A Data-Driven Method Utilizing Linear Programming And MPCE Analysis To Address Economic Disparities And Optimize Smart City Strategies

Abstract

Smart cities use data-driven policy making to improve social justice and economic sustainability. This study evaluates regional disparities, economic unpredictability, and resource allocation difficulties by analysing Monthly Per Capita Expenditure (MPCE) data from several Indian states and socioeconomic groupings. We highlight notable economic inequality by computing standard deviation, percentiles, and urban-rural discrepancies using statistical methods. In order to maximize welfare impact within budgetary limits, we also use linear programming to optimize budget allocation. With a high MPCE variation (σ = and an urban-rural differential, the results highlight the necessity of focused policy initiatives. Our research offers a mathematical model for smart city economic planning optimization, facilitating effective resource allocation to lower income disparity. The development of future smart cities requires optimized economic planning that ensures equitable growth across both urban and rural regions. This study presents a Linear Programming (LP) model for optimizing budget allocation between urban and rural areas based on Monthly Per Capita Expenditure (MPCE) data. The model aims to minimize economic disparity, ensuring that financial resources are allocated efficiently while adhering to constraints such as minimum allocation limits and urban-rural spending ratios.