1 Introduction

Have you ever wondered what’s behind those hefty numbers of traffic citations dished out daily in Manhattan? Picture this: a whopping 2,500 citations every single day, breaking down to 104 per hour and a staggering 2 per minute. That’s no small figure; it’s a statistic that impacts the community both economically and personally.

In a revealing article by Melissa Sanchez and Sandhya Kambhampati, the ripple effects of traffic citations on individuals’ lives and the economy are laid bare. Take, for instance, this eye-opening excerpt: “Legal experts say what’s happening in Chicago’s bankruptcy courts is unique. Parking, traffic, and vehicle compliance tickets prompt so many bankruptcies the court here leads the nation in Chapter 13 filings…” (Sanchez and Kambhampati). It’s a stark reminder of the profound consequences these citations can have, often plunging individuals into debt and despair rather than serving as a corrective measure.

So, what’s our mission? We’re delving deep into the variables influencing the number of traffic tickets in Manhattan. But before we dive headfirst into the nitty-gritty, let’s clarify why these tickets are even issued in the first place. In conversation with Professor Ira Promisel, a seasoned expert in Criminal Justice at St Thomas Aquinas College, we’ve identified three key reasons.

First up, there’s the punitive aspect. In today’s society, punishment is often seen as the go-to method for teaching lessons, and traffic violations are no exception. Then there’s the aim to ease traffic congestion and bolster public safety—tickets serve as a means to keep traffic flow smooth and accidents at bay. Last but not least, let’s not overlook the financial aspect. In a world where money makes the world go ’round, traffic tickets are a lucrative revenue stream for both the city and various organizations.

1.1 Hypothesis:

Hypothesis: If the number of low-level English speakers in a census tract increases in Manhattan, then the number of traffic citations in the census track will increase.
Depend variable: The number of traffic citations.
Independent variable: The number of low-level English speakers.

Hypothesis: If the population has a higher income, then the number of traffic citations in New York City will increase.
Depend variable: The number of traffic citations.
Independent variable: Income

By exploring this angle, we hope to shed light on which demographics are more likely to find themselves on the receiving end of a citation. Ultimately, our goal is to uncover insights that could potentially lead to a reduction in the number of traffic citations, thus positively impacting the fabric of our community’s daily life. Ready to hit the road with us on this investigative journey? Let’s buckle up and explore the road ahead.

1.2 Methods

Data Collection: We collected data from two main sources: NYC open data and US Census data. The NYC open data included over 100,000,000 citations, providing details on where tickets were given and the demographic groups of the recipients. Data Manipulation Platform: We used RStudio, an IDE for the R programming language, to manipulate and analyze the collected data.

Data Manipulation Procedure: Our data manipulation process involved executing specific code provided by our project advisor, Professor Andrew Lee, and mentor Jojo Josie. This code facilitated reading, cleaning, and utilizing the collected data. Once cleaned, we applied various statistical techniques such as exploratory data analysis and regression modeling to extract insights from the data. Overall, our methodology aimed to utilize NYC open data and US Census data to analyze the distribution and demographics of citations issued in the city using RStudio’s statistical capabilities.

2 English Level vs Spanish Level

In this section we will be comparing the number of citations given regarding English or Spanish level. Is it true that the higher your English level the lower the number of citations received? Lets find out.

Using a heat map lets compare the number of people in Manhattan who speak English vs Spanish in each tract.

2.0.0.1 Heat map showing the English level in sections of Manhattan

2.0.0.2 Heat map showing the Spanish level in sections of Manhattan

2.1 Scatter plot Comparison

2.1.0.1 Scatter plot and Regression Line comparing the count of citations given in an area to the income English level in sections of Manhattan

2.2 Observations:

2.2.0.1 English Level

Ascending regression line shows that as the number of persons who speak English on the x axis increases the number of citations given in the area also increase.

Based on the graph and the regression line created we see that when the number of persons speaking English is zero (0) the number of citations given is 390.22.

