City2vec: Urban knowledge discovery based on population mobile network

Zhang, Yan; Zheng, Xufei; Helbich, Marco; Chen, Nengcheng; Chen, Zeqiang

doi:10.1016/j.scs.2022.104000

Cited by 27 publications

(8 citation statements)

References 58 publications

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“…The grid point counts in vessel trajectories (868) are substantially smaller than the vocabulary size in languages like Chinese and English. Drawing inspiration from City2vec [28], we explore low-dimensional embedding techniques for this application.…”

Section: Word Embedding Training Using Word2vecmentioning

confidence: 99%

biSAMNet: A Novel Approach in Maritime Data Completion Using Deep Learning and NLP Techniques

Li,

Wang

2024

JMSE

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In the extensive monitoring of maritime traffic, maritime management frequently encounters incomplete automatic identification system (AIS) data. This deficiency poses significant challenges to safety management, requiring effective methods to infer corresponding ship information. We tackle this issue using a classification approach. Due to the absence of a fixed road network at sea unlike on land, raw trajectories are difficult to convert and cannot be directly fed into neural networks. We devised a latitude–longitude gridding encoding strategy capable of transforming continuous latitude–longitude data into discrete grid points. Simultaneously, we employed a compression algorithm to further extract significant grid points, thereby shortening the encoding sequence. Utilizing natural language processing techniques, we integrate the Word2vec word embedding approach with our novel biLSTM self-attention chunk-max pooling net (biSAMNet) model, enhancing the classification of vessel trajectories. This method classifies targets into ship types and ship lengths within static information. Employing the Taiwan Strait as a case study and benchmarking against CNN, RNN, and methods based on the attention mechanism, our findings underscore our model’s superiority. The biSAMNet achieves an impressive trajectory classification F1 score of 0.94 in the ship category dataset using only five-dimensional word embeddings. Additionally, through ablation experiments, the effectiveness of the Word2vec pre-trained embedding layer is highlighted. This study introduces a novel method for handling ship trajectory data, addressing the challenge of obtaining ship static information when AIS data are unreliable.

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Section: Word Embedding Training Using Word2vecmentioning

confidence: 99%

biSAMNet: A Novel Approach in Maritime Data Completion Using Deep Learning and NLP Techniques

Li,

Wang

2024

JMSE

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“…The grid point counts in vessel trajectories (868) are substantially smaller than the vocabulary size in languages like Chinese and English. Drawing inspiration from City2vec [26], we explore low-dimensional embedding techniques for this application.…”

Section: Word Embedding Training Using Word2vecmentioning

confidence: 99%

biSAMNet: A Novel Approach in Maritime Data Completion Using Deep Learning and NLP Techniques

Li,

Wang

2024

Preprint

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In the extensive monitoring of maritime traffic, maritime management frequently encounters incomplete automatic identification system (AIS) data, particularly in critical static fields like vessel types. This shortfall presents substantial challenges in safety management, necessitating robust methods for data completion. Distinct from road traffic, where road width imposes constraints, maritime vessels of identical classifications often choose significantly divergent routes, deviating by several nautical miles. Sole dependence on trajectory clustering is insufficient. Addressing this issue, we segments the maritime area into spatiotemporal grids, transforming vessel paths into sequences of grid encodings. Utilizing natural language processing techniques, we integrate the Word2vec word embedding approach with our novel biLSTM self-attention chunk-max pooling net (biSAMNet) model, enhancing the classification of vessel trajectories. This method is crucial for determining static details like vessel types. Employing the Taiwan Strait as a case study and benchmarking against CNN, RNN, and methods based on the attention mechanism, our findings underscore our model’s superiority. The biSAMNet achieves an impressive vessel classification F1 score of 0.94, using only 5-dimensional word embeddings, highlighting the effectiveness of Word2vec pre-trained embedding layers. This research introduces a novel paradigm for processing vessel trajectory data, greatly enhancing the accuracy of filling in incomplete vessel type details.

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“…In other words, these models are still lacking in the feature mining of transportation networks and population migration networks. Therefore, Zhang et al [28] modified the node2vec proposed by Grover et al [27] into City2vec and applied it in the discovery of spatial characteristics of city population migration networks. However, the functional evolution network's temporal and spatial characteristics should be considered.…”

Section: The Significance Of Function2vecmentioning

confidence: 99%

“…Node2vec is a well-established graph embedding proposed by Grover et al in 2016. It converts the connection features between network nodes into feature vectors using two sampling strategies: Depth-First Search (DFS) and Breadth-First Search (BFS) [27]. In 2022, Zhang et al [28] modified Node2vec to create City2vec, which has been successfully applied in the field of urban geography to mine knowledge from the population mobility network.…”

Section: Introductionmentioning

confidence: 99%

Function2vec: A Geographic Knowledge Graph Model of Urban Function Evolution and Its Application

Li,

Zheng,

Wang

et al. 2023

IJGI

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Urban function evolution (UFE) has become more and more complex in emerging cities. However, insufficient theoretical support exists for the visual expression of the spatial correlation between UFE patterns. In order to fill this gap, we use the 2013 and 2022 Point-of-Interest (POI) data of Shenzhen city to implement the funtion2vec model based on the node2vec model and urban tree theory. In this model, we first divide UFE patterns into three categories: Function Replace (FR), Function Newly Added (FNA), and Function Vanishing (FV). Then, we calculate the correlation between those UFE patterns using their functional vectors, resulting in a graph structure representing the urban function evolution network (UFEN). Based on our case study, we obtained the following conclusions: (1) From 2013 to 2022, the UFE in Shenzhen was primarily dominated by FR (89.44%). (2) FV and FNA exhibit a long-tailed distribution, adhering to the 20–80 law. (3) Through the UFEN based on FR, healthcare services are well suited to form mutual complementarities with other functions; science, education, and cultural services demand a higher complementarity with other functions; administrative offices exhibit a strong diversity in their evolutionary patterns; and the integration of transportation hubs with other functions results in a significantly deviating urban function evolution from its original pattern. The above conclusions suggest that function2vec can well express UFE in emerging cities by adding spatial correlation in UFE.

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City2vec: Urban knowledge discovery based on population mobile network

Cited by 27 publications

References 58 publications

biSAMNet: A Novel Approach in Maritime Data Completion Using Deep Learning and NLP Techniques

biSAMNet: A Novel Approach in Maritime Data Completion Using Deep Learning and NLP Techniques

biSAMNet: A Novel Approach in Maritime Data Completion Using Deep Learning and NLP Techniques

Function2vec: A Geographic Knowledge Graph Model of Urban Function Evolution and Its Application

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