1. IS 6833 Professor Vicki Sauter
NYC Stop, Question and Frisk
Analytics Assignment
10 May 2017
Group C
Sam Clervi
Kory Mitchell
Rachel Petzoldt
Mark Phillips
Sabina Yuan

2. Outline Data Preparation and Approach Analysis Findings
Did the NYC SQF program decrease crime?
Did the NYC SQF program decrease weapons?
Was there bias in the SQF program?

3. Data Preparation & Approach
Obtained outside NYC crime statistics
Historical NYC crime data
2000 – 2016
NYC crime and enforcement activity by race
2008 – 2015

4. Analysis Findings NYC SQF found to decrease overall crime rate
NYC SQF found not to impact overall weapons arrests
Racial bias found in NYC SQF program
Based on NYC population by race
Based on NYC criminal activity data

5. Did the program reduce crime?
Results:
Presence of the NYC SQF showed statistical significance in decreased overall crime
Methodology:
Linear regression to test impact of NYC SQF on number of arrests
Investigated SQF variables affecting overall crime, but no statistically significance
NYC Arrests = -11,036.9 * Year – 38,309.7 * SQF + 22,828,043

6. Did the program reduce weapons?
Results:
The NYC SQF did not show statistical significance in weapon reduction
Methodology:
Linear regression to test impact of NYC SQF on number of arrests
Investigated SQF variables affecting overall crime, but no statistically significance
NYC Weapons = * Year * SQF – 327,796

7. Was there bias in the program?
Results:
All races showed statistical significance except Hispanic
Bias in SQF toward Black
Methodology:
Paired t-Test to compare SQF stops by race and NYC population by race
Race
SQF Pop.
NYC Pop.
Pearson
t-Stat
API
3.7 %
13.4 %
0.85
-61.61
Black
52.7 %
23.2 %
-0.58
58.31
White
10.2 %
34.1 %
-0.41
-35.57
Hispanic
30.2 %
29.3 %
---

8. Was there bias in the program?
Results:
All races showed statistical significance
Bias in SQF toward API and Black
Methodology:
Paired t-Test to compare SQF stops by race and NYC arrests by race
Race
SQF Arrests
NYC Arrests
Pearson Coeff.
t-Stat
API
3.7 %
3.4 %
0.89
2.23
Black
52.7 %
48.6 %
-0.53
4.05
White
10.2 %
12.8 %
0.28
-6.74
Hispanic
30.2 %
35.1 %
-0.57
-4.99

9. Thank You! Sam Clervi Kory Mitchell Rachel Petzoldt Mark Phillips
Sabina Yuan