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Extract error while installing data package on an external drive Product - US Business Insights data package When installing the US Business Insights data package on an external drive that is connected by USB, an extract error from the Alteryx Data Manager may occur. Cause Larger files over 4 GB in the data set could not be installed and are missing. Right click on the drive in File Explorer and go to the Properties. If the file system is FAT32, the drive will need to be reformatted, as the maximum file size on FAT32 is 4 GB. Solution Right-click on the drive, select Format, and then choose NTFS. Note: working with data from a USB drive will be slower than having the data installed on the computer’s local internal drive. Also, the associated drive letter for the USB drive must remain the same for the data to be found by the Designer initialization files. The use of a file server and UNC paths are recommended to maintain a stable file path. Additional Resources Location Data Knowledge Base
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.
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.
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 .
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!
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.
Akshatha Madhan Gopal
Data Products Specialist
The Q1 2019 Business and Location Insights data packages were released today. The packages include analytics-ready data from a variety of vendors as well as data-specific analysis tools to get the most from the packaged data. All data packages are available via the Downloads & Licenses portal; US & Canada Business Insights customers should receive their hard drives later this week, but you are also welcome to download the data from the portal.
Here are a couple important notes specific to this release. Please see the product specific documentation for more details.
US & Canada: Install now only includes country specific CASS dataset (i.e., US Business Insights will only install US CASS by default)
Canada: Forward Sortation Area data was not updated due to vendor source issue. The vendor confirmed there was no change from Q4 to Q1.
All documentation packages are included in the attached .7z archive. As a side note, our team is working on content to help customers get started with their Insights packages. If you haven't already, be sure to take a look at some of the recent articles in the Location Data Knowledge Base regarding things like installation, mapping, and many more to come! Shout out to @TravisR for his work!
Feel free to add any questions in the comments below.
Team Lead, Data Products
Platform Product: Allocate/Behavioral Issues – Working with Alteryx Customer Support Engineers (CSEs)
To EXPEDITE the resolution of your case, please include the below information.
Allocate/Behavioral - Requested Information
*** Suggestion: copy/paste the questions below and email the supporting documentation to email@example.com
1. Detailed Description of the Issue
2. Screenshot of Alteryx Version
3. Screenshot of Error
4. What dataset and vintage are you using?
5. Please send a copy of your workflow (*.yxmd or *.yxzp)
6. Please send your .ini files in the data install folder for the data set. (optional)
Allocate/Behavioral – Requested Information (Detailed Instructions):
1. Detailed Description of the Issue – What issues are you having? Has it worked in the past? When did the issue start? Are all users affected or just some? What are the steps to reproduce your issue? What have you tried to resolve the issue? Have you searched the Alteryx Community?
2. Screenshot of Alteryx Version– Our CSEs need to know the precise version of Alteryx so we can replicate any issues. In Alteryx, click Help > About and provide a screenshot.
The screenshot will include whether it is Server or Designer. In addition, whether it is “Running Elevated” Admin vs. Non-Admin.
3. Screenshot of Error or Exact Text of Error- Click CTRL-Print-screen to capture the error and paste into your e-mail. Also include where was the error encountered – Gallery, Designer, Scheduler?
Note: You may wish to Google the error text research the issue. The Knowledgebase is also a great place to search the error text as well!
4. What dataset and vintage are you using? From the Allocate Input tool, click the Drop Down for Choose a Dataset. What dataset is selected? If “Most Recent Vintage”, what is the Dataset below? i.e. Experian US 2018A (Q4 2018).
5. Please send a copy of your workflow (*.yxmd or *.yxzp) and sample data if possible. Either create a .yxzp and include macros and data by clicking Options>Export Workflow. Or, include the workflow *.yxmd and sample data if possible.
6. Please send your .ini files in the data install folder for the data set. (optional) C:\ProgramData\Alteryx\DataProducts\DataProducts.ini
Renaming Allocate Variables
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.
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.
Data Products 101
Part 1: Installing your Data (Network & Command Line)
This article will walk through the Network data installation process and is intended for Admins. If you are looking for a guide on the standard local installation process, then check out our article on Local Data Installation.
