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Spatiotemporal evolution and driving factors of urban–rural integration in the Jinan metropolitan area
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  • Published: 10 April 2026

Spatiotemporal evolution and driving factors of urban–rural integration in the Jinan metropolitan area

  • Dawei Mei1,
  • Ningning Liu1 &
  • Haijiao Yu1 

Scientific Reports (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Ecology
  • Environmental sciences
  • Environmental social sciences
  • Geography

Abstract

Research on the spatiotemporal evolution of urban–rural integration areas within metropolitan regions has received increasing attention with the growing availability of remote sensing data and spatial analytical methods. However, existing studies often rely on single data sources or static frameworks, limiting their ability to capture the dynamic evolution and spatial heterogeneity of urban–rural integration. Based on National Polar-Orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data from 2017 to 2023, this study develops an integrated framework combining nighttime light threshold segmentation, an improved gravity model, and spatiotemporally weighted geographic regression (GTWR) to identify urban–rural integration areas and examine their spatiotemporal evolution and driving mechanisms in the Jinan Metropolitan Area. The main findings are as follows: (1) Urban–rural integration areas expanded significantly from 2017 to 2023, with fragmented patches evolving into a more continuous spatial pattern, particularly toward the northeast and east. (2) The spatial association pattern showed dynamic shifts in dominant centers between Licheng–Lixia and Taishan–Daiyue, while remaining relatively stable overall and exhibiting increased association intensity. (3) Spatial interaction intensity was concentrated in a few core districts, whereas the socioeconomic locational index showed pronounced spatial differentiation, with declining trends in central areas and increases in peripheral regions. (4) The effects of economic development level, industrial upgrading, fiscal health, the consumer price index, urban land expansion efficiency, and air pollution displayed significant spatial heterogeneity, varying in both magnitude and direction across regions. Compared with existing studies, this research integrates multi-source data and dynamic spatial econometric modeling to capture the identification, evolution, and driving mechanisms of urban–rural integration areas. Overall, urban–rural integration in metropolitan areas is characterized by spatial expansion and strengthening of spatial linkages, providing a replicable framework for targeted policymaking and coordinated regional development.

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Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

References

  1. Moreno-Monroy, A. I., Schiavina, M. & Veneri, P. Metropolitan areas in the world. Delineation and population trends. J. Urban Econ. 125, 103242 (2021).

    Google Scholar 

  2. Randazzo, M. & Currid-Halkett, E. Rethinking the Urban–Rural Divide: Economic Growth in America’s Heartland. Econ. Dev. Q. 39, 124–135 (2025).

    Google Scholar 

  3. Pandey, B., Brelsford, C. & Seto, K. C. Rising infrastructure inequalities accompany urbanization and economic development. Nat. Commun. 16, 1193 (2025).

    Google Scholar 

  4. Wei, X., Yang, Z., Yan, Y. & Sun, J. Rural E-commerce, Digital finance, and urban–rural common prosperity: A Quasi-natural Experiment Based on China’s Comprehensive Demonstration of E-commerce Entering Rural Areas Policy. Finance Res. Lett 69, (2024).

  5. Wu, Y., Zhou, Y. & Liu, Y. Exploring the outflow of population from poor areas and its main influencing factors. Habitat Int. 99, 102161 (2020).

    Google Scholar 

  6. Zhan, L., Wang, S., Xie, S., Zhang, Q. & Qu, Y. Spatial path to achieve urban-rural integration development – analytical framework for coupling the linkage and coordination of urban-rural system functions. Habitat Int. 142, 102953 (2023).

    Google Scholar 

  7. Guan, X., Wei, H., Lu, S., Dai, Q. & Su, H. Assessment on the urbanization strategy in China: Achievements, challenges and reflections. Habitat Int. 71, 97–109 (2018).

    Google Scholar 

  8. Zhang, C., Fan, Y. & Fang, C. When will China realize urban-rural integration? A case study of 30 provinces in China. Cities 153, 105290 (2024).

    Google Scholar 

  9. Yu, Z., Lin, J., Zhao, Y. & Liu, W. Spatiotemporal dynamics and driving mechanisms of coupled coordination between rural–urban integration and rural resilience in Southwest China. Sci. Rep. 15, 23944 (2025).

    Google Scholar 

  10. Yang, Y., Bao, W., Wang, Y. & Liu, Y. Measurement of urban-rural integration level and its spatial differentiation in China in the new century. Habitat Int. 117, 102420 (2021).

    Google Scholar 

  11. García-Nieto, A. P. et al. Impacts of urbanization around Mediterranean cities: Changes in ecosystem service supply. Ecol. Indic. 91, 589–606 (2018).

