Towards semantic enrichment for spatial interactions

References

• Barrat, A., M. Barthélemy, and A. Vespignani. 2005. “The Effects of Spatial Constraints on the Evolution of Weighted Complex Networks.” Journal of Statistical Mechanics: Theory and Experiment 2005 (5): 49–68. https://doi.org/10.1088/1742-5468/2005/05/P05003.
   Google Scholar
• Batty, M. 2008. “The Size, Scale, and Shape of Cities.” Science 319 (5864): 769–771. https://doi.org/10.1126/science.1151419.
   PubMed Web of Science ®Google Scholar
• Batty, M. 2013. The New Science of Cities. Cambridge, MA: MIT press.
   Google Scholar
• Bengio, Y., A. Courville, and P. Vincent. 2013. “Representation Learning.” A Review and New Perspectives.”ieee Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1798–1828. https://doi.org/10.1109/TPAMI.2013.50.
   Google Scholar
• Brockmann, D., and D. Helbing. 2013. “The Hidden Geometry of Complex, Network-Driven Contagion Phenomena.” Science 342 (6164): 1337–1342. https://doi.org/10.1126/science.1245200.
   PubMed Web of Science ®Google Scholar
• Chen, X., J. Xie, C. Xiao, B. Lu, and J. Shan. 2022. “Recurrent Origin–Destination Network for Exploration of Human Periodic Collective Dynamics.” Transactions in GIS 26 (1): 317–340. https://doi.org/10.1111/tgis.12849.
   Web of Science ®Google Scholar
• Chi, G., J.-C. Thill, D. Tong, L. Shi, and Y. Liu. 2016. “Uncovering Regional Characteristics from Mobile Phone Data: A Network Science Approach.” Papers in Regional Science 95 (3): 613–632. https://doi.org/10.1111/pirs.12149.
   Web of Science ®Google Scholar
• Fotheringham, A. S. 1981. “Spatial Structure and Distance-Decay Parameters.” Annals of the Association of American Geographers 71 (3): 425–436. https://doi.org/10.1111/j.1467-8306.1981.tb01367.x.
   Web of Science ®Google Scholar
• Gong, L., X. Liu, L. Wu, and Y. Liu. 2016. “Inferring Trip Purposes and Uncovering Travel Patterns from Taxi Trajectory Data.” Cartography and Geographic Information Science 43 (2): 103–114. https://doi.org/10.1080/15230406.2015.1014424.
   Web of Science ®Google Scholar
• Guimera, R., S. Mossa, A. Turtschi, and L. A. N. Amaral. 2005. “The Worldwide Air Transportation Network: Anomalous Centrality, Community Structure, and cities’ Global Roles.” Proceedings of the National Academy of Sciences of the United States of America 102 (22): 7794–7799. https://doi.org/10.1073/pnas.0407994102.
   PubMed Web of Science ®Google Scholar
• Guo, H., W. Zhang, H. Du, C. Kang, and Y. Liu. 2022. “Understanding China’s Urban System Evolution from Web Search Index Data.” EPJ Data Science 11 (1): 20. https://doi.org/10.1140/epjds/s13688-022-00332-y.
   PubMed Web of Science ®Google Scholar
• Jin, C., J. Cheng, and J. Xu. 2018. “Using User-Generated Content to Explore the Temporal Heterogeneity in Tourist Mobility.” Journal of Travel Research 57 (6): 779–791. https://doi.org/10.1177/0047287517714906.
   Web of Science ®Google Scholar
• Kang, C., Z. Jiang, and Y. Liu. 2022. “Measuring Hub Locations in Time-Evolving Spatial Interaction Networks Based on Explicit Spatiotemporal Coupling and Group Centrality.” International Journal of Geographical Information Science 36 (2): 360–381. https://doi.org/10.1080/13658816.2020.1863411.
   Web of Science ®Google Scholar
• Kang, C., Y. Liu, and L. Wu. 2015. “Delineating Intra-Urban Spatial Connectivity Patterns by Travel-Activities: A Case Study of Beijing, China.” In Proceedings of the 23rd International Conference on Geoinformatics, 1–7, Wuhan, China.
   Google Scholar
• Kang, C., Y. Liu, and L. Wu. 2016. “Delineating Intra-Urban Spatial Connectivity Patterns by Travel-Activities: A Case Study of Beijing, China.” In Proceedings of the 23rd International Conference on Geoinformatics, 7378616. https://doi.org/10.1109/GEOINFORMATICS.2015.7378616.
