Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data
Bibliographic Data
| ID | 22034133 |
|---|---|
| Authors | Xudong Liu (0000-0001-6873-0606, Southwest University), Yongzhong Tian (0000-0002-6938-2006, Southwest University, corresponding author), Xueqian Zhang (0000-0002-0460-6486, Southwest University), Zuyi Wan (Southwest University) |
| Year | 2020 |
| Volume | 9 |
| Issue | 3 |
| Pages | 158 |
| Publication date | 2020-03-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9030158 |
| OpenAlex | W3010383195 |
| Language | EN |
| Citations received | 17 |
| References cited | 28 |
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
Cluster analysis · Data mining · Dynamic time warping · Geography · Machine learning · Medoid · Time series · Trajectory · Computer Science · Human Mobility and Location-Based Analysis · Impact of Light on Environment and Health · Land Use and Ecosystem Services · Artificial Intelligence
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| Citations per year | 2.83 |
|---|---|
| Citation span | 2020 - 2025 (6) |
| Highly cited | No |