A knowledge-based approach for estimating the distribution of urban mixed land use

Abstract

Estimating the proportion of land-use types in different regions is essential to promote the organization of a compact city and reduce energy consumption. However, existing research in this area has a few limitations: (1) lack of consideration of land-use distribution-related factors other than POIs; (2) inability to extract complex relations from heterogeneous information; and (3) overlooking the correlation between land-use types. To overcome these limitations, we propose a knowledge-based approach for estimating land-use distributions. We designed a knowledge graph to display POIs and other related heterogeneous data and then utilized a knowledge embedding model to directly obtain the region embedding vectors by learning the complex and implicit relations present in the knowledge graph. Region embedding vectors were mapped to land-use distributions using a label distribution learning method integrating the correlation between land-use types. To prove the reliability and validity of our approach, we conducted a case study in Jinhua, China. The results indicated that the proposed model outperformed other algorithms in all evaluation indices, thus illustrating the potential of this method to achieve higher accuracy land-use distribution estimates.

Keywords

• knowledge graph
• land-use distribution
• region embedding
• label distribution learning
• label correlation