Big data sources are of paramount importance today more than ever before. During the COVID-19 pandemic, big data can provide critical insights into the dynamic state, spread, and impact of the coronavirus. Recognizing this, Cuebiq is proud to be leading the COVID-19 Data Collaborative.
Cuebiq’s Data for Good Program
For years, Cuebiq has been dedicated to providing social value through location data. Through our Data for Good Program, we provide access to anonymous, privacy-compliant location data for academic research and humanitarian initiatives related to human mobility.
Having had this Data for Good Program in place since 2017, we were in a prime position to launch the data collaborative, as we’d already established existing relationships with many organizations who use our data. These collaborations have resulted in projects ranging from working on natural disaster relief, to analyzing social inequality, to assisting with epidemiology efforts such as measuring the impact of travel restrictions during the Zika epidemic of 2016. As such, at the outset of the coronavirus pandemic, Cuebiq was poised to start contributing data to existing and new partners alike to aid in the COVID-19 crisis.
COVID-19 Data Collaborative
The COVID-19 Data Collaborative consists of 15 groups including researchers, practitioners, and data scientists across academia, international and humanitarian organizations, and the private sector. Participants’ domain expertise includes epidemiology, applied mathematics, data governance and responsibility, computational social science, and big data modeling.
Among other research areas, participants of the Data Collaborative are currently using Cuebiq data to analyze the effects of COVID-19 and public response efforts on human mobility and its impacts on society, while also modeling the spread of COVID-19.
Initial Outputs From the Collaborative
While the COVID-19 Data Collaborative is still in its early days, it has already produced some revelatory outputs. Cuebiq has provided data and insights to a number of organizations mapping mobility patterns during the pandemic, including:
Cuebiq has partnered with the University of Oxford to help illustrate how life in the United Kingdom has changed since the coronavirus outbreak began. Using Cuebiq location data, a team of AI and big data researchers at Oxford created an online dashboard to understand and predict the impact of the UK’s COVID-19 social distancing measures on population movements nationwide.
Researchers at MIT have been applying Cuebiq location data to analyze the effectiveness of the social distancing policies adopted in the New York metropolitan area in response to the coronavirus pandemic. The initial findings reveal that the area’s social distancing policies have led to major changes in where people spend their time and how they interact with each other. For more detail, check out the coverage of this project in the Wall Street Journal.
Researchers at Northeastern MOBS Lab are using Cuebiq data to analyze changes in mobility, commuting patterns, and contacts in cities around the United States, while also developing predictive computational tools for the analysis of the spatial spread of COVID-19.
If you are interested in using Cuebiq’s location data for academic research or humanitarian purposes, please reach out to our Data for Good team.
Mobility and Store Visitation Insights
In addition to these academic projects, Cuebiq is also providing free COVID-19 mobility and store visitation insights to businesses that are navigating the market uncertainty. These insights will help brands and agencies monitor their business performance, inform or modify their national and local ad strategies, and support brick-and-mortar locations as they begin to reopen.
To get more granular, Cuebiq is also offering brand and vertical-level insights directly within our platform, Clara, to help businesses monitor brand health with real-time offline intelligence.
To access these vertical and brand-level visitation insights, simply request credentials to log in to our platform.