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Acquired | Acquired

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About StreetLight Data

StreetLight Data makes it easy and intuitive to use big data for transportation and urban planning, retail, and real estate. The company transforms trillions of anonymous location records from mobile devices into analytics that describe group travel patterns. Its easy-to-use online platform, StreetLight InSight, takes the hassle out of using big data. It lets you design, visualize, and download custom metrics in just a few mouse clicks.On February 7th, 2022, StreetLight Data was acquired by Jacobs. Terms of the transaction were not disclosed.

StreetLight Data Headquarter Location

677 Harrison St.

San Francisco, California, 94107,

United States


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Expert Collections containing StreetLight Data

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

StreetLight Data is included in 2 Expert Collections, including Real Estate Tech.


Real Estate Tech

2,258 items

Startups in the space cover the residential and commercial real estate space with a focus on consumers. Categories include buying, selling and investing in real estate (iBuyers, marketplaces, investment/crowdfunding platforms), and also tenant experience, property management, et


Smart Cities

1,912 items

StreetLight Data Patents

StreetLight Data has filed 1 patent.

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Application Date

Grant Date


Related Topics



Income distribution, Real estate, Geolocation, Global Positioning System, Professional titles and certifications


Application Date


Grant Date


Related Topics

Income distribution, Real estate, Geolocation, Global Positioning System, Professional titles and certifications



Latest StreetLight Data News

National study offers new bike count models: Combining traditional counters and emerging GPS data

