Simulating inter-city population flows based on graph neural networks

References

• Bhagat RB, Mohanty S. 2009. Emerging pattern of urbanization and the contribution of migration in urban growth in India. Asian Population Studies. 5(1):5–20. doi:10.1080/17441730902790024.
   Google Scholar
• Bieger T, Wittmer A. 2006. Air transport and tourism—Perspectives and challenges for destinations, airlines and governments. J Air Transp Manag. 12(1):40–46. doi:10.1016/j.jairtraman.2005.09.007.
   Web of Science ®Google Scholar
• Burkart N, Huber MF. 2021. A survey on the explainability of supervised machine learning. jair. 70:245–317. doi:10.1613/jair.1.12228.
   Google Scholar
• Cao Y, Hua Z, Chen T, Li X, Li H, Tao D. 2023. Understanding population movement and the evolution of urban spatial patterns: an empirical study on social network fusion data. Land Use Policy. 125:106454. doi:10.1016/j.landusepol.2022.106454.
   Web of Science ®Google Scholar
• Carey HC. 1859. Principles of social science. London: Lippincott.
   Google Scholar
• Chen X. 2020. Application of GNN in Urban Computing. In 2020 International Conference on Communications, Information System and Computer Engineering (CISCE). Presented at the 2020 International Conference on Communications, Information System and Computer Engineering (CISCE), IEEE, Kuala Lumpur, Malaysia, pp. 14–17. doi:10.1109/CISCE50729.2020.00010.
   Google Scholar
• Chen Y, Geng M, Zeng J, Yang D, Zhang L, Chen X. 2023. A novel ensemble model with conditional intervening opportunities for ride-hailing travel mobility estimation. Physica A. 628:129167. doi:10.1016/j.physa.2023.129167.
   Web of Science ®Google Scholar
• Chen Y, Zhang D. 2021. Evaluation and driving factors of city sustainability in Northeast China: an analysis based on interaction among multiple indicators. Sustain Cities Soc. 67:102721. doi:10.1016/j.scs.2021.102721.
   Web of Science ®Google Scholar
• Cheong TS, Wu Y. 2014. The impacts of structural transformation and industrial upgrading on regional inequality in China. China Eco Rev. 31:339–350. doi:10.1016/j.chieco.2014.09.007.
   Web of Science ®Google Scholar
• Choe K, Laquian AA. 2008. City cluster development: toward an urban-led development strategy for Asia, Urban development series. Asian Development Bank, Mandaluyong City, Metro Manila, Philippines.
   Google Scholar
• Coffey WJ. 2000. The geographies of producer services. Urban Geogr. 21(2):170–183. doi:10.2747/0272-3638.21.2.170.
   Web of Science ®Google Scholar
• Defferrard M, Bresson X, Vandergheynst P. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst. 29:3837–3845.
   Google Scholar
• Devi PS, Sudarsan PK. 2021. Determinants of Migration to Goa, India: a gravity model analysis. Ind J Labour Econ. 64(2):485–498. doi:10.1007/s41027-021-00323-z.
   Google Scholar
• Dou X. 2018. Labor mobility under the background of industrial relocation.
   Google Scholar
• Du J, Zhang S, Wu G, Moura JM, Kar S. 2017. Topology adaptive graph convolutional networks. arXiv preprint arXiv:1710.10370.
   Google Scholar
• Fan CC, Li T. 2020. Split households, family migration and Urban settlement: findings from China’s 2015 National Floating Population Survey. SI. 8(1):252–263. doi:10.17645/si.v8i1.2402.
   Google Scholar
• Gu H, Shen J, Chu J. 2023. Understanding intercity mobility patterns in rapidly Urbanizing China, 2015–2019: evidence from longitudinal Poisson gravity modeling. Ann Am Assoc Geographers. 113(1):307–330. doi:10.1080/24694452.2022.2097050.
   Web of Science ®Google Scholar
• Hamilton W, Ying Z, Leskovec J. 2017. Inductive representation learning on large graphs. Adv Neural Inf Process Syst. 30:1024–1234.
   Google Scholar
• He C, Chen T, Mao X, Zhou Y. 2016. Economic transition, urbanization and population redistribution in China. Habitat International. 51:39–47. doi:10.1016/j.habitatint.2015.10.006.
   Web of Science ®Google Scholar
• Hong J, Tang M, Wu Z, Miao Z, Shen GQ. 2019. The evolution of patterns within embodied energy flows in the Chinese economy: a multi-regional-based complex network approach. Sustain Cities Soc. 47:101500. doi:10.1016/j.scs.2019.101500.
   Web of Science ®Google Scholar
• Jia X-Y, Liu E-J, Chen C-Y, He Z, Yan X-Y. 2022. An interactive city choice model and its application for measuring the intercity interaction. Front Phys. 10:850415. doi:10.3389/fphy.2022.850415.
