The smart city movement’s first wave brought tons of stationary sensors to our cities, especially in the context of transportation. These sensors are passively collecting valuable travel pattern information at traffic lights, parking lots, bus stops, sidewalks, and more. But if we want cities that are truly smart – if we want to solve the challenges exposed by our stationary sensors – we have to go beyond them. In this blog post, I will use New York City as a case study to explain why.
For even the most seasoned consulting firms, winning a government contract through competitive bid is no cakewalk. However, there are ways to make your proposals rise to the top – and today, StreetLight Data is introducing a new way to differentiate with StreetLight InSight®, our easy-to-use online platform for turning Big Data into transportation Metrics. Our new Consultant Subscription allows consultants to customize, visualize and download Metrics derived from Massive Mobile Data in ways designed to help grow their businesses. Keep reading this blog post for all the details.
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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!
Since Donald Trump's election on November 8th, 2016, we’ve noticed a major uptick in complaints about traffic in his Manhattan neighborhood. That’s no small feat, especially given that New Yorkers are known for complaining about traffic – just ask Jerry Seinfeld. (For the record, we complain about traffic a ton in San Francisco, too.) However, we hesitate to use subjective grumbling to measure the impact of events. Thanks in part to cognitive biases, people have a tendency to exaggerate traffic and other negative events. Since we were curious about exactly how much travel patterns changed in New York after the election, we decided to use Big Data to crunch the numbers ourselves. (Note: Our study originally appeared in USA Today. Click here to read the article.)
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.