A hybrid model for high spatial and temporal resolution population distribution prediction

References

• Adamatzky, Andrew. 2010. Game of Life Cellular Automata. Vol. 1. Bristol: Springer.
   Google Scholar
• Akagi, Yasunori, Takuya Nishimura, Takeshi Kurashima, and Hiroyuki Toda. 2018. “A Fast and Accurate Method for Estimating People Flow from Spatiotemporal Population Data.” Paper presented at the IJCAI.
   Google Scholar
• Alessandretti, Laura, Ulf Aslak, and Sune Lehmann. 2020. “The Scales of Human Mobility.” Nature 587 (7834): 402–407. doi:10.1038/s41586-020-2909-1.
   PubMed Web of Science ®Google Scholar
• Barlacchi, Gianni, Marco De Nadai, Roberto Larcher, Antonio Casella, Cristiana Chitic, Giovanni Torrisi, Fabrizio Antonelli, Alessandro Vespignani, Alex Pentland, and Bruno Lepri. 2015. “A Multi-Source Dataset of Urban Life in the City of Milan and the Province of Trentino.” Scientific Data 2 (1): 1–15. doi:10.1038/sdata.2015.55.
   Google Scholar
• Bock, Hans Georg, Thomas Carraro, Willi Jäger, Stefan Körkel, Rolf Rannacher, and Johannes P Schlöder. 2013. Model Based Parameter Estimation: Theory and Applications. Vol. 4. Berlin: Springer Science & Business Media.
   Google Scholar
• Bzdok, Danilo, Naomi Altman, and Martin Krzywinski. 2018. “Statistics Versus Machine Learning.” Nature Methods 15 (4): 233–234. doi:10.1038/nmeth.4642.
   PubMed Web of Science ®Google Scholar
• Chen, Jiandong, Wei Fan, Ke Li, Xin Liu, and Malin Song. 2019. “Fitting Chinese Cities’ Population Distributions Using Remote Sensing Satellite Data.” Ecological Indicators 98: 327–333. doi:10.1016/j.ecolind.2018.11.013.
   Web of Science ®Google Scholar
• Chen, Xiaoxuan, Xia Wan, Fan Ding, Qing Li, Charlie McCarthy, Yang Cheng, and Bin Ran. 2019. “Data-driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data.” Journal of Advanced Transportation 2019. doi:10.1155/2019/9401630.
   Web of Science ®Google Scholar
• Chin, Kimberley, Haosheng Huang, Christopher Horn, Ivan Kasanicky, and Robert Weibel. 2019. “Inferring Fine-Grained Transport Modes from Mobile Phone Cellular Signaling Data.” Computers, Environment and Urban Systems 77: 101348. doi:10.1016/j.compenvurbsys.2019.101348.
   Web of Science ®Google Scholar
• Clarke, Keith C. 2014. “Cellular Automata and Agent-Based Models.” Handbook of Regional Science, 1217–1233. doi:10.1007/978-3-642-23430-9_63.
   Google Scholar
• Cooper, Crispin HV, Ian Harvey, Scott Orford, and Alain J Chiaradia. 2018. “Testing the Ability of Multivariate Hybrid Spatial Network Analysis to Predict the Effect of a Major Urban Redevelopment on Pedestrian Flows.” arXiv preprint arXiv:1803.10500. doi: 10.48550/arXiv.1803.10500.
   Google Scholar
• Devi, U., and R. Mohan. 2020. “Long Short-Term Memory with Cellular Automata (LSTMCA) for Stock Value Prediction.” In Data Engineering and Communication Technology, 841–848. Springer. doi:10.1007/978-981-15-1097-7_70.
   Google Scholar
• Duives, Dorine C, Guangxing Wang, and Jiwon Kim. 2019. “Forecasting Pedestrian Movements Using Recurrent Neural Networks: An Application of Crowd Monitoring Data.” Sensors 19 (2): 382. doi:10.3390/s19020382.
