The Traj2Vec model to quantify residents' spatial trajectories and estimate the proportions of urban land-use types

The formulation of mixed urban land uses is not only intended to find the ideal scenario of land use but also regarded as a way toward sustainable urban development. We propose a geo-semantic mining approach Traj2Vec to quantify the trajectories of residents as high-dimensional semantic vectors. Then, a random forest (RF) method is used to model the relationship between the semantic vectors and mixed urban land uses. The proposed Traj2Vec approach can obtain the highest accuracy (OA = 0.7733, kappa = 0.7245) in urban land-use classification and a high average proportion accuracy (64.0%) in capturing the proportions of urban land-use types. Diversity analysis indicates that Shenzhen has a high degree of mixed urban land use at the scale of a street block. By analyzing the mixing index and the travel distance, we find a weak but significant negative correlation between them (), which not only confirms the conclusion that an increase in the degree of mixing will reduce the travel distances of residents but also verifies the mixing index. This suggests that urban planning should focus on mixed urban land uses, which can reduce the travel distances of residents, reduce energy consumption, and make cities more compact.

Automated summary

The Traj2Vec method and random forest model can accurately quantify the semantic vectors of residents' trajectories and effectively classify mixed urban land use. The study shows that Shenzhen has a high degree of mixed urban land use at the street block scale, emphasizing that urban planning should focus on mixed land use to reduce residents' travel distances, lower energy consumption, and make cities more compact.