Network Data Install (Advanced)
Considering that the entire US Business Insights bundle is around 180GB, we often have cases where a user will run out of hard drive space during the data install. If you find yourself in this boat and are on a company network, you can resolve your space issues by performing a network install with the Alteryx Data Installer.
This can also potentially save a huge amount of installation time when you have multiple users for the Data Bundle. Instead of performing this lengthy install numerous times – once for each user – you can install once and be done.
The downside to a Network Install is performance. A Local Install will almost always have better performance, as a Network Install is limited to network speeds and read/write times on your network drive.
A Network Install consists of two operations:
1. Preparing the Network Installation – contents of the Alteryx Data Install are copied to and prepared on your network location.
2. Registering the Network Installation – where each of your users navigate to the prepared network location in order to register the data sets to their machine.
To Prepare a Network Installation:
1. Follow Steps 1 – 4 within the (Local Installation process article)[hyper-link here].
2. You should now be at the Location screen - choose ‘Prepare a Network Install’ and browse to the shared network location where you wish to install. Be sure that all users have the same configuration if you're using a mapped network drive.
At a minimum, each of your users will need read access to this location. If you plan to install CASS to this network location, then your users will also need write access to this location.
Click ‘Finish’ after selecting the network location and let the Alteryx Data Installer run.
3. Once the installation is complete, each of your users will need to register the Prepared Network Install to their computers.
To Register a Network Installation:
1. Each user will need to navigate to the location where you Prepared the Network Install. Once there, run ‘AlteryxDataInstall.exe’ and follow Steps 1 – 4 as outlined under the Local Data Install guide. On the Location screen, select ‘Register from a Network Location and click ‘Finish’.
2. The installation process should take no more than a couple minutes to copy over a handful of files to the user’s computer (sample data, etc.). Once done registering, the user can immediately start accessing the installed data in Alteryx.
Command Line Install (Advanced)
The Alteryx Data Installer also has command line operations for all of our IT users. Please refer to our Command Line documentation for more details.
As of the Q4 2018 data release, Experian's CAPE offering accessed through Alteryx's Demographics Analysis toolset will move to an annual update schedule instead of semi-annual. The change was made by the vendor, whose research determined there isn't enough variance to warrant updating the Annual Update to the Thin Update (“A” to “B” in Alteryx terms).
This affects the following data sets:
CAPE Demographics, “Current Year Estimates” and “Five Year Projections” (CYE & FYP)
CAPE Seasonal Population
CAPE Daytime Population
CAPE Consumer Expenditure, CYE & FYP
CAPE Retail Demand & Retail Supply (Scaled)
American Community Survey, Mosaic Workplace and Mosaic Residential are already on an annual update schedule and will remain on that release cycle. However, the annual Mosaic updates will be delivered with the rest of the CAPE updates beginning with the Q2 2019 release.
This change does not affect the quarterly Alteryx Data delivery frequency to our customers, as geographies and other data sets follow their own release cycles. We will continue to deliver Alteryx Data quarterly as the other data sets included within the offering follow their own update release cycles.
Data Products 101
Part 1: Installing your Data (Standard)
So, you just bought one of the Data Bundles from Alteryx and you’re excited to dive in and start exploring Location and Business Insights for your company – but where do you start? How do you get access to this Data? Well, unlike a SQL database or CSV you might be used to working with, you’re going to start by installing these Data Products. This article will walk through the standard data installation process. If you are an Admin and are interested in a Network or Command Line installation, check out our article on Data Installation for Admins.
First and foremost, do you have your Data Package yet?
If you purchased either the US or Canada Business Insights bundles, you should have received a hard drive in the mail. If you have your hard drive plugged in and read, then go ahead and skip to Step 1 below.
If you purchased one of our Location Insights spatial bundles, then head to licensing and download portal and download the data. Assuming you have your Spatial license key activated, it’ll be under the Data Packages tab on the left – just click on the bundle you wish to download and then the .7z file you wish to download. Pay attention to the vintage tag as you’ll want the most recent quarter’s data.
Be sure to extract the .7z package after you’ve downloaded it. We recommend creating a new folder that you will extract into – instead of extracting everything onto your desktop.
