Cities in an urban agglomeration closely interact with each other through various flows. Information flow, as one of the important forms of urban interactions, is now increasingly indispensable with the fast development of informatics technology. Thanks to its timely, convenient, and spatially unconstrained transmission ability, information flow has obvious spillover effects, which may strengthen urban interaction and further promote urban coordinated development. Therefore, it is crucial to quantify the spatial spillover effect and influencing factors of information flows, especially at the urban agglomeration scale. However, the academic research on this topic is insufficient. We, therefore, developed a spatial interaction model of information flow (SIM-IF) based on the Baidu Search Index and used it to analyze the spillover effects and influencing factors of information flow in the three major urban agglomerations in China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in the period of 2014–2019. The results showed that the SIM-IF performed well in all three agglomerations. Quantitative analysis indicated that the BTH had the strongest spillover effect of information flow, followed by the YRD and the PRD. It was also found that the hierarchy of cities had the greatest impact on the spillover effects of information flow. This study may provide scientific basis for the information flow construction in urban agglomerations and benefit the coordinated development of cities.

The spatial pattern and mechanism of human flow are of great significance for urban planning, economic development, transportation planning and so on. In this study, we used cell phone location data to represent the human flow network in Guangdong Province, China, using the 21 cities in Guangdong as “nodes” and the human flow intensity among them as “edges”. Then we explored macro and micro features of the human flow network, by using the index of degree distribution, alter-based centrality and alter-based power, respectively. Finally, we proposed a human flow estimation model which integrates individual urban characteristics, intercity links, and differences to further analyze the affecting factors of human flow. We found that the human flow network in this region is significantly scale-free, with Guangzhou, Shenzhen, Foshan, and Dongguan being the most important cities. We also found that the newly proposed model can explain the human flow in the study area, with an R² of 0.914. Analysis results show that the factors of employment in tertiary sector, intercity internet attention, intercity differences in the number of tertiary workers, differences in population size, and distance have significant impacts on the human flow. This study may provide insights into human activity mechanisms that can contribute to urban planning and management.