data Knowledge Base

Data methodologies, and Release schedules.
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Canada Business Insights customers, formerly "Canada Data", may have noticed that we've built up our 2016 Statistics Canada demographic variables inventory over the past handful of releases. The 2016 demographic data is accessed via the Demographic Analysis toolset by selecting Statistics Canada 2016 Census in the drop down. In addition, we deliver 2011 Statistics Canada demographics as Canada Data within the same drop down. Both data sets receive quarterly updated Forward Sortation Areas and geography summarized business counts.   Effective with the Q3 2019 Insights data release, update efforts will be focused on the 2016 census only; we will no longer update the 2011 Statistics Canada data set. We will continue to provide the 2011 data set without updates in the installation package through the Q2 2020 release.   Please let us know if you have any questions.
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Update Allocate Append tool using XML   You’re running a process to select certain variables to be used within a model. You’ve built your process, but you’re getting tired of having to run it twice. Once to pull thousands of variables to check for relevance, and a second time with just the variables you want to include in the final model based on the tests you’ve run.   There’s good news! You can use the Action tool within the Interface toolset to update the Raw XML of the Allocate Append tool to dynamically select the variables you want to use, and it’s not as hard as you might think.   The first thing we need to do is find out what the XML code is for the variables we want to use, and the format it needs to be in for the Allocate Append tool to recognize it. You can enable the XML view from the User Settings menu (Options -> User Settings -> Edit User Settings). On the Advanced tab, there is a check box to “Display XML in Properties Window”:   Once you’ve checked the box, return to your Allocate Append tool, or any tool on your canvas, and you’ll see a new option on the right hand side that will allow you to see the XML code the tool is creating.   From here you can get the format you need for the XML code that we’ll pass into the macro to be created later.   Once you know the variables you want to use, you can use the variable name (code, not description) to build out the XML string as show above. If you select multiple variables, what you’ll notice is that they are each on their own line under the “<Variables>” tag in the XML code. The list you make must follow the same format:   In the sample workflow attached, you’ll see that I am using a Text Input tool to simulate the data stream that contains the fully compiled XML strings needed. As you will most likely see in your data, I have one variable per record. The problem is I need all of the variables in the same cell, on their own line. So how do we combine the records into one, and add a new line?   The answer is we use the Summarize tool. Within the Summarize tool we can use the Concat function to combine the XML strings into a single cell, and in the concatenate Properties section, we can indicate that we want to use a new line as the separator by typing in \n.   Now that the prep work is done, all we need to do is pass this new variable list into the Allocate Append tool through XML. This can be achieved with a simple Batch Macro. For the Control Parameter you want to use the Variable list that we just created. The Control Parameter gets connected to the Allocate Append tool which adds the Action tool as shown below. In your Action tool, select the option to “Update Raw XML with Formula”, expand the options under Allocate Append until you see “Variables” and highlight that section. You’ll want to update the Inner XML, and the formula to use is the connection from the Control Parameter as shown below.        Once you have this set up, simply add your Macro Input (for your incoming data stream) and Macro Output (to feed back into your workflow) to complete the macro set up.   Return to your original workflow, insert your newly created Batch Macro and connect your inputs. Your variable list stream will feed into the ¿ input, and your main data stream to the other.   You’re now set to dynamically change the variables you are pulling! Simply run your process for selecting relevant variables, build your XML strings through the Formula tool and pass them into your macro.
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Greetings Everyone! The Q2 2019 Business and Location Insights data packages are now available for analysis. The packages include analytics-ready data from a variety of vendors as well as data-specific analysis tools which will empower users to get the most from the packaged data. All data packages are available via the  Downloads & Licenses  portal. US & Canada Business Insights customers will receive their hard drives later this week, but they are also welcome to download the data from the portal. All documentation packages are included in the attached .zip archive. Be sure to take a look at some of the recent articles in the Location Data Knowledge Base which will help you get started with your Insights packages. There are articles related to installation, mapping, and many more. Please let us know if you have any questions or concerns by commenting below. Happy Alteryxing!   Akshatha Madhan Gopal Data Products Specialist
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Greetings Everyone!   The Q3 2019 Business and Location Insights data packages are now available for analysis. The packages include analytics-ready data from a variety of vendors as well as data-specific analysis tools which will empower users to get the most from the packaged data. All data packages are available via the  Downloads & Licenses  portal. US & Canada Business Insights customers will receive their hard drives later this week, but they are also welcome to download the data from the portal.   All documentation packages are included in the attached .zip archive. Be sure to take a look at some of the recent articles in the  Location Data Knowledge Base  which will help you get started with your Insights packages. There are articles related to installation, mapping, and many more.   Please let us know if you have any questions or concerns by commenting below.   Happy Alteryxing!