   Google Scholar
• Kang, C., and K. Qin. 2016. “Understanding Operation Behaviors of Taxicabs in Cities by Matrix Factorization.”computers.” Environment and Urban Systems 60: 79–88. https://doi.org/10.1016/j.compenvurbsys.2016.08.002.
   Web of Science ®Google Scholar
• Kang, C., L. Shi, F. Wang, and Y. Liu. 2020. “How Urban Places are Visited by Social Groups? Evidence from Matrix Factorization on Mobile Phone Data.” Transactions in GIS 24 (6): 1504–1525. https://doi.org/10.1111/tgis.12654.
   Web of Science ®Google Scholar
• Kroeger, P. 2018. Analyzing Meaning: An Introduction to Semantics and Pragmatics. Berlin: Language Science Press.
   Google Scholar
• Leung, X., F. Wang, B. Wu, B. Bai, K. A. Stahura, and Z. Xie. 2012. “A Social Network Analysis of Overseas Tourist Movement Patterns in Beijing: The Impact of the Olympic Games.” International Journal of Tourism Research 14 (5): 469–484. https://doi.org/10.1002/jtr.876.
   Web of Science ®Google Scholar
• Liben-Nowell, D., J. Novak, R. Kumar, P. Raghavan, and A. Tomkins. 2005. “Geographic Routing in Social Networks.” Proceedings of the National Academy of Sciences of the United States of America 102 (33): 11623–11628. https://doi.org/10.1073/pnas.0503018102.
   PubMed Web of Science ®Google Scholar
• Liu, Y. 2016. “Revisiting Several Basic Geographical Concepts: A Social Sensing Perspective.” Acta Geographica Sinica 71 (4): 564–575.
   Google Scholar
• Liu, Y., X. Liu, S. Gao, L. Gong, C. Kang, Y. Zhi, G. Chi, and L. Shi. 2015. “Social Sensing: A New Approach to Understanding Our Socioeconomic Environments.” Annals of the Association of American Geographers 105 (3): 512–530. https://doi.org/10.1080/00045608.2015.1018773.
   Web of Science ®Google Scholar
• Liu, Y., Z. Sui, C. Kang, and Y. Gao. 2014. “Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data.” PLoS ONE 9 (1): e86026. https://doi.org/10.1371/journal.pone.0086026.
   PubMed Web of Science ®Google Scholar
• Liu, Y., F. Wang, Y. Xiao, and S. Gao. 2012. “Urban Land Uses and Traffic ‘Source-Sink areas’: Evidence from GPS-Enabled Taxi Data in Shanghai.” Landscape and Urban Planning 106 (1): 73–87. https://doi.org/10.1016/j.landurbplan.2012.02.012.
   Web of Science ®Google Scholar
• Liu, Y., X. Yao, Y. Gong, C. Kang, X. Shi, F. Wang, J. Wang, et al. 2020. “Analytical Methods and Applications of Spatial Interactions in the Era of Big Data.” Acta Geographica Sinica 75 (7): 1523–1538.
   Google Scholar
• Liu, S., J. Zhang, P. X. Liu, Y. F. Xu, L. Xu, and H. L. Zhang. 2023. “Discovering Spatial Patterns of Tourist Flow with Multi-Layer Transport Networks.” Tourism Geographies 25 (1): 113–135. https://doi.org/10.1080/14616688.2020.1850849.
   Google Scholar
• Maggo, C., and P. Garg. 2022. “From Linguistic Features to Their Extractions: Understanding the Semantics of a Concept.” In Proceedings of 2022 5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022, Sonepat, India, 427–431.
   Google Scholar
• Masucci, A. P., J. Serras, A. Johansson, and M. Batty. 2013. “Gravity versus Radiation Models: On the Importance of Scale and Heterogeneity in Commuting Flows.” Physical Review E 88 (2): 022812. https://doi.org/10.1103/PhysRevE.88.022812.
   Web of Science ®Google Scholar
• Mikolov, T., I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013. “Distributed Representations of Words and Phrases and Their Compositionality.” In Advances in Neural Information Processing Systems, Vol. 26, 3111–3119.
   Google Scholar
• Miller, H. J., and H. J. Miller. 2004. “Tobler’s First Law and Spatial Analysis.” Annals of the Association of American Geographers 94 (2): 284–289. https://doi.org/10.1111/j.1467-8306.2004.09402005.x.
