Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning
Population mobility between cities significantly affects traffic congestion, disease spread, and societal well-being. As globalization and urbanization accelerate, understanding the dynamics of population mobility becomes increasingly important. Traditional population migration models reveal the fac...
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MDPI AG
2024-10-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/13/11/379 |
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| author | Jingjing Liu Lei Xu Le Ma Nengcheng Chen |
| author_facet | Jingjing Liu Lei Xu Le Ma Nengcheng Chen |
| author_sort | Jingjing Liu |
| collection | DOAJ |
| description | Population mobility between cities significantly affects traffic congestion, disease spread, and societal well-being. As globalization and urbanization accelerate, understanding the dynamics of population mobility becomes increasingly important. Traditional population migration models reveal the factors influencing migration, while machine learning methods provide effective tools for creating data-driven models to handle the nonlinear relationships between origin and destination characteristics and migration. To deepen the understanding of population mobility issues, this study presents GraviGBM, an expandable population mobility simulation model that combines the gravity model with machine learning, significantly enhancing simulation accuracy. By employing SHAPs (SHapley Additive exPlanations), we interpret the modeling results and explore the relationship between urban characteristics and population migration. Additionally, this study includes a case analysis of COVID-19, extending the model’s application during public health emergencies and evaluating the contribution of model variables in this context. The results show that GraviGBM performs exceptionally well in simulating inter-city population migration, with an RMSE of 4.28, far lower than the RMSE of the gravity model (45.32). This research indicates that distance emerged as the primary factor affecting mobility before the pandemic, with economic factors and population also playing significant roles. During the pandemic, distance remained dominant, but the significance of short distances gained importance. Pandemic-related indicators became prominent, while economics, population density, and transportation substantially lost their influence. A city-to-city flow analysis shows that when population sizes are comparable, economic factors prevail, but when economic profiles match, living conditions dictate migration. During the pandemic, residents from hard-hit areas moved to more distant cities, seeking normalcy. This research offers a comprehensive perspective on population mobility, yielding valuable insights for future urban planning, pandemic response, and decision-making processes. |
| format | Article |
| id | doaj-art-02ada0efc4274abcba6ed523cbbc96be |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-02ada0efc4274abcba6ed523cbbc96be2025-08-20T02:28:06ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-10-01131137910.3390/ijgi13110379Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine LearningJingjing Liu0Lei Xu1Le Ma2Nengcheng Chen3Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, ChinaNational Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaHubei Luojia Laboratory, Wuhan University, Wuhan 430079, ChinaPopulation mobility between cities significantly affects traffic congestion, disease spread, and societal well-being. As globalization and urbanization accelerate, understanding the dynamics of population mobility becomes increasingly important. Traditional population migration models reveal the factors influencing migration, while machine learning methods provide effective tools for creating data-driven models to handle the nonlinear relationships between origin and destination characteristics and migration. To deepen the understanding of population mobility issues, this study presents GraviGBM, an expandable population mobility simulation model that combines the gravity model with machine learning, significantly enhancing simulation accuracy. By employing SHAPs (SHapley Additive exPlanations), we interpret the modeling results and explore the relationship between urban characteristics and population migration. Additionally, this study includes a case analysis of COVID-19, extending the model’s application during public health emergencies and evaluating the contribution of model variables in this context. The results show that GraviGBM performs exceptionally well in simulating inter-city population migration, with an RMSE of 4.28, far lower than the RMSE of the gravity model (45.32). This research indicates that distance emerged as the primary factor affecting mobility before the pandemic, with economic factors and population also playing significant roles. During the pandemic, distance remained dominant, but the significance of short distances gained importance. Pandemic-related indicators became prominent, while economics, population density, and transportation substantially lost their influence. A city-to-city flow analysis shows that when population sizes are comparable, economic factors prevail, but when economic profiles match, living conditions dictate migration. During the pandemic, residents from hard-hit areas moved to more distant cities, seeking normalcy. This research offers a comprehensive perspective on population mobility, yielding valuable insights for future urban planning, pandemic response, and decision-making processes.https://www.mdpi.com/2220-9964/13/11/379urban mobilityCOVID-19geospatial datadata-driven algorithmslightGBM |
| spellingShingle | Jingjing Liu Lei Xu Le Ma Nengcheng Chen Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning ISPRS International Journal of Geo-Information urban mobility COVID-19 geospatial data data-driven algorithms lightGBM |
| title | Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning |
| title_full | Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning |
| title_fullStr | Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning |
| title_full_unstemmed | Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning |
| title_short | Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning |
| title_sort | modeling population mobility flows a hybrid approach integrating a gravity model and machine learning |
| topic | urban mobility COVID-19 geospatial data data-driven algorithms lightGBM |
| url | https://www.mdpi.com/2220-9964/13/11/379 |
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