Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data
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 id...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/9/3/158 |
| _version_ | 1828352316608086016 |
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| author | Xudong Liu Yongzhong Tian Xueqian Zhang Zuyi Wan |
| author_facet | Xudong Liu Yongzhong Tian Xueqian Zhang Zuyi Wan |
| author_sort | Xudong Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T01:52:12Z |
| format | Article |
| id | doaj.art-0b905be85a27478e83585ed386d3d5bc |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-14T01:52:12Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-0b905be85a27478e83585ed386d3d5bc2022-12-22T02:19:17ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-03-019315810.3390/ijgi9030158ijgi9030158Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series DataXudong Liu0Yongzhong Tian1Xueqian Zhang2Zuyi Wan3School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaSchool of Geographical Sciences, Southwest University, Chongqing 400715, ChinaSchool of Geographical Sciences, Southwest University, Chongqing 400715, ChinaSchool of Geographical Sciences, Southwest University, Chongqing 400715, ChinaOverall 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.https://www.mdpi.com/2220-9964/9/3/158urban function regionstrajectory datatime seriesdynamic time warpingchengdu |
| spellingShingle | Xudong Liu Yongzhong Tian Xueqian Zhang Zuyi Wan Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data ISPRS International Journal of Geo-Information urban function regions trajectory data time series dynamic time warping chengdu |
| title | Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data |
| title_full | Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data |
| title_fullStr | Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data |
| title_full_unstemmed | Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data |
| title_short | Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data |
| title_sort | identification of urban functional regions in chengdu based on taxi trajectory time series data |
| topic | urban function regions trajectory data time series dynamic time warping chengdu |
| url | https://www.mdpi.com/2220-9964/9/3/158 |
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