If you haven’t received your US/CAN Business Insights hard drive yet, or don’t see the Data Packages tab on the Licensing and Downloads portal, you should reach out to our Fulfillment Team for support.
Local Data Installation Steps
The local data install is the standard installation type. First, you’ll want to make sure Alteryx is closed. Next, either plug in your Alteryx Data hard drive or navigate to wherever you extracted the data from the Licensing and Downloads portal and run the ‘AlteryxDataInstall.exe’ to launch the Alteryx Data Installer.
1. Click Next when the Welcome Screen appears.
2. Read and Accept the license agreement then click Next.
3. Select the data sets you would like to install. If you want all of them just click the All button on the right. Otherwise, you can select individual Data Products by selecting the check box next to them. After you’ve made your selection, click Next.
4. Choose any previously installed Data Products that you would like to uninstall by selecting them similarly to the previous screen. You don't have to choose anything here if you want to keep everything, however, keep in mind that these data bundles can be very large and you may not have enough space to keep multiple vintages installed locally. After you’ve made your selection, click Next.
5. Leave ‘Install to a Local Directory’ selected and browse to the file path you would like to install the data to. The default path will be auto-populated but if you'd like to install it somewhere else just update the path. Make sure the hard drive you install the data to has enough space. For instance, the US Business Insights bundle takes up over 180GB and we recommend 500GB of space for Alteryx Designer – so you will want to have at least 700GB available.
Once you’ve selected the install path, hit Finish and let the Alteryx Data Installer run.
Feel free to kick back and relax now – as this will take some time. An install of the entire US Business Insights bundle may take well over 3 hours depending on your hard drive write speed.
6. When you get to the ‘Complete’ screen you’re done. Now you can load up Alteryx and start diving into Business and Location Insights!
If you’re unsure where to get started with actually using these Data Products, then be sure to check out Part 2 of my Data Products 101 series. Stayed tuned for more to come!
Data Products 101
Part 2: Maps
This article focuses on one key area of our Location Insights products – Maps. I'll use the US Location Insight product for this example, but all of our international Location Insight products are functionally the same unless I call it out specifically as a unique part of the US package.
As a quick recap before I begin this article, let's cover two frequently asked questions:
What do the Location Insight packages include?
All Location Insight packages include:
Satellite imagery maps (with optional road overlay)
Detail rich street maps
Drivetime data to generate custom trade areas and point-to-point calculations
API-based Forward and Reverse Geocoding tools
In addition to the above, Canada and the US include:
CASS data for CASS certification and Address Standardization
On premise, forward geocoding tools
Rooftop and parcel centroid address point data
In addition to the above, the US also includes:
2010 US Census data summarized to the Block Group
What countries does Alteryx have Location Insight products for (as of Q4 2018)?
The United States (which includes: American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and the US Virgin Islands)
The United Kingdom and the Republic of Ireland
Australia and New Zealand
Europe (which includes: Andorra, Austria, Belgium, Croatia, Czechia, Denmark, Finland, France, Germany, Gibraltar, Hungary, Italy, Liechtenstein, Luxembourg, Monaco, the Netherlands, Norway, Poland, Portugal, San Marino, Slovakia, Slovenia, Spain, Sweden, Switzerland, the Vatican City, and some coverage of Western Russia)
Let's get started:
Using Alteryx Maps
In Part 1 we covered installing our Data Products – now let’s jump into actually using it.
First, let’s drag a Map Input tool onto our canvas and review what maps we have available to us by clicking the ‘Base Map’ drop down.
As we can see here, we have multiple maps at our disposal. Carto Positron and Dark Matter come pre-loaded with Alteryx and function well as a basic map for your data – but the TomTom and Digital Globe maps you get with the Location Insights packages bring more accuracy and a lot more utility.
Let’s examine a quick comparison below to understand the features of each Base Map.
DigitalGlobe Aerials with Streets
As we can see above, there are some big differences between the 4 main Base Maps.
TomTom US offers a feature rich visual in a familiar and modern styling that makes it easy to immediately distinguish map layers from one another. There’s no mistaking a park with a lake, or an interstate highway with an avenue. Details which could be critical when trying to analyze where your customers live.