   PubMed Web of Science ®Google Scholar
• Eagle, Nathan, Michael Macy, and Rob Claxton. 2010. “Network Diversity and Economic Development.” Science 328 (5981): 1029–1031. doi:10.1126/science.1186605.
   PubMed Web of Science ®Google Scholar
• Eon, Christine, Xin Liu, Gregory M Morrison, and Joshua Byrne. 2018. “Influencing Energy and Water use Within a Home System of Practice.” Energy and Buildings 158: 848–860. doi:10.1016/j.enbuild.2017.10.053.
   Web of Science ®Google Scholar
• González, Marta C., César A. Hidalgo, and Albert-László Barabási. 2008. “Understanding Individual Human Mobility Patterns.” Nature 453 (7196): 779–782. doi:10.1038/nature06958.
   PubMed Web of Science ®Google Scholar
• Greff, Klaus, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. 2016. “LSTM: A Search Space Odyssey.” IEEE Transactions on Neural Networks and Learning Systems 28 (10): 2222–2232. doi:10.1109/TNNLS.2016.2582924.
   PubMed Web of Science ®Google Scholar
• Gu, Danan, Kirill Andreev, and Matthew E. Dupre. 2021. “Major Trends in Population Growth Around the World.” China CDC Weekly 3 (28): 604–613. doi:10.46234/ccdcw2021.160.
   PubMedGoogle Scholar
• Gu, Hengyu, Ziliang Liu, Tiyan Shen, and Xin Meng. 2019. “Modelling Interprovincial Migration in China from 1995 to 2015 Based on an Eigenvector Spatial Filtering Negative Binomial Model.” Population, Space and Place 25 (8): e2253. doi:10.1002/psp.2253.
   Web of Science ®Google Scholar
• Guo, Xifeng, Xinwang Liu, En Zhu, and Jianping Yin. 2017. “Deep Clustering with Convolutional Autoencoders”. Paper presented at the International conference on neural information processing. doi:10.1007/978-3-319-70096-0_39.
   Google Scholar
• Hoffmann, Eike Jens, Karam Abdulahhad, and Xiao Xiang Zhu. 2022. “Using Social Media Images for Building Function Classification.” arXiv preprint arXiv:2202.07315. doi: 10.48550/arXiv.2202.07315.
   Google Scholar
• Huang, Guilin. 2021. “Missing Data Filling Method Based on Linear Interpolation and Lightgbm.” Paper presented at the Journal of Physics: Conference Series. doi:10.1088/1742-6596/1754/1/012187.
   Google Scholar
• Huang, Lida, Tao Chen, Yan Wang, and Hongyong Yuan. 2015. “Forecasting Daily Pedestrian Flows in the Tiananmen Square Based on Historical Data and Weather Conditions.” ISCRAM 18: 1315–1320.
   Google Scholar
• Huang, Xiao, Cuizhen Wang, Zhenlong Li, and Huan Ning. 2021. “A 100 m Population Grid in the CONUS by Disaggregating Census Data with Open-Source Microsoft Building Footprints.” Big Earth Data 5 (1): 112–133. doi:10.1080/20964471.2020.1776200.
   Google Scholar
• Itami, Robert M. 1994. “Simulating Spatial Dynamics: Cellular Automata Theory.” Landscape and Urban Planning 30 (1-2): 27–47. doi:10.1016/0169-2046(94)90065-5.
   Web of Science ®Google Scholar
• Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. “Lightgbm: A Highly Efficient Gradient Boosting Decision Tree.” Advances in Neural Information Processing Systems 30: 3146–3154.
   Google Scholar
• Kim, B. S., and T. G. Kim. 2019. “Cooperation of Simulation and Data Model for Performance Analysis of Complex Systems.” International Journal of Simulation Modelling 18 (4): 608–619.