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Data Products 101   Part 3: Drivetimes   In Part 2 of my Data Products 101 series I covered giving context to our spatial data with Maps, but what if you’ve never worked with spatial data before? What does a typical spatial use case even look like? We’ll cover getting you started with spatial data and more using the Drivetime Data available with each Location Insights package in this article.   Before we dive in, have you installed your data yet? If not be sure to check out Part 1 of this series first.   Let's get started:   Taking Drivetimes for a Spin   So, what is Drivetime Data anyways? At a high level, it’s a catalog of road segments and their associated speed values. Alteryx has a few tools which can leverage this data to determine point-to-point drivetimes or how far can you drive in any direction from a point within a set interval of time (aka "isochrone"). The latter is especially useful for a very common use case in Alteryx – trade area analysis defined by a drivetime threshold rather than a fixed distance radius.   Let’s jump in and build our first trade area. I'll use US data from the Location or Business Insights package, but this example will work for any of the countries we offer Location Insights data for.   First, pull in a Map Input tool onto our canvas and select the TomTom US base map. Add a point to the map via the configuration option at the top left. This point will represent the store that our trade area will be built around.     Next, bring a Trade Area tool onto our canvas and connect it to the Map Input tool. We'll use the default selections for now, but I highly recommend checking out the Trade Area Tool Mastery article on the Community whether you’re new to the Trade Area tool or a seasoned spatial veteran. This tool is jam packed with features and the Tool Mastery article is a great deep dive.     A few things to note here:   The SpatialObj field will automatically configure since we only have one field containing spatial data. Be sure to select the correct field for your analysis if you have multiple spatial object fields.   By default, the Trade Area tool will be configured to build radius-based trade areas using miles as the unit.   Let’s leave the tool in the default configurations so that we can visually compare the differences between radius-based and drivetime-based trade areas. Go ahead and throw a Browse tool after the Trade Area and run the workflow.     As we can see, our trade area is a perfect circle with a radius of 5 miles. The point we see in the center of the circle is our store location that we created in the Map Input tool.   Let’s go back to our Trade Area tool and reconfigure it to create a drivetime-based trade area as pictured below.     You probably noticed a few things when applying these changes:   There are three different versions of drivetime data – Peak, OffPeak, and Night. OffPeak is the ‘medium traffic’ option, Peak is the ‘heavy traffic’ option, and Night is the ‘light traffic’ option.   We didn’t change the value for ‘Specific Value’. As you may have guessed based on the units – it’s no longer a 5-mile radius, or even a 5-mile drive distance, but rather a 5-minute drivetime. This means that the edge of our trade area will be the maximum distance you can drive in 5 minutes from the point we built our trade area around.     After rerunning our workflow – we can now see a trade area with a completely different shape. Every point within the green boundary of our trade area is 5 minutes or less drivetime from our store. You may notice that our trade area has “arms” that extend beyond the “body” of the trade area. This is due to some streets having better traffic flow (e.g. higher speed limits, less traffic stops, etc). You may also notice areas where the trade area doesn’t extend much into and this can be due to opposite (i.e. low speed limits and many traffic stops) or there may just not be roads present.   Using Drivetime Data to Drive your Analysis   Great, so now that we have our drivetime trade area – what do we do with it? Trade area analysis covers a gammut of topics; ranging from understanding current customers, analyzing the competition, identifying potential new customers, and much more. For our purposes – we’ll walk through a couple of simple customer analysis cases. However, the sky is the limit and here are a few use cases for Trade Area analysis to spark your imagination:   The Right Location for Healthcare Facilities Using Spatial Analytics for Retail Site Analysis Understanding Consumer Behavior with Mobile Device Location Analysis   A classic entry point to trade area analysis is answering the simple question of: “How many of my customers live within X miles/minutes of my store?” For this example – I’m going to explore how many of my customers live within 10 minutes of our store.   First, I load in my customer file which consists of a unique ‘CustomerID’, a ‘Centroid’ SpatialObject point which represents the customers address, ‘Weekly_Visits’ count, and ‘Visit_Spend’ amount.     In my example customer file, I already have a SpatialObject point representing the customers address. However, in many cases your customer file may only have an address. This is a perfect use case for the Geocoder which is included in every Location Insights package.   Next, I’m going to create a 10-minute trade area around our store.       