Spatiotemporal Exploration of Chinese Spring Festival Population Flow Patterns and Their Determinants Based on Spatial Interaction Model

Abstract

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.

Anxiety and Depression in Chinese Students During the COVID-19 Pandemic: A Meta-Analysis

“…Based on risk theory and the spatial relationship of population outflow, two factors-pandemic risk areas and pandemic development progression-may be related to the prevalence of anxiety and depression (19,20). Furthermore, the substantial levels of anxiety and depression caused by COVID-19 and their severity may also be associated with increased age (21,22).…”

What Is Essential Travel? Socioeconomic Differences in Travel Demand in Columbus, Ohio, during the COVID-19 Lockdown

“…Based on the criteria of localization and distance measuring, SWIM is categorized into three major branches: origin-focused models, destinationfocused models, and flow-focused models. Few studies applied the SWIM models in identifying the socioeconomic determinants of O-D flow (Zhang, Cheng, and Jin 2019;Pulford, Cheng, and Jin 2020;Zhou et al 2020).…”

“…SI models using the geographically weighted regression framework, however, account for spatial nonstationarity and are limited in addressing spatial dependence within the interaction data set. Past studies using this approach assumed that spatial flows are independent over space and adopted a fully local modeling technique where local models are calibrated separately around each origin and destination using spatial weights (Fotheringham, Brunsdon, and Charlton 2003;Nissi and Sarra 2011;Kordi and Fotheringham 2016;Zhang, Cheng, and Jin 2019;Pulford, Cheng, and Jin 2020;Zhou et al 2020). There are other modified SI models to address spatial dependence in data sets using spatial modeling techniques like spatial lag autoregression (Fischer and Griffith 2008;LeSage and Fischer 2010) and eigenvector spatial filtering (Chun 2008;Fischer and Griffith 2008;Patuelli et al 2015).…”

What is essential travel? Socio-economic differences in travel demand during the COVID-19 lockdown

“…Based on the criteria of localization and distance measuring, SWIM is categorized into three major branches: origin-focused models, destination-focused models, and flow-focused models. Few studies applied the SWIM models in identifying the socio-economic determinants of OD flow (Zhang, Cheng, and Jin 2019;Pulford, Cheng, and Jin 2020;Zhou et al 2020).…”

“…However, spatial interaction models using the GWR framework account for spatial nonstationarity and are limited in addressing spatial dependence within the interaction dataset. Past studies using this approach assumed that spatial flows are independent over space and adopted a fully-local modeling technique where local models are calibrated separately around each origin and destination using spatial weights (Fotheringham, Brunsdon, and Charlton 2003;Nissi and Sarra 2011;Kordi and Fotheringham 2016;Zhang, Cheng, and Jin 2019;Pulford, Cheng, and Jin 2020;Zhou et al 2020). There are other modified SI models to address spatial dependence in datasets using spatial modeling techniques like spatial lag autoregression (Fischer and Griffith 2008;LeSage and Fischer 2010) and eigenvector spatial filtering (Chun 2008;Fischer and Griffith 2008;Patuelli et al 2015).…”