   Web of Science ®Google Scholar
• Lenormand, Maxime, Miguel Picornell, Oliva G Cantú-Ros, Thomas Louail, Ricardo Herranz, Marc Barthelemy, Enrique Frías-Martínez, Maxi San Miguel, and José J Ramasco. 2015. “Comparing and Modelling Land use Organization in Cities.” Royal Society Open Science 2 (12): 150449. doi:10.1098/rsos.150449.
   PubMed Web of Science ®Google Scholar
• Liu, Shaojun, Yi Long, Ling Zhang, and Hao Liu. 2021. “Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data.” ISPRS International Journal of Geo-Information 10 (8): 545. doi:10.3390/ijgi10080545.
   Web of Science ®Google Scholar
• Liu, Xin, Joseli Macedo, Tao Zhou, Liyin Shen, Yilan Liao, and Yulin Zhou. 2018. “Evaluation of the Utility Efficiency of Subway Stations Based on Spatial Information from Public Social Media.” Habitat International 79: 10–17. doi:10.1016/j.habitatint.2018.07.006.
   Web of Science ®Google Scholar
• Liu, Chi Harold, Chengzhe Piao, Xiaoxin Ma, Ye Yuan, Jian Tang, Guoren Wang, and Kin K Leung. 2021. “Modeling Citywide Crowd Flows using Attentive Convolutional LSTM.” Paper presented at the 2021 IEEE 37th International Conference on Data Engineering (ICDE). doi:10.1109/ICDE51399.2021.00026.
   Google Scholar
• Liu, Jiamin, Bin Xiao, Yueshi Li, Xiaoyun Wang, Qiang Bie, and Jizong Jiao. 2021. “Simulation of Dynamic Urban Expansion Under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata.” Remote Sensing 13 (8): 1499. doi:10.3390/rs13081499.
   Web of Science ®Google Scholar
• Louail, Thomas, Maxime Lenormand, Oliva G. Cantu Ros, Miguel Picornell, Ricardo Herranz, Enrique Frias-Martinez, José J. Ramasco, and Marc Barthelemy. 2014. “From Mobile Phone Data to the Spatial Structure of Cities.” Scientific Reports 4 (1): 1–12. doi:10.1038/srep05276.
   Google Scholar
• Lu, Lili, Ching-Yao Chan, Jian Wang, and Wei Wang. 2017. “A Study of Pedestrian Group Behaviors in Crowd Evacuation Based on an Extended Floor Field Cellular Automaton Model.” Transportation Research Part C: Emerging Technologies 81: 317–329. doi:10.1016/j.trc.2016.08.018.
   Web of Science ®Google Scholar
• Lu, Zhenyu, Jungho Im, Lindi Quackenbush, and Kerry Halligan. 2010. “Population Estimation Based on Multi-Sensor Data Fusion.” International Journal of Remote Sensing 31 (21): 5587–5604. doi:10.1080/01431161.2010.496801.
   Web of Science ®Google Scholar
• Lundberg, Scott M, and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” Paper presented at the Proceedings of the 31st international conference on neural information processing systems.
   Google Scholar
• Lutz, Wolfgang, Warren Sanderson, and Sergei Scherbov. 1997. “Doubling of World Population Unlikely.” Nature 387 (6635): 803–805. doi:10.1038/42935.
   PubMed Web of Science ®Google Scholar
• Mahmoud, Amira. 2018. “The Impact of Built Environment on Human Behaviors.” International Journal of Environmental Science & Sustainable Development 3 (1). doi:10.21625/essd.v2i1.157.g69.
   Google Scholar
• Mirjalili, Vahid, Sebastian Raschka, Anoop Namboodiri, and Arun Ross. 2018. Semi-adversarial networks: Convolutional autoencoders for imparting privacy to face images. Paper presented at the 2018 International Conference on Biometrics (ICB). doi:10.1109/ICB2018.2018.00023.
   Google Scholar
• Mourad, Lablack, Heng Qi, Yanming Shen, and Baocai Yin. 2019. “ASTIR: Spatio-Temporal Data Mining for Crowd Flow Prediction.” IEEE Access 7: 175159–65. doi:10.1109/ACCESS.2019.2950956.