   Web of Science ®Google Scholar
• Mou, N., Y. Zheng, T. Makkonen, T. Yang, J. J. Tang, and Y. Song. 2020. “Tourists’ Digital Footprint: The Spatial Patterns of Tourist Flows in Qingdao, China.” Tourism Management 81:104151. https://doi.org/10.1016/j.tourman.2020.104151.
   Web of Science ®Google Scholar
• O’Kelly, M. E., W. Song, and G. Shen. 1995. “New Estimates of Gravitational Attraction by Linear Programming.” Geographical Analysis 27 (4): 271–285. https://doi.org/10.1111/j.1538-4632.1995.tb00911.x.
   Web of Science ®Google Scholar
• Parent, C., S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko, V. Bogorny, M. L. Damiani, et al. 2013. “Semantic Trajectories Modeling and Analysis.” ACM Computing Surveys 45 (4): 1–32. https://doi.org/10.1145/2501654.2501656.
   Web of Science ®Google Scholar
• Ratti, C., S. Sobolevsky, F. Calabrese, C. Andris, J. Reades, M. Martino, R. Claxton, S. H. Strogatz. 2010. “Redrawing the Map of Great Britain from a Network of Human Interactions.” PLoS ONE 5 (12): e14248. https://doi.org/10.1371/journal.pone.0014248.
   PubMed Web of Science ®Google Scholar
• Ren, M., Y. Lin, M. Jin, Z. Duan, Y. Gong, and Y. Liu. 2020. “Examining the Effect of Land-Use Function Complementarity on Intra-Urban Spatial Interactions Using Metro Smart Card Records.” Transportation 47 (4): 1607–1629. https://doi.org/10.1007/s11116-019-09977-7.
   Web of Science ®Google Scholar
• Roy, J. R., and J. C. Thill. 2004. “Spatial Interaction Modelling.” In Fifty Years of Regional Science, edited by R.J.G.M. Florax and D.A. Plane, 339–361. Heidelberg: Springer.
   Google Scholar
• Rozenfeld, H. D., D. Rybski, X. Gabaix, and H. A. Makse. 2011. “The Area and Population of Cities: New Insights from a Different Perspective on Cities.” American Economic Review 101 (5): 2205–2225. https://doi.org/10.1257/aer.101.5.2205.
   Web of Science ®Google Scholar
• Schläpfer, M., L. Dong, K. O’Keeffe, P. Santi, M. Szell, H. Salat, S. Anklesaria, M. Vazifeh, C. Ratti, and G. West. 2021. “The Universal Visitation Law of Human Mobility.” Nature 593 (7860): 522–527. https://doi.org/10.1038/s41586-021-03480-9.
   PubMed Web of Science ®Google Scholar
• Schneider, C. M., V. Belik, T. Couronné, Z. Smoreda, and M. C. González. 2013. “Unravelling Daily Human Mobility Motifs.” Journal of the Royal Society Interface 10 (84): 20130246. https://doi.org/10.1098/rsif.2013.0246.
   PubMed Web of Science ®Google Scholar
• Simini, F., G. Barlacchi, M. Luca, and L. Pappalardo. 2021. “A Deep Gravity Model for Mobility Flows Generation.” Nature Communications 12 (1): 6576. https://doi.org/10.1038/s41467-021-26752-4.
   PubMed Web of Science ®Google Scholar
• Simini, F., M. C. González, A. Maritan, and A.-L. Barabási. 2012. “A Universal Model for Mobility and Migration Patterns.” Nature 484 (7392): 96–100. https://doi.org/10.1038/nature10856.
   PubMed Web of Science ®Google Scholar
• Sun, L., J. G. Jin, K. W. Axhausen, D. H. Lee, and M. Cebrian. 2015. “Quantifying Long-Term Evolution of Intra-Urban Spatial Interactions.” Journal of the Royal Society Interface 12 (102): 20141089. https://doi.org/10.1098/rsif.2014.1089.
   PubMed Web of Science ®Google Scholar
• Taaffe, E. 2001. “Spatial interaction.” In International encyclopedia of the social & behavioral sciences, edited by N. J. Smelser and P. B. Baltes, 14792–14794. Oxford: Pergamon.
   Google Scholar
• Ullman, E. L. 1980. Geography as Spatial Interaction. Seattle, WA: University of Washington Press.
   Google Scholar
• Wang, J. 2017. “Economic Geography: Spatial Interaction.” In International Encyclopedia of Geography, edited by D. Richardson, N. Castree, and M. Goodchild, 1–4, Hoboken, NJ: John Wiley & Sons, Ltd.
