Smart City Gnosys
Smart city article details
| Title | High-Dimensional Urban Dynamic Patterns Perception Under The Perspective Of Human Activity Semantics And Spatiotemporal Coupling |
|---|---|
| ID_Doc | 29091 |
| Authors | Lv Y.; Yang J.; Xu J.; Guan X.; Zhang J. |
| Year | 2025 |
| Published | Sustainable Cities and Society, 121 |
| DOI | http://dx.doi.org/10.1016/j.scs.2025.106192 |
| Abstract | As urbanization accelerates, megacities are emerging globally. Various human activities shape dynamic urban spaces, understanding dynamic performance implicit within them is essential for developing smart cities. Previous studies on urban dynamic patterns mainly focused on the spatiotemporal dimensions, unable to explain the joint effects of higher-dimensional patterns. In fact, large-scale social media data encapsulate human activity features across multiple dimensions, including semantics, space, and time, whose combined effects drive the formation of high-dimensional urban dynamic patterns. This study proposes a framework that expands the activity semantics dimension on top of spatiotemporal dimensions and perceive these patterns through high-dimensional feature coupling. Activity semantics are extracted from social media texts using ERNIE 3.0, a large-scale knowledge-enhanced pre-trained model. Data with three features dimensions are coupled into high-order tensors, and tensor decomposition uncovers key patterns. A case study using Weibo check-in records within Beijing's Sixth Ring Road extracted ten distinct activity semantics, and interpretable patterns along each dimension. Through core tensors, we identified eight urban dynamic patterns under various states and their corresponding activity complexity changes. Additionally, correlations between activity semantics (dynamic attributes) and fixed facility configurations (static attributes) were explored using Point of Interest (POI) data. The results confirm the advantages of our method in exploring high-dimensional urban dynamic patterns. © 2025 |
| Author Keywords | Activity semantics; High-dimensional pattern mining; Pre-trained language model; Social media data; Tensor decomposition; Urban dynamic patterns |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
20530  | 0.902 | Cao J.; Wang X.; Chen G.; Tu W.; Shen X.; Zhao T.; Chen J.; Li Q. | Disentangling The Hourly Dynamics Of Mixed Urban Function: A Multimodal Fusion Perspective Using Dynamic Graphs | Information Fusion, 117 (2025) |
| 25830 | 0.877 | Liu C.; Zhang H.; Zhu G.; Guan H.; Kwong S. | Exploring Trajectory Embedding Via Spatial-Temporal Propagation For Dynamic Region Representations | Information Sciences, 668 (2024) |
| 3083 | 0.867 | Emek M.S.; Parlak I.B. | A New Method Of Smart City Modeling Using Big Data Techniques | Lecture Notes in Networks and Systems, 759 LNNS (2023) |
| 60280 | 0.864 | Li Z.; Xia L.; Tang J.; Xu Y.; Shi L.; Xia L.; Yin D.; Huang C. | Urbangpt: Spatio-Temporal Large Language Models | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2024) |
| 14008 | 0.862 | Natapov A.; Fisher-Gewirtzman D. | Cities As Visuospatial Networks | Springer Optimization and Its Applications, 125 (2017) |
| 20485 | 0.861 | Liu C.; Yang Y.; Yao Z.; Xu Y.; Chen W.; Yue L.; Wu H. | Discovering Urban Functions Of High-Definition Zoning With Continuous Human Traces | International Conference on Information and Knowledge Management, Proceedings (2021) |
| 25800 | 0.86 | Liu S.; Xu M.; Long Y. | Exploring The Spatiotemporal Pattern Of Urban Human Flows From The Perspective Of Dynamic Network | 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2021 (2021) |
| 17754 | 0.858 | Hu J.; Liang Y.; Fan Z.; Liu L.; Yin Y.; Zimmermann R. | Decoupling Long- And Short-Term Patterns In Spatiotemporal Inference | IEEE Transactions on Neural Networks and Learning Systems, 35, 11 (2024) |
| 60533 | 0.857 | Brelsford C.; Thakur G.; Arthur R.; Williams H. | Using Digital Trace Data To Identify Regions And Cities | Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019 (2019) |
| 21407 | 0.856 | Mazzamurro M.; Wu Y.; Guo W. | Dynamic Spatial Cluster Process Model Of Geo-Tagged Tweets In London | 5th IEEE International Smart Cities Conference, ISC2 2019 (2019) |