   Web of Science ®Google Scholar
• Jiang Y. 2017. Population migration and brain drain in Northeast China. China Popul Dev Stud. 1(2):71–80. doi:10.1007/BF03500925.
   Google Scholar
• Johnson KM, Beale CL. 2010. The recent revival of widespread population growth in nonmetropolitan areas of the United States1. Rural Sociol. 59(4):655–667. doi:10.1111/j.1549-0831.1994.tb00553.x.
   Google Scholar
• Kingma DP, Ba J. 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980.
   Google Scholar
• Kipf TN, Welling M. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
   Google Scholar
• Lan F, Gong X, Da H, Wen H. 2020. How do population inflow and social infrastructure affect urban vitality? Evidence from 35 large- and medium-sized cities in China. Cities. 100:102454. doi:10.1016/j.cities.2019.102454.
   Web of Science ®Google Scholar
• Lee ES. 1966. A theory of migration. Demography. 3(1):47–57. doi:10.2307/2060063.
   Web of Science ®Google Scholar
• Li Z-C, Sheng D. 2016. Forecasting passenger travel demand for air and high-speed rail integration service: a case study of Beijing-Guangzhou corridor, China. Trans Res Part A: Policy Pract. 94:397–410. doi:10.1016/j.tra.2016.10.002.
   Web of Science ®Google Scholar
• Liu E-J, Yan X-Y. 2020. A universal opportunity model for human mobility. Sci Rep. 10(1):4657. doi:10.1038/s41598-020-61613-y.
   PubMed Web of Science ®Google Scholar
• Liu Wangbao SE. 2016. Spatial pattern of population daily flow among cities based on ICT: a case study of “Baidu Migration. Acta Geographica Sinica. 71:1667. doi:10.11821/dlxb201610001.
   Google Scholar
• Liu Z, Miranda F, Xiong W, Yang J, Wang Q, Silva C. 2020. Learning geo-contextual embeddings for commuting flow prediction. AAAI. 34(01):808–816. doi:10.1609/aaai.v34i01.5425.
   Google Scholar
• Pan J, Lai J. 2019. Spatial pattern of population mobility among cities in China: case study of the National Day plus mid-autumn festival based on Tencent migration data. Cities. 94:55–69. doi:10.1016/j.cities.2019.05.022.
   Web of Science ®Google Scholar
• Qizhi M, Ying L, Kang W. 2016. Spatio-temporal changes of population density and urbanization Pattern in China (2000–2010). China City Planning Review. 25:8–14.
   Google Scholar
• Reia SM, Rao PSC, Barthelemy M, Ukkusuri SV. 2022. Spatial structure of city population growth. Nat Commun. 13(1):5931. doi:10.1038/s41467-022-33527-y.
   PubMed Web of Science ®Google Scholar
• Robinson C, Dilkina B. 2018. A machine learning approach to modeling human migration. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies. pp. 1–8. doi:10.1145/3209811.3209868.
   Google Scholar
• Rodrigue J-P. 2020. The geography of transport systems. New York: Routledge.
   Google Scholar
• Rong C, Feng J, Li Y. 2019. Deep learning models for population flow generation from aggregated mobility data. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. Presented at the UbiComp ’19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, London United Kingdom, pp. 1008–1013. doi:10.1145/3341162.3349319.
   Google Scholar
• Sandryhaila A, Moura JMF. 2013. Discrete signal processing on graphs: graph fourier transform. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Presented at the ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Vancouver, BC, Canada, pp. 6167–6170. doi:10.1109/ICASSP.2013.6638850.
   Google Scholar
• Shao S, Tian Z, Yang L. 2017. High speed rail and urban service industry agglomeration: evidence from China’s Yangtze River Delta region. J Trans Geogr. 64:174–183. doi:10.1016/j.jtrangeo.2017.08.019.
   Web of Science ®Google Scholar
• Simini F, Barlacchi G, Luca M, Pappalardo L. 2021. A deep gravity model for mobility flows generation. Nat Commun. 12(1):6576. doi:10.1038/s41467-021-26752-4.
   PubMed Web of Science ®Google Scholar
• Simini F, González MC, Maritan A, Barabási A-L. 2012. A universal model for mobility and migration patterns. Nature. 484(7392):96–100. doi:10.1038/nature10856.
   PubMed Web of Science ®Google Scholar
• Spadon G, Carvalho A, Rodrigues JF, Jr, Alves LGA. 2019. Reconstructing commuters network using machine learning and urban indicators. Sci Rep. 9(1):11801. doi:10.1038/s41598-019-48295-x.
   PubMedGoogle Scholar
• Stouffer SA. 1940. Intervening opportunities: a theory relating mobility and distance. Am Sociol Rev. 5(6):845–867. doi:10.2307/2084520.
   Google Scholar
• Wajdi N, Adioetomo SM, Mulder CH. 2017. Gravity models of interregional migration in Indonesia. Bull Indones Econ Stud. 53(3):309–332. doi:10.1080/00074918.2017.1298719.