   Web of Science ®Google Scholar
• Okwuashi, Onuwa, and Christopher E Ndehedehe. 2021. “Integrating Machine Learning with Markov Chain and Cellular Automata Models for Modelling Urban Land use Change.” Remote Sensing Applications: Society and Environment 21: 100461. doi:10.1016/j.rsase.2020.100461.
   Google Scholar
• Reichstein, Markus, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, and Nuno Carvalhais. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204. doi:10.1038/s41586-019-0912-1.
   PubMed Web of Science ®Google Scholar
• Robinson, Caleb, Fred Hohman, and Bistra Dilkina. 2017. “A Deep Learning Approach for Population Estimation from Satellite Imagery.” Paper presented at the Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities. doi:10.1145/3149858.3149863.
   Google Scholar
• Roy, S. S., S. Dey, and S. Chatterjee. 2020. “Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework.” IEEE Sensors Journal 20 (18): 10792–10800. doi:10.1109/JSEN.2020.2995109.
   Web of Science ®Google Scholar
• Suganuma, Masanori, Mete Ozay, and Takayuki Okatani. 2018. “Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search.” Paper presented at the International Conference on Machine Learning. doi:10.1109/JSEN.2020.2995109.
   Google Scholar
• Vu, Long, Quang Do, and Klara Nahrstedt. 2011. “Jyotish: A Novel Framework for Constructing Predictive Model of People Movement from Joint Wifi/Bluetooth Trace.” Paper presented at the 2011 IEEE international conference on pervasive computing and communications (PerCom). doi:10.1109/PERCOM.2011.5767595.
   Google Scholar
• Wang, Dehua, Yang Zhang, and Yi Zhao. 2017. “LightGBM: An effective miRNA Classification Method in Breast Cancer Patients.” Paper presented at the Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics. doi:10.1145/3155077.3155079.
   Google Scholar
• Wang, Shuwei, Ronggui Zhou, and Lin Zhao. 2015. “Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network.” Discrete Dynamics in Nature and Society 2015. doi:10.1155/2015/749181.
   Web of Science ®Google Scholar
• Weinstein Agrawal, Asha, Marc Schlossberg, and Katja Irvin. 2008. “How Far, by Which Route and Why? A Spatial Analysis of Pedestrian Preference.” Journal of Urban Design 13 (1): 81–98. doi:10.1080/13574800701804074.
   Google Scholar
• Wolch, Jennifer, John P Wilson, and Jed Fehrenbach. 2005. “Parks and Park Funding in Los Angeles: An Equity-Mapping Analysis.” Urban Geography 26 (1): 4–35. doi:10.2747/0272-3638.26.1.4.
   Web of Science ®Google Scholar
• Yuan, Jing, Yu Zheng, and Xing Xie. 2012. “Discovering Regions of Different Functions in a City Using Human Mobility and POIs.” Paper presented at the Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. doi:10.1145/2339530.2339561.
   Google Scholar
• Zakria, M., and F. Muhammad. 2009. “Forecasting the Population of Pakistan Using ARIMA Models.” Pakistan Journal of Agricultural Sciences 46 (3): 214–223.
   Google Scholar
• Zhang, Yifei. 2018. “A Better Autoencoder for Image: Convolutional Autoencoder.” Paper presented at the ICONIP17-DCEC. Accessed March 23, 2017. http://users.cecs.anu.edu.au/Tom.Gedeon/conf/ABCs2018/paper/ABCs2018_paper_58.pdf.
   Google Scholar
• Zhao, Miaoxi, Gaofeng Xu, Martin de Jong, Xinjian Li, and Pingcheng Zhang. 2020. “Examining the Density and Diversity of Human Activity in the Built Environment: The Case of the Pearl River Delta, China.” Sustainability 12 (9): 3700. doi:10.3390/su12093700.
   Web of Science ®Google Scholar