    Google Scholar 

  12. Zhou, Y., Li, C., Zheng, W., Rong, Y. & Liu, W. Identification of urban shrinkage using NPP-VIIRS nighttime light data at the county level in China. Cities 118, 103373 (2021).

    Google Scholar 

  13. Potter, R. B. & Unwin, T. Urban-rural interaction: physical form and political process in the Third World. Cities 12, 67–73 (1995).

    Google Scholar 

  14. Guo, C., Zhou, W., Jing, C. & Zhaxi, D. Mapping and measuring urban-rural inequalities in accessibility to social infrastructures. Geogr. Sustain. 5, 41–51 (2024).

    Google Scholar 

  15. Rahman, M. A. et al. Measuring the level of rurality in the Southwestern region of Bangladesh. Front. Urban Rural Plan. 1, 20 (2023).

    Google Scholar 

  16. Thompson, C. A., Saxberg, K., Lega, J., Tong, D. & Brown, H. E. A cumulative gravity model for inter-urban spatial interaction at different scales. J Transp. Geogr 79, (2019).

  17. Yang, T., Zhou, C., Xiao, T. & Xu, Q. Coordinated Development of the Digital Economy and Urban–Rural Integration in the Yangtze River Delta and Its Spatial Correlation Structure. Sustainability 17, 4144 (2025).

    Google Scholar 

  18. Wang, J., Peng, L., Chen, J. & Deng, X. Impact of rural industrial integration on farmers’ income: Evidence from agricultural counties in China. J. Asian Econ. 93, 101761 (2024).

    Google Scholar 

  19. Gao, J., Song, G. & Sun, X. Does labor migration affect rural land transfer? Evidence from China. Land. Use Policy. 99, 105096 (2020).

    Google Scholar 

  20. Chen, K., Long, H., Liao, L., Tu, S. & Li, T. Land use transitions and urban-rural integrated development: Theoretical framework and China’s evidence. Land. Use Policy. 92, 104465 (2020).

    Google Scholar 

  21. Zhou, Y., Li, X. & Liu, Y. Rural land system reforms in China: History, issues, measures and prospects. Land. Use Policy. 91, 104330 (2020).

    Google Scholar 

  22. Wang, Y., Tian, L., Wang, Z., Wang, C. & Gao, Y. Effects of Transfer of Land Development Rights on Urban–Rural Integration: Theoretical Framework and Evidence from Chongqing, China. Land 12, (2023). (2045).

  23. Wu, R., Li, Y. & Wang, S. Will the construction of high-speed rail accelerate urban land expansion? Evidences from Chinese cities. Land. Use Policy. 114, 105920 (2022).

    Google Scholar 

  24. Zeng, H., Chen, J. & Gao, Q. The Impact of Digital Technology Use on Farmers’ Land Transfer-In: Empirical Evidence from Jiangsu. China Agriculture. 14, 89 (2024).

    Google Scholar 

  25. Fang, C., Chen, Z., Liao, X., Sun, B. & Meng, L. Urban-rural digitalization evolves from divide to inclusion: empirical evidence from China. Npj Urban Sustain. 4, 51 (2024).

    Google Scholar 

  26. Ge, T., Hao, Z. & Chen, Y. How environmental policy synergy can enhance urban ecological resilience: insights from text mining analysis in China. Humanit. Soc. Sci. Commun. 12, 656 (2025).

    Google Scholar 

  27. Pan, W., Wang, J., Li, Y., Chen, S. & Lu, Z. Spatial pattern of urban-rural integration in China and the impact of geography. Geogr. Sustain. 4, 404–413 (2023).

    Google Scholar 

  28. Li, J. & Li, W. Ecological compensation, financial support, and rural revitalization. Finance Res. Lett. 85, 108233 (2025).

    Google Scholar 

  29. Fotheringham, A. S. & Brunsdon, C. Local Forms of Spatial Analysis. Geogr. Anal. 31, 340–358 (1999).

    Google Scholar 

  30. Chen, J., Shin, Y. & Zheng, C. Estimation and inference in heterogeneous spatial panels with a multifactor error structure. J. Econom. 229, 55–79 (2022).

    Google Scholar 

  31. Yang, J. & Huang, X. The 30 m annual land cover datasets and its dynamics in China from 1985 to 2024. Zenodo https://doi.org/10.5281/zenodo.15853565 (2025).

  32. Minh, N. Q., Huong, N. T. T., Khanh, P. Q., Hien, L. P. & Bui, D. T. Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations. Remote Sens. 16, 819 (2024).

