Intercity Population Migration Conditioned by City Industry Structures

Abstract

One of the key concerns in geographical and social sciences is to analyze and predict population migration due to its close association with urban planning, industrial upgrade, and urban development. Although the most prevailing framework, the gravity model, has been applied in its various versions, there is little information available about how city industry structure functions as the invisible distance in the modeling of intercity population migration. Here, we introduce a family of improved gravity models by considering city industry structure proximity, complementarity, and diversities. The resulting models predict population migration patterns in good agreement with the flows observed. Our best model (GM_COM) outperforms the benchmark model (GM_O) by 24.6 percent in terms of mean absolute percentage error. Further analysis shows the improved models offer several advantages with respect to the base models. They have better prediction accuracies for flows with high intensities and long distances. The best model demonstrates obvious improvement when flows occur in eastern China. Given the significant improvement of the proposed models, this study broadens existing research by absorbing city industry structure features into the gravity model and deepens our understanding in the population migration as a function of distance.

人口迁移与城市规划、产业升级和城市发展有密切的关系, 地理学和社会学的一个重点是分析和预测人口迁移。尽管最流行的重力模型框架内的各种版本已经得到应用, 但在城际人口迁移建模中, 却很少涉及到城市产业结构做为无形距离的作用。我们考虑了城市产业结构的邻近性、互补性和多样性, 引入了一系列改进的重力模型。模型预测的人口迁移模式与观测的迁移量比较吻合。在平均绝对百分比误差方面, 最佳模型（GM_COM）比基准模型（GM_O）的精度高24.6%。深入的分析表明, 改进的模型比基础模型有几个优点。对于高强度和长距离的迁移, 它们具有更高的预测精度。当中国东部出现迁移时, 最佳模型在精度上有明显提高。鉴于模型的显著改进, 我们将城市产业结构特征融合到重力模型中, 拓宽了现有研究, 加深了我们对人口迁移是一个距离函数的理解。

Una de las preocupaciones claves de las ciencias geográficas y sociales es analizar y predecir la migración de la población en cuanto a la cercana asociación del tema con la planificación urbana, el fomento industrial y el desarrollo urbano. Aunque se ha aplicado el marco de mayor preferencia, el modelo de gravedad, en sus diferentes versiones, se dispone de poca información sobre el funcionamiento de la estructura industrial de la ciudad como distancia invisible en la modelización del modo como migra la población entre ciudades. En este trabajo introducimos una familia de modelos gravitacionales mejorados, considerando la proximidad de la estructura industrial de la ciudad, la complementariedad y las diversidades. Los modelos resultantes predicen patrones migratorios de la población muy concordantes con los flujos observados. Nuestro mejor modelo (GM_COM) supera al modelo de referencia (GM_O) en un 24. 6 por ciento en términos de error porcentual medio absoluto. Un análisis más avanzado muestra que los modelos mejorados ofrecen varias ventajas respecto a los modelos de base. Aquellos tienen mayor exactitud en la predicción para los flujos con intensidades altas y largas distancias. El mejor de los modelos demuestra mejoras obvias cuando los flujos se producen en el oriente de China. Dada la mejora significativa de los modelos propuestos, este estudio amplía la investigación existente absorbiendo las características de la estructura industrial de la ciudad en el modelo de gravedad, y profundiza en nuestro entendimiento de la migración de la población como una función de la distancia.

Key Words:

• city industry structure
• function complementarity
• gravity model
• intercity population migration

关键词:

• 城市产业结构
• 功能互补
• 重力模型
• 城际人口迁移。

Palabras clave:

• estructura industrial de la ciudad
• función de complementariedad
• migración de la población entre ciudades
• modelo de gravedad

Acknowledgments

The authors are thankful to the editor and anonymous referees for their valuable comments and detailed suggestions that improved this article. We also thank Dr. Lei Dong for the data processing and Professor Fahui Wang for the inspiration of economic distance. Xia Li served as the corresponding author for this article.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFA0604402), Director's Fund of Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University (Grant No. KLGIS2021C01), and the Fundamental Research Funds for the Central Universities.

Notes on contributors

Yuxia Wang

YUXIA WANG is a Postdoctoral Researcher in the School of Geographic Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China. E-mail: [email protected]. Her research interests include human mobility, spatiotemporal data mining, and complex urban systems.

Xia Li

XIA LI is a Professor in the School of Geographic Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China. E-mail: [email protected]. His research interests include cellular automata, land use modeling, global land use simulation, and spatial optimization.

Xin Yao

XIN YAO is a Machine Learning Engineer at Alibaba Group, Beijing 100102, China. E-mail: [email protected]. His research interests include anomaly detection, time series modeling, and geospatial artificial intelligence.

Shuang Li

SHUANG LI is an Associate Researcher in the Center for Historical Geography Studies, Fudan University, Shanghai 200433, China. E-mail: [email protected]. Her research interests include historical geography and geographic information systems applications.

Yu Liu

YU LIU is a Professor of GIScience in the Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China. E-mail: [email protected]. His research concentration is in GIScience and big geodata.