DigitalGlobe Aerials offers a pure satellite imagery map that makes it easy to pick out specific buildings or sight property lines between farms when manually creating spatial data.
DigitalGlobe Aerials with Streets offers a hybrid between the TomTom map and the DigitalGlobe Aerials – literally. In fact, DigitalGlobe Aerials with Streets uses the very street data used within the TomTom map. The inclusion of street data can give geographic context which is often lacking in a pure satellite imagery map.
Carto – our free map – offers similar features as the TomTom map that will help give context to your spatial data, but it doesn’t include as many layers as TomTom and it is not as easy to distinguish these layers from one another.
While the Map Input tool is useful for creating spatial data at the beginning of our analytical journey, it doesn’t help provide context to the data we are currently working with. Thankfully, the Browse tool has a built-in map function when working with spatial data.
All you have to do is select the ‘Map’ tab on the top left, then select your desired Base Map in the drop down. Now you can see your data visualized on one of our maps. Let’s pause and take a moment to reflect upon the fact that Maps are the OG in Visualytics.
Now we’ve seen how the Map Input tool can help start our analytical journey, and how maps within the browse tool provide context to the data we are working with - the last mapping tool in our toolkit we need to discuss is the Report Map tool.
Report Map is a powerful tool for visualizing your spatial data. It provides spatial context to the results at the end of your analytical journey. I highly recommend you check out the Tool Mastery article for it as well as the One Tool Sample found in the ‘Help’ menu in Alteryx.
As the Tool Mastery article provides plenty of context on how to configure the tool, I’ll point out that the TomTom base map within the Report Map tool allows users to customize individual geography layers. For example, your analysis is based near airports – the Report Map tool allows you to customize the appearance of the airport polygon layer and even other surrounding layers to show importance. Select the TomTom map in the Reference Base Map configuration to enable base layer customization.
You should now be equipped to use the Map portion of your Location Insights package. If you’re wondering where to start with diving deeper into spatial analytics then be sure to stay tuned for Part 3 of my Data Products 101 series - Drivetime Data.
Let's circle back to an earlier point about the three main maps of the Location Insights packages – the "DigitalGlobe Aerials with Streets" map uses the exact same street network data as the TomTom map. You might be wondering how that works, so let's take a quick dive into how maps work in Alteryx.
There are two ways Alteryx creates maps – fetching map tiles from an API (Carto and DigitalGlobe Aerials use this method) and rendering spatial data with a .map file (TomTom uses this method). In a practical sense, the TomTom map consists of a file inventory of .yxdb's, and the styling, layering, and rendering of the data within those .yxdb's is orchestrated by a .map file. The DigitalGlobe Aerials with Streets map is actually a hybrid of the road network spatial data in select .yxdb’s rendered on top of the satellite imagery tiles from the API.
Why does this matter?
Location Insights customers can take advantage of the base .yxdb’s for use in their spatial analysis. Perhaps you’re interested in lake polygons.
These raw .yxdb’s can be found wherever you installed the Location Insights package. For instance, the files should be found in C:\Data_Install\AlteryxMap\TomTom_US_2018_Q4\Data if I installed the Q4 2018 US data to C:\Data_Install.
To make this even more convenient, our US Location Insights users have a TomTom Layer Extraction app included with their installation. Access it within Alteryx via: Options > Run Analytical Apps > TomTom Layer Extraction App > Run
Configure the app to according to your needs – narrow down to which state geography(s) you want to extract from, then select the specific layer(s) you want data for, and finally select your output and if you want to merge the geographies.
Now you can integrate the spatial data for these layers directly into your workflow!
As mentioned previously, the styling, layering, and rendering of the spatial data within the .yxdb's is orchestrated by a .map file. Users can actually edit those .map files to make styling tweaks or add new elements. This process can be a bit tricky, especially for users without a GIS background. I won't dive into the process, but here is a Community Article that discusses .map file customization if you are interested.
I have a list of zip codes, can I use Alteryx to determine the city they're in?