    Google Scholar 

  33. Xu, G., Su, J., Xia, C., Li, X. & Xiao, R. Spatial mismatches between nighttime light intensity and building morphology in Shanghai, China. Sustain. Cities Soc. 81, 103851 (2022).

    Google Scholar 

  34. Yang, Y., Ma, M., Tan, C. & Li, W. Spatial Recognition of the Urban-Rural Fringe of Beijing Using DMSP/OLS Nighttime Light Data. Remote Sens. 9, 1141 (2017).

    Google Scholar 

  35. Yan, W. U. & Hongbo, L. I. Spatial change and correlations of desakota regions in a metropolitan area using NPP/VIIRS nighttime light data: A case study of Wuhan City. Prog Geogr. 39, 13–23 (2020).

    Google Scholar 

  36. Liu, M. et al. Nighttime light intensity and brightness suitability in urban functional zones. Sci. Rep. 15, 25113 (2025).

    Google Scholar 

  37. Ozga, F., Onnela, J. P. & DeGruttola, V. Bayesian method for inferring the impact of geographical distance on intensity of communication. Sci. Rep. 10, 11775 (2020).

    Google Scholar 

  38. Wang, Q. & Lu, S. The influence of hybrid accessibility on tourism economy in prefecture-level cities: Evidence from China’s high-speed rail network. J. Transp. Geogr. 104, 103417 (2022).

    Google Scholar 

  39. Wen, H., Zhao, D., Wang, W., Hua, X. & Yu, W. Exploring the spatial distribution structure of intercity human mobility networks under multimodal transportation systems in China. J. Transp. Geogr. 123, 104144 (2025).

    Google Scholar 

  40. Li, L., Sun, Z. & Long, X. An empirical analysis of night-time light data based on the gravity model. Appl. Econ. 51, 797–814 (2019).

    Google Scholar 

  41. Fu, B. & Xue, B. Temporal and Spatial Evolution Analysis and Correlation Measurement of Urban–Rural Fringes Based on Nighttime Light Data. Remote Sens. 16, 88 (2024).

    Google Scholar 

  42. Jiang, P. et al. Study on the Efficiency, Evolutionary Trend, and Influencing Factors of Rural–Urban Integration Development in Sichuan and Chongqing Regions under the Background of Dual Carbon. Land 13, 696 (2024).

    Google Scholar 

  43. Li, L., Yang, L., Wang, H., Yang, S. & Yan, Z. Spatiotemporal Evolution Characteristics and Impact Factors of Urban-rural Integrated Development in China. Chin. Geogr. Sci. 35, 802–818 (2025).

    Google Scholar 

  44. Jing, C. et al. Gridded value-added of primary, secondary and tertiary industries in China under Shard Socioeconomic Pathways. Sci. Data. 9, 309 (2022).

    Google Scholar 

  45. Yan, Y., He, C., Liu, T. & Yang, H. Regional fiscal disparities in Chinese cities: Revenue-expenditure perspective. Appl. Geogr. 170, 103362 (2024).

    Google Scholar 

  46. Wu, X. & Ma, Y. Research on the comparison effect of urban residents’ consumption. J. Bus. Res. 160, 113812 (2023).

    Google Scholar 

  47. Jiang, H. et al. Projections of urban built-up area expansion and urbanization sustainability in China’s cities through 2030. J. Clean. Prod. 367, 133086 (2022).

    Google Scholar 

  48. Wang, C. & Cao, J. Air pollution, health status and public awareness of environmental problems in China. Sci. Rep. 14, 19861 (2024).

    Google Scholar 

  49. Liu, B., Qi, C., Xue, B. & Yang, Z. Measuring urban–rural integration through the lenses of sustainability and social equity: Evidence from China. Habitat Int. 165, 103559 (2025).

    Google Scholar 

  50. Zheng, Q., Seto, K. C., Zhou, Y., You, S. & Weng, Q. Nighttime light remote sensing for urban applications: Progress, challenges, and prospects. ISPRS J. Photogramm Remote Sens. 202, 125–141 (2023).

    Google Scholar 

  51. Guo, J., Zhang, F., Zhao, H., Pan, B. & Mei, L. Global reconstruction of three decades of fine-grained nighttime light data with analysis of large-scale infrastructure and landmarks. Remote Sens. Environ. 331, 115036 (2025).

    Google Scholar 

  52. Duarte, D. & Fonte, C. C. Combining readily available population and land cover maps to generate non-residential built-up labels to train Sentinel-2 image segmentation models. Int. J. Appl. Earth Obs Geoinf. 135, 104272 (2024).

