BIG MOUNTAIN DATA

Data-driven solutions to Intimate Partner Violence

Women Data Science Conference

February 3, 2017

Who am I?

Susan Scrupski,

Entrepreneur

30-yr Career in

Technology

Have “lived

experience” with

domestic violence

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

Domestic Violence

captures a lot of DATAThe 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.

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).

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

Projects SF Hackathon

Testing our ThesisBayes 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

Projects First U.S. Data Dive

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

Projects Predictive Analytics

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

Projects Hashtag Analysis

#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.

Projects High Point Film

DV Offenders are a Threat

to Society

Omar Mateen, Pulse

Markeith Loyd,

“Manhunt”

Esteban Santiago,

Ft. Lauderdale

shooter

Technology Partners