Discovery of Time-Sensitive Thematic Patterns in Urban Functional Areas
LIU Junling1,2, DING Sibo1,2, SUN Huanliang1,2, YU Ge3, XU Jingke1,2
1. School of Computer Science and Engineering, Shenyang Jian-zhu University, Shenyang 110168; 2. Liaoning Provincial Big Data Management and Analysis Laboratory of Urban Construction, Shenyang Jianzhu University, Shenyang 110168; 3. School of Computer Science and Engineering, Northeastern University, Shenyang 110169
Abstract The analysis of urban spatial function structure is a hot research direction in the field of urban geographic information. Correct analysis of spatial function can reasonably plan resources and facilitate residents to utilize urban space. Therefore, a model for discovery of time-sensitive thematic patterns in urban functional areas is proposed to analyze the dynamic urban functional area structure changing with time. In the model, the urban space is gridded into multiple spatial units, and the spatial units are embedded and represented by combining user access data and point of interest data. After clustering the theme feature vectors in the time dimension, the feature distribution matrix with differences is obtained to complete the period division. In the spatial dimension, the adjacent areas with similar feature distribution are merged to obtain a time-sensitive urban function theme model. Based on the shared bicycle trajectory data of Beijing and Baidu map query data, the objective dynamic functional areas are divided, the rationality of functional area division is visualized, and the effectiveness of the proposed model is verified via clustering evaluation measures.
Fund:National Key Research and Development Program of China(No.2021YFF0306303), Key Program of the Joint Fund of the National Natural Science Foundation of China(No.U1811261), Natural Science Foundation of Liaoning Province(No.2019-MS-264), and Project of the Educational Department of Liaoning Province(No.LJKZ0582)
Corresponding Authors:
SUN Huanliang, Ph.D., professor. His research interests include spatial data management and data mi-ning.
About author:: LIU Junling, Ph.D., associate professor. Her research interests include spatio-temporal data query and data mining. DING Sibo, master student. His research interests include spatio-temporal data query. YU Ge, Ph.D., professor. His research interests include database theory and system. XU Jingke, Ph.D., professor. His research interests include spatio-temporal database and data mining.
[1] YUAN J, ZHENG Y, XIE X.Discovering Regions of Different Functions in a City Using Human Mobility and POIs // Proc of the 18th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining. New York, USA: ACM, 2012: 186-194. [2] ZHU J W, TAO C, LIN X, et al. A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data. International Journal of Geo-Information, 2021, 10(2). DOI: 10.3390/ijgi10020066. [3] QI G D, LI X L, LI S J, et al. Measuring Social Functions of City Regions from Large-Scale Taxi Behaviors // Proc of the IEEE International Conference on Pervasive Computing and Communications Workshops. Washington, USA: IEEE, 2011: 384-388. [4] HU Y F, HAN Y Q.Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone. Sustainability, 2019, 11(5). DOI: 10.3390/su11051385. [5] GAO S, JANOWICZ K, COUCLELIS H.Extracting Urban Functional Regions from Points of Interest and Human Activities on Location-Based Social Networks. Transactions in GIS, 2017, 21(3): 446-467. [6] WANG S D, LIU Y B, ZHI W, et al. Discovering Urban Functional Polycentricity: A Traffic Flow-Embedded and Topic Modeling-Based Methodology Framework. Sustainability, 2020, 12(5). DOI: 10.3390/su12051897. [7] ZHANG X, SUN G Z, PAN Y G, et al. Students Performance Mo-deling Based on Behavior Pattern. Journal of Ambient Intelligence and Humanized Computing, 2018, 9(5): 1659-1670. [8] HUANG B T, LIU Z G, CHEN J H, et al. Behavior Pattern Clustering in Blockchain Networks. Multimedia Tools and Applications, 2017, 76: 20099-20110. [9] CHOVANAK T, KASSAK O, KOMPAN M, et al. Fast Streaming Behavioural Pattern Mining. New Generation Computing, 2018, 36(4): 365-391. [10] HUSSAIN S M, KANAKAM P, SURYANARAYANA D, et al. Forensics Data Analysis for Behavioral Pattern with Cognitive Predictive Task // Proc of the International Conference on Next Gene-ration Computing Technologies. Berlin, Germany: Springer, 2017: 549-557. [11] SADILEK A, KRUMM J.Far Out: Predicting Long-Term Human Mobility. