Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data

Xudong Liu,Xueqian Zhang,Zuyi Wan,Yongzhong Tian

doi:10.3390/ijgi9030158

Xudong Liu, Xueqian Zhang + Show 2 more

Open Access

https://doi.org/10.3390/ijgi9030158

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Abstract

Overall scientific planning of urbanization layout is an important component of the new period of land spatial planning policies. Defining the main functions of different spaces and dividing urban functional areas are of great significance for optimizing the land development pattern. This article identifies and analyses urban functional areas from the perspective of data mining. The results of this method are consistent with the actual situation. In this paper, representative taxi trajectory data are selected as the research basis of urban functional areas. First, based on trajectory data from Didi Chuxing within the high-speed road surrounding Chengdu, we generated trajectory time sequence data and used the dynamic time warping (DTW) algorithm to generate a time series similarity matrix. Second, we utilized the K-medoid clustering algorithm to generate preliminary results of land clustering and selected the results with high classification accuracy as the training samples. Then, the k-nearest neighbour (KNN) classification algorithm based on DTW was performed to classify and identify the urban functional areas. Finally, with the help of point-of-interest (POI) auxiliary analysis, the final functional layout in Chengdu was obtained. The results show that the spatial structure of Chengdu is complex and that the urban functions are interlaced, but there are still rules that are followed. Moreover, traffic volume and inflow data can better reflect the travel rules of residents than simple taxi on–off data. The original DTW calculation method has high temporal complexity, which can be improved by normalization and the reduction of time series dimensionality. The semi-supervised learning classification method is also applicable to trajectory data, and it is best to select training samples from unsupervised learning. This method can provide a theoretical basis for urban land planning and has auxiliary and guiding value for urbanization layout in the context of land spatial planning policies in the new era.

Highlights

An urban functional area is a product of nature, society and the economy that enables a city to simultaneously support a variety of human activities, such as work, life, dining and entertainment [1]
To make the results more reliable, this study selected some data with good classification effects from the above clustering results as training samples according to the baselines of the time series of each cluster (Figure 7)
k-nearest neighbour (KNN)–dynamic time warping (DTW) classification based on the training samples was conducted on the remaining data to obtain the final result of the functional area classification (Figure 8)

Summary

Introduction

An urban functional area is a product of nature, society and the economy that enables a city to simultaneously support a variety of human activities, such as work, life, dining and entertainment [1]. The distribution of urban spatial structures and functions has consistently been an important topic in urban research. Traditional studies on urban spatial structure are based on the analysis of the interaction between urban functions and social processes [2]. Many scholars have applied data with location information to the study of urban space. Ahas et al [6] first proposed that mobile phone location data could be applied to the study of urban spatial structure. Ratti et al [7], based on Ahas, demonstrated the results of analysing the spatial and temporal changes of urban activities using thermal maps to mobile phone data, opening the research field of the application of mobile positioning data to a large sample, a large range and a dynamic understanding of urban systems

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Discussion

Conclusion