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This article is an excerpt from Everything Happens Somewhere: Unlock new dimensions in your analyses with location intelligence. Download the full e-book here.
Applying location analytics to your data transforms rows of spreadsheet records into a visual treasure map of spatial relationships and connections. Key insights practically reveal themselves. There are many remarkable ways to uncover location intelligence now that you have obtained, cleaned, and prepped your spatial data. Let’s explore our top seven.
1 Pin: Put Points on a Map
Everything happens somewhere, so it all starts with a location. In spatial analytics terms, “location” refers to a point referenced onto the Earth’s surface. A large amount of locational data can be stored in a point format, such as stores or customers.
The first step in location analytics is geocoding, or simply creating that point on a map for the location of a standardized address. A latitude and longitude are assigned to the address, effectively “geo-coding” or adding the latitude and longitude “codes” to the location on the Earth’s surface.
Once you geocode a location, you can create a spatial object point, which can not only be shown on a map, but also used for spatial processes such as calculating the distance between points, finding the nearest point, spatially matching point layers to polygon layers, and more.
2 Relate: Connect Points to Form Shapes
All lines and polygons are made up of a series of points (i.e. a series of latitude and longitude geocoded coordinates). You can use lines and shapes to:
- Create a custom polygon trade area or region
- Digitize a proposed travel route line
- Generate a set of points to show locations, such as gas wells, nonprofit organizations, or public libraries
Many datasets are available in ESRI Shapefile .shp, MapInfo .tab, Google Earth .kml and other spatial formats pre-digitized and ready for use.
3 Measure: Find the Distance Between Points
Finding the relationship between your customers and stores, hospitals and patients, or distribution centers to each other in terms of distance is integral to understanding their spatial correlation. It also gives you the ability to provide deeper insights into questions like how much time it takes for your patients to drive to a hospital, or how many customers reside within a 10-minute drivetime area.
Distance can be analyzed with the following categories:
- Direct (straight-line) distance
- Drivetime distance
- Finding the nearest location(s)
Often with these types of analytics, a specified time to travel is set, such as 10 minutes. Using this option, a drivetime polygon can be created, which shows the area covered by travel from one center point (origin) within a specified amount of time. For a 10-minute drivetime trade area, the polygon boundary created shows where one could travel in 10 minutes from the origin point, using all streets in the underlying network.
With Alteryx, the street network is provided by TomTom data and updated quarterly so you have fresh data. This is essential in analyzing high-growth areas where population is increasing rapidly and the road infrastructure is expanding.
4 Layer: Discover Relationships in Datasets
Discovering how various datasets relate to each other gives you a major advantage in understanding your data. Just as layers of maps can be overlaid and aligned with each other, allowing you to view how the individual layers interact, you can overlay points, polylines, and polygons to align them within the same geographic references on the Earth’s surface.
5 Enhance: Apply Demographic and Segmentation Data
A treasure trove of demographic data within geographic boundaries can be used to enhance your spatial datasets and analytics. Alteryx empowers analysts to leverage industry data sources like Experian to provide highly accurate and recent demographic, segmentation, and other variables for seamless use within workflows. The many datasets provided by Experian include:
- Current and projected five-year demographics (updated quarterly)
- MOSAIC Household Lifestyle Segmentation with 71 segments
- Consumer expenditure
- Psychographic, lifestyle and behavioral variables
- Simmons Survey Data
- Census Data, including the American Community Survey (ACS) Data
- And much more
Demographic data are available in many geographic boundaries, from Census block groups and tracts to Designated Market Areas (DMA) as well as zip codes. Many of these boundaries are useful when viewing demographic characteristics, and the data can be accessed immediately using the Allocate Tools in Alteryx.
In addition, appending demographic and segmentation data to your customer, patient, or client locations can provide excellent insight into who they are and the areas where they reside. You can also append the demographic data within your custom trade areas, allowing comparisons of existing store markets to each other, and to prospect market areas.
Using the Spatial Match Tool, you can overlay your geocoded customer points spatially onto the geographic boundaries you choose, matching the points and polygons together within the same geographic references on the Earth’s surface. Then, each of the Experian variables can be accessed for the geographic boundaries you select, allowing you to create customized demographic, segmentation and consumer profiles for your clients, patients, and customers.
6 Map: Share Insights Visually
They say a picture is worth a thousand words, so a map must be worth a million! Maps are essential for conveying extensive amounts of information, all in one image. A well-designed map will be able to deliver that information in a concise, easy-to-read, understandable, and attractive format.
Using the Report Map Tool in Alteryx, various spatial data layers can be added in Point, Polyline and Polygon formats simultaneously. For instance, a point layer of financial donors can be added as a map layer, along with a polygon layer of development outreach areas.
Alteryx provides extensive map layers via TomTom data; these include streets, highways, lakes, rivers, parks, and many other geographic features. It is also possible to modify the base map layers, customizing the map to your preferred color symbology.
Thematic layers, such as those that are popular in population or income maps to show distinction between defined locations, can be easily created. Custom ranges, equal records, equal ranges, and other options like ramped or preset unique value coloring are available for smoothed or individual themes. Map legend fonts, icons, and other elements can be adjusted to get the exact look you need.
7 Act: Select Ideal Sites
Site selection, or the process of performing spatial and demographic analytics to determine the optimal site for a new location, is widely used for not only retail stores, but also for hospitals, malls, government agencies, bank branches, schools, libraries, nonprofit organizations, and more. It is an intricate process, a balance between art and science.
True site selection involves many factors, including population growth, economic measures, urbanicity, evaluation of competitors, retail synergy, existing locations, real estate markets, statistical models and so on. However, the core of the analytic process is spatial analytics using demographic and lifestyle segmentation data, such as the Experian Demographic Data in Alteryx.
The methods explored in this e-book all play a part in the site selection process: Geocoding, distance and drivetime analytics, finding the nearest locations and competitors, appending demographic data within custom or predefined geographic boundaries, and of course, presenting your discoveries and proposed locations on a map.
Many corporations use Alteryx as an integral part of their site selection process, allowing GIS analysts to perform detailed and proprietary location analytics rapidly and with high accuracy. The Reporting Tools enable maps and demographic results to be produced in batch processes, distributing to a database or via email to recipients. Workflows can be packaged and shared with other Alteryx users, facilitating an enterprise-wide analytic environment.
