Applying Predictive Analytics to Optimize Government Operations and Improve Public Service Delivery in the United States

Abstract

The United States governmental agencies are turning to the use of data-driven strategies to improve operational efficiency and increase the delivery of public services. This study deploys predictive analytics to the NYC 311 Customer Service Requests dataset, which is a high volume of administrative data regarding the citizen-reported non-urgent problems to determine how sophisticated analytical methods could streamline government work. The dataset provides detailed data of type, agency, venue, and time specific service measures, which can be used to perform a detailed evaluation of the dynamics of service demand processes and agency responsiveness. The research will create a new performance measure, Request_Closing_Time, to identify the speed and performance efficiency of their services by carrying out extensive data preprocessing, temporal transformation, and feature engineering.This study uses forecasting tools, such as ARIMA and LSTM, to forecast the volumes of service requests so that governmental agencies can predict the changes in workloads and resource allocation effectively. Service delays and the factors that have the most significant impact on response times are predicted using machine learning algorithms, including Random Forest, SVM, and the XGBoost. Statistical tests also determine that the response time among complaints of different types and the correlations between different types of complaints and geographical locations are significantly different. The findings indicate evident time trends, high agency performance deviation, and high spatial response of the citizen service needs. This study has revealed the benefits of predictive analytics in facilitating proactive decision-making, backlog reduction, and responsiveness of services in urban governments. The lessons also offer a viable roadmap to the incorporation of data-driven projections, categorizing, and statistical evaluation into the work of the U.S. government. Finally, this study emphasizes that predictive analytics may offer a significant change in the way more efficient, transparent, and citizen-centered government services are designed.