Calculation: 0.042/1000 = 42

For every additional 1000 people who speak English well,an additional 42 traffic citations were given.

2.2.0.2 Scatter plot and Regression Line comparing the count of citations given in an area to the income Spanish level in sections of Manhattan

2.3 Observation:

2.3.0.1 Spanish Level

Descending regression line shows that as the number of persons who speak Spanish on the x axis increases the number of citations given in the area decreases.

Based on the graph and the regression line created we see that when the number of persons speaking English is zero the number of citations given is 570.76.

Calculations: 0.090/1000 = 90

For every additional 1000 people who speak Spanish well,an additional 90 traffic citations were given.

2.4 Discussion of Findings:

Our hypothesis suggested that an increase in the number of low-level English speakers in a census tract in Manhattan would lead to a rise in the number of traffic citations in that area. However, the observed data paints a different picture. The ascending regression line indicates that as the number of English speakers increases, so does the number of traffic citations issued in the census tract. Upon closer examination of the data, for every additional 1000 people who speak English well, there were approximately 42 more traffic citations issued on average. This indicates a positive correlation between the number of English speakers and the number of traffic citations. Hence, contradicting our hypothesis. On the same page, the other graph that compares the Spanish Level with the number of traffic citations shows that the more fluent people are in Spanish, the fewer traffic citations they get, contradicting our hypothesis and supporting the first graph related to English Levels.

3 Income Level

3.0.0.1 Heat map showing the income quintile level in sections of Manhattan (Hundred Thousand Dollars)

3.0.0.2 Scatter plot and Regression Line comparing the count of citations given in an area to the income quintile level in sections of Manhattan (Hundred Thousand Dollars)

3.1 Observation

The ascending regression line indicates a positive correlation between the number of individuals with higher incomes, represented on the x-axis, and the number of citations issued in the area. This finding aligns with the hypothesis proposed at the beginning of our research.


Based on the graph and the regression line created we see that when the number of persons speaking English is zero (0) the number of citation given on average is 206.78

Calculation: 0.003/1000 = 3

For every additional $1000 of median income in the area, an additional 3 traffic citations were given.

3.2 Discussion of Findings

Our hypothesis suggested that an increase in population income would correspond to a rise in the number of traffic citations issued in New York City. The data we collected supports this hypothesis. The ascending regression line demonstrates a positive relationship between the number of individuals with higher incomes and the number of citations issued in the area, confirming our initial expectations. The regression analysis reveals a clear trend: for every additional $1000 of median income in the area, an average of 3 more traffic citations were issued. This reaffirms our hypothesis and indicates a direct correlation between income levels and traffic violations in New York City.

4 Conclusion

English Level:

In conclusion, the analysis indicates a positive correlation between the number of high-level English speakers and traffic citations in New York City, as shown by the ascending regression line. However, the increase of 42 citations per 1000 additional English speakers does not represent a statistically significant change. After using the other graph comparing the Spanish Level and the number of traffic citations, we saw that the more fluent Spanish speakers, the fewer traffic citations, supporting the first graph on the English Level. This contrasts with the initial hypothesis linking lower English levels to increased traffic citations. The regression analysis suggests that while English proficiency may have some influence on citation rates, other factors beyond income levels also play a role.  

Income:

In conclusion, the analysis of the data supports the hypothesis that higher income leads to an increase in traffic citations. This suggests that factors or behaviors associated with higher income levels, like more cars and facilities to pay these citations, contribute to a greater incidence of traffic violations in New York City.

4.1 Reference

Sanchez, Melissa, and Sndhya Kambhampati. “How Chicago Ticket Debt Sends Black

Motorists into Bankruptcy.” ProPublica.  

https://features.propublica.org/driven-into-debt/chicago-ticket-debt-bankruptcy/. 

Mello, Stephen. “Fines and Financial Wellbeing.” Under review.

https://mello.github.io/files/fines.pdf