Two of the questions we’re often asked here at StreetLight Data are: “What percentage of the population is creating the location records in your sample?" and "Does the location data in your sample fairly represent all income groups, or is it biased?” In this blog post, we’re pulling back the curtain on our internal evaluation process with a deep-dive analysis of our newest data source: Location-Based Services data. We started using this data source chiefly because of its large sample size and representativeness, so in this blog post, I will show you our process for determining these characteristics. (Click here to read more about Location-Based Services data in general.)
I’m excited to share that we updated StreetLight InSight® again this week – and it’s an update that I’ve been eagerly anticipating for quite a long time. Beginning now, our clients can access Metrics derived from our new Location-Based Services data source directly from our StreetLight InSight web app – that’s our one-stop, cloud-based platform for the best Big Data resources and the processing software that makes them useful. So, why am I so excited about this? It means some of our most popular Metrics are even more comprehensive and accurate than before. That’s because our device sample size now represents about 10% of the U.S. population. We’re processing roughly 60 billion location data points into travel pattern analytics every month – and counting!
Get the latest news about Big Data and mobility analytics for the transportation, retail, and real estate industries.
At last week’s Transportation Research Board Annual Meeting, I attended an excellent panel discussion about transportation data. Towards the end of the panel, the moderator challenged the group with a somewhat loaded question. I don’t recall the exact phrasing, but it was along these lines: “As transportation professionals, we know that we have a huge amount of work to do to upgrade, maintain, and repair our infrastructure. The backlog of projects that we have not yet begun is overwhelming. Given our clear mandate - and the often politicized process of infrastructure investments - does all this new data actually impact our decisions in the real world?” It’s an important question to ask - but based on audience feedback and in my own opinion, the answer is clearly yes.
Occasionally, when I introduce myself as an employee of StreetLight Data, people ask: “Oh, so you measure the data from streetlights?” We do not do that - yet! (But stay tuned for more information on our new partnership with Current Powered by GE down the road)
We're beginning to hear this question often enough that we wanted to publish a blog post about our name. And now is the right time to tell our story: Next week, we will be exhibiting at the Transportation Research Board Annual Meeting, and the StreetLight Data booth (#639) is located near a traffic signal manufacturer.
A few weeks ago, one of my best friends from graduate school moved to Colorado to work for my old employer, Rocky Mountain Institute. It’s a nonprofit research and educational foundation dedicated to efficient and sustainable use of resources. The downside – from my perspective – is that he and his equally awesome wife moved to Boulder, CO for this job. The upside – from his perspective – is that now he can go skiing every weekend. The question is: where should he go skiing?
Ever since we launched StreetLight InSight, our transportation clients have asked about scaling the StreetLight Trip Index to estimate actual vehicle trip counts. Our recommendation has always been to do this manually using trusted local calibration data. In yesterday’s StreetLight InSight update, we transformed that manual process into an automated one with our new, BETA calibration feature.
This means that if you have average daily travel data that you trust for roads that are nearby (or even within) your project, you can enter that information directly into StreetLight InSight and automatically scale Metrics to estimated counts.