Finally, we need to do a spatial join. This is going to use a tool that may be new to you – the Spatial Match tool. This tool has a variety of configuration options which the help documents cover in a great visual format. For our purposes – I’m going to connect our Customer data stream to the T input (Targets) and the Store trade area stream to the U input (Universe). I also need to make sure that the ‘SpatialObject_TradeArea’ is selected as my Spatial Object Field for the Universe input as this is our trade area and the ‘SpatialObject’ field is the point for our store. The final step is to set the join condition to ‘Where Target Within Universe’.     Now that we’ve configured our workflow, let’s throw a browse tool on the M output (Matched) and the U output (Unmatched) and run the workflow so that we can visually see our data.     These 24 red points are our customers that are within 10 minutes driving from our store.     And these 438 red points are all our customers who are outside our 10-minute trade area.   Going the Distance with your Analysis   Great – now we know that most of our customers live much further away than 10 minutes driving from our store – but we don’t know how far the furthest customer is. If we knew how far our furthest customer lived, then we would be able to create a trade are which encapsulates all our customers without extra bloat. So how can we accomplish this?   Another important tool which leverages the drivetime data is the Distance tool. As the name implies, the primary function of the Distance tool is to measure distance from point A to pint B – including drivetime and driving distance. We can use this tool, in combination with a few others, to figure out how far our furthest customer lives. First, we need to append our store location to our customer data. We will do this with an Append Fields tool. Hook the customer data stream to the S input (Source) of the Append Fields tool and the store data stream to the T input (Target). Also, be sure to set the option to allow all appends.     Next, we will configure a Distance tool. By default, ‘Output Distance’ will be selected. This option measures straight line distance, not driving distance. We want to de-select this option, and then select the ‘Output Drivetime & Distance to Destination Centroid’ option. We need to make sure that ‘TomTom US OffPeak – Most Recent Vintage’ is selected as our Dataset as this is the same dataset we are using to generate our trade areas.   I’m going to leave ‘Route Optimized by’ as ‘Time’ because we are creating drivetime-based trade areas. I’ll also leave ‘Allow Reverse Routing for Optimum Speed’ checked as this speeds up the run of the workflow and I’m not too concerned about incredibly precise results. I’m going to set ‘Maximum Minutes’ to 45 instead of keeping the default of 30. This is because I am familiar with the Denver metro area and I think that some of my points may fall further than 30 minutes, so I want to make sure that all results are included.     Finally, I am going to sort my data by DriveTime in Descending order because I want to know how far the furthest customer is, but also what the next few farthest customers are just in case the furthest is an outlier. Now let’s run the workflow and check out the results.     From these results, I can see that our furthest customers live almost 25 minutes away from our store. Therefore, I know that a drivetime-based trade area of 25 minutes should cover all our customers. Great, now what can I do with this information? Well, we have a count of weekly visits and spend per visit in our customer data – maybe there’s a correlation with drivetime distance.   Put a Ring on your Insights   Let’s revisit our initial trade area workflow and take advantage of one of the more hidden functionalities – trade area rings. I’m going to analyze customers based on the following drivetime distance brackets: 0 to 15 minutes, 15 to 20 minutes, and 20 to 25 minutes.   The way we configure a ring in the trade area tool is to specify the start and end values separated by a hyphen (e.g. 10-15). We can also specify multiple trade areas within a single tool by separating our unique values with a comma (e.g. 10,15). The ‘Specific Value’ that we need to use then is: ’15,15-20,20-25’.     Note that our initial value is just ‘15’ and not ‘0-15’. This is because our initial trade area ‘ring’ isn’t actually a ring, it’s just a polygon, therefore we don’t need to specify a starting distance of ‘0’.     Now that we have our customer points matched to our trade area rings, lets create an ‘Average Weekly Visits’ and an ‘Average Visit Spend’ field for each trade area ring.     Finally, let’s wrap it up by creating a ‘Total Weekly Spend’ value for each of our rings by using a formula tool.     Now let’s inspect the results.     As we can see above, the customers who live nearest to our store visit the most, spend the least per visit, but also spend the most overall. And the opposite holds true for our customers who live the furthest away. This is the type of spatial driven analysis which could help our marketing campaign better target our customers and drive better results.   Bonus Section   One of the most powerful features of trade areas in Alteryx is the ability to append demographic values to them. For those of you who have purchased the Business Insights package, you may have explored the demographic analysis tools, or at least you may have seen the names ‘Experian US’, ‘Experian Full US ACS’, and ‘US Census’ when installing your data. These are your demographic datasets and you can append any field from those datasets directly to your trade areas.   The most exciting part of this feature is that you can calculate demographics for the trade area itself, and not just for given geographies that fall within your trade area, like ZIP Codes (although you can append those too).   