This project delves into the multifaceted dynamics influencing the issuance of traffic tickets in Manhattan, focusing on income levels and English proficiency as primary variables. This is done with the use of data from NYC open data and US Census data and manipulated with the RStudio platform. The investigation examines whether higher income correlates with increased citations due to greater financial capacity for payment, or, conversely, if lower-income areas witness higher citation rates. Additionally, it explores whether individuals with lower English proficiency face higher citation rates, potentially stemming from difficulties in interpreting signage or communicating with law enforcement. Visual representations include an analysis of ticket distribution overlaid with shades indicating concentrations of public assistance recipients. The study raises critical questions regarding the underlying purposes of traffic tickets, positing punitive measures, traffic management, and revenue generation as primary motives. Through this research, we aim to elucidate the intricate interplay between socioeconomic factors, language proficiency, and law enforcement practices in shaping traffic citation trends in Manhattan.

Collected and analyzed personal data over a 3-week period to understand data collection processes, data cleaning, visualization, and analysis.

Conducted a comprehensive analysis of a dataset, aligning with personal interests and specified criteria, to identify trends in the fast-food industry.