Modeling smart growth of cities through entropy and logistics

TL;DR

This paper presents a novel predictive algorithm for urban smart growth, combining entropy-based metrics and logistic models to simulate and evaluate growth plans for cities with populations over 100,000.

Contribution

It introduces a differential model that assesses growth proposals through a weighted entropy metric, enabling effective simulation and optimization of urban development strategies.

Findings

The algorithm accurately predicts city growth patterns.

Simulations identify optimal growth proposals.

The model provides actionable insights for urban planners.

Abstract

We introduce a predictive algorithm for the smart growth of cities with populations upward of 100,000, allowing for extensive simulations of growth plans and their effects upon an urban populous. A smart growth metric is calculated to evaluate the progress of a city at each phase of its adaptation of the growth plan, which is measured using a weighted entropy method. The predictive algorithm itself is built from a unique differential model, which calculates the growth of a city from smart growth proposals that are individually assessed by a logistic weight model. These proposals are then sorted based on effectiveness and efficiency observed from the simulations, giving insight into the best approach to providing the target cities with a hopeful future.