If you haven’t purchased the Business Insights package but would like to test out this powerful feature, you can download the ‘US CENSUS 2010 - FREE DATA’ package from the downloads and licensing portal under the ‘Data Packages’ section. Follow my installation guide in case you forget how to navigate the downloads page or the data installer.   Let’s go back to our multi-ring trade area and add in an additional trade area that covers the entire 25-minute area. This new trade area will serve as a baseline that we can compare our rings to. Our ‘Specific Value’ should now be: 15,15-20,20-25,25.     Next, we’re going to bring in a new tool - the Allocate Append tool - and connect it to our data stream. We’re going to select the US Census 2010 dataset as our dataset. We need to set the ‘Records Are:’ dropdown to ‘Custom Geographies’ and the ‘Spatial Field’ as our trade area field: ‘SpatialObject_TradeArea’.     Next, we are going to select our demographic variables. I’m going to select ‘Total Population’, ‘%Total Female’, and ‘% Hispanic or Latino’ as the demographic variables I’m interested in.     Finally, let’s review our results.     As we can see here, a marketing campaign which targets potential Hispanic/Latino costumers will be most effective in our 15-minute radius, and least effective in our 20 to 25-minute radius. A general marketing campaign would have the most reach in our 20 to 25-minute radius as that ring has the highest population. A marketing campaign which targets women consumers would have fairly equal effectiveness across our entire trade area as one which targets men.   Appending demographics to drivetimes is a popular and powerful way to leverage Location Insights with Business Insights. If you want to learn more about our demographic data and the Business Insights packages, then be sure to stay tuned for Part 4 of my Data Products 101 series – All Things Allocate.   Bonus Bonus   You may notice that my demographic fields have very descriptive field names, rather than a more coded field name that you will see when exploring our demographic datasets. This is because I used a simple trick to rename my fields using descriptions from the Allocate Metainfo tool. If you want to apply this process to your workflows, be sure to check out the Allocate Rename Fields Macro .
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The Q3 2018 US Data package includes analytics-ready data from TomTom, Experian, Dun & Bradstreet and the US Census as well as data-specific analysis tools to get the most from the packaged data. The documentation package attached includes –   Release notes, variable list and change log Experian CAPE demographic data methodology document, Mosaic segment descriptions Experian ConsumerView Analytical file – user guide and penetration report Simmons overview D&B Analytical file - data description, SIC lookup code, penetration report Kalibrate Technologies traffic count overview and metadata Spatial products include documentation on drive time methodology and Alteryx map layers   What's new in this release?   Sample workflows included with the data installs are now grouped within a top-level "Data Install Samples" category Annual updates for Places, Other Name Places, CBSAs, and CCDs/MCDs   Please download and extract the attached 'Q32018_US_Data.zip' for the complete documentation.
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Have you recently updated your Alteryx version, and now are getting an error when you try to run workflows that use Spatial Data? This article outlines the symptoms, diagnosis, and solution for the Error: “The Designer .x64 reported: InboundNamedPipe::ReadFile: Not enough bytes read. Overlapped I/O operation is in progress.” message associated with Spatial Tools.
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Question Where can I find the latest CASS dataset?? Help! Answer The most recent CASS datasets are available online through the Licensing and Download Portal which can be accessed at http://licenses.alteryx.com. For information on how to access the licensing portal and download the data you are licensed for, click on the below link -   https://help.alteryx.com/licensing/2018.2/Download/DownloadDatasets.htm
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Hi, we had shared in our release documentation that end users are reminded if they have not installed the geocoder and reverse geocoder macros from the Q3 2017 release that they do so due to several key changes implemented in that release. The below changes were -   Geocoder (affects UK/ROI, AU/NZ, BR and EU Spatial installs only) Migration to TomTom’s Online Search API and license key update therefore geocoders installed prior to the Q3 2017 release will not be active after December 31, 2017 The macro has been updated to meet the currently supported version of Alteryx Designer 10.5.9.15014 Updated Customer Support error messaging Reverse geocoder – last two bullets plus, Adding logic to replace missing latitude and longitude values with 0 and alerting users when these missing values are converted to 0 Migration to TomTom’s Online Search API and license key update therefore reverse geocoders installed prior to the Q3 2017 release will not be active after December 31, 2017 Please ensure that you have installed the Geocoder (where applicable) and Reverse Geocoder macros from installs including and after Q3 2017.   If you have any questions, please contact support@alteryx.com.   Thank you -
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Shipping and Gallery schedule for data delivered to licensees of Alteryx Designer with Data, Alteryx Designer with Spatial and CASS.  
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Why is my geocoding workflow hanging? It used to run fine – but now it seems to be stuck indefinitely!