    Google Scholar 

  53. Badshah, A. et al. Big data applications: overview, challenges and future. Artif. Intell. Rev. 57, 290 (2024).

    Google Scholar 

  54. Huang, L. et al. Reconstructing human activities via coupling mobile phone data with location-based social networks. Travel Behav. Soc. 33, 100606 (2023).

    Google Scholar 

  55. Zhang, F. et al. Uncovering inconspicuous places using social media check-ins and street view images. Comput. Environ. Urban Syst. 81, 101478 (2020).

    Google Scholar 

  56. Islam, M. A., Mohammad, M. M., Das, S., Ali, M. & S. S. & E. A survey on deep learning based Point-of-Interest (POI) recommendations. Neurocomputing 472, 306–325 (2022).

    Google Scholar 

  57. Wang, C., Liu, H., Zhang, M. & Wei, Z. The border effect on urban land expansion in China: The case of Beijing-Tianjin-Hebei region. Land. Use Policy. 78, 287–294 (2018).

    Google Scholar 

  58. Lubida, A., Veysipanah, M., Pilesjo, P. & Mansourian, A. Land-use planning for sustainable urban development in africa: A spatial and multi-objective optimization approach. Geod. Cartogr. 45, 1–15 (2019).

    Google Scholar 

  59. Farber, S. & Li, X. Urban sprawl and social interaction potential: an empirical analysis of large metropolitan regions in the United States. J. Transp. Geogr. 31, 267–277 (2013).

    Google Scholar 

  60. He, X., Cao, Y. & Zhou, C. Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion. Remote Sens. 13, 3639 (2021).

    Google Scholar 

  61. Huang, M. & Zhao, S. Tracing trajectories and co-evolution of metropolitan urbanization in the United States, Europe, and China. Sci. Total Environ. 945, 173894 (2024).

    Google Scholar 

  62. Xu, Y., Chen, C., Deng, W., Dai, L. & Yang, T. Spatial eco-socio-economic trade-offs inform differentiated management strategies in mega-urban agglomerations. Npj Urban Sustain. 5, 43 (2025).

    Google Scholar 

  63. Kawai, M. East Asian economic regionalism: progress and challenges. J. Asian Econ. 16, 29–55 (2005).

    Google Scholar 

  64. Wang, Y. & Li, L. Digital economy, industrial structure upgrading, and residents’ consumption: Empirical evidence from prefecture-level cities in China. Int. Rev. Econ. Finance. 92, 1045–1058 (2024).

    Google Scholar 

  65. Zhu, B., Zhang, S., Chen, W., Shi, H. & Wang, Y. The positive impact of urban-rural integration on consumption gap among residents in China. Cities 168, 106454 (2026).

    Google Scholar 

  66. Sun, D. & Zhao, G. Urban Environment Quality and Migrant Settlement Intentions: Evidence from China’s Hygienic Cities Initiative. Sustainability 15, 13093 (2023).

    Google Scholar 

  67. Liang, W., Liu, R. & Kou, P. Nighttime light dynamics reveal peri-urban brightening and population decoupling in the Chengdu Chongqing megaregion. Sci. Rep. 16, 4601 (2026).

    Google Scholar 

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Funding

This work was supported by the National Natural Sciences Foundation of China (42301361, 42201228), the Natural Science Foundation of Shandong Province (ZR2024QD140), and the Shandong Provincial College Students’ Innovation Training Program (S202510452057).

Author information

Authors and Affiliations

  1. College of Resources and Environment, Linyi University, Linyi, 276005, China

    Dawei Mei, Ningning Liu & Haijiao Yu

Authors
  1. Dawei Mei
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  2. Ningning Liu
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  3. Haijiao Yu
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Contributions

Author Contributions: Conceptualization, N.L., H.Y., and D.M.; methodology, N.L. and D.M.; software, N.L. and H.Y.; validation, N.L. and H.Y.; formal analysis, N.L.; inves-tigation, N.L. and H.Y.; resources, D.M.; data curation, D.M.; writ-ing—original draft preparation N.L.; writing—review and editing, D.M. and Y.H.; visuali-zation, N.L.; supervision, H.Y. and D.M.; project administration, Y.H. and D.M.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Haijiao Yu.

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Mei, D., Liu, N. & Yu, H. Spatiotemporal evolution and driving factors of urban–rural integration in the Jinan metropolitan area. Sci Rep (2026). https://doi.org/10.1038/s41598-026-46744-y

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  • Received: 07 February 2026

  • Accepted: 27 March 2026

  • Published: 10 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-46744-y

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Keywords

  • Urban-rural integration
  • Nighttime lights
  • Measurement of spatial association
  • Spatiotemporal geographically weighted regression model
  • Jinan Metropolitan Area
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