The answer is yes! But with a few caveats. Zip Codes can be notoriously difficult to pair with cities they belong in because they exist in two forms; points and polygons. Point Zip Codes are generally associated with businesses or universities, while polygons generally encompass residential areas. Alteryx data does have Zip Codes with Points data; however, it is not immediately accessible and will require some configuration on the part of the user to get access to that level of data.
Zip Codes are also frequently adjusted, deprecated, and consolidated by the USPS, so depending on the age/vintage of the zip codes in your data as compared to that in the Alteryx Spatial Database, there may be some slight variation there as well. All said, users should still expect a reasonably high match rate.
To start, make sure you either have the Alteryx Data Package installed (available with a license), or you have the 2010 US Census data installed (available for free at http://downloads.alteryx.com/data.html).
With the data installed, the first thing you will bring down is an Allocate Input Tool. Here you will choose the relevant dataset (Experian data or US Census) from the drop down, then check the box for Zip Codes under Pick Geography.
From here you will use the Join Tool to join your data to the Allocate Input data based on your Zip Code field and the Key field of the Allocate Input data. [NOTE: the Zip Code and Key fields will both need to be either String fields or numeric fields. Either is fine as long as it is consistent]
The resulting data that comes from the J output anchor of the Join tool will contain all of your Zip Codes that matched those in the dataset. The field "Name" that comes from the Allocate Input is formatted as the 5 digit Zip Code, followed by the City name. From here a simple Text to Columns tool configured to create 2 columns and parse on the space, will create a field specifically for the City and an extra Zip code field that can be deselected and discarded as the data moves down your workflow.
See an example of the process in the attached workflow below.
The Q4 2018 Business and Location Insights data packages includes analytics-ready data from a variety of vendors as well as data-specific analysis tools to get the most from the packaged data. All data packages will be available via the Downloads & Licenses portal later today. US & Canada Business Insights customers should receive their hard drives later this week, but you are also welcome to download the data from the portal.
Here are a couple important notes for this release. Please see the product specific release notes for more information.
Product name change (does not affect product contents):
US and Canada "Data", which includes demographics, business lists, etc., are now called "Business Insights"
All "Spatial" packages are now called "Location Insights"
DigitalGlobe Satellite Maps base URL changed to whitelist.alteryx.com/v1/dgmaps/v1. See this post for more details.
All documentation packages included in the attached .zip file. Please let us know if you have any questions or concerns.
Team Lead, Data Products
Note that this post includes a corrected version of the US Data Variable List that was not included on the hard drive or via the Downloads & Licenses portal. The correction corresponds to TrueTouch variables within the ConsumerView dataset - there were no new variables with the Q4 2018 release.
Have you ever used the ConsumerView Analytical File in US Core Data and stared wide-eyed at the codes returned? There is now an alternative to looking in the documentation for the coded values! The ConsumerView Renaming Macro allows you to rename the codes into readable data.
If you need more geographical information on a coordinate, try converting it into a spatial object and using the Find Nearest Tool to find coinciding Experian geographical data from an Allocate Input Tool.
Have you ever used the Allocate tools and received back some strange looking variable names? You're not alone! The Allocate Rename Fields Macro will allow you to rename your fields into readable variables.
The macro can be downloaded here. Note: This will navigate you to the Alteryx Gallery. Select "Download & Install the Allocate Rename Fields Macro" and follow the prompts to install.
USING THE TOOL
The Allocate tools allow users to enrich their workflows with third party data provided from Experian and the US Census. This data contains demographic and household information by geography. Allocate tools can be found under the “Demographic Analysis” tab in the Alteryx toolbar; they include the Allocate Input, Allocate Append, Allocate Report, and Allocate Metainfo.
Allocate Input and Allocate Append tools allow users to select variables to display by geography. Once configured, the fields returned look something like this:
Add the Allocate Rename macro after the Allocate Input/Append. In the Configuration window, select the Dataset that you are pulling from. Press Run for the magic!
Voila! Your field names are now human-readable.
What if my company blocks access to downloading new tools/macros from the Gallery?
In the case that you cannot download this macro, you can use Alteryx to dynamically rename the field names. See is it possible to get the variable name I see in the Allocate tool?