The effective data mining of social media has become increasingly recognized for its value in informing decision makers of public welfare. However, existing studies do not fully exploit the underlying merit of big data. In this study, we develop a data-driven framework that integrates machine learning with spatial statistics, and then use it on Xiamen Island, China to delineate urban population dynamic patterns based on hourly Baidu heat map data collected from August 25 to September 3, 2017. The results showed that hot grids are primarily clustered along the main street through the downtown area during working days, whereas cold grids are often observed at the edge of the city during the weekend. The mixed use (of commercial and life services, restaurants and snack bars, offices, leisure areas and sports complexes) is the most significant contributing factor. A new cold grid emerged near conference venues before the Brazil, Russia, India, China, and South Africa Summit, revealing the strong effects of regulations on population dynamics and its evolving patterns. This study demonstrates that the proposed data-driven framework might offer new insights into urban population dynamics and its driving mechanism in support of sustainable urban development.
Keywords: Baidu heat map; Deep mining approach; Dynamic patterns; Urban population.
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