case-study

Partner with New York, Seattle, New Orleans, and Microsoft to explore how data science can help the Vision Zero movement, which aims to reduce traffic-related deaths and severe injuries to zero
Help New York City’s Department of Transportation improve traffic safety on its streets by understanding what existing safety interventions are working and where there is potential for improvement so the city can better allocate resources
Inform Seattle’s Department of Transportation’s Bicycle and Pedestrian Safety Analysis to provide policy makers and engineers with actionable information to best allocate funding for future safety interventions and to find out what factors may contribute to crashes–such as traffic volume, street characteristics and environmental variables
Help the City of New Orleans’ Office of Performance Accountability understand how effective street treatments, like bike lanes, traffic signage and other interventions, are at preventing traffic injuries and fatalities to inform future efforts

Build a predictive model using John Jay’s data to identify students who are at risk of dropping out, to aid John Jay’s efforts to provide better and more timely support to these students and reduce dropout rates.

Help DC Child and Family Services Agency (CFSA) caseworkers determine their clients’ eligibility for multiple programs and social services at once. 
Create a protoype of a tool and codebase that can easily be repurposed and replicated by other agencies facing similar challenges.

Develop a “proof of concept” water demand forecasting model using flow data at the microzone level for potential future scaling to other retailers in the California Data Collaborative, a unique water manager-led public private partnership that brings together utilities across the state to leverage data to help water managers ensure reliability.  

Analyze the impact of different factors VOTO Mobile can control to increase mobile survey response rates among rural women in developing countries
Develop a tool to enable VOTO Mobile to forecast response rates and design surveys to access the hardest to reach and the least represented populations in the world

Use historical customer payment behavior data to predict which new applicants are likely to be a good fit for their pay-as-you-go solar power systems program

Analyze 15 years of data to uncover predictors for the best engagements between pro bono consultants, nonprofits, and Taproot Foundation
Understand what makes a nonprofit or pro bono consultant a good fit for Taproot’s long-standing and new program delivering pro bono services to nonprofits.

Begin to understand and define “college readiness” for students enrolled at KIPP LA elementary and middle schools
Determine what factors, if any, can predict a KIPP LA student’s level of college readiness, given the available data

Prepare and anonymize never-before-released American Red Cross domestic disaster data and open data to gain insights
Explore the data descriptively with statistics and visually with maps and graphics to understand data quality and identify patterns
Prototype a model that predicts and maps which counties American Red Cross should target for its next smoke alarm installation campaigns

Prototype a tool to harness Citizen Advice’s data to better identify and react to emerging social issues in the UK
Build awareness among Citizens Advice staff of new methods for mining and using data, and encourage them to open their data
Share learnings with other charities on how to better manage and use data