StreetLight InSight® Update: Better Trip Purpose Metrics, the Liberty Bell, and Faster Visualizations
I’m thrilled to announce our latest monthly update of StreetLight InSight—that’s our online platform for transforming Big Data into transportation Metrics in minutes. With this release, our platform does even more to close the gap between Big Data and actionable analytics.
We’ve achieved this by adding several new Metrics, updating our databases with the latest location data sets from our suppliers, and implementing features that make StreetLight InSight more user-friendly and responsive. In this blog post, we’ll dive into the most important updates.
New and Improved Metrics
As usual, the improvements we’re most excited about in this release are our new Metrics. These new analytics make analyzing trip purpose and visitor activity much easier.
New Metric: Home-Work Trip Purpose
Our Home-Work Trip Purpose Metric infers why groups of people travel from one place to another by analyzing devices’ aggregated behavior in the prior thirty days. It estimates the share of trips in an analysis that are:
- Home-Based Work: Travel between home and work in either direction
- Home-Based Other: Travel to and from the home, to anywhere other than work
- Non-Home Based: All travel not to or from home
Basically, this means StreetLight InSight can now tell you the share of trips in an Origin-Destination or Zone Activity study that go from home to work or to other locations, and the share of trips that don’t involve the home. These Trip Purposes allow for a much more accurate understanding of commuters, congestion, and general movement throughout an area.
This Metric is based on Location-Based Services data (LBS data), which are location records created by smartphone apps where a user opts-in to location tracking, i.e. dating apps, shopping apps, etc. This is different from our older Simple Trip Purpose Metric, which uses navigation-GPS data.
That older Metric uses parcel designations (Commercial, Residential, or Other) to estimate trip purposes instead of devices’ travel patterns. Click here for more information about how we measure home and work locations.
The key benefit of our new Home-Work Trip Purpose Benefits is that planners can now gather spatially precise trip purpose information empirically, using real-world data, instead of relying on models or on surveys, which are infrequent and tend to have very low response rates. Using Big Data also ensures that short trips (for example, a quick run into the grocery store on the commute home) are captured accurately. Those short trips tend to get undercounted in surveys.
New Metric: Visitor Activity Index
Our new Visitor Activity Index measures the relative volume of visitors that go to a specific location, or “Zone.” It’s focused on activity levels instead of trips, so you can easily evaluate the amount of people who spend time at places of interest. While our Retail customers have seen this Metric before, many of our transportation customers are getting access for the first time. Like our new Home-Work Trip Purpose Metric, it is also based on LBS data.
The Visitor Activity Index is a game-changer for transportation planners because it helps them quickly compare volume of activity that takes place at different locations. This is particularly important for places that seem similar at first, but actually generate very different levels activity.
Many models lump together similar places – for example, grocery stores or tourist attractions – in trip generation formulas without accounting for potential variation within a single category of location. But we all know that a grocery store downtown will generate different types of travel patterns than a grocery store on the outskirts of the city.
By combining our Visitor Activity Index Metrics with our vehicle-based O-D Metrics, planners can get a much clearer understanding of relative trip generation. To show you how it works, we ran an example Project in StreetLight InSight using major landmarks in in Philadelphia, Pennsylvania: The Liberty Bell and Betsy Ross’ House. Next, we created a chart in Excel to compare the average volume of visitors to each attraction on weekends and weekdays (see Figure 1 below).
Figure 1: This chart compares visitor activity at Betsy Ross’ House and the Liberty Bell. Note that this Metric is provided as an index value, not as a count of visitors. Indexing is critical because the number of devices in our sample changes over time, typically increasing. To make sure that our customers can analyze trends over time, we normalize the data. For more information, check out our FAQ page.
Figure 1 (above) shows us that the Liberty Bell experiences more than 2x as many visitors as the Betsy Ross House. This information is useful, especially for transportation planning around potentially crowded downtown areas.
For example, if a Philadelphia transportation agency wanted to create a shuttle service to popular historical landmarks, the data from our Visitor Index Metric would show that they need twice as many shuttles to the Liberty Bell as Betsy Ross’ House. Traditional data collection methods can be costly and inefficient, and without this data, the number of shuttles required per day would be harder to predict.
Because this Metric is based on (LBS) data, it has excellent spatial precision. Even though Betsy Ross’s home occupies a space smaller than one city block, we can measure the visitor activity levels because our LBS location records show devices’ locations with better than ~25 meter accuracy on average.
Figure 2: This diagram shows the impact of a location record’s spatial precision. The purple square represents Betsy Ross’ house. The circles show the potential location of a device at different levels of spatial precision. If location records’ spatial precision exceeds ~20-25 Meters, it is not possible to confirm that the devices are in Betsy Ross house.
Updated Metric – StreetLight Volume: 2016 AADT Metrics
We recently released our new StreetLight Volume: 2016 AADT Metrics, which provides 2016 Annual Average Daily Traffic counts for almost any road in the US. With this update, we have refined our algorithms to use more data, and we now have chart visualizations in StreetLight InSight (see Figure 3 below).
Figure 3: This chart from StreetLight InSight shows AADT for several streets North of Union Square, San Francisco.
Now you don’t even have to download a spreadsheet to get AADT values from StreetLight InSight. We will be adding more volumetric Metrics in the coming months. Contact us to find out more about this Metric.
Welcome to a Better Interface
Next, I’ll outline the key interface and user experience changes that we’ve incorporated into StreetLight InSight.
Faster, Improved Map Visualizations
Our map visualizations for large Zone sets and big Projects are much faster and cleaner thanks to this update. Maps now render 5x to 10x more quickly, and users will see the biggest difference in speed in their largest and most complex Projects. Exploring maps that have already rendered will also be much quicker. Figure 4 (below) shows the type of map that renders much faster now than previously.
Figure 4: This is an example of the type of map visualization that will now load about 10x faster in StreetLight InSight. It visualizes the relative volume of trips that start or stop, on average, in one of 1,408 TAZs in Denver.
Simpler O-D by Pre-Set Geography Download Format
We also introduced a new format for downloads of O-D by Pre-set Geography Projects. This Project creates O-D matrices that compare Zones of interests to ZIPs, TAZs, and Census Blocks Groups. It’s a great analysis to run when you’re not sure what you want to study yet, but simply need an overview of how specific Zones interact with the larger region. Simply upload your Zones of interest and select the appropriate pre-set geography – no need to track down additional shapefiles for these standard Zones, or to guess at what your full study area should be.
Thanks to our update, the format of these O-D by Pre-set Geography projects now match our standard O-D Analysis Projects. This makes it easier to blend different types of Metrics from our platform in tools like MS Excel and modeling simulation software.
New Months of Data
We’ve added a few new months of data to include in your analyses. In the “Project” side of StreetLight InSight, you can now analyze travel patterns using our two different data sources for the following months:
- Navigation-GPS Data: January 2014 – May 2017
- Location-Based Services Data: January 2016 – March 2017
Our Next Release
We are enthusiastic about our latest updates to StreetLight InSight, and we hope you are too! Keep checking back because our next update to StreetLight InSight is right around the corner.
Want to see a new feature? Do you have thoughts on our latest release? Let us know in the comments!