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 excited to share that StreetLight Data has officially announced its new data partnership with Cuebiq, a next generation location intelligence company. As a result of this partnership, StreetLight’s total device sample size will increase to more than 30M devices – that represents over 10% of the adult US population.
In this blog post, we highlight how to use Big Data to measure the performance of transportation policies and infrastructure projects. Since we’re deeply concerned by the rise in traffic fatalities in 2015, we decided to evaluate the performance of a popular safety measure: a road diet.
At StreetLight Data, we’re excited that Vehicle Miles Traveled (VMT) is gaining traction as a transportation performance measure. In this blog post, we explore the reasons behind increasing interest in VMT and discuss why StreetLight InSight®, our easy-to-use web app for transportation Metrics, is a great tool for calculating and exploring VMT. If you’re not familiar with StreetLight InSight, click here to learn more by watching our demo videos.
Bike shares are riding a wave of popularity in the intermodal transit planning community. They are great because they help address several hot-button urban challenges simultaneously; for example, congestion, air pollution, “last mile” transit gaps, and even sedentary lifestyles. From 2004 to 2015, the number of bike share systems worldwide grew from 14 to 855 – that’s over 6,000% growth!1
However, there are still only 54 bike share systems1 across America’s 486 urbanized areas.2 That means there is plenty of room for expansion in the U.S. We wanted to find out how Big Data could play a role in this expansion.