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Big Mountain Data

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This presentation was a lightening talk (15 min) at the #WIDS2017 conference in Sarasota, FL. It explains how we address intimate partner violence by developing data-driven solutions.

Published in: Data & Analytics
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Big Mountain Data

  1. 1. BIG MOUNTAIN DATA Data-driven solutions to Intimate Partner Violence Women Data Science Conference February 3, 2017
  2. 2. Who am I? Susan Scrupski, Entrepreneur 30-yr Career in Technology Have “lived experience” with domestic violence
  3. 3. What is Big Mountain Data? • An early stage social impact startup, founded Fall 2014 • We focus on OFFENDERS and their CRIMES associated with domestic violence • We take a STEM approach to solving a societal issue typically addressed with a victim-centric emotional appeal for charity ($4B spent annually) • We partner with leading edge technology companies that align with our vision and goals
  4. 4. Domestic Violence captures a lot of DATA The CDC reports that 1 out of 4 women have experienced severe physical violence from an intimate partner. That’s ~40 MILLION women in the U.S. On the flipside of that equation, there are an equal number of OFFENDERS. Even if we consider one batterer stands to abuse 1 - x number of women, the scale of the “problem” still runs into the millions. We have data on these offenders today. Further, we can experiment with new models to merge unstructured data with the structured data already in law enforcement databases.
  5. 5. What can the data tell us? • Who the most dangerous repeat offenders are and their criminal history • When repeat offenders are more likely to commit an act of violence. • How at-risk a victim is to being re-victimized by her abuser. • The probability of whether a first-time offender is a good candidate for behavioral change. • Where domestic violence occurs (everywhere).
  6. 6. What data? Data that already exists in law enforcement systems. ● Calls for Service (Computer- Aided Dispatch, CAD) 911 calls ● Arrest Data (Record Management Systems) ● Incident Reports
  7. 7. Projects SF Hackathon
  8. 8. Testing our Thesis Bayes Impact inaugural hackathon: November 15-16, 2014. Five teams tackled the High Point PD challenge. Justice League - Location-aware app that can discover if offenders that need a preventative visitation is nearby Hack DV Offenders - Predictive tool that can determine the likelihood that a person would engage in severe domestic violence Will It Blend - Predictive analytics on who is likely to engage in domestic violence To Arrest or Not to Arrest - Machine learning tool to predict when an arrest should be made All About the Bayes - Identifies the addresses where domestic violence is most likely to happen
  9. 9. Projects First U.S. Data Dive
  10. 10. A response to the Obama Administration's President’s Task Force on 21st Century Policing 14 of 74 of the key issues identified had to do with data and transparency Orlando experimented with the first community “data dive” exploring domestic violence and sexual assault data. PDI Today: 129 Jurisdictions Orlando Data Dive
  11. 11. Projects Predictive Analytics
  12. 12. Portland Police Used IBM’s SPSS to assess the risk of recidivism and brought the most dangerous offenders to justice. “We wanted to find a data- driven repeatable method that would help us prioritize the most important cases without bias.” - Sergeant Greg Stewart
  13. 13. Projects Hashtag Analysis
  14. 14. #WhyIStayed #WhyILeft An analysis of the social media phenomenon that erupted over the Ray Rice NFL scandal. We analyzed 225K rows of data to ask a simple question: WHY DID THEY? We published the results. Then, we open sourced the data.
  15. 15. Projects High Point Film
  16. 16. DV Offenders are a Threat to Society Omar Mateen, Pulse Markeith Loyd, “Manhunt” Esteban Santiago, Ft. Lauderdale shooter
  17. 17. Technology Partners

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