2017 Urban Science Intensive
Social Impact & Criminal Justice

 



1. Prosecutorial Data Justice

CUSP Students: Hrafnkell Hjörleifsson, Michelle M. Ho, Christopher Prince, Achilles Saxby

CUSP Mentor: Federica B. Bianco

USI Sponsor: BetaGov and The District Attorney’s Office of Santa Clara County (SCC)

The District Attorney’s office of Santa Clara County (SCC), California has observed long durations for their prosecution processes. It is interested in assessing the drivers of prosecutorial delays and determining whether there is evidence of disparate treatment of accused individuals in pre-trial detention and criminal charging practices.

This Capstone has two goals: to create a visualization tool aimed that facilitates the DA’s office exploration of their own data and analytical models that identify drivers of prosecutorial delays.

The visualization tool allows a comparison of four phases of the prosecutorial process for SCC criminal cases: case issuing to arraignment, arraignment to preliminary hearing, preliminary hearing to plea, plea to disposition, and also post-disposition court events. It enables aggregation, filtering, and extraction of statistical quantities of prosecutorial features (e.g., the crime type, the number of defendants on the case, and the number of charges) and demographic features (e.g., race/ethnicity, gender, age). It is designed to run in the CUSP computational environment, assuring protection of identifiable data.

The prosecutorial process duration and outcome are modeled with decision tree algorithms (random forest and gradient boosted trees). The models enable the identification of the most significant features associated with prosecutorial delays, and an assessment of the importance of demographic features in determining prosecution process duration and outcome.


2. Predicting “Failure to Appear” for Misdemeanor Offenses

CUSP Students: Tashay Green, John Hall, Henry Lin, and Jordan Vani

CUSP Mentors: Ravi Shroff

USI Sponsor: New York County District Attorney’s Office

Each year, The New York County District Attorney’s Office (DANY) prosecutes approximately 100,000 violation, misdemeanor, and felony arrests. Of these 100,000 arrests, approximately 25,000 cases involve defendants who have received a Desk Appearance Ticket (DAT) for misdemeanor offenses. Unlike a typical arrest, where defendants are detained from arrest to arraignment (within 24 hours), DAT recipients are instructed to return to court for arraignment at a future date, normally 3-7 weeks after arrest.

Nearly one-in-four DAT defendants fail to appear (FTA) for their initial arraignment date and subsequently receive a warrant for arrest. The New York County District Attorney’s Office would like to know what risk factors are associated with a defendant neglecting to attend their arraignment date after receiving a DAT. Ultimately, by using historical DAT data and machine learning methods, with a focus on interpretability, this work aims to support prosecutorial interventions, such as strategic scheduling, to reduce FTA rates.


3. Piercing the Landlord Corporate Veil

CUSP Students: Sebastian Bana, Nathan Weber, Shalmali Kulkarni, Xinge Zhong

CUSP Mentors: Debra Laefer and Huy Vo

USI Sponsor: New York State Office of the Attorney General

Landlords often obfuscate their identities by purchasing individual buildings with individual corporations and Limited Liability Companies (LLCs). This practice makes locating a landlord’s entire portfolio or even the true owner of a single property difficult. However, the research department in the Office of the New York State Attorney General (“OAG”) has found that landlords frequently use the same address for many of their building transactions, including: purchases, mortgages, and registration.

Given this insight, the veil piercing team, made up of Master Students from New York University’s Center for Urban Science and Progress (NYU CUSP) have built a web-based data exploration tool that can be queried by non-technical end users. This tool will help OAG to improve their understandings towards the ownership network which consists of multiple buildings, multiple LLCs and potential true owners behind. This heightened understanding of owner’s portfolios will enable OAG to strengthen its aggressive efforts to combat harmful landlord practices, such as tenant harassment, deed theft, bank fraud, and other harmful violations affecting tenants and homeowners.


4. SmartShelters: Using Technology and Data Science to Improve Outcomes for Homeless Families in NYC 

CUSP Students: Xueqi (Claire) Huang, Kristi Korsberg, Dara Perl, and Avikal Somvanshi

CUSP Mentor: Constantine Kontokosta, Boyeong Hong, and Awais Malik

USI Sponsor: Women In Need NYC

New York City (NYC) faces the challenge of an ever increasing homeless population with more than 70,000 people living in city shelters in 2016. In 2015, 17% of families with children that exited a homeless shelter returned to the shelter system within a year of leaving. On an average a family stays in the shelter for 11 months. This suggests that “long term stayers” and “repeat entrants” contribute significantly to the homeless population in NYC.

This capstone focuses to understand the factors that affect the readmission and length of stay of homeless families at NYC based Women-in-Need shelters to predict the likelihood of length of stay and re-entry on exit. It also explore deployment of technologies that can help improve service delivery and performance the homeless shelters.