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 26(1): 814-820. [12] FURLETTI B, CINTIA P, RENSO C, et al. Inferring Human Activities from GPS Tracks // Proc of the 2nd ACM SIGKDD International Workshop on Urban Computing. New York, USA: ACM, 2013. DOI: 10.1145/2505821.2505830. [13] CAO H C, XU F L, SANKARANARAYANAN J, et al. Ha-bit2vec: Trajectory Semantic Embedding for Living Pattern Recognition in Population. IEEE Transactions on Mobile Computing, 2020, 19(5): 1096-1108. [14] MAITI S, SUBRAMANYAM R B V. Mining Behavioural Patterns from Spatial Data. Engineering Science and Technology(An International Journal), 2019, 22(2): 618-628. [15] XU X Z, LIANG T M, ZHU J, et al. Review of Classical Dimensionality Reduction and Sample Selection Methods for Large-Scale Data Processing. Neurocomputing, 2019, 328: 5-15. [16] YAN J, LIU N, YAN S C, et al. Trace-Oriented Feature Analysis for Large-Scale Text Data Dimension Reduction. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(7): 1103-1117. [17] TAO H Y, WANG K L, ZHUO L, et al. Re-examining Urban Region and Inferring Regional Function Based on Spatial-Temporal Interaction. International Journal of Digital Earth, 2019, 12(3): 293-310. [18] PAPADAKIS E, RESCH B, BLASCHKE T.Composition of Place: Towards a Compositional View of Functional Space. Cartography and Geographic Information Science, 2019, 47(1): 28-45. [19] PAPADAKIS E, GAO S, BARYANNIS G.Fusing Knowledge-Based and Data-Driven Techniques for the Identification of Urban Functional Regions. ISPRS International Journal of Geo-Information, 2019, 8. DOI: 10.3390/ijgi8090385. [20] PARENTE J, PEREIRA M G, TONINI M.Space-Time Clustering Analysis of Wildfires: The Influence of Dataset Characteristics, Fire Prevention Policy Decisions, Weather and Climate. Science of the Total Environment, 2016, 559: 151-165. [21] PACI L, FINAZZI F.Dynamic Model-Based Clustering for Spatio-Temporal Data. Statistics and Computing, 2018, 28: 359-374. [22] MELIKER J R, JACQUEZ G M.Space-Time Clustering of Case-Control Data with Residential Histories: Insights into Empirical Induction Periods, Age-Specific Susceptibility, and Calendar Year-Specific Effects. Stochastic Environmental Research and Risk Assessment, 2007, 21(5): 625-634. [23] ZHANG D Z, LEE K, LEE I.Hierarchical Trajectory Clustering for Spatio-Temporal Periodic Pattern Mining. Expert Systems with Applications, 2018, 92. DOI: 10.1016/j.eswa.2017.09.040. [24] PEREIRA M G, CARAMELO L, OROZCO C V, et al. Space-Time Clustering Analysis Performance of an Aggregated Dataset: The Case of Wildfires in Portugal. Environmental Modelling and Software, 2015, 72: 239-249. [25] 宋泽宇,李旸,李德玉,等.融合标签关系的法律文本多标签分类方法.模式识别与人工智能, 2022, 35(2): 185-192. (SONG Z Y, LI Y, LI D Y, et al. Multi-label Classification of Legal Text with Fusion of Label Relations. Pattern Recognition and Artificial Intelligence, 2022, 35(2): 185-192.) [26] LEYLI-ABADI M, LABIOD L, NADIF M.Denoising Autoencoder as an Effective Dimensionality Reduction and Clustering of Text Data // Proc of the Pacific-Asia Conference on Knowledge Disco-very and Data Mining. Berlin, Germany: Springer, 2017: 801-813. [27] WANG Y S, YAO H X, ZHAO S C.Auto-Encoder Based Dimensionality Reduction. Neurocomputing, 2016, 184: 232-242. [28] 张永,刘浩科,张洁.基于类属特征和实例相关性的多标签分类算法.模式识别与人工智能, 2020, 33(5): 439-448. (ZHANG Y, LIU H K, ZHANG J.Multi-label Classification Algorithm Based on Label-Specific Features and Instance Correlations. Pattern Recognition and Artificial Intelligence, 2020, 33(5): 439-448.)
2022, Vol. 35 
