We just passed our one-year anniversary of using Location-Based Services (LBS) data, so we decided to update some key sample size figures. The results are exciting: Our sample size has doubled to more than 62 million devices in the US and Canada in the past year. In other words, now our analytics anonymously describe the travel behavior of 23% of the US and Canadian adult population.
There are many reasons for this increase, including our main LBS data partner, Cuebiq, doing a great job. However, the most important reason is that Location-Based Services are becoming more and more widely adopted by consumers. As a result, our clients can now analyze the aggregate travel patterns of nearly ¼ of the population in just a few mouse clicks.
That’s a large sample by any measure, but when you consider the “status quo” methods of collecting travel behavior data, it’s even more dramatic. Imagine how much it would cost – and how long it would take – to collect household travel surveys from 62 million people, or to install sensors and traffic counters on the roads they use every day. It just wouldn’t be feasible. In this blog post, I’ll explain how we calculate sample size (hint: accuracy is more important to us than flashiness) and why it’s grown so much in just one year.
Locational Big Data – the geospatial data created by mobile devices – is ubiquitous. Smartphones, connected cars, fitness trackers, and more create trillions of location records as their users go about their daily lives. There are many benefits of this Big Data, but one, of course, is large sample size. But just knowing something is “large” is not always enough. Many of our clients want to know more detailed info on sample size for individual projects. It helps them understand certainty of results.
In this blog post, I’ll explain how we’ve updated StreetLight InSight® (that's our easy-to-use online platform for transforming Big Data into transportation analytics) so that our clients always know the size of the sample they’re working with.
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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.
After many years of client requests, we’re releasing the first cut of our StreetLight Volume: 2016 AADT Metric on the StreetLight InSight® platform. These beta Metrics provide a very robust estimate of 2016 Annual Average Daily Traffic (AADT) for almost any road in the US. To learn more, keep reading this blog post, or watch our recorded webinar on the new Metrics. Click here to watch the webinar.
We believe this Metric provides estimates that are comparable or better than most of the standard AADT estimation practices for three reasons: