Smart city analysis using spatial data and predicting the sustainability

TL;DR

This paper proposes a spatial data-driven approach for smart city planning that predicts optimal locations for industries to promote sustainability and resource efficiency, reducing environmental impact and maximizing ROI.

Contribution

It introduces a novel prediction method using spatial data and genetic algorithms to identify suitable industry locations for sustainable urban development.

Findings

Effective prediction of industry locations using spatial data

Reduced environmental exploitation through optimized placement

Enhanced ROI for investors with sustainable planning

Abstract

Smart city [1] planning is crucial as it should balance among resources and the needs of the city .It allows to achieve good eco-friendly industries, there by supporting both the nature and the stake holders. Setting up an industry is a difficult problem, because it should optimize the resources and allocating it in an effective manner. Weighted sum approach [2] uses the spatial data for finding appropriate places to set up the industry based on the weight assigned to each constraint. The user can predict the possible places in the search space, where the industry can be set with low time complexity using spatial data. Diversity being introduced by using multipoint crossover and mutation operations. It will help to bring exploration in the search space, thereby bring the diversity factor into the solution space. The prediction approach will help to avoid the human exploitation on nature for resources. This in turn helps the investors to maximize the Return on Investment (ROI).