Large-scale population flow reshapes the economic landscape and is affected by unbalanced urban development. The exploration of migration patterns and their determinants is therefore crucial to reveal unbalanced urban development. However, low-resolution migration datasets and insufficient consideration of interactive differences have limited such exploration. Accordingly, based on 2019 Chinese Spring Festival travel-related big data from the AMAP platform, we used social network analysis (SNA) methods to accurately reveal population flow patterns. Then, with consideration of the spatial heterogeneity of interactive patterns, we used spatially weighted interactive models (SWIMs), which were improved by the incorporation of weightings into the global Poisson gravity model, to efficiently quantify the effect of socioeconomic factors on migration patterns. These SWIMs generated the local characteristics of the interactions and quantified results that were more regionally consistent than those generated by other spatial interaction models. The migration patterns had a spatially vertical structure, with the city development level being highly consistent with the flow intensity; for example, the first-level developments of Beijing, Shanghai, Chengdu, Guangzhou, Shenzhen, and Chongqing occupied a core position. A spatially horizontal structure was also formed, comprising 16 closely related city communities. Moreover, the quantified impact results indicated that migration pattern variation was significantly related to the population, value-added primary and secondary industry, the average wage, foreign capital, pension insurance, and certain aspects of unbalanced urban development. These findings can help policymakers to guide population migration, rationally allocate industrial infrastructure, and balance urban development.
Big data can provide new insights for smart city planning. This study exploits mobile-phone locating-request (MPLR) data as a proxy for real-time intra-urban population distribution. It models the relationship between the dynamic population distribution and the urban built environment using geographically and temporally weighted regression (GTWR), which can account for spatial and temporal non-stationarity simultaneously. A case study is undertaken based on MPLR records in Shenzhen, China and points of interest-based urban environment data aggregated to grid zones. Compared with previous models, GTWR yields a better result. Furthermore, the spatiotemporal coefficients are analyzed and compared. The results suggest that the patterns of urban population distribution are more complex during weekends than during weekdays. The coefficients of the company density variable are significantly higher during weekdays than weekends, while the coefficients associated with residential buildings are lower during weekday afternoons. Hence, the urban built environment plays an important role in the dynamic distribution of the population at different times. The findings show that the GTWR model in combination with MPLR and points of interest-based urban environment data can assist urban planners in gaining a better understanding of the spatiotemporal dynamics of the population distribution, thereby providing potential inputs to the rational allocation of public resources over space and time.
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