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Data-driven framework for delineating urban population dynamic patterns: Case study on Xiamen Island, China.

Lei Fang1,2, Jinliang Huang1, Zhenyu Zhang1

  • 1Fujian Key Laboratory of Coastal Pollution Control, Xiamen University, 361102, Xiamen, China.

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This study reveals urban population dynamics using big data analysis. Mixed land use and events significantly influence population distribution, offering insights for sustainable urban planning.

Keywords:
Baidu heat mapDeep mining approachDynamic patternsUrban population

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Area of Science:

  • Urban Planning
  • Data Science
  • Spatial Statistics

Background:

  • Social media data mining is crucial for public welfare decision-making.
  • Existing big data utilization in urban studies is often limited.
  • Understanding urban population dynamics is key for sustainable development.

Purpose of the Study:

  • To develop a data-driven framework integrating machine learning and spatial statistics.
  • To delineate urban population dynamic patterns using big data.
  • To identify factors influencing population distribution in Xiamen Island.

Main Methods:

  • Developed a framework combining machine learning and spatial statistics.
  • Utilized hourly Baidu heat map data from Xiamen Island (August 25–September 3, 2017).
  • Analyzed population distribution patterns in relation to land use and events.

Main Results:

  • Hot grids clustered downtown during weekdays; cold grids appeared at city edges on weekends.
  • Mixed land use (commercial, residential, recreational) was a significant factor.
  • A cold grid emerged near summit venues, showing regulatory impact on population dynamics.

Conclusions:

  • The data-driven framework provides insights into urban population dynamics.
  • Findings support sustainable urban development strategies.
  • Spatial statistics and machine learning effectively model population shifts.