   Web of Science ®Google Scholar
• Wang Y, Dong L, Liu Y, Huang Z, Liu Y. 2019. Migration patterns in China extracted from mobile positioning data. Habitat Int. 86:71–80. doi:10.1016/j.habitatint.2019.03.002.
   Web of Science ®Google Scholar
• Wang Y, Li X, Yao X, Li S, Liu Y. 2022. Intercity population migration conditioned by city industry struct. Ann Am Assoc oGeogr. 112(5):1441–1460. doi:10.1080/24694452.2021.1977110.
   Web of Science ®Google Scholar
• Wei Qi FJ, Liu S. 2017. Calculation and spatial evolution of population loss in Northeast China. Scientia Geographica Sinica. 37:1795. doi:10.13249/j.cnki.sgs.2017.12.002.
   Google Scholar
• Williams CC. 1996. Understanding the role of consumer services in local economic development: some evidence from the Fens. Environ Plan A. 28(3):555–571. doi:10.1068/a280555.
   Web of Science ®Google Scholar
• Wu J, Yu Z, Wei YD, Yang L. 2019. Changing distribution of migrant population and its influencing factors in urban China: economic transition, public policy, and amenities. Habitat Int. 94:102063. doi:10.1016/j.habitatint.2019.102063.
   Web of Science ®Google Scholar
• Xiao L, Wu X, Wang G. 2019. Social network analysis based on Graph SAGE. in 2019 12th International Symposium on Computational Intelligence and Design (ISCID). Presented at the 2019 12th International Symposium on Computational Intelligence and Design (ISCID), IEEE, Hangzhou, China, pp. 196–199. doi:10.1109/ISCID.2019.10128.
   Google Scholar
• Xu F, Uszkoreit H, Du Y, Fan W, Zhao D, Zhu J. 2019. Explainable AI: a brief survey on history, research areas, approaches and challenges. In: Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part II 8. Springer, pp. 563–574.
   Google Scholar
• Yao X, Gao Y, Zhu D, Manley E, Wang J, Liu Y. 2021. Spatial origin-destination flow imputation using graph convolutional networks. IEEE Trans Intell Transport Syst. 22(12):7474–7484. doi:10.1109/TITS.2020.3003310.
   Web of Science ®Google Scholar
• Ying Z, Bourgeois D, You J, Zitnik M, Leskovec J. 2019. Gnnexplainer: generating explanations for graph neural networks. Adv Neural Inf Process Syst. 32:9240–9251.
   Google Scholar
• You Z, Yang H, Fu M. 2018. Settlement intention characteristics and determinants in floating populations in Chinese border cities. Sustain Cities Soc. 39:476–486. doi:10.1016/j.scs.2018.02.021.
   Web of Science ®Google Scholar
• Zhang P. 2008. Revitalizing old industrial base of Northeast China: process, policy and challenge. Chin Geogr Sci. 18(2):109–118. doi:10.1007/s11769-008-0109-2.
   Web of Science ®Google Scholar
• Zhang W, Chong Z, Li X, Nie G. 2020. Spatial patterns and determinant factors of population flow networks in China: analysis on tencent location Big Data. Cities. 99:102640. doi:10.1016/j.cities.2020.102640.
   Web of Science ®Google Scholar
• Zhang Y, Zheng X, Helbich M, Chen N, Chen Z. 2022. City2vec: urban knowledge discovery based on population mobile network. Sustain Cities Soc. 85:104000. doi:10.1016/j.scs.2022.104000.
   Web of Science ®Google Scholar
• Zhao Y, Zhang G, Zhao H. 2021. Spatial network structures of urban agglomeration based on the improved gravity model: a case study in China’s two urban agglomerations. Complexity. 2021:1–17. doi:10.1155/2021/6651444.
   Web of Science ®Google Scholar
• Zhao Z, Wei Y, Yang R, Wang S, Zhu Y. 2019. Gravity model coefficient calibration and error estimation: based on Chinese interprovincial population flow. Dili Xuebao/Acta Geographica Sinica. 74:203–221. doi:10.11821/dlxb201902001.
   Google Scholar
• Zhdanov M, Steinmann S, Hoffmann N. 2022. Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer. In 2022 26th International Conference on Pattern Recognition (ICPR). Presented at the 2022 26th International Conference on Pattern Recognition (ICPR), IEEE, Montreal, QC, Canada, pp. 5155–5161. doi:10.1109/ICPR56361.2022.9956201.
   Google Scholar
• Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M. 2020. Graph neural networks: a review of methods and applications. AI Open. 1:57–81. doi:10.1016/j.aiopen.2021.01.001.
   Google Scholar
• Zipf GK. 1946. The P1 P2/D Hypothesis: on the intercity movement of persons. AmSociol Rev. 11(6):677–686. doi:10.2307/2087063.
   Web of Science ®Google Scholar