Data-Science Investigation of 311 Request Drivers, Smartphone Reporting Bias, and Performance KPIs

New York City’s 311 system is among the most comprehensive non-emergency municipal reporting systems globally. It enables citizens to report issues related to public services, ask for information, and track government responsiveness. Having received over 40 million requests since its launch, the 311 data offer a rich source to study the performance and equity of urban service provision. 

This project employed a data science case study methodology to examine the NYC 311 data with U.S. Census data (gender, population, and income metrics) for every NYC ZIP code. The study aimed to discover hidden biases in service outcomes related to smartphone use, demographic, and spatial variation. This study offered actionable recommendations to improve the effectiveness of 311 services by comparing key performance indicators.