Public service resource allocation evaluation and site selection optimization decision making method using PCA-SOM based on autoencoder

Abstract

The distribution of urban planning and allocation of public service resources currently lacks consistency and suffers from inefficient siting.Electricity consumption data for public service resources was combined with resource quantity and regional population size to evaluate the allocation of public service resources in each region using principal component analysis (PCA).Additionally, the self-organizing mapping (SOM) algorithm was utilized to optimize the siting of educational resources in Lanzhou City as a case.The power data demonstrated the inadequacy of resource allocation and offered accurate guidance for optimal allocation, especially in Lanzhou City.By utilizing the SOM algorithm, the efficiency of educational resource siting was enhanced, and resource allocation was fairly promoted.This study offers a well-researched justification for public service resource allocation in Gansu Province, and serves as a significant reference point for similar research in other regions.

Keywords

• allocation of public service resource
• power data
• principal component analysis
• self-organizing mapping
• site selection optimization