As a pioneer of the “Data for Good” movement, Cuebiq drives positive social impact through the ethical and responsible use of location-based data.
Beyond simply providing data, the Cuebiq Data Platform supports scientific inquiry, provides policy makers with actionable insights, and fosters innovation ecosystems around data equity and privacy.
Historically, performing analyses with location and mobility data was a painful and lengthy process that required in-depth industry knowledge, a sophisticated development space, and a significant amount of time for research, development, and redundant data processing. Beyond technical obstacles, many institutions must reconcile research objectives with strict privacy and ethics requirements.
On the Cuebiq Data Platform, users innovate, customize, and analyze location data within a secure and privacy-centric environment, accelerating their path to research, insights, and results.
Segregation leads to adverse health, educational, and economic outcomes for society. However, most studies on segregation in cities are at the neighborhood level, which fails to capture the intricacies of how communities move and interact throughout the day. Using Cuebiq mobility data, census, and points-of-interest data, MIT’s Human Dynamics Lab created the Atlas of Inequality, illustrating how people from different income segments interact within public and commercial venues. Through a more nuanced understanding of place-based inequality, MIT’s research provides new methods and insights for city planners and policymakers looking to create more equitable public spaces.
When natural disasters strike, it is critical for emergency managers to gain situational awareness into whether communities shelter in place, evacuate, or become permanently displaced. Cuebiq mobility data provides an empirical and privacy-preserving view of evacuation behaviors, powering research at leading institutions such as Cornell University, Texas A&M, University of Maryland, University of Delaware, and the World Bank. Beyond generating descriptive analyses, researchers at the University of Washington have also developed machine-learning models to predict the likelihood of evacuations at a neighborhood level, providing unprecedented insight for disaster preparedness efforts.
In many developing regions across the world, public transportation infrastructure is insufficient to meet user demand, and private transportation options can be too expensive, overcrowded, and even dangerous. By overlaying Cuebiq mobility data with crowdsourced maps of Maputo, Mozambique, researchers at Politecnico di Milano can demonstrate where public transit supply falls short of demand. Politecnico di Milano demonstrates the synergetic effects of combining big data with more traditional data collection practices by working with community-based organizations that crowdsource mapping data through inclusive and participatory design practices.
Throughout the COVID-19 pandemic in the United States, eviction moratoria helped keep millions of Americans in their homes. As policy makers debated how long to maintain the moratoria, researchers at Northeastern University and the University of Pennsylvania developed models to predict the effects of evictions on COVID-19 spread. By incorporating Cuebiq mobility data into their models, the researchers were able to demonstrate that evictions could lead to increased transmission of the virus not only among those directly affected by evictions, but also in the general population, as well.
Cuebiq is proud to be involved in innovations in Open Differential Privacy & Smartnoise Technology. As an Early Adopter of Microsoft’s SmartNoise Accelerator program, Cuebiq applies true Differential Privacy to its humanitarian data products. By leveraging Differential Privacy, Cuebiq can offer granular insights into evacuation patterns during natural disasters without sacrificing the privacy of individual users who entrust Cuebiq with their data.
In order to create sustained impact over the long term, Cuebiq takes a hybrid approach to data philanthropy and creating shared value for society and the research community:
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