   Google Scholar
• Wang, X., J. Chen, T. Pei. 2021. “I–Index for Quantifying an Urban location’s Irreplaceability.” Computers, Environment and Urban Systems 90:101711. https://doi.org/10.1016/j.compenvurbsys.2021.101711.
   Web of Science ®Google Scholar
• Wang, H., and Z. Li. 2017. “Region Representation Learning via Mobility Flow.” In Proceedings of the 2017 ACM on Conference on Information & Knowledge Management, New York, NY, USA, 237–246. https://doi.org/10.1145/3132847.3133006.
   Google Scholar
• Wang, Y., X. Li, X. Yao, S. Li, and Y. Liu. 2021. “Intercity Population Migration Conditioned by City Industry Structures.” Annals of the American Association of Geographers 112 (5): 1441–1460. https://doi.org/10.1080/24694452.2021.1977110.
   Web of Science ®Google Scholar
• Weber, A., and C. J. Friedrich. 1929. Alfred Weber’s Theory of the Location of Industries. Chicago, IL: Univ. Chicago Press.
   Google Scholar
• Wesolowski, A., G. Stresman, N. Eagle, J. Stevenson, C. Owaga, E. Marube, T. Bousema, C. Drakeley, J. Cox, and C. O. Buckee. 2014. “Quantifying Travel Behavior for Infectious Disease Research: A Comparison of Data from Surveys and Mobile Phones.” Scientific Reports 4 (1): 1–7. https://doi.org/10.1038/srep05678.
   Google Scholar
• Wu, L., L. Yang, Z. Huang, Y. Wang, Y. Chai, X. Peng, and Y. Liu. 2019. “Inferring Demographics from Human Trajectories and Geographical Context.” Computers, Environment and Urban Systems 77:101368. https://doi.org/10.1016/j.compenvurbsys.2019.101368.
   Web of Science ®Google Scholar
• Xiao, Y., F. Wang, Y. Liu, and J. Wang. 2013. “Reconstructing Gravitational Attractions of Major Cities in China from Air Passenger Flow Data, 2001-2008: A Particle Swarm Optimization Approach.” Professional Geographer 65 (2): 265–282. https://doi.org/10.1080/00330124.2012.679445.
   Web of Science ®Google Scholar
• Xu, Y., J. Li, A. Belyi, and S. Park. 2021. “Characterizing Destination Networks Through Mobility Traces of International Tourists — a Case Study Using a Nationwide Mobile Positioning Dataset.” Tourism Management 82:104195. https://doi.org/10.1016/j.tourman.2020.104195.
   Web of Science ®Google Scholar
• Yao, X., L. Wu, D. Zhu, Y. Gao, and Y. Liu. 2019. “Visualizing Spatial Interaction Characteristics with Direction–Based Pattern Maps.” Journal of Visualization 22 (3): 555–569. https://doi.org/10.1007/s12650-018-00543-4.
   Web of Science ®Google Scholar
• Yue, M., C. Kang, C. Andris, K. Qin, Y. Liu, and Q. Meng. 2018. “Understanding the Interplay Between Bus, Metro, and Cab Ridership Dynamics in Shenzhen, China.” Transactions in GIS 22 (3): 855–871. https://doi.org/10.1111/tgis.12340.
   Web of Science ®Google Scholar
• Zeng, B. 2018. “Pattern of Chinese Tourist Flows in Japan: A Social Network Analysis Perspective.” Tourism Geographies 20 (5): 810–832. https://doi.org/10.1080/14616688.2018.1496470.
   Web of Science ®Google Scholar
• Zhong, C., S. M. Arisona, X. Huang, M. Batty, and G. Schmitt. 2014. “Detecting the Dynamics of Urban Structure Through Spatial Network Analysis.” International Journal of Geographical Information Science 28 (11): 2178–2199. https://doi.org/10.1080/13658816.2014.914521.
   Web of Science ®Google Scholar
• Zhong, Y., N. J. Yuan, W. Zhong, F. Zhang, and X. Xie. 2015. “You are Where You Go: Inferring Demographic Attributes from Location Check-Ins.” In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, New York, NY, USA, 295–304. ACM. https://doi.org/10.1145/2684822.2685287.
   Google Scholar
• Zhou, Y., and Y. Huang. 2018. “Deepmove: Learning Place Representations Through Large Scale Movement Data.” In Proceedings of 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, 2403–2412. IEEE.
   Google Scholar