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The US Data package includes analytics-ready data from TomTom, Experian, Dun & Bradstreet and the US Census as well as data-specific analysis tools to get the most from the packaged data. The documentation package includes –   Release notes, variable list and change log Experian CAPE demographic data methodology document, Mosaic segment descriptions Experian ConsumerView Analytical file – user guide and penetration report Simmons overview D&B Analytical file - data description, SIC lookup code, penetration report Kalibrate Technologies traffic count overview and metadata Spatial products include documentation on drive time methodology and Alteryx map layers    Release Documentation download: http://downloads.alteryx.com/ReleaseDocumentation/Q32017_US_Data.zip
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  In the Q3 2017 US Data release there were six reports that incorrectly reference the D&B Business Summary Employees and Establishments vintage as “Q2 2017” instead of the correct vintage of “Q3 2017”. This issue is purely cosmetic in the report – the data is still from the Q3 2017 vintage. The reports can be found in the dataset, “Experian US 2017A (Q3 2017 Release)(v.8.6)”, within the Demographic Analysis tools in Alteryx.   Affected Reports: Demographic Snapshot Comparison Report Demographic Snapshot Index Comparison Demographic Summary with Index Demographic Trend Summary Executive Demographic Population Comparison Report   Action Required: Replace the incorrect report template files. Close any open instances of Alteryx or Allocate Download, unzip and extract Q3_2017_Fixed_Report_Templates.7z from the Community Article to the local Alteryx data install location. You need to place these in the Reports folder – found under […]\Portfolio\ALTERYX_US_Experian_17A_17B\Reports. A typical install location is C:\Program Files (x86)\Alteryx\DataProducts\Portfolio\ALTERYX_US_Experian_17A_17B\Reports Overwrite the existing templates with the updated templates   How to Validate: Generate a report using a corrected template. Launch Alteryx Drag out an Allocate Input tool Select the dataset “Experian US 2017A (Q3 2017 Release)(v.8.6)” Select any single geography Drag out and connect an Allocate Report tool to the Allocate Input tool Select the dataset “Experian US 2017A (Q3 2017 Release)(v.8.6)” Select the “Executive Demographic” report Click the Index button and select any State geography Press run – the report should now reference “Q3 2017” – and not “Q2 2017” – for the Employee and Establishment fields If you are an Allocate GUI user, the updated reports can be validated there.   If you have other concerns or need assistance with these instructions, please contact our Customer Support team at support@alteryx.com.
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The Europe (EU) Spatial Data includes analytics-ready data from TomTom as well as data-specific analysis tools to get the most from the packaged data. The documentation package includes –   Release notes Spatial products include documentation on drive time methodology and Alteryx map layers Countries included in this install are:   Andorra Austria Belgium Croatia Czech Republic Denmark Finland France Germany Gibraltar (British Overseas Territories) Hungary Italy (including Vatican City) Liechtenstein Luxembourg Monaco Netherlands Norway Poland Portugal San Marino Slovakia Slovenia Spain Sweden Switzerland   Release Documentation download: http://downloads.alteryx.com/ReleaseDocumentation/Q32017_EU_Spatial.zip
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The UK and Republic of Ireland (ROI) Spatial Data includes analytics-ready data from TomTom as well as data-specific analysis tools to get the most from the packaged data. The documentation package includes –   Release notes Spatial products include documentation on drive time methodology and Alteryx map layers   Release Documentation download: http://downloads.alteryx.com/ReleaseDocumentation/Q32017_UKROI_Spatial.zip
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The  ConsumerView Matching macro  enables users to match their customer file to the Experian ConsumerView data. Starting with customer information such as name and address you can leverage the ConsumerView macro in Alteryx to append a variety of information about your customers such as household segmentation, home purchase price, presence of children in a home, estimated education and income levels, length of residence, and many more!
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The Brazil (BR) Spatial Data includes analytics-ready data from TomTom as well as data-specific analysis tools to get the most from the packaged data. The documentation package includes –   Release notes Spatial products include documentation on drive time methodology and Alteryx map layers   Release Documentation download: http://downloads.alteryx.com/ReleaseDocumentation/Q32017_BR_Spatial.zip
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The Australia and New Zealand Spatial Data includes analytics-ready data from TomTom as well as data-specific analysis tools to get the most from the packaged data. The documentation package includes –   Release notes Spatial products include documentation on drive time methodology and Alteryx map layers   Release Documentation download: http://downloads.alteryx.com/ReleaseDocumentation/Q32017_AUNZ_Spatial.zip
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I updated a dataset and now my workflow is looking for a weirdly named vintage?
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