Jul 15, 2022

Credit: Photo by Cait McCusker In order to make sure bicyclists’ needs are considered when improving a transportation system, planners and engineers need to know how many people are biking, and where. Traditional bicycle counters can provide data for limited sections of the bike network, often these counters are installed at important locations like trails or bridges. While limited in location, they count everyone who bikes by. Meanwhile, GPS & mobile data cover the entire transportation network, but that data only represents those travelers who are using smartphones or GPS. Combining the traditional location-based data sources with this new, crowdsourced data could offer better accuracy than any could provide alone. “Knowing how many people are bicycling on a street is really important for a number of reasons. As just a few examples, bicycle volumes give you a way to understand safety data and determine crash rates. They provide insight into where and how bicycle trips are taking place, which can help plan for new or improved facilities,” said Nathan McNeil of Portland State University. Supported by a pooled fund grant administered by the National Institute for Transportation and Communities (NITC), Dr. Sirisha Kothuri of Portland State University led a research project aimed at fusing traditional and emerging data sources together, to derive bicycle volumes for an entire transportation network. They developed three models and tested them in six cities: Dallas, Texas; Portland, Bend and Eugene, Oregon; Boulder, Colorado; and Charlotte, North Carolina. Learn more about the project in this research highlight video. DEVELOPING THREE BIKE COUNT MODELS With Kothuri as principal investigator, the research team included Joe Broach and Nathan McNeil of PSU; Kate Hyun, Stephen Mattingly and Md. Mintu Miah of the University of Texas at Arlington; Krista Nordback of the University of North Carolina’s Highway Safety Research Center, and Frank Proulx of Frank Proulx Consulting LLC. First, the team conducted a literature review while cataloging and evaluating the available third-party data sources and existing applications. They chose the six study sites to represent a variety of urban and suburban contexts, with plenty of geographical diversity, and existing bike data available. Of the six, Boulder, Charlotte and Dallas constituted basic sites, where one year of data (2019) was used for modeling. Portland, Bend, and Eugene in Oregon were considered enhanced sites, where three years of data (2017–2019) were used for model estimation. The team chose three relatively new data sources: Strava, Streetlight Data, and GPS data from bike share systems in the case study cities. After collecting demographic, network, bike count and emerging data from the new sources for each of the cities, they developed three sets of models:  One with pooled data from all six cities,  another with just the pooled data from the three Oregon cities,  and finally a set of city-specific models. The researchers then applied the results to each of the six study sites. The city-specific models generally performed the best, showing the most accuracy in predicting bicycle volumes. The scripts used to run the models will soon be published to GitHub, and a link will be posted on the project page for anyone interested in accessing the models. In general, the various data sources appeared to be complementary to one another; that is, adding any two data sources together tended to outperform each data source on its own. Adding even more data should continue to refine accuracy. The findings from this study indicate that rather than replacing conventional bike data sources and count programs, big data sources like Strava and StreetLight actually make the old “small” data even more important. “We will need more ground-truth counts for low-volume sites to capture the variety of locations, and that will make more robust models,” said Kate Hyun of UTA. BETTER MODELS PROVIDE MORE ACCURATE PERFORMANCE MEASURES FOR TRANSPORTATION AGENCIES Josh Roll, Research Analyst & Data Scientist at the Oregon Department of Transportation, served as the chair for the project’s technical advisory committee. He believes the outcome of this research could help transportation agencies get a better handle on how many people are biking in their communities. “At ODOT we just adopted “Bicycle Miles Traveled” as a new key performance measure, and we need a way to measure it, so this project very much helps to fill the gap on how we’re going to do that. This research used cutting-edge data fusion techniques that could lay the groundwork for how transportation agencies like ODOT monitor bicycle activity across the system,” Roll said. For transportation agencies wishing to support active travel to meet various sustainability, public health, and climate-related goals, quickly having accurate data for the entire network would be a giant leap in the right direction. Robust, organized, and accessible count programs will be essential to get the most out of emerging data sources. The more good, vetted data are available, the better models based on emerging sources will perform, so professionals managing bicycle count programs should focus on making data uniform and widely usable. “In order to integrate all of these disparate data sources – automated and manual counts, opt-in apps like Strava, passively collected background data like Streetlight, and GPS-enabled bike sharing systems — into one coherent system, data professionals should organize their data to best take advantage of these new data fusion possibilities. This means making sure nonmotorized data are accurate, consistent, and useful,” said Sirisha Kothuri, lead researcher on the project. ABOUT THE PROJECT Exploring Data Fusion Techniques to Estimate Network-Wide Bicycle Volumes Sirisha Kothuri, Joe Broach and Nathan McNeil, Portland State University; Kate Hyun and Stephen Mattingly, University of Texas at Arlington Download the Final Report (PDF) Download the Project Brief (PDF) Watch a recorded March 10, 2022 Webinar Watch the research highlight video This research was funded by a pooled fund grant through the National Institute for Transportation and Communities, with additional support from the Oregon Department of Transportation, Virginia DOT, Colorado DOT, Central Lane MPO, Portland Bureau of Transportation, District DOT, and Utah DOT. Photo by Cait McCusker RELATED RESEARCH Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections Data-Driven Mobility Strategies for Multimodal Transportation Biking and Walking Quality Counts: Using “BikePed Portal” Counts to Develop Data Quality Checks The National Institute for Transportation and Communities (NITC) is one of seven U.S. Department of Transportation national university transportation centers. NITC is a program of the Transportation Research and Education Center (TREC) at Portland State University. This PSU-led research partnership also includes the Oregon Institute of Technology, University of Arizona, University of Oregon, University of Texas at Arlington and University of Utah. We pursue our theme — improving mobility of people and goods to build strong communities — through research, education and technology transfer.

StreetLight Data Web Traffic

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StreetLight Data Rank

  • When was StreetLight Data founded?

    StreetLight Data was founded in 2011.

  • Where is StreetLight Data's headquarters?

    StreetLight Data's headquarters is located at 677 Harrison St., San Francisco.

  • What is StreetLight Data's latest funding round?

    StreetLight Data's latest funding round is Acquired.

  • How much did StreetLight Data raise?

    StreetLight Data raised a total of $40.53M.

  • Who are the investors of StreetLight Data?

    Investors of StreetLight Data include Jacobs, Osage University Partners, Macquarie Capital, Ajax Strategies, Active Capital and 5 more.

  • Who are StreetLight Data's competitors?

    Competitors